the impact of automatic store replenishment systems on...

The Impact of Automatic Store

Replenishment Systems on Retail

DISSERTATION

of the University of St. Gallen

Graduate School of Business Administration,

Economics, Law and Social Sciences (HSG)

to obtain the title of

Doctor of Business Administration

submitted by

Alfred Angerer

from

Austria

Approved on the application of

Prof. Dr. Daniel Corsten

and

Prof. Fritz Fahrni, PhD

Dissertation no. 3123

The University of St. Gallen, Graduate School of Business Administration,

Economics, Law and Social Sciences (HSG) hereby consents to the printing of the

presented dissertation, without hereby expressing any opinion on the views herein

expressed.

St. Gallen, November 17, 2005

The President

Prof. Ernst Mohr, PhD

dedicado a las dos mujeres más importantes de mi vida:

mi madre y Anne

VII

Foreword

Fast moving consumer goods retailing is a highly competitive market. European

retailers are continuously aiming to improve customer loyalty by offering good

service. At the same time, they are struggling to reduce costs in order to stay

competitive. One technology that promises to decrease the number of out-of-stocks

while simultaneously reducing store handling costs is automatic store replenishment

(ASR). At the heart of ASR systems lies software that automatically places an order

to replenish stocks. Many European grocery retailers have started to implement such

decision support systems.

Surprisingly, although several retailers have automated their order process in the last

few years, there is almost no academic source examining this topic at the level of the

store. It is worth noting that other technologies in retail, such as RFID (Radio

Frequency Identification) and the introduction of the barcode, have received far

greater attention from the public and from researchers. Furthermore, while the topic

of extent and root-causes of retail out-of-stock has received substantial interest over

the course of the last years, the question to what extent existing and new practices

remedy OOS is largely unanswered. In particular, there is a debate whether ASR

improve or worsen OOS. Therefore, Dr. Alfred Angerer has well chosen a topic of

both managerial and academic relevance.

Although there are many success stories from practitioners describing the enormous

advantages of introducing automatic store replenishment systems there has been

limited empirical proof of this. To the best of my knowledge no conceptual framework

exits that can help practitioners to choose an adequate automatic replenishment

system. In order to develop such a model research on relationship between

replenishment performance (e.g. OOS rate, inventory levels) and contextual variables

(such as store and product characteristics) is required. Finally, it is not clear how

retailers have to adapt its organization and processes to best support the chosen

ASR system.

Dr. Angerer confidently identifies and covers several research gaps and manages to

give answers to this research gaps by a skilful combination of quantitative and

qualitative research methodologies. In a first part an exhaustive data set of a

European retailer is examined. With this data analysis the performance of

replenishment system before and after the introduction of ASR systems is compared.

VIII

Dr. Angerer is able to statistically prove and quantify the positive impact of such

systems on inventory levels and out-of-stock rates. In the second part, several case

studies illustrate how ASR systems are implemented in practice. The given

recommendations on store processes help retailers to benefit most from automatic

replenishment systems.

Overall, this thesis makes an important contribution to the field of retail operations −

in practice and theory. I personally wish Dr. Angerer's work wide attention in both

academic and practitioner circles.

Prof. Dr. Daniel Corsten

IX

Acknowledgment

Rarely is a doctoral thesis the contribution of a single person. Many people supported

and consulted me during my three years of research at the University of St.Gallen.

Therefore, I would like to express my thanks to everyone who supported me in

finalising this work.

I am greatly indebted to my two advisors, Prof. Dr. Daniel Corsten and Prof. Dr. Fritz

Fahrni. They guided me through the inevitable ups and downs that characterise such

a long research project. I want to specially thank Daniel Corsten who supported my

research from the very outset. Without his never ending striving for improvement, the

present results would not have been gained. I also cordially thank my second

adviser, Fritz Fahrni, who always helped me to look for the big picture in my work.

Further, I am thankful to Prof. Dr. Frank Straube and Prof. Dr. Wolfgang Stölzle for

their backup as directors of the KLOG.

A special thanks goes to my colleagues Lars Dittmann and Christian Tellkamp, with

whom I shared several "research camps". They decisively influenced my research.

Jens Hamprecht deserves a special thank for his numerous advices as well as

Dorothea Wagner does. Her extensive knowledge about the consumer goods

industry was always a valuable contribution.

My time a the University of St.Gallen would only have been half the fun without my

colleagues. I want to thank Gunther Kucza, Marion Peyinghaus, Jörg Hofstetter, Jan

Felde, Jan Frohn, Elias Halsband, Florian Hofer, Petra Seeger, Dirk Voelz and all

other colleagues at the Kuehne-Institute for Logistics and the Institute for Technology

Management for being such good colleagues and for all the good moments we

shared.

Throughout the last years, I received valuable contributions form researchers and

students. Especially, I want to thank Johanna Småros and Michael Falck from the

HUT for the enriching discussions and research projects I shared with them. I would

also like to express my thanks to all the students whose bachelor and master thesis I

coached. Their interviews provided a basic foundation for my research.

Without the support from practitioners, this research would only have been a

theoretical contribution. A very warm thank you to Roland S., who invested his time to

provide me the data for the quantitative research. Further, I am very grateful to

X

Marianne S., Daniel B. and all the interview partners for the time they invested in my

research project.

Finally, I want to thank my mother and father, Toni, Lydia, Nic and Anne for their

never-ending moral support. Despite the distance, I always felt their affection

throughout my education and career.

St. Gallen, November 2005 Alfred Angerer

XI

Content Overview

1. Introduction ................................................................................................................................... 1

1.1. Logistics Contribution to Retail Excellence.............................................................................. 1 1.2. Excellence in Store Operations ............................................................................................... 3 1.3. New Technologies Enable Automatic Store Replenishment Systems..................................... 6 1.4. Research Deficit ...................................................................................................................... 8 1.5. Research Questions.............................................................................................................. 12 1.6. Thesis Structure .................................................................................................................... 14

2. Research Framework and Design ............................................................................................. 16

2.1. Research Framework ............................................................................................................ 16 2.2. Research Methodology ......................................................................................................... 18 2.3. Research Process ................................................................................................................. 23

3. Literature Research .................................................................................................................... 26

3.1. Inventory Management Perspective ...................................................................................... 26 3.2. Logistics and Operations Management Perspective ............................................................. 30 3.3. Business Information Systems Perspective .......................................................................... 41 3.4. Contingency Theory Perspective........................................................................................... 47 3.5. Literature Research Overview............................................................................................... 51

4. Development of Models.............................................................................................................. 53

4.1. A Descriptive Model of Replenishment Systems................................................................... 53 4.2. Classification of Automatic Replenishment Systems............................................................. 64 4.3. Explanatory Model................................................................................................................. 68

5. Quantitative Analysis.................................................................................................................. 85

5.1. Sample and Methodology...................................................................................................... 85 5.2. Hypothesis Testing: Dataset1................................................................................................ 99 5.3. Dataset2: Pretest/Posttest................................................................................................... 122 5.4. Quantitative Research–Conclusions ................................................................................... 128

6. Field Research and Managerial Implications.......................................................................... 132

6.1. Research Sample................................................................................................................ 132 6.2. Replenishment Processes................................................................................................... 137 6.3. Organizational Changes and Personnel Issues .................................................................. 151 6.4. ASR Performance ............................................................................................................... 157 6.5. Lessons Learned and Recommendations for Management ................................................ 161

7. Conclusion ................................................................................................................................ 181

7.1. Theoretical Contributions..................................................................................................... 181 7.2. Contribution for Practitioners ............................................................................................... 183 7.3. Further Research Fields ...................................................................................................... 186

8. Appendix and References ........................................................................................................ 189

8.1. Statistical Appendix ............................................................................................................. 189 8.2. References .......................................................................................................................... 193 8.3. List of Interviews.................................................................................................................. 210

XIII

Table of Contents

1. Introduction ........................................................................................................................1

1.1. Logistics Contribution to Retail Excellence ...................................................................1 1.2. Excellence in Store Operations.....................................................................................3 1.3. New Technologies Enable Automatic Store Replenishment Systems..........................6 1.4. Research Deficit............................................................................................................8 1.5. Research Questions....................................................................................................12 1.6. Thesis Structure ..........................................................................................................14

2. Research Framework and Design ..................................................................................16

2.1. Research Framework..................................................................................................16 2.2. Research Methodology ...............................................................................................18 2.3. Research Process.......................................................................................................23

3. Literature Research .........................................................................................................26

3.1. Inventory Management Perspective............................................................................26 3.1.1. Optimization in Inventory Management Research ...............................................27 3.1.2. Theoretical Sources on OOS ...............................................................................28 3.1.3. Contributions and Deficits of an Inventory Management Perspective..................29

3.2. Logistics and Operations Management Perspective...................................................30 3.2.1. Supply Chain Management and ECR ..................................................................31 3.2.2. Automatic Replenishment Programmes...............................................................33 3.2.3. Operations Management in Retail........................................................................39 3.2.4. Contributions and Deficits of a Logistics and Operations Management

Perspective ..........................................................................................................40 3.3. Business Information Systems Perspective................................................................41

3.3.1. Characteristics of ERP Systems ..........................................................................42 3.3.2. ERP Implementation and Selection .....................................................................42 3.3.3. ERP and Human Agency .....................................................................................44 3.3.4. Contributions and Deficits of a Business Information Systems Perspective ........46

3.4. Contingency Theory Perspective ................................................................................47 3.4.1. Contingency Theory at the Organizational Level .................................................47 3.4.2. Contingency Theory on Information Technology and Processes.........................49 3.4.3. Contributions and Deficits of a Contingency Perspective ....................................50

3.5. Literature Research Overview.....................................................................................51

4. Development of Models...................................................................................................53

4.1. A Descriptive Model of Replenishment Systems ........................................................53 4.1.1. Inventory Visibility ................................................................................................56 4.1.2. Replenishment Logic............................................................................................57 4.1.3. Order Restrictions ................................................................................................60 4.1.4. Forecasts .............................................................................................................61

4.2. Classification of Automatic Replenishment Systems ..................................................64 4.3. Explanatory Model ......................................................................................................68

4.3.1. Purpose and Structure of the Explanatory Model ................................................68 4.3.2. Hypothesis Development: Product Characteristics ..............................................71 4.3.3. Hypothesis Development: Store Characteristics..................................................77 4.3.4. Hypothesis Development: ASR Characteristics ...................................................81

XIV

5. Quantitative Analysis...................................................................................................... 85

5.1. Sample and Methodology........................................................................................... 85 5.1.1. Dataset1: Testing of Out-of-Stock Hypotheses ................................................... 85 5.1.2. Dataset2: Pretest/Posttest Analysis .................................................................... 93

5.2. Hypothesis Testing: Dataset1 .................................................................................... 99 5.2.1. Influence of Product Characteristics.................................................................... 99 5.2.2. Influence of Store Characteristics ..................................................................... 113

5.3. Dataset2: Pretest/Posttest........................................................................................ 122 5.4. Quantitative Research–Conclusions ........................................................................ 128

6. Field Research and Managerial Implications.............................................................. 132

6.1. Research Sample..................................................................................................... 132 6.1.1. Company Selection ........................................................................................... 132 6.1.2. Market Characteristics....................................................................................... 134 6.1.3. Supply Chain Structure ..................................................................................... 134 6.1.4. Chains and Store Formats ................................................................................ 136 6.1.5. Delivery Frequency and Order-to-Deliver Lead Times...................................... 136

6.2. Replenishment Processes........................................................................................ 137 6.2.1. Inventory Visibility.............................................................................................. 138 6.2.2. Forecasts and Replenishment Logic ................................................................. 146 6.2.3. Order Restrictions ............................................................................................. 151

6.3. Organizational Changes and Personnel Issues ....................................................... 151 6.3.1. Structural Changes and Setup .......................................................................... 152 6.3.2. Personnel and Change Management................................................................ 154

6.4. ASR Performance .................................................................................................... 157 6.4.1. Performance Measurement............................................................................... 157 6.4.2. Inventory Level Performance ............................................................................ 158 6.4.3. OOS Reduction and Overall Performance ........................................................ 159

6.5. Lessons Learned and Recommendations for Management..................................... 161 6.5.1. The Adequate Automation Level: Recommendations ....................................... 161 6.5.2. ASR Introduction ............................................................................................... 165 6.5.3. Technical and Organizational Requirements .................................................... 166 6.5.4. Store Operations: Recommended Action.......................................................... 170 6.5.5. Cost-Benefit Analyses ....................................................................................... 175

7. Conclusion..................................................................................................................... 181

7.1. Theoretical Contributions ......................................................................................... 181 7.2. Contribution for Practitioners.................................................................................... 183 7.3. Further Research Fields........................................................................................... 186

8. Appendix and References ............................................................................................ 189

8.1. Statistical Appendix .................................................................................................. 189 8.1.1. Calculation of the Inventory Level ..................................................................... 189 8.1.2. ANOVA Considerations and Prerequisites........................................................ 191

8.2. References ............................................................................................................... 193 8.3. List of Interviews....................................................................................................... 210 8.4. Curriculum Vitae....................................................................................................... 212

XV

List of Abbreviations and Acronyms ANOVA Analysis Of Variance

ARP Automatic Replenishment Programme

ASR Automatic Store Replenishment

ASRx Automatic Store Replenishment System level x

CD Cross-Docking

CU Consumer Unit

CU/TU Consumer Unit per Trading Unit (=case pack size)

CRP Continuous Replenishment Planning

CPFR Collaborative Planning Forecasting and Replenishment

EAN European Article Numbering

ECR Efficient Consumer Response

EDI Electronic Data Interchange

ERP Enterprise Resource Planning

DC Distribution Centre

DSD Direct Store Delivery

DSS Decision Support System

HQ Headquarters

IS Information System

IT Information Technology

ITEM Institute for Technology Management

KLOG Kuehne-Institute for Logistics

KPI Key Performance Indicator

MAD Mean Absolute Deviation

MAPE Mean Absolute Percent Error

OOS Out-Of-Stock

OR Operations Research

OSA On-Shelf Availability

PC Personal Computer

PDA Personal Digital Assistant (handhelds)

POS Point Of Sales

QR Quick Response

SC Supply Chain

SCM Supply Chain Management

SKU Stock Keeping Unit

TU Trading Unit

VMI Vendor Managed Inventory

XVII

Figures

Figure 1: The importance of logistics for different industries ..................................... 2

Figure 2: Percentage of logistics costs on total costs by industry (in %) ................... 3

Figure 3: Summary of OOS root causes.................................................................... 5

Figure 4: Thesis structure ........................................................................................ 15

Figure 5: Focus of research ..................................................................................... 16

Figure 6: Integrative research procedure................................................................. 19

Figure 7: Case study research as iterative process between theory and

empiricism................................................................................................. 22

Figure 8: Research activities in this research project .............................................. 23

Figure 9: Spectrum of misfit resolution strategies.................................................... 44

Figure 10: Descriptive model of replenishment systems ........................................... 54

Figure 11: Exemplary time dependent course of the inventory stock level................ 56

Figure 12: Qualitative and quantitative forecasting techniques ................................. 62

Figure 13: Classification of automatic replenishment systems .................................. 65

Figure 14: Overview of hypotheses, product characteristics ..................................... 83

Figure 15: Overview of hypotheses, store characteristics ......................................... 84

Figure 16: Overview of hypotheses, ASR characteristics .......................................... 84

Figure 15: Distribution of the 84 products in Dataset1............................................... 88

Figure 16: Comparison of the replenishment systems by store................................. 92

Figure 17: Estimated OOS (order-related) rate by sales coefficient of variance...... 100

Figure 18: Estimated inventory range of coverage by sales coefficient of

variance ................................................................................................. 102

Figure 19: Estimated OOS (order-related) rate by speed of turnover...................... 103

Figure 20: Estimated inventory range of coverage by speed of turnover ................ 104

Figure 21: Estimated OOS (order-related) rate by price .......................................... 105

Figure 22: Estimated inventory range of coverage by price..................................... 106

Figure 23: Estimated OOS (order-related) rate by CU/TU group............................. 107

Figure 24: Estimated Inventory range of coverage by case pack ............................ 108

Figure 25: Estimated OOS (order-related) rate by product size .............................. 109

Figure 26: Estimated inventory range of coverage by product size ......................... 110

Figure 27: Estimated OOS (order-related) rate by shelf life..................................... 111

Figure 28: Estimated inventory range of coverage by shelf life ............................... 112

Figure 29: Estimated OOS (order-related) rate by store.......................................... 114

Figure 30: Estimated inventory range of coverage by store .................................... 115

Figure 31: Estimated inventory coefficient of variance by store............................... 116

Figure 32: Relationship between shrinkage and OOS............................................. 117

Figure 33: Relationship of OOS and SKU density ................................................... 118

XVIII

Figure 34: Relationship of OOS and number of personnel per m2 ...........................118

Figure 35: Relationship of OOS and number years of the store manager

working in the store.................................................................................119

Figure 36: Relationship of OOS and the size of the backroom ................................120

Figure 37: Relationship of OOS and customer satisfaction......................................121

Figure 38: Mean inventory range of coverage in days of dairy products and

Control1 ..................................................................................................123

Figure 39: Means of the repeated ANOVA on the coefficient of variance of

the stock level, ASR3 group and Control1 .............................................125

Figure 40: Mean inventory range of coverage in days of non-food

products and Control2.............................................................................126

Figure 41: Estimated means of the repeated ANOVA on the inventory range of

coverage, ASR2 group............................................................................127

Figure 42: Estimated means of the repeated ANOVA on the inventory

range of coverage, ASR2* group............................................................127

Figure 43: Mean inventory coefficient of variance in days of ASR2, ASR2* group

and Control2 ...........................................................................................128

Figure 44: Supply chain structure of sample ............................................................135

Figure 45: Delivery frequency of sample..................................................................137

Figure 46: Inventory storage places and product flow processes ............................138

Figure 47: Comparison of inventory records and real inventory in one store...........142

Figure 48: Decision tree for practitioners .................................................................162

Figure 49: Cost of forecasting versus cost of inaccuracy.........................................168

Figure 50: Overview of store operations recommendations.....................................170

Figure 51: Comparison of inventory on shelf and total store inventory for a

glue stick.................................................................................................172

Figure 52: Costs in relation to replenishment level ..................................................177

Figure 53: Theoretical contribution of thesis ............................................................181

Figure 54: Contribution for practitioners ...................................................................183

Figure 55: Relative inventory level curve without zero line ......................................189

Figure 56: Absolute inventory level curve after the correction .................................190

XIX

Tables

Table 1: Overview of research deficits................................................................... 12

Table 2: Overview of basic theoretical sources reviewed (excerpt)....................... 26

Table 3: Implementation of ARP-related items ...................................................... 35

Table 4: Effectiveness in achieving automatic replenishment-related goals.......... 36

Table 5: Information systems capabilities .............................................................. 37

Table 6: Summary of research streams perspectives............................................ 52

Table 7: Inventory notations................................................................................... 55

Table 8: Basic inventory decision rules.................................................................. 57

Table 9: Exemplary order restrictions .................................................................... 60

Table 10: Characteristics of automatic replenishment levels................................... 68

Table 11: Overview of hypotheses concerning product characteristics ................... 77

Table 12: Overview of hypotheses concerning store characteristics ....................... 81

Table 13: Overview of the utilization of the two datasets for hypothesis testing...... 85

Table 14: Overview of variables used in the analysis .............................................. 90

Table 15: Product characteristics of Dataset1 by replenishment system ................ 91

Table 16: OOS rates (order-related)of the sample .................................................. 92

Table 17: Inventory range of coverage of Dataset1................................................. 93

Table 18: Dataset for the pretest/posttest................................................................ 94

Table 19: Descriptive statistics of the dairy products (ASR3) and Control1

group (ASR0)........................................................................................... 95

Table 20: Descriptive statistics of the beauty and household group (ASR2) and

Control2 (ASR0) ...................................................................................... 97

Table 21: Descriptive statistics of the non-food group (ASR2*) and Control2

(ASR0) ..................................................................................................... 97

Table 22: ASR level and sales coefficient of variance ANOVA on OOS

(order-related)........................................................................................ 100

Table 23: ASR level and sales coefficient of variance ANOVA on inventory

range of coverage.................................................................................. 101

Table 24: ASR level and speed of turnover ANOVA on OOS (order-related)........ 103

Table 25: ASR level and speed of turnover ANOVA on inventory range of

coverage ................................................................................................ 104

Table 26: ASR level and price ANOVA on OOS (order-related)............................ 105

Table 27: ASR level and price ANOVA on inventory range of coverage ............... 106

Table 28: ASR level and CU/TU ANOVA on OOS (order-related)......................... 107

Table 29: ASR level and CU/TU on inventory range of coverage.......................... 108

Table 30: ASR level and product size ANOVA on OOS (order-related) ................ 109

Table 31: ASR level and product size ANOVA on inventory range of coverage.... 110

XX

Table 32: Regression of shelf life and shelf life squared on OOS..........................112

Table 33: Correlation between OOS per week and store characteristics

(Dataset1)...............................................................................................113

Table 34: ASR level and Store ANOVA on OOS (order-related) ...........................114

Table 35: ASR level and Store ANOVA on inventory range of coverage...............115

Table 36: ASR level and store ANOVA on inventory coefficient of variance..........116

Table 37: ASR level ANOVA on OOS (order-related) ............................................121

Table 38: ASR level ANOVA on inventory range of coverage ...............................122

Table 39: Performance of ASR3 group compared to the Control1 (ASR0)............124

Table 40: Repeated ANOVA on inventory range of coverage, ASR3 group ..........124

Table 41: Repeated ANOVA on the coefficient of variance of the stock

level, ASR3 group ..................................................................................125

Table 42: Repeated ANOVA on mean inventory range of coverage,

ASR2 group............................................................................................126

Table 43: Results overview: product characteristics hypotheses...........................129

Table 44: Results: store characteristics hypotheses ..............................................130

Table 45: Results: ASR hypotheses.......................................................................130

Table 46: Overview of the results of the hypotheses tested...................................131

Table 47: Selected companies for the field research .............................................133

Table 48: Inventory range of coverage of European grocery retailers in days.......158

Table 49: Technical requirements and recommendations on operations and

organization structure.............................................................................169

Table 50: Overview of possible benefits and costs following an ASR

system introduction ................................................................................177

Table 51: Overview of further research opportunities ............................................186

XXI

Abstract

European fast moving consumer goods retailers face a mature market with low

margins and high competition. To improve their situation, retailers are looking for

technologies and concepts to increase consumer satisfaction while at the same time

reducing costs. One technology that promises to increase the availability of the

products on the shelf while simultaneously reducing store handling costs is automatic

store replenishment (ASR). At the heart of ASR systems lies software that

automatically places an order to replenish stocks of a certain product. A majority of

European grocery retailers have implemented such decision support systems. Yet

research in this area is practically non-existent. Therefore, this thesis aims to

investigate the impact of this technology on retail, taking into account financial,

organizational and personnel aspects.

To answer this main research question, a quantitative and a qualitative methodology

was chosen. First of all, based on theoretical sources and more than 50 interviews, a

descriptive model and an ASR classification system is developed. Next, an

explanatory model is developed with a view to enabling identification of the

characteristics of products, stores and replenishment systems that influence the

replenishment performance of retail stores. To be able to test the hypothesis derived

from this explanatory model, exhaustive data from a grocery retailer is examined. The

quantitative analysis clearly shows that even simple automatic replenishment

systems are able to dramatically reduce the average shelf out-of-stock rate and at the

same time lower inventory level. In addition, a major advantage of automatic systems

over manual ones is that they show constant results, independently of product

characteristics. Yet the analysis also shows that badly-parameterised automatic

systems will fail to deliver the desired results. In order to better understand how ASR

systems are best implemented in practice, four major grocery retailers are analysed

in detail. These case studies illustrate the necessary technological and organizational

changes and highlight the influence of ASR systems on the working behaviour of

employees.

Overall, this thesis makes contributions to both practice and theory. On the one hand,

the results presented are a first stepping stone towards the creation of a basic theory

of ASR systems. A descriptive model enables further researchers to make

differentiated statements on the impact of ASR based on the classification

developed. Another contribution is the explanatory model which tests existing and

demonstrates new relationships hypothesised in inventory and operations

management research. On the other hand, practitioners receive an overview of the

XXII

existent systems by which they may automate store replenishment. The

determination of ASR benefits and necessary requirements help them to make a

cost-benefit analysis. In addition, the several implications of the automation of their

replenishment system for the organization and for human working patterns are

illustrated. Practical recommendations on store processes help retailers to benefit

most from automatic replenishment systems. And finally, a decision tree helps

practitioners to identify the best-suited ASR system for each product category.

1. Introduction 1

1. Introduction

Grocery retailing is a highly competitive market (e.g. Keh and Park 1997). European

retailers are continuously aiming to improve customer loyalty by offering good

service. At the same time, they are struggling to reduce costs in order to stay

competitive. The effort to achieve customer service excellence has only been partly

successful, as the low average product shelf availability rates of 92–95% (Gruen,

Corsten et al. 2002; Roland Berger 2003b) and a sunk store loyalty underline. The

major part of retailer costs are personnel costs, and in particular it is the operations in

the store that require intensive staff dedication (Broekmeulen, van Donselaar et al.

2004a). The German retailer Globus has calculated that the logistics costs of the last

50 meters in the store, i.e. from the backroom to the shelf, are three times as

expensive as the first 250 kilometres from the producer to the store gate (Shalla

2005). A technique that promises to reduce the out-of-stock (OOS) rate by

simultaneously reducing the store handling costs are so-called automatic store

replenishment (ASR) systems, the main research subject of this thesis.

This chapter provides an introduction to the business challenges faced by retailers

and the valuable role of logistics in retail, followed by a short introduction to ASR

systems. Later, research deficits in the literature are identified and the research

questions of this thesis are derived. Finally, an overview of the structure of this

research study is given.

1.1. Logistics Contribution to Retail Excellence

The major market developments that make retail challenging started in the 1990s and

still are prevalent today, namely high cost pressure, shorter innovation cycles,

increasing consumer expectations and globalization (Baumgarten and Wolf 1993;

Lee 2001). The common response of retailers has been a so-called quantity strategy:

They introduced more product variants, invested in new channels of distribution,

diversified store formats and expanded into new countries. However, the benefits

harvested from such a strategy seem to have come to an end, as the market has

become saturated. The fraction of private consumption that flows into food and near-

food retail has decreased continuously in the last two decades. In Germany, for

example, it sank from 44.2% in 1990 to 29.3% in 2004 (Körber 2003), and this trend

is typical for many developed countries. Nevertheless, a small group of retailers was

able to defy this trend and outperformed the market. As a study by Accenture (2000)

reports, approximately one-third of 63 examined retailers outperformed the other two-

thirds by far and showed a yearly revenue increase of at least 10% coupled with a

2 1. Introduction

higher-than-average increase in stock price. According to the study, this group had

developed the right strategy by focusing their investments in areas where the most

efficiency potentials were located.

One of the areas with such potential is without doubt logistics, as effective and

efficient logistics is the fundamental to successful retailing. Hans Joachim Körber

(2003), CEO of Metro AG, describes logistics as "the physical accomplishment of the

concern strategy."

Figure 1 depicts the great importance of logistics for retail and various industry

sectors under the aspects "differentiation" (i.e. logistics as a marketing tool) and

"rationalisation" (i.e. logistics as a method of saving costs).

Diffe

rentia

tion

Rationalisation

Retail/consumer goods

Automotive

Chemical industry

Machinebuilding

Paperindustry

Electronics

Plant construction

Figure 1: The importance of logistics for different industries1

The importance of logistics for the retail sector is based on the nature of the products

sold. Most consumer goods, for example daily food items, are relatively cheap and

the consumer generally buys without lengthy quality or price comparisons.

Nevertheless, the importance of logistics in other sectors is increasing as well, as

Pfohl (2004) stresses.

1 Source: Kowalski (1992).

1. Introduction 3

A precise estimation of the logistics costs is rather difficult. Pfohl compared studies

measuring the logistics costs as a percentage of turnover. The large differences in

the results can often be explained by geographical differences between countries and

their infrastructure levels. Yet even within a single country like Germany, there are

several studies with significantly divergent figures. This is the result of the varying

definition of logistics costs. One of the most cited studies is that by Baumgarten and

Thoms (2002). They estimate the retailers' logistics costs at up to 27% of total costs

(see Figure 2).

8.2% 12.8% 27.6%

Automotive Consumer

goods Retail

7.6% 12.2% 26.7%

Figure 2: Percentage of logistics costs on total costs by industry (in %)2

Even if other researchers have clearly lower estimations (e.g. Klaus 2003), there is a

common agreement that there exists a large savings potential. Two studies from the

year 1999 estimate the savings potentials at about 12–25% (Baumgarten and Wolf

1993; European Logistics Association and A.T. Kearney 1999).

In order to achieve these savings, new advanced logistics-technology is employed.

But logistics should never be reduced to its cost-reducing effect, as logistics concepts

can also be utilized to improve service and consequently increase sales (Angerer and

Corsten 2004). The next section deals with one of the most important measures used

to quantify customer-service levels: the on-shelf availability rate.3

1.2. Excellence in Store Operations

A high availability rate of products on the shelves is of utmost importance for

retailers. All the efforts made to improve the supply chain are futile if, in the end, the

consumer is unable to buy the product because it is not available on the shelf. There

2 Source: Baumgarten and Thoms (2002).

3 The on-shelf availability rate is the percentage of products that are available for purchasing on the store's

shelves at a particular moment in time.

4 1. Introduction

exist studies that show that out-of-stocks (OOS) in stores are the most frequently

mentioned cause of frustration for dissatisfied customers in retail (Sterns, Unger et al.

1981). Interviews with practitioners confirm the importance of high shelf availability:

"The three criteria that decide the success of a product are the right price,

the right forms of advertisement and high on-shelf availability. (...) In

particular, if there is a promotion, there is nothing more important than

having the goods on the shelf!"4

Obviously, the impact of an OOS depends on the reaction of the customer:

"The reaction of customers [on OOS] differs a great deal. If the customer

buys a different brand, we are happy. If he or she does not buy anything at

all, then we are not content. And if the customer buys the product in a

competitor's store, that is a catastrophe! Seventy percent of customers

change to the competition for good if they experience repeated OOS; and

that is a complete catastrophe!"5

Furthermore, there is a strategic component to high shelf availability, as it ensures an

advantage in increasingly competitive markets:

"If we want to compete with new aggressive retailers such as LIDL which are

planning to enter the Swiss market, we have to increase the turnover per

square meter. For that, we need to increase the on-shelf availability (...) to

make our stores more interesting for customers."6

The importance of a high availability is underlined by the research of Drèze, Hoch et

al. (1994) among others. They show that the total amount of money spent on any

store visit is an elastic quantity and is highly dependent on product presentation and

quantity on the shelf. Although the on-shelf availability rate plays such an important

role in the business of retailers, it seems that only a minority of European grocery

retailers systematically measures this important KPI (key performance indicator). A

case study of 12 leading European grocery retailers has shown that only four

companies have established a process for daily availability check (Småros, Angerer

et al. 2004a). Only one retailer had implemented an electronic-based system for

automatic checks. The magnitude of the OOS problem still appears not to have been

identified by many retailers. They tend to derive the availability rate in their stores

from the service level at their distribution centres (DCs). Their argument is that if the

4 Source: Arthur Mathys, Director Logistics, Denner, 04.08.2003.

5 Source: Wolfgang Mähr, Director IT, Spar Switzerland, 16.02.2004.


1. Introduction 5

DC can fulfil 99% of the store orders, then one can expect an on-shelf availability rate

of 99%. This thought is not quite correct, as the work of Gruen, Corsten et al. (2002)

demonstrates. Their meta-study proves that in the last few decades the OOS rate

has not decreased. It seems to have remained rather stable at a level of about 8%.

This high figure is rather surprising for manufacturers and retailers, as they expected

a far better rate considering the progress made in technology and new logistic

concepts introduced in the last few years. In order to tackle this problem, the study

further examines the reasons for OOSs, as depicted in Figure 3.

Store forecasting

13%

Store ordering34%

Store shelving

25%

Distribution centre

10%

Retail HQ ormanufacturer

14%

Other cause

4%

Figure 3: Summary of OOS root causes7

Surprisingly, almost three-quarters of stock-outs are the direct result of retail store

practices and shelf restocking processes. A very similar result was found in a KLOG

project carried out with the European grocery retailer MYFOOD8. Sixty percent of the

OOS situations at this retailer were caused by the ordering behaviour in the stores. In

10% of the cases the goods were in the store but not on the shelves. Here lies a

possible answer for the ineffectiveness of existing ECR (Efficient Consumer

Response) activities on the OOS rate. Many of these ECR activities concentrate on

the smooth transportation of items up to the store gate. How replenishment orders

are placed, how order quantities are determined and processes in the so-called last

50 metres in the store still remains an area for research.

To see the financial implications of OOS incidents, Gruen, Corsten et al. (2002)

conducted an estimate of the overall effect of OOS on sales that takes into account

the response of consumers. The result is that on average, retailers might lose up to

7 Source: Gruen, Corsten et al. (2002).

8 The name of this retailer and of three others that are examined in the case study section 6 have been made

anonymous for confidentiality reasons.

6 1. Introduction

4% of their turnover due to SKUs (stock keeping units) being absent from the

shelves.

1.3. New Technologies Enable Automatic Store Replenishment

Systems

As half of all OOSs arise from incorrect ordering and forecasting processes, it is

sensible to have a closer look at stores' replenishment processes and systems.

Some decades ago, there was no alternative to manual store replenishment systems.

A planner, for example the store manager, was responsible for deciding the two main

parameters of replenishment systems, namely the amount to be ordered and the

when to place the order. In order to do this, the planner had to check manually the

quantity in stock. In the last decade, there has been an impressive diffusion of large-

scale information packages such as ERP (Enterprise Resource Planning) in

organizations (Kallinikos 2004). In addition, identification technology (such as

barcodes and scanners) and communication tools (such as EDI9) have become very

cheap, their implementation and use is nearly routine (Kuk 2004). Today, these and

other new technologies make it increasingly possible to automate the replenishment

decision-making. The interviews conducted with practitioners as well as other

surveys (Bearing Point 2003; Småros, Angerer et al. 2004a) clearly reveal a trend in

retailing towards automating store replenishment processes:

"The normal replenishment process has been until now consisted of store

personnel deciding what quantity to order by looking on the shelf. Now,

retailers want to let the systems take this decision."10

Semi-automatic systems merely support the planner in his decision, for example, by

showing him electronically the inventory and order restrictions. Advanced automatic

store replenishment systems are IT-based software systems that automatically

decide when to order which quantity. Nevertheless, there are several differences in

the complexity and performance of such ASR systems. The simplest systems just

place an order as soon as an article is sold or when a certain minimum stock level is

reached. No forecasts are made; the quantity to be ordered is calculated with a very

simple algorithm (e.g. fill up to a certain level). This kind of automatic system is, for

example, used by the Swiss retailers Mobile Zone, Marionnaud and Fust. One

example of a complex, state-of-the art ASR system comes from the company SAF

AG (Switzerland). The main advantage of their ASR software "SuperStore" is that it

9 Electronic Data Interchange.


1. Introduction 7

makes a separate forecast for every item in every store. This is in contrast to other

software programs, which make their calculations at SKUs/stores clusters due to IT

performance restrictions (Beringe 2002). Furthermore, such forecasts do not rely only

on historic sales. Their sophisticated causal models also consider price, promotion,

seasons, holiday and other events when predicting demand. The introduction of this

product in the German over-the-counter chemist retailer dm-drogeriemarkt resulted in

a 70–80% reduction in OOS incidents and simultaneously reduced the inventory

stock level by 10–20% (Beringe 2002). A detailed classification of the various existing

ASR systems is provided in section 4.1.

Several technological developments were necessary to enable the implementation of

such sophisticated replenishment systems:

� Electronic inventory systems

� Identification technologies (barcodes, scanners)

� Data warehousing capacities (for historical sales data)

� Electronic data interchange (EDI)

� IT computation power (for forecasts at SKU level)

� Enterprise Resource Planning (ERP) systems

First, inventory management systems were introduced that made it possible to

manage quantities of a product in the electronic systems. These electronic inventory

systems profited markedly from identifying technologies such as the barcode. The

order process was simplified by using fax and electronic connections (e.g. EDI)

between companies. IT systems' storing capacity increased, making it possible to

handle larger amounts of data. The storing of huge quantities of POS (point of sales)

and inventory data became feasible with new data warehouses and storage

mediums. Furthermore, not only was it internal data that was more easily accessible;

thanks to larger communication bandwidths, it has became possible to access large

quantities of external data as well. This new external data includes competitive

information (e.g. the price of a competitor's products), market data (e.g. from

marketing institutes) and collaborative data (e.g. collaborative forecasts with

suppliers) (Beringe 2002). The increased IT-power performance has made it possible

to calculate in fractions of a second increasingly complicated forecasts at SKU level.

In a nutshell, IT-capabilities do not seem to be the decisive restriction anymore. This

statement is underlined by a study by Sabath, Autry et al. (2001) which shows that

the information system capabilities (such as timeliness of information or compatibility

of the IT) of the surveyed manufacturers and retailers are on average on a rather high

8 1. Introduction

level.11 The authors conclude that these companies already have the basic

requirements to operate automatic replenishment programmes.

Yet, IT is only one step towards ASR systems; other questions concerning the

organization and logistical processes arise. The introduction of ASR systems goes

hand in hand with the implementation of large-scale ERP systems, and thus has

radical implications for the organizations and processes of firms. For Kallinikos (2004,

p. 8), the introduction of these packages marks "a distinctive stage in the history of

computer-based information technology's influence in organizations." Their main

achievement is the new possibility for integrating operations and information across

functions, departments and modules. Therefore, the organizational and behavioural

implications have to be considered. One example is the role of employees; the

introduction of such automatic systems can result in a dramatic change of their

working habits:

"The changes resulting from the introduction of ASR systems are enormous. (...) It

is a change of paradigm. Who has today the same job as 5 years ago? (...)

Especially elderly employees have problems with the changes. Our planners have

been doing their jobs for 25 years; one has to take this into account."12

The importance of ordering for the store employees can be seen on the following

statement of a grocery employee, which altered René Descartes famous statement:

"I order, therefore I am."

1.4. Research Deficit

What is the contribution of academic research in the field of ASR? Surprisingly,

although several retailers have automated their order process in the last few years

(Småros, Angerer et al. 2004a), there is almost no academic source examining this

topic at the level of the store. It is worth noting that other technologies in retail, such

as RFID (Radio Frequency Identification) and the introduction of the barcode, have

received far greater attention from the public and from scholars.13 One explanation

could be that ASR is a technology working in the background. If it works properly,

consumers should notice it only indirectly, such as through higher availability in the

stores. Yet this would not explain the interest received by other technologies, such as

EDI, which also work in the background. To sum it up, the questions surrounding

11

See Table 5. 12

Source: Stefan Gächter, DC-director, COOP, 16.02.2004. 13

This statement can be illustrated by a look at the agenda of European ECR initiatives. There exist several

working groups dealing with RFID and barcodes, but none that deals with automatic replenishment.

1. Introduction 9

ASR systems arising for practitioners and researchers can only be partially answered

through a review of the literature. The few sources related to this topic are presented

in the following.

General Inventory Management Literature

The contributions that come from general inventory management literature are rather

basic. Existing academic sources of inventory modelling sources seek to answer the

two primary questions that arise when dealing with replenishment systems, namely

when should which quantity be ordered (Wagner 2002). Many papers in the

operations research (OR) field concentrate on the modelling of replenishment

systems, and try to identify an optimum under certain conditions (see Groote 1994;

Silver, Pyke et al. 1998; Bassok 1999; Gudehus 2001). Algorithms calculate minimal

inventory levels by choosing the right order quantity and order point so that certain a

priori set objectives are fulfilled (e.g. a certain percentage of service level, a

maximum out-of-stock rate, etc.). In general, much of the inventory management

literature remains very theoretical. The implementation of these models in daily

business is rather difficult (Wagner 2002). Many simplifications are made. When

calculating the optimum, the specific situation of retailers at store level is not taken

into account. The critical costs of retailers at store level are not inventory holding

costs, but handling costs, which can be between 3–5 times as large as the former

(Broekmeulen, van Donselaar et al. 2004b). Yet, store handling costs are seldom

taken into account in these mathematical models (cf. van Donselaar, van Woensel et

al. 2004).

ASR Related Literature

One of the few sources dealing directly with ASR systems is a dissertation published

by Norman Götz (1999). His main achievement has been to develop software that

enables the automation of order placement. This program uses existing forecasting

heuristics and combines them into a new one. The benefits of Götz's program are

shown with a simulation based on real data from two stores of a German drug

retailer. The theoretical benefit compared to the old system is an average cost

reduction of 14.5%, mostly from a reduction in the inventory holding costs. Götz's

simulation shows a strong effect of the automation on the OOS rate: Out-of-stocks

are reduced by almost 80%. The time savings for the stores are calculated at about 5

hours per ordering day. The remarkable contribution of this thesis is that for the first

10 1. Introduction

time the benefits of such systems were calculated, at least in theory.14 Götz stresses

that one of the main advantages of the system is that retailers have the power to

realize the described benefits on their own, independently from the rest of the market.

Nevertheless, his work is only the very beginning of the research on this topic. The IT

systems described in Götz's work are no longer state-of-the art.15 The overall focus of

his work is rather mathematical; the main goal is the development of an optimal

forecasting heuristic. Consequently, many aspects in the context of ARP systems

such as optimal implementation and organizational influences are not considered.16

Supply Chain Management

As valuable as Götz and other contributions from inventory management research

are, they show the limits of focusing strongly on mathematical or IT aspects when

dealing with ASR systems. An approach to the topic from a more abstract level could

be helpful, as the implementation of ASR systems is a fundamental change in the

way the flow of materials is triggered in a supply chain. Therefore, SCM research,

which deals with the importance of having demand-based replenishment systems

(pull systems), is examined in this thesis.17 One effect of such a pull-supply chain is a

major increase in efficiency and performance (Fiorito, May et al. 1995; Cottrill 1997;

Closs, Roath et al. 1998). Because the competition between grocery retailers is so

fierce, Bell, Davies et al. (1997) regard pull-supply chains as a necessity for every

retailer. A practical implementation of the idea of a demand driven supply chain are

automatic replenishment programmes (ARP). Common ARPs are Vendor Managed

Inventory (VMI), Continuous Replenishment Planning (CRP), Quick Response (QR)

and Collaborative Planning, Forecasting and Replenishment (CPFR).18 Overall, the

sources on this topic (e.g. Daugherty, Myers et al. 1999; Ellinger, Taylor et al. 1999;

Myers, Daugherty et al. 2000; Sabath, Autry et al. 2001) show the major benefits of

such programmes, because OOS and handling costs are reduced while the inventory

turn increases.

14

Götz (1999) used real data for his analysis. Yet, he did not prove that such systems would also work under real

life conditions. 15

For example, in order to save computing power, the products are clustered into 4 groups that have a common

forecast function. Today's systems have evolved rapidly in the last few years so that this constraint is no longer

relevant for today's ERP systems. 16

Only once does Götz acknowledge that performance could depend on the satisfaction of employees with the

new software and their commitment to it (Götz 1999, p. 186). 17

A pull system is driven by demand at the lowest point of the chain (Christopher 1998). In the context of this

thesis this would be the shopper in the store. 18

See for an explanation of these concepts Christopher 1998, chapter 7; Seifert 2002; Alicke 2003, pp.168-169

1. Introduction 11

Although the research methodology and findings of these researchers concerning the

performance and context influences of ARPs are remarkable, the transfer of their

results to the ASR systems context is limited. The research deficit in this field is that

the theoretical sources have concentrated up to now on pull systems from the

manufacturer to the retailer's distribution centres. In the context of ASR systems, it is

also necessary to consider the replenishing of stores. In ASR systems the demand is

being driven by the shopper in the store.19

Myers, Daugherty et al. (2000) recommend focusing future research on

replenishment automation in a single industry. Sabath, Autry et al. (2001, p. 103)

further state that "the issue of information systems capabilities is vital as well and

deserves further study." Furthermore, the authors point out the importance of making

additional investigations concerning organizational structure for developing decision

guidelines. All these research recommendations are considered in the conception of

this thesis.

Overview of Research Deficits

To sum up, more research on this topic is required because the subject of ASR has

not received in theory the attention it deserves considering its importance for

practitioners. The first deficit is that there is no academic source describing the

different systems in use; thus, a descriptive model and a classification has to be

developed. Furthermore, an examination of retailers has revealed that the

introduction of ASR was often part of changes taking places in the entire ERP

system, therefore significant financial and managerial inputs are necessary (Keh and

Park 1997). Although there are many success stories from practitioners describing

the enormous advantages of introducing automatic store replenishment systems

(see, for example, Beringe 2002; Anderson 2004; Hopp and Arminger 2005) there

has been only limited examination of such statements from academic sources.

Consequently, it is not surprising that some of the retailers interviewed are sceptical

about the sense of such systems. Even if practitioners are convinced as to the utility

of ASR, they remain insecure on the question of which system to choose for different

types of products. There is no conceptual framework available at the moment which

would help practitioners to choose an adequate replenishment system. In order to be

able to develop such a model more needs to be known about the relationship

between replenishment performance (e.g. OOS rate, inventory levels) and contextual

19

For a detailed explanation of the limitations of ARP see section 3.2.2.

12 1. Introduction

variables (such as store and product characteristics). Finally, it is not clear how

retailers have to adapt its organization and processes to best support the chosen

ASR system.

The research deficits in the context of ASR systems are summarized in Table 1.

Research Deficits Concerning ASR Systems

Missing:

� descriptive model and classification

� qualitative study on benefits

� knowledge about relationship between store and product

characteristics and the performance of replenishment systems

� method determining necessary replenishment system for each

product category

� knowledge on the change retailers' organization and

processes

Table 1: Overview of research deficits

1.5. Research Questions

In the last sections it was demonstrated that numerous unknowns exist in the context

of ASR systems. Therefore, this research aims to answer following main question:

Q: Under which conditions can retailers benefit from automatic store

replenishment systems?

Practitioners (cf. Bearing Point 2003) often speak of automatic replenishment

systems without taking into account that these systems can vary from very simple

heuristic- based decision systems to highly sophisticated ones with self-optimization

and complex forecasting models. This thesis aims to help practitioners choose the

right system for their business. This implies a need for identification as well as

categorization of the automatic replenishment systems available. For that, this thesis

first develops a descriptive model from which a classification is derived.

Consequently, the first sub-question, which provides support in answering the main

research question is:

Q1: How can automatic store replenishment systems be classified?

1. Introduction 13

The kind and magnitude of benefits discussed in theory and practice is very broad;

therefore, they are addressed in this thesis in particular. For three of the possible

benefits (fewer OOS incidents, lower inventory levels and lower inventory variability)

quantitative examinations are carried out. The second sub-question in this thesis is:

Q2: What benefits can a company expect from the implementation

of replenishment systems?

Practitioners want to know if the replenishment systems they are using are best

suited for them. To be able to choose the right systems for a given business and

product category, it is necessary to perceive the interrelations between the elements

of such systems and to understand the influence of environmental setting on

performance. Therefore, knowledge about the influence of store and product

characteristics on the ASR system outcome is necessary. Consequently, the next

sub-question is:

Q3: How is the performance of ASR systems influenced by product and

store characteristics?

A new automatic replenishment system does not only influence the performance of

replenishment it can also influence the entire distribution system, delivery frequencies

and employee working behaviour. Consequently, the choice of a new replenishment

system with all its implications for an organization is a strategic decision and will be

one focus of this thesis. Practitioners need a methodology for choosing the right

system. For this reason, the next sub-question is:

Q4: Which ASR system is recommended given the characteristics of a

certain product and retailer?

It can be assumed that some companies will not have a system that adequately

meets their needs. Consequently, the recommendation will be to implement another

type of ASR system. For an automatic replenishment system to reach its full

potential, next to technical requisites it is necessary to adapt the retailers'

organization and internal processes. Therefore, the last question that arises and

which will be examined is:

Q5: Which intra-organizational aspects of a company have to be changed to

adapt a new ASR system?

14 1. Introduction

1.6. Thesis Structure

This thesis is structured as follows:

� Chapter 1 highlights the major contribution of logistics for retailers and

describes current challenges in retail. This chapter ends with the derivation of

the principal research questions from the research deficits. The methodology

for addressing these research questions is depicted in

� Chapter 2. This chapter sets out the research framework, the methodologies

used to address the research questions and illustrates the activities

undertaken by the author to accompany and guide the research process.

� Chapter 3 highlights the contribution of theoretical sources for this thesis. Four

research perspectives are examined, namely inventory management, logistics

and operations management, business information systems and contingency

theory. The theoretical findings from this chapter, together with evidence from

interviews, are the foundation of

� Chapter 4. In this chapter, hypotheses and models concerning automatic

store replenishment systems are developed. First, a descriptive model and a

classification structure for ASR systems are presented (Q1). Second, an

explanatory model is developed that explores the correlation between the

performance of retailers and the store, product and ASR system

characteristics. The created hypotheses are tested in

� Chapter 5, in a quantitative analysis with the help of real inventory and sales

data from a grocery retailer. One part of the statistical analysis shows how the

OOS rate and the stock levels of the automated goods changed compared to

a control group (Q2). Another part of the chapter illustrates the correlation

between store/product characteristics and ASR performance (Q3). A major

finding described in this chapter is that ASR systems are indeed beneficial for

retailers, yet their contribution depends strongly on how they are implemented.

Therefore, in

� Chapter 6 there is a description of how four European retailers have

implemented sophisticated ASR systems. The case studies show the required

organization and processes for successful ASR implementation (Q5). This

chapter ends with several action recommendations for mangers as regards

store operations and choice of system (Q4).

� Finally, Chapter 7 summarizes contributions to theory and practice.

Figure 4 illustrates the structure of this thesis.

1. Introduction 15

2. Research Framework and Design

How can the research questions be answered?Result: Research framework and methodology


How can the research questions be answered?Result: Research framework and methodology

8. References & Appendix8. References & Appendix

4. Development of Models

Descriptive model and classification of ASR systemsExplanatory modelResult: Models and hypotheses to describe and explain different ASR systems


Descriptive model and classification of ASR systemsExplanatory modelResult: Models and hypotheses to describe and explain different ASR systems

1. Introduction

The challenge of store replenishment for retailersResearch deficits and research questions

1. Introduction

The challenge of store replenishment for retailersResearch deficits and research questions

3. Literature Research

Four theoretical perspectives: inventory management, logistics/operations management, business information systems and contingency theory Result: Theoretical foundation of thesis


Four theoretical perspectives: inventory management, logistics/operations management, business information systems and contingency theory Result: Theoretical foundation of thesis

6. Field Research and Managerial Implications

Case studies from four European retailers Result: Required changes for successful ASR implementation

Decision tree: adequate ASR system Recommended store operations


Case studies from four European retailers Result: Required changes for successful ASR implementation

Decision tree: adequate ASR system Recommended store operations

5. Quantitative Analysis

Sample and statistical methodologyTesting of hypothesesResult: Proof of the benefits of ASR systems


Sample and statistical methodologyTesting of hypothesesResult: Proof of the benefits of ASR systems

7. Conclusion

Contribution to practice and theoryResearch limitations

7. Conclusion

Contribution to practice and theoryResearch limitations

combined with interview results is the basis of

developed hypotheses are tested in

how to introduce ASR systems in practice is shown in

defines main focus of

Q1Q1

Q4Q4 Q5

Q5

Q2Q2 Q3

Q3

Figure 4: Thesis structure

16 2. Research Framework and Design


The outcome of any research is strongly affected by the choice of the research

methods and strategies. As Scandura and Williams state (2000, p. 1249) "[a]ny

research method chosen will have inherent flaws, and the choice of that method will

limit the conclusions." This means that design choices about instrumentation, data

analysis and construct validation may affect the types of conclusions that are drawn

(Sackett and Larson 1990). Therefore, this chapter gives an overview of the research

methodology chosen for this thesis. First, a research framework narrows the research

field down. Second, the qualitative and quantitative research methods are depicted

before, finally, the research activities that led to this thesis are presented.

2.1. Research Framework

The research framework mainly focuses on the role of automatic replenishment

systems for European grocery retailers. The main research field of this thesis is not

the distribution system to the store itself, but the ordering logic that lies behind it

(see also Figure 5). This means that the focus of the thesis is the logic of the system

that decides at what time and in what quantity the goods are replenished in a store.

The routes taken by the goods or their mode of transportation are only secondary in

this context. Consequently, whenever in this thesis the term "supplier" is used, there

is no differentiation between products that have come from a retailer's own

distribution centre or directly from the manufacturer. The shop-floor logistics (i.e. how

the goods are transported from the backroom to the shelves) are a relevant part of

the research framework, especially when talking about changes in process due to

ASR implementation.

DC retailerDirect supplier

DC retailerDirect supplier

Store ordering processes

(major European

grocery retailers)

Store ordering processes

(major European

grocery retailers)Flow of material

(replenishment of products)

Flow of information(order-, inventory level-,

POS-data ...)

Pre-supplierPre-

supplier SupplierSupplier CustomerCustomer

Focus of thesis

Figure 5: Focus of research

2. Research Framework and Design 17

The focus on Europe does not mean that such systems are not interesting for other

retailers from other world regions as well. The OOS studies mentioned in the

introduction show a similar level of operational problems all over the world (Gruen,

Corsten et al. 2002; Roland Berger 2003b), and replenishment systems play an

important role for North American retailers as well. A Bearing Point technology study

(2003) shows that although 61% of the North American retailers studied use

automatic replenishment systems, only 38% have a well-documented and

established inventory management strategy and thus a clear view of how such

systems are best implemented within the organization. As there are no fundamental

differences in the logistics or store operations between these two regions, one can

presume that the findings will be significant for American retailers as well.20 The

same holds true for retailers from other regions so that the result of the European

research will be most probably globally relevant.

The grocery retailers group is among the most interesting ones in retail, as the

leading grocery retailers have in the last decades broadened their categories to

products that are not related to the food sector at all. Guptill and Wilkens (2002)

describe a grocery store as a retail store with at least 1,500 different food items

and/or $2 million in annual sales that sells dry grocery, canned goods and non-food

items plus some perishable items. A grocer is, according to the Oxford English

Dictionary, a "trader who deals in spices, dried fruits, sugar and, in general, all

articles of domestic consumption except those that are considered the distinctive

wares of some other class of tradesmen."21 This last definition does not seem to be

precise enough nowadays. The leading retailers sell through their distribution

channels almost all kinds of consumer goods that were some decades ago only

available at certain speciality stores (e.g. apparel, computers, household goods,

garden articles, etc.). The term modern grocery22 is sometimes used to separate the

core business of groceries (food and near food) from more exotic products (e.g.

financial services). This large variety of articles force retailers to have separate

logistics strategies for their items, depending on various factors such as speed of

turnover, value, demand volatility, etc. Some retailers have adopted different

strategies and channels to distribute their products. There exists a large variance of

stores, ranging from rather small supermarkets (200 m2) to huge hypermarkets

(10,000 m2 and larger). This breadth of product range and store size among grocery

20

This is a so-called analytical generalization as described by Punch (1998). 21

Source: definition found on the site http://www.oed.com (accessed 01.09.2005). 22

The term modern grocery is taken from the definition of Planet Retail: modern grocery distribution includes both

grocery and non-food sales from modern grocery distribution formats. It excludes sales from independent

specialist formats and wet markets. Source: http://www.planetretail.org (accessed 01.09.2005).


retailers will facilitate the generalisation towards other retailers with similar product

categories or distribution channels. Another advantage of this group is the high

variance that exists within it: some grocery retailers still rely on completely manual

systems, while others have implemented very sophisticated systems with complex

forecasting algorithms (Småros, Angerer et al. 2004a). Grocery retailers have to fulfil

consumers' wishes immediately, as supermarket visitors are not normally willing to

wait for the delivery of their goods. This business has further a strong demand

volatility as many products are easily substituted, making SKU-level forecasts

difficult. Therefore, all the grocery retailers have adopted a make-to-stock strategy.23

Another benefit of studying grocery retailers is that they are rather well organized in

initiatives such as ECR-Europe. There are several project groups analysing their

supply chains and the benefit of collaboration, standardization, electronic data

interchange, identification and more so that additional information sources are more

easily available for researchers. And last but not least, the choice of grocery retailers

follows the research tradition of the institutes ITEM and KLOG at the University of

St.Gallen, where this subgroup of retailers has been examined for several years.

2.2. Research Methodology

The long tradition of the University of St.Gallen to focus on topics that are relevant to

business practice is fully continued by this work. One of the main advocates of this

statement is without doubt Hans Ulrich. For him, business science is understood as a

leading and managing-science and has thus the central objective of giving

practitioners the ability to act and to make decisions in a scientific way (Ulrich 1981).

The starting point for each research project in business science is an analysis of

existing practical problems. First of all, interesting situations, correlations and

contexts are observed from a practical point of view and then are conceptualised

(Ulrich 1981). The concepts that are developed are tested in practice again and again

and become gradually more refined. This iterative learning process will finally

generate at the end of the research process theoretical and practical solutions to the

identified problems that can again be tested in practice (Kromrey 2002). Ulrich's

design of a 7-step research process was the base for the structural approach of this

thesis (see Figure 6). The main characteristics of this procedure are the iterative

approach and the deep contact of the researcher with the practice.

23

For an explanation of make-to-stock see Alicke (2003, p. 50).


Step 1: Identifying and structuring problems and their potential solutions that are relevant to business reality

Step 1: Identifying and structuring problems and their potential solutions that are relevant to business reality

Step 2: Identifying and interpreting theory and hypotheses of the empirical social sciences relevant for the targeted problem

Step 2: Identifying and interpreting theory and hypotheses of the empirical social sciences relevant for the targeted problem

Step 3: Identifying and specifying formal scientific procedures that are relevant for the targeted problem

Step 3: Identifying and specifying formal scientific procedures that are relevant for the targeted problem

Step 4: Identifying and assessing the relevant application contextStep 4: Identifying and assessing the relevant application context

Step 5: Deriving rules and modelsStep 5: Deriving rules and models

Step 6: Testing rules and models in the application contextStep 6: Testing rules and models in the application context

Step 7: Documenting research results and consulting of practitioners Step 7: Documenting research results and consulting of practitioners

Practice

Practice

Practice

Formalsciences

Empiricalsocial sciences

Figure 6: Integrative research procedure24

In the first part of the research process (steps 1 to 4), a mix of qualitative research

methodologies is used. These research steps are documented in this thesis in

chapter 2 and 3. Campbell and Fiske (1959) have stressed the importance of using

several methodologies to overcome the main deficiency of qualitative research: the

limited generalisation. Denzin (1978; 1989) in turn develops the term "multiple

triangulation" that applies when researchers combine multiple observers, theoretical

perspectives, sources of data and methodologies in one investigation. He further

states that "all the advantages that derive from triangulating single forms are

combined into a research perspective that surpasses any single-method approach"

(Denzin 1978, p. 304). The term triangulation is a metaphor from navigation which

"use[s] multiple reference points to locate an object's exact position" (Smith 1975, p.

273). The triangulation of data collection settings affects the external validity of the

results (McGrath 1982). Several researchers from social and business sciences,

such as Webb, Campbell et al. (1966), Smith (1975) and Jick (1979), recommend

triangulation. The research process used in this thesis adopts this methodology by

simultaneously combining different research methods and data collection forms.

24

Source: adapted and translated from Ulrich (2001, p. 222).


Mc Grath (1982) defines eight research strategies used in management research.

The one strategy corresponding closest to the methodology used in this thesis is the

field study. Field studies investigate behaviour in its natural setting. The data is

collected by the researchers themselves on site. Scandura and Williams (2000, p.

1251) give the advantages and risks of this method:

"This strategy maximizes realism of context, but it can be low on precision of

measurement and control of behavioural variables (there is lack of experimental

control). It can also be low on generalizability to the population, with the study

population not representative of the target population."

Despite these drawbacks, this research strategy is extremely popular. In a review of

385 papers of the journals Academy of Management Journal, Administrative Science

Quarterly and Journal of Management from the years 1995–97, Scandura and

Williams (2000) found out that 67.5% of all papers had chosen field studies as the

research strategy. Ten years previously the percentage was only 54.1%, therefore a

significant increase had taken place. An example of this strategy in this thesis is the

KLOG OOS-project conducted for the grocery retailer MYFOOD. This was not an

experiment, as no variable was manipulated, and the researchers acted merely as

observers.

The data collection and its analysis comprise qualitative and quantitative research

strategies. Friedli, Billinger et al. (2005) state that the three most frequently used

methods of qualitative research are action research, grounded theory and case

study. The main methodology used in this thesis is the last. The basic idea behind

case studies is to investigate only a small number of cases, sometimes even only

one, yet these in a great detail. As Punch (1998, p. 150) states, "the general objective

is to develop as full an understanding of the case as possible." According to Yin

(1988, p. 23), a case is an empirical inquiry that:

" � Investigates a contemporary phenomenon within its real-life context; when

� the boundaries between phenomenon and context are not clearly evident;

and in which

� multiple sources of evidence are used."

As demonstrated in section 1.4, there are almost no existing academic sources that

investigate the topic of this thesis. The focus on the case study method seems

therefore to be most appropriate for this research project, as this methodology is

recommended for researching topics in new areas (Eisenhardt 1989). The external

validity of qualitative studies is sometimes considered to be limited. It is true that case

study research is not able to give statistical generalisation, hence the external validity


comes from analytical generalisation (Gassmann 1999). In case study research,

however, generalisation is not always the goal. Some cases might be so important or

interesting that they deserve a study in their own right (Punch 1998). To achieve

generalisation, it is necessary to conduct the research at a sufficient level of

abstraction:

"The more abstract the concept, the more generalisable it is. Developing abstract

concepts and propositions raises the analysis above simple descriptions, and in

this way a case study can contribute potentially generalisable findings" (Punch

1998, p. 155).

The main advantage of case studies is that they are most appropriated for describing

and understanding complex social systems (Marshall and Rossmann 1995). Another

argument for the case study approach is that the phenomenon observed in this thesis

happens in the presence of but cannot be influenced by the researcher (Yin 1988).

The main reason for choosing the case study method is that case studies have a

holistic focus and aim to preserve and understand the wholeness and unity of a case

(Punch 1998).

More than 50 interviews with practitioners were conducted, with the aim of reaching

an in-depth understanding of retailers' logistics (see the interviews listed in section

1.1). These interviews are conducted orally, as the actuality and explorative character

of this research phase would not support a written interview technique (Lamnek

1993). The intimate connection with empirical reality that is demanded by Glaser and

Strauss (1967) in order to develop a sound, relevant and testable theory is

guaranteed with this approach. There are, nevertheless, also elements from another

two popular qualitative methods. The main idea from grounded theory is that

qualitative research has the goal of generating new theories (Punch 1998; Friedli,

Billinger et al. 2005). And from action research the idea is adopted that the starting

point for a research project is not a theory but a problem in practice (Coughlan and

Coghlan 2002). The aim of the action researcher is to attempt to change the

examined system and obtain a desired new status (Ulrich 1981; Coughlan and

Coghlan 2002). The author was not personally involved in this change; nevertheless

the last chapter of the thesis gives recommendations to managers as regards

technical and organizational changes that a retailer may undergo to reach a new

level of automation.

In order to derive and test rules and models (steps 5 and 6), four case studies of

retailers using advanced ASR systems are described (chapters 4 and 5 in this

thesis). This is the part of the research project with the greatest interaction with


practice; several iterative loops between theory generation and theory verification are

carried out. On the one hand, the information gained from the interaction with the

chosen companies is used to verify and refine the descriptive model developed in

sections 4.1 and 4.2. On the other hand, the information gathered is the main source

for developing the explanatory model. Closure is achieved when the differences

between collected data and developed theory is small (Bansal and Roth 2000). A

good illustration of this iterative process can be seen in Figure 7.

Questions to reality

Questions to reality

Iterative learning

processData collectionData collection

Problems ofpractice

Problems ofpractice

Phenomenonof practice

Phenomenonof practice

Critical reflectionof gained picture

of reality

Critical reflectionof gained picture

of reality

Differentiation,abstraction, change

of perspective

Differentiation,abstraction, change

of perspective

Theoreticalcomprehension

Theoreticalcomprehension

Literaturereview

Literaturereview

Own constructs

Own constructs

THEORY EMPIRICISM

Figure 7: Case study research as iterative process between theory and empiricism25

For the creation and verification of the models, the author does not rely only on

qualitative data. As stated by Yin (1988) and Eisenhardt (1989), the data collection

method and the case study process can also include more qualitative elements.

While experiments have great internal validity because of their precise control of

variables, an additional external validity can be achieved by accompanying surveys

(Scandura and Williams 2000). Therefore, next to several in-depth interviews

(qualitative data), archival documents review, participant observation and analysis of

the data gained from the ERP system of one retailer is included. This process allows

the researcher to gain an even broader understanding of the company, or as Jick

(1979, p. 603) states: "a more complete, holistic and contextual portrayal of the

unit(s) under study." In addition, the quantitative analysis is a critical part of the

testing of the models and hypotheses created in section 4.3.

Some researchers criticise the simultaneous use of quantitative and qualitative

methods, as every method has different aims and purposes (Dey 1993). The

traditional distinction between two schools of social sciences, one oriented towards

25

Source: translated from Gassmann (1999).


the qualitative development of theories, the other directed at the quantitative testing

of theories, is criticised by Baumard and Ibert (2001). The authors point out the

importance of not being dogmatic, as both forms of data are useful for constructing

and testing theories. Or, as Glaser and Strauss (1967, p. 17) formulate:

"There is no fundamental clash between the purpose and capacities of qualitative

and quantitative methods or data. (...) Each form of data is useful for both

verification and generation of theory."

The final step in this research project (step 7) is the developing of recommendations

for managers (chapter 6 of this thesis) and the documentation of the findings in form

of the presenting thesis.

2.3. Research Process

The research process was guided by several activities which can be seen in

Figure 8.

Summer2005

(1) Literature review(1) Literature review

(7) Synthesis of the results: writing of thesis(7) Synthesis of the results: writing of thesis

October2002

(6) Field research: qualitative and quantitative analysis(6) Field research: qualitative and quantitative analysis

(5) Consulting

projects

(5) Consulting

projects(2) Activities

with

ECR-Europe

(2) Activities

with

ECR-Europe

(4) European

grocery

research

study

(4) European

grocery

research

study

(3) Supervision

of Bachelor

and Master

theses

(3) Supervision

of Bachelor

and Master

theses

(5) Consulting

projects

(5) Consulting

projects(2) Activities

with

ECR-Europe

(2) Activities

with

ECR-Europe

(4) European

grocery

research

study

(4) European

grocery

research

study

(3) Supervision

of Bachelor

and Master

theses

(3) Supervision

of Bachelor

and Master

theses

Figure 8: Research activities in this research project

The investigation began, as expected, with a thorough review of the available

literature and other information sources (Activity 1). Due to the lack of academic

papers dealing with ASR systems, the research started with more general inventory

theory and logistics literature. Later, the research concentrated on papers targeting

typical retail problems, such as data accuracy, inventory visibility and other logistics


challenges. A new perspective came from the study of papers dealing with business

IT and contingency theory.

The involvement in the ECR Europe community was critical to obtain first hand

information from practitioners (Activity 2). In several dialogues and meetings, the

author deepened his understanding of business practices and current challenges in

the retail and consumer goods industry. The involvement of the author in the ECR

community was:

� Personal involvement in the ECR-Switzerland working group "On-shelf

availability"

� Participation in the quarterly ECR-Switzerland working meetings and the

official ECR Europe Conferences in Berlin (2003), Brussels (2004) and Paris

(2005).

� Participation in various activities of the ECR Europe Academic Partnership,

including the publishing of the ECR Journal and the organization of the annual

ECR Student Award.

The supervision of bachelor and master theses was an additional important source of

first hand information from practitioners in the industry (Activity 3). All supervised

works focused on actual problems of enterprises in the consumer goods sector and

gave a broad overview of the performance of logistics within retailers:

� CPFR in the sports industry. Simon Steiner (2003)

� CPFR in the fashion and apparel sector. Marion Brägger (2003)

� CPFR in the German grocery retail market. Juerg Neuenschwander (2004)

� CPFR in the consumer electronics industry. Thomas Köhl (2004)

� Chances and limits of CPFR in the Swiss grocery retail. Diego Rütsch (2004)

� Limits and possibilities of CPFR in the Swiss grocery retail. Dominic Loher

(2004)

� Supply Chain Management in the fashion industry: critical factors. Benjamin

Brechbühler (2004)

� The operational control of stores with inventory management systems. Remo

Maggi (2004)

� Efficient Consumer Response. Impacts on consumer and welfare. Gabriella

Todt (2005)

The supervision of this work resulted in over 45 documented interviews with directors

and managers in charge of logistics and supply chain management, category

management, or information technology. The chosen companies were mainly

retailers, followed by logistics and technology services providers. This more


explorative approach led to a deep understanding of the current logistics processes

within retailers. The result of this inductive qualitative approach was the creation of

the descriptive and parts of the explanatory model (see chapter 4.3).

The next information source for the creation of the models and hypotheses was the

pan-European study "Logistics processes of European grocery retailers" (Activity 4).

It was launched in autumn 2003 and lasted for about a year. The aim of the study

was to investigate the current logistics processes and performance of leading

European grocery retailers. The study was conducted as a collaborative project

involving researchers representing five different European universities.

The consulting projects conducted together with industrial partners were another

important source of information (Activity 5). The most valuable project for the

research into on-shelf availability was conducted with the grocery retailer MYFOOD.

The data gained through this project is the main data source for the quantitative

analysis (see chapter 5). In addition, close contact with employees from MYFOOD

gave helpful insights into retailers' replenishment operations.

The last activity before finishing the thesis (Activity 7) was the creation of field

research of European retailers that have ASR systems in use (Activity 6). First, in

short interviews (about 15 minutes), some 30 German, Swiss and Austrian retailers

from very different consumer goods sectors were asked about their replenishment

practices. The information gained in this survey was used to choose the four

companies for the case study and also to design a semi-structured interview for the

analysis. The selected companies and the outcome can be seen in chapter 6.

26 3. Literature Research


There exists almost no academic source that deals directly with automatic store

replenishment systems at retail store level. Yet, even if theory cannot provide direct

answers to the research questions, it is fruitful to consider the presented practical

problem from different theoretical perspectives in order to obtain new impulses for

this research project. Therefore, four theoretical research sources are addressed

below in detail to verify, what contribution further theoretical streams can offer. The

literature research focuses on sources dealing with inventory management, logistics

and operations management, business IT and contingency theory (see Table 2).

Perspective Contribution to the Thesis/ Research Question

Inventory Management

� Definition of decisions an ASR system has to take (descriptive model) � Existing replenishment logics and strategies for replenishment systems

(classification) � Performance of different replenishment logics � OOS research: reasons and interrelations � Forecasting methods and typologies

Q1

Q3

Logistics and Operations Management

� Benefits of well specified and organized logistics systems and programmes

� Best practice in store operations � Coordination of structures, processes and systems to increase the

efficiency of the SC

Q2

Q5

Business IT Theories

� Role of Enterprise Resource Planning (ERP) systems � Organizational and human agency changes due to new technology

Q4

Q5

Contingency Theory

� Importance of the choice of the right ASR system for a given environment � Organizational perspective: influence of the contextual factors within an

organization on performance of ASR systems

Q4

Q5

Table 2: Overview of basic theoretical sources reviewed (excerpt)

3.1. Inventory Management Perspective

The first perspective that is used to examine the research problems focuses on the

challenge of right inventory management. Existing academic sources in inventory

modelling seek to answer the two primary questions that arise when dealing with a

replenishment system, namely when should which quantity be ordered (Wagner

2002). Many sources in the operations research (OR) field concentrate on the

modelling of replenishment systems, and try to identify an optimum under certain

restrictions (see Groote 1994; Silver, Pyke et al. 1998; Bassok 1999; Gudehus 2001).

They seek to obtain a minimal inventory level by choosing the right order quantity and

order point so that certain a priori set objectives are fulfilled (e.g. a certain percentage

3. Literature Research 27

of service level, a maximum rate of out-of-stock, etc.). This approach has been widely

used in practice for many years (cf. Chang 1967). More sophisticated models seek to

optimize a specific utility function. The challenge here is to identify and quantify all

the relevant logistics costs that should be included in this function. Inventory costs

include factors such as the cost of carrying stock, order costs, safety stock costs,

transport costs and an estimate of the out-of-stocks costs. For example, Galliher,

Morse et al. (1959) and Dalrymple (1964) explicitly consider OOS-costs in their

inventory control systems.26 The financial importance of optimal inventory control for

companies is stressed by many authors such as Vollmann, Berry et al. (1992) and

Dubelaar, Chow et al. (2001). Some sources even investigate the importance of the

inventory for entire economies. In the United States, for example, the inventory value

as a percentage of the GDP was in 1993 as high as 17.7% (Silver, Pyke et al. 1998).

In the following sections, first theoretical inventory management sources dealing with

mathematical models are highlighted. Papers and studies that in particular examine

OOS situations in retail form the second part of the inventory management

perspective.

3.1.1. Optimization in Inventory Management Research

Inventory control systems have the aim of balancing demand and supply, reducing

overall inventory costs and assuring an adequate service level (Wegener 2002).

There are countless papers dealing with the modelling of sophisticated mathematical

solutions for such inventory management systems (see e.g. Chang 1967; Inderfurth

and Minner 1998; Ketzenberg, Metters et al. 2000).27 Wagner (2002) criticises the

rather theoretical approach of these papers; their implementation in real life

applications is problematic.

Several other authors concentrate on retailers and their specific inventory

management challenges. An identified central strategic issue that influences the

success of a retailer is the setting of the right service level (Balachander and

Farquhar 1994; Gudehus 2001; Zinn, Mentzer et al. 2002). Other papers deal directly

with the optimal ordering policy for retailers from a marketing point of view, i.e. they

look for the optimal assortment and shelf combination to maximize sales. In the

1960's and 70's, several experiments were conducted to measure the effect of shelf

facings and inventory quantity on sales (e.g. Kotzan and Evanson 1969;

26

A detailed discussion of this cost function can be found in section 4.1.2. 27

For an overview of the history of OR research on inventory control, see Wagner (2002).


Krueckenberg 1969; Cox 1970; Curhan 1972). In the following decades, other

models sought to calculate an optimum in order to minimize inventory, shelf space

costs and backorder (c.f. Corstjens and Doyle 1981; Cachon 2001). In these models,

the handling costs are normally ignored. An exception is the paper by Broekmeulen,

van Donselaar et al. (2004a). These last researchers state that the inventory carrying

costs are low compared to the handling costs, therefore they suggest a

replenishment logic that takes shelf space and package restrictions more strongly

into account.

3.1.2. Theoretical Sources on OOS

A group of inventory management papers reviewed deals directly with OOSs. These

papers can be split into two sub-groups. The first group empirically studies the extent

and causes of OOSs. The first OOS study, conducted nearly 40 years ago, reports

an average OOS rate of 12.2% (Progressive Grocer 1968). More recent studies

report an OOS rate between 7% and 10% (Andersen Consulting 1996; Gruen,

Corsten et al. 2002; Roland Berger 2003b; Stölzle and Placzek 2004). The second

sub-group takes a marketing and behavioural perspective and studies the reaction of

consumers towards out-of-stock that crucially influence retailers' sales (e.g.

Emmelhainz, Stock et al. 1991; Drèze, Hoch et al. 1994; Zinn and Liu 2001; Sloot,

Verhoef et al. 2002). Retailers try to increase their sales with two groups of

market-driven tactics. On the one hand, there are "out-of-stores" tactics, which try to

bring more consumers into the stores. Avoiding OOSs is, by contrast, an "in-store

tactic." The latter tactics generally attempt to extract more surplus from shoppers

once they are in the store. An attractive, full shelf attracts the attention of the

consumer, making a purchase more probable. As shelf space is expensive28, retailers

have to decide whether to place another facing of a certain product (to increase its

visibility and/or reduce the OOS probability) or to place an additional SKU (Drèze,

Hoch et al. 1994). Several studies prove the value of such store specific micro-

merchandising; consumer decision-making can be strongly influenced. Only one third

of purchases are specifically planned in advance of a shopping trip (Dagnoli 1987).

Many buying decisions are made on a low level of involvement and very quickly

(Hoyer 1984). In addition, the average shopper shops in 3–4 supermarkets each

week (Coca-Cola Retailing Research Council 1994). With these facts in mind it is

easy to see the magnitude of the impact OOS still has today for grocery retailers'

28

In the USA store occupancy costs range between $20 per square foot for dry grocery and $70 per square foot

for frozen goods (Drèze, Hoch et al. 1994).


sales. The knowledge of consumer behaviour is necessary to calculate the losses

connected with OOSs.

3.1.3. Contributions and Deficits of an Inventory Management Perspective

The inventory management perspective has a significant contribution to this thesis:

� Basis of descriptive model

� Foundation of relationships in explanatory model

� OOS magnitude and impact

The descriptive and explanatory models developed in chapter 4 are based on

contributions from inventory management sources.29 In particular, the sources that

deal with challenges of replenishment in retail are of major value for this thesis. The

papers and studies dealing with OOS highlight the magnitude of this problem in

practice, illustrate the underlying reasons for low product availability and demonstrate

possible solutions.

Nevertheless, there are also some limits to the potential contribution of the theoretical

inventory approach to this research field:

� Simplified view of reality

� Context parameters are regarded as given

� Missing organizational and contextual aspects

First, all mathematical inventory models assume a very simplified view of reality so

that it is often very difficult to apply such systems a given real-life situation. Second, it

is often assumed that the parameters in such systems are given. What is overlooked

is that many parameters can be changed by the organization (e.g. delivery frequency,

case pack size) so that the strategic dimension of such decisions are not examined.

And third, these OR papers normally neglect the organizational and contextual

aspects of replenishment systems, which are nonetheless critical for the performance

of such systems. An exception to the last statement is the paper by Zomerdijk and

Vries (2002), as the authors place their research focus on environmental influences

of inventory control systems. The basic message of the authors is that, beside the

traditional points of attention such as order quantities and replenishment strategies, it

is critical to take care of contextual and organizational factors. The authors identify

four significant aspects in the organizational context of inventory management: task

allocation, decision-making and communication processes as well as the behaviour

29

For an overview of authors see Table 6.


of personnel. The notion of examining the contextual focus of replenishment systems

is a fundamental concept that is incorporated into the research questions (see

section 1.5).

Overall, a purely mathematical approach to inventory management is not appropriate,

as Wagner (2002) states. He sees a major problem in the fact that classical inventory

research is blind to all the "dirty data" issues that challenge companies (i.e. the data

in the system is not accurate enough to be used for control and calculations). As

Wagner further states, inventory modelling research is far removed from the

entrenched software that now drives supply chain systems. Therefore, it is necessary

to implement a comprehensive discussion of ERP and inventory holding systems, as

will be accomplished in section 3.3.

3.2. Logistics and Operations Management Perspective

Academic sources dealing with logistics and operations management form the

second perspective addressed in this thesis. Logistics research can be defined as the

systematic and objective search for and analysis of information relevant to the

identification and solution of any problem in the field of logistics (Chow and

Henriksson 1993). The basic assumption behind logistics research is that a particular

course of action will be correlated with logistics performance (Chow, Heaver et al.

1994). The problem starts with the definition of performance, sometimes hard

measures are meant (such as delivery time or net income), and sometimes more soft

measures are in the focus (such as consumer happiness ratings or flexibility). Both

perspectives have their strengths and weaknesses. A comprehensive overview of the

literature on this topic is made by Chow, Heaver et al. (1994). Their main criticizing

point is that none of these studies examines logistics performance in the context of

supply chains. Yet this statement has to be revised today in the light of the

comprehensive literature on this topic (cf. Stölzle, Heusler et al. 2001; Karrer,

Placzek et al. 2004; Stölzle 2004).

The logistics research cannot be completely separated from another research stream

that is of great relevance for this thesis: operations management. Operations

management is an area of business that is concerned with the production of goods

and services, and involves the responsibility of ensuring that business operations are

efficient and effective.30

30

Definition by Wikipedia (URL: http://www.wikipedia.org, accessed 1.09.2005).


In the following, three research streams are looked at: sources dealing with Supply

Chain Management (SCM) and Efficient Consumer Response (ECR), with Automatic

Replenishment Programmes (ARP) and sources examining retail operations.

3.2.1. Supply Chain Management and ECR

Supply Chain Management describes an approach between interdependent firms to

collaboratively manage material and information flows (Corsten 2002). One of the

basic principles of SCM is the management of connected activities that involve

several units in a supply chain. The term "units" can refer to departments within an

organization, but SCM also deals with "the management of upstream and

downstream relationships with suppliers and customers" (Christopher 1998) that are

outside the company. One of the goals of SCM is to reduce the inefficiencies, e.g. the

bullwhip effect (Lee, Padmanabhan et al. 1997a), observed in many supply chains.

The term SCM is understood here in broader terms; the explicit distinction between

logistics management and supply chain management that is sometimes found in the

American literature is not made.

Many SCM papers focus on the ideal design of supply chains (Lee and Billington

1992; Anupindi 1999; Gudehus 2001; Gudehus 2004; Pfohl 2004), how to control

them (Lee and Billington 1993), how to cope with the uncertainty of demand (Fisher,

Hammond et al. 1994; Feitzinger and Lee 1997) and the importance of sharing

information between firms (Lee, Padmanabhan et al. 1997b; Cachon and Fisher

2000; Chen, Drezner et al. 2000). Hines, Holweg et al. (2000), for instance, have built

a very colourful model of supply chains, comparing supply chain systems with waves

breaking on a beach. The book of Womack and Jones (1996) brought new

momentum to logistics and operations management by describing the authors' notion

of a lean supply chain.

One field of SCM deals with the creation of pull-based systems and is therefore

closely related to the ASR topic. Several authors stress the need for push systems

for obtaining efficient supply chains (Fiorito, May et al. 1995; Cottrill 1997). As Bell,

Davies et al. (1997) state, one of the main opportunities and at the same time

challenges for food retailers nowadays is the transformation of supply chains from a

push to a pull strategy. A pull system is driven by demand at the lowest point of the

chain (Christopher 1998). In the framework of this thesis, the lowest point is the

shopper. The simulation of Closs, Roath et al. (1998) demonstrates the benefits of a

response-based system. Their pull-replenishment model is able to reduce uncertainty

by sharing information between supply chain partners. The created automatic


replenishment system reduces (at least in theory) system-wide inventory by 25%,

delivers 2–3 percentage points better service level and gives more robust results

across a wide range of environments than traditional retail order-based systems. An

additional finding of major interest is that most of the inventory reduction happens at

retailer level. As a study of 12 major European groceries shows (Småros, Angerer et

al. 2004a), this is the part of the supply chain with the most need for improvement in

the stock situation. The new pull-replenishment system described by Closs, Roath et

al. (1998) creates benefits for all partners in the supply chain. A prerequisite for this

system, apart from investment in information technology, is the integrated inter-

organizational management of relationships. This means a change in the mindset of

the buyers and sellers, as the risks and the rewards have to be shared in a

cooperative approach. The first hurdle in such a cooperative attitude is the initial cost

associated with the IT introduction. A drawback of Closs, Roath et al.'s (1998)

approach is that their model stops at the retailer DCs, and does not take into account

retailers' stores. Yet, the aim of a responsive supply chain cannot be solved by just

the implementation of such technical systems. How retailer's processes and

structures have to change to adapt such a demand-based supply chain is, for

example, partly described in the case study by Fisher, Hammond et al. (1994) on the

apparel retailer Obermeyer. Again the contextual situation plays a major role when

designing the supply chain and replenishment systems and processes.

Lee (2001) stresses that it is impossible for the human mind to optimize all the

operations necessary for such demand-based systems. Lee therefore promotes the

utilization of computer-based tools in retail. He has created a list with characteristics

and requirements necessary for such systems to perform, such as the capability of

doing precise demand forecasts based on marketing data and the capability of

self-learning to adapt to new environments.

Finally, there are some very useful articles by practitioners highlighting the

importance of processes and organization for the performance of the entire supply

chain, and how changes in these two factors can influence the performance of the

entire company (Duffy 2004). Processes are in many of today's companies supported

by Enterprise Resource Systems (ERPs).31 An interesting study that examines the

influence of ERP systems on SCM is the work of Akkermans, Bogerd et al. (1999).

The authors argue that once an ERP system is installed, this system can act as the

backbone supporting business developments in many areas, among them SCM.

However, the original ERP systems were never designed to specifically support

31

See also section 3.3 for more details on ERP systems.


SCM, and companies may find that such a system becomes a disadvantage, as

Akkermans, Bogerd et al. (1999, p. 20) state:

"Their architectural advantage of being fully integrated for one firm becomes a

strategic disadvantage in this new business environment, where modular, open

and flexible IT solutions are required."

Since this Delphi study was conducted, several of the ERP software vendors have

adapted to the trend towards inter-company SCM and have enhanced their ERP

systems.32 Time will show how successful these new systems are in supporting SCM

processes across multiple enterprises.

Efficient Consumer Response (ECR) started originally as a supply chain initiative

for the fast-moving consumer goods industry (Corsten 2002). Its main purpose is the

collaboration between suppliers and retailers (Kurt Salmon Associates 1993). Since

then ECR has developed and now includes marketing elements such as category

management alongside SC elements (ECR Europe 2000). There are several

publications of the ECR community that are cited in this thesis, dealing mainly with

store operations and OOS. Yet the core element of ECR, collaboration, is not within

the scope of this work.

3.2.2. Automatic Replenishment Programmes

Automatic Replenishment Programmes (ARP) are a practical implementation of the

idea of pull-supply chains. In these programmes, the vendor is responsible for the

replenishing of the retailer's stock. To be able to do this, the vendor receives data

such as inventory level information and POS data from the retailer. Replenishment is

then pulled by the actual sales; thus no orders have to be actively placed by the

retailer. The first successful implementation of such a system was between Wal-Mart

and Procter & Gamble in the late 1980's (Kuk 2004). Common ARPs are Vendor

Managed Inventory (VMI), Continuous Replenishment Planning (CRP), Quick

Response (QR) and Collaborative Planning, Forecasting and Replenishment (CPFR).

According to Davis (1993), the benefits of ARP concepts such as VMI are generally

accepted, namely the possibility to reduce OOS rates and inventory levels.

An interesting ARP paper related closely to the ASR topic is the contribution of

Achabal, McIntyreet al. (2000). In this paper a decision support system (DSS) is

described that was used by an apparel manufacturer to forecast the demand of

32

For example, the company SAP has introduced a software package called "mySAP SCM."


selected VMI retailers. It is worth noting that the system described uses a forecasting

model to optimize the supplier retailer deliveries to the retail DC. Yet, the system

does not take into account the replenishment at the store level. Nevertheless, the

contribution of this paper is that for the first time a DSS in use is described that

combines inventory replenishment decisions with advanced causal forecasting

models derived from marketing theory. Their multiplicative forecasting model takes

into account effects from price elasticity, promotional influences and seasonal effects

and bases on pooled, weekly product data. The DSS system was tested for 30

retailers in a three-year period. The result was a dramatic OOS rate reduction of

70%. In addition, the service level at the DC increased from about 73% to 92%. A

minor drawback was a slight fall in the inventory turnover. Further benefits for the

vendor were better production planning, as retailers' orders were more stable and

reliable with the new system. This is mostly attributed to the fact that an inflation of

orders, resulting from "shortage gaming" (cf. Lee, Padmanabhan et al. 1997b), is

avoided. In addition, the retailers had the usual benefits of VMI systems: they had

lower costs and expended less effort on order placement and they profited from a

cost-effective and more precise sales forecasting tool. The increase in forecasting

accuracy is attributed to the utilization of several vendors' data and to the fact that

more time is spent on the customization of the models. The main achievement of

Achabal, McIntyre et al. (2000) is to show in practice that such DSS systems can

dramatically improve inventory efficiency and are profitable for vendors and retailers.

Yet, their paper concentrates mainly on the development of an efficient forecasting

model and less on the automatic replenishment or on the contextual prerequisites of

such systems. A deficit of their contribution is the missing description of new

processes necessary for the effectiveness of automatic replenishment systems.

A major empirical contribution to the investigation of ARPs comes from an American

research group that based their findings on a survey of 75 American manufacturers

and 23 retailers (Ellinger, Taylor et al. 1999; Stank, Daugherty et al. 1999). The

authors see the main problem in modern retailing as the increased number of SKUs

that have to be handled. The retailer's aim is to cope with this complexity without

increasing the number of OOSs or inventory levels. In their first papers (Daugherty,

Myers et al. 1999; Ellinger, Taylor et al. 1999), the research group studies the

appearance and effects of such systems. First, the implementation of ARP-related

elements in companies is measured. These are elements that are considered by the

authors necessary for the implementation of ARP. As can be seen in Table 3, it is not

only technological capabilities that are mentioned but also operations and

organizational elements are to a certain degree a prerequisite for ARP. This table has


a major relevance for this work, as in section 6.5.3 the technological and

organizational requirements for ASR systems are examined.

Item Mean* Standard Deviation

Bar coding at retail level 5.66 2.31

Electronic data interchange 5.46 1.77

Automatic replenishment of basic goods 5.45 1.76

Automatic forecasting for staple goods 5.33 1.90

Pre-season planning with trading partners 5.26 1.93

Cross-functional teams 5.26 1.65

Automatic forecasting for fashion/seasonal goods 5.22 2.45

Bar coding on container labels 5.20 1.86

Direct store delivery of products 4.91 2.42

Joint planning or replenishment of products 4.79 1.84

Electronic payment 4.74 1.91

Electronic point-of-sale scanners 4.66 2.74

Advanced ship notices 4.53 2.18

Cross-docking operations 4.46 2.17

Joint forecasting 4.21 1.89

Vendor-marked merchandise 3.93 2.56

Activity-based costing 3.77 2.19

*7-point scale: 1=not implemented 4=somehow implemented 7=fully implemented

Table 3: Implementation of ARP-related items33

Ellinger, Taylor et al. (1999) come to the conclusion that for a basic involvement in

ARP programmes, the most important elements are bar coding at retail level and

electronic data interchange. With these elements in place, a basic level of automation

is possible. For more sophisticated replenishment programmes, advanced elements

such as joint planning and electronic payments, are necessary. The second question

Ellinger, Taylor et al. (1999) looked at was the effectiveness of such programmes.

Effectiveness was judged with the help of several scales as can be seen in Table 4.

The authors showed that the group having a high ARP implementation level (more

than 20% of total dollar sales involved automatic replenishment) has significantly

faster inventory turns and at the same time reduced product handling, inventory

holdings and costs. A reduction of OOSs could not be proved. However, higher

profitability and overall relationship performance for the high ARP involvement group

could be shown. These are important findings, as in this thesis the effect of ASR

systems will be measured on three scales: inventory level reduction, inventory level

variance and OOS.

33

Source: Ellinger, Taylor et al. (1999, p. 29).


Goal High ARP

involvement

Low

ARP involvement

Improved/increased customer service 5.61 5.40

Fewer stock-outs 5.42 5.33

Improved reliability of deliveries 5.42 5.19

Reduction of discounting 5.35 4.86

Reduced return and refusals 5.12 5.03

Faster inventory turns 5.35 4.44

Reduced over-stocks 4.95 4.92

Reduced product damage 4.81 5.06

Reduced inventory holdings 5.28 4.17

Reduced handling 4.93 4.23

Reduced costs 5.08 4.06

*7-point scale: 1=not at all effective 7=extremely affective. Bold: significant difference between groups (p<.05)

Table 4: Effectiveness in achieving automatic replenishment-related goals34

In a third paper Myers, Daugherty et al. (2000) test a series of hypotheses related to

the antecedents and outcomes of automatic replenishment programmes. In their

model, the authors postulate that the effectiveness (cost and service quality) of ARP

systems will depend on three factors:

� Transaction-specific factors (factors influencing transaction costs, such as the

standardization level of the ARP)

� Strategic factors (the market and profit orientation of the firm)

� Organizational factors (characteristic factors of the firm, such as the firm size)

This model could only be partly supported by the data. Market-oriented companies

had indeed as expected a greater service effectiveness at the cost of lower cost

effectiveness. Service effectiveness was positively influenced by the firm size, but

astonishingly, larger firms had lower cost effectiveness. A similar finding was

reported by Kuk (2004). In his analysis of 25 electronics industry companies, the

members of small rather than large organizations expected and perceived higher

returns from ARPs. Another surprise was that decentralized companies did not have

lower service effectiveness as expected. Not surprising was the fact that with

increasing managerial commitment also the cost and service effectiveness rises.

Further, Myers, Daugherty et al. (2000) expected a positive correlation between

companies markets' competitiveness and their service effectiveness. According to the

34

Source: adapted from Ellinger, Taylor et al. (1999, p. 33). The results depicted in this table vary slightly from

paper to paper of the American research group, though they come from the same empirical base. The authors

do not explain the reasons for these differences.


data, the opposite was true, in highly competitive markets the ARP-related service

effectiveness went down. This effect is explained by the fact that the fast-changing

demand for goods has made accurate replenishment very difficult for ARPs. The

authors also looked at the implications of ARP effectiveness for overall firm

performance. They could prove a significant positive relation between the

effectiveness of ARP and strategic performance (creation of barriers to competitors).

The results of this paper deliver important insights for this thesis. Myers' and

Daugherty's paper pinpoints that many of the factors influencing ARP success lie in

organizational structure and human agency. Firm size, centralization of

replenishment decisions and commitment to ASR seem to play an important role. But

also the environment has to be taken into account, as the degree of market

competitiveness plays a role.

The last work analysed that is based on this American survey is by Sabath, Autry et

al. (2001). In this paper, the authors first of all analyse the information systems

capabilities of the companies in the sample using nine items. As can seen in Table 5,

most IT systems are able to provide basic information, yet their capability for internal

and external connectivity as well as speed and exception management are perceived

to be rather low.

Information Systems Capabilities Overall Mean

(n=97) Centralized Group

Decentralized Group

Daily download of information 5.70 5.49 5.88

Accuracy of information 5.33 5.19 5.45

Timeliness of information 5.16 4.97 5.33

Formatted to facilitate usage 5.07 5.11 5.03

Availability of information 5.01 4.58 5.39

Internal connectivity/compatibility 4.88 5.31 4.49

Formatted on exception basis 4.65 4.41 4.87

Real-time information 4.59 4.19 4.95

External connectivity/compatibility 4.58 4.29 4.85

*7-point scale: 1=not at all effective; 7=extremely affective; Bold: significant difference between groups at .01

Table 5: Information systems capabilities35

Another question posed by the authors is whether there is a significant difference

between the IT capabilities of decentralized companies compared to centralized

ones. This is not a trivial question, as the theories on this topic are ambiguous. For

example, the coordination theory states that to decide whether new communication

technology will lead to more or less centralization, one has to look at which cost types

are reduced (Gurbaxani and Whang 1991). If the cost to collect the information

35

Source: adapted from Sabath, Autry et al. (2001, p. 100).


required to take a decision falls, then the result will be greater centralization of the

organization. On the other hand, if the agency costs sink (i.e. the costs of the

controlling of the single independent units, in the case of this thesis, the stores), the

utilization of new coordination technology will lead to greater decentralization.36

According to the survey of Sabath, Autry et al. (2001), the decentralized group tends

to have higher IT capabilities. This could be a sign that the agency costs are more

effectively reduced by the implementation of such IT technologies. An exception here

is the internal compatibility that is stronger in the centrally-organized group. This

phenomenon is explained by the authors on the basis of coordination theory. In

centralized organizations it is necessary to have the information in the upper levels of

the organization, therefore internal connectivity and compatibility is most important.

Further, Sabath, Autry et al. (2001) examine the influence of the degree of

centralization on the performance of ASR systems. Their basic result is that, in all

examined areas, a decentralized organization will tend to achieve better results.

Decentralized firms with ASR systems in use tend to perform better in relation to

OOSs, reduction of overstocks, returns, handling costs and product damages. This

strong argument for a decentralized system is contrary to the trend of power

centralization which has been observed within European grocery firms (Småros,

Angerer et al. 2004a).

This American research group explains the sometimes ambiguous results of their

studies by the relatively small number of companies in the sample and by the relative

short period these automatic programmes have been in place. In addition, three more

problematic issues can be seen. First of all, the way the effectiveness was measured

could be misleading. The benefits of the systems are measured by a self-estimation

of the interviewees on a 7-step Likert scale.37 Consequently, it is possible that

companies that introduce sophisticated replenishment systems will also have a

clearer view of the costs and performance of their supply chains. Problems that were

hidden before are now visible; the benefits from ASR systems are therefore not

adequately estimated. Second, it could be problematic that the researchers do not

differentiate between the effects of ARP for retailers and manufacturers. It is a

common statement from practitioners and researchers that, for example, VMI

36

For an overview on agency costs and theory, see Kieser (2002, p. 209). 37

In the experience of the author, with this approach there is the risk of obtaining biased results. In the European

survey by Småros, Angerer et al. (2004a) the authors asked a group of retailers about their happiness with their

OOS rate. It is remarkable that the retailers who did not have implemented systematic methods to measure

their OOS rate tended to be more satisfied with their on-shelf availability than the other group.


programmes create different costs and benefits for retailers and manufacturers.38

And third, although the replenishment programmes are called "automatic," this term is

not used in the significance of this thesis (i.e. "not manual; executed by a machine").

The term "automatic" means in this context that the replenishment is managed by the

vendor and not by the retailer. But whether the replenishment itself really happens

automatically, i.e. the vendors' IT system takes the replenishment decisions, is not

differentiated in these papers. Thus the contribution of these papers on the

implications of having an IT system take over the replenishment decisions is limited.

3.2.3. Operations Management in Retail

The sources studied from the operations management field have a focus on

operations in the retail environment. In the following, some challenges found in the

literature concerning store operations and replenishment systems are highlighted.

One weighty issue when dealing with automatic replenishment systems is the data

accuracy. As noted by both practitioners and researchers, the performance of

automatic replenishment systems dramatically depends on the accuracy of the data.

There are very few papers that investigate this subject (Raman, DeHoratius et al.

2001b; Wagner 2002; Fleisch and Tellkamp 2004; Tellkamp, Angerer et al. 2004),

although it is highly relevant for retailers. In a case study with a US retailer, Raman

(2000) found that inventory was inaccurate for over 70% of stock-keeping units

(SKUs) in the store. These figures are based on physical inventory counts at six

stores. Each store had on average 9,000 SKUs. Physical inventory was below book

inventory for 42% of SKUs and above for 29% of SKUs. The total difference was

61,000 units or a mean of 6.8 units per SKU. This compares to an average inventory

of 150,000 units per store.

One of the major challenges of accurate inventories is shrinkage. A German study

examining 104 German retailers estimates the inventory difference at 4.1 billion euro

in the year 2004 (Horst 2005), and half of these losses are due to theft. The inventory

differences add up to 1.1% of the gross product value. According to another US

survey, shrinkage in the retail industry amounted to 1.7% of sales in 2002 (Hollinger

and Davis 2002). The importance of this topic for practice is seen in the high sums

invested in anti-theft measures, for instance, Germany's companies spent 900 million

euro (Horst 2005).

38

See for a list of benefits and disadvantages, e.g. Alicke (2003, p. 171).


The replenishment of the shelves is one of the most time-consuming store

operations found. The German grocery retailer Globus has calculated that 47% of its

internal logistics costs are caused by in-store shelf replenishment. In comparison, the

costs for the transport of the goods to the store (and back in the case of returns)

represents only 14% of total costs (Shalla 2005). The replenishment effort is

influenced by the size of the shelf, the available space in the backroom, the case

pack size (CU/TU) and the store replenishment frequency. Retailers, such as Globus

and Manor (Switzerland), have therefore started to design stores without a backroom

in hopes of reducing this costly and error-prone process.

Most of the European grocery retailers examined are centrally organized.

Nevertheless, the role of the store manager in the performance of the store is still

important. This person is responsible for several employees, has many freedoms

regarding the processing of store operations and has decision rights over operating

expenses such as labour costs. One of the ways discussed in the literature to

oversee and motivate store managers is through appropriate incentives (see e.g.

DeHoratius and Raman 2005). The role of store managers during and after

introduction of an ASR system was found during the research for this thesis to be a

critical element. The store manager's influence on the success of the replenishment

system change was stressed by all interviewees.

Overall, the review of the operations literature was very fruitful. The literature helped

to identify the relevant fields for the field research in this paper. This was possible as,

contrary to the literature about mathematical inventory management, the operations

management literature examines topics very close and relevant to retailers' daily

challenges.

3.2.4. Contributions and Deficits of a Logistics and Operations Management

Perspective

The logistics and operations management research explores a very wide range of

topics. This is due to the integrating function that logistics has in today's enterprises

(Pfohl 2004). As the implementation of ASR impacts so many areas of a retailer's

business, logistics' holistic approach is a valuable knowledge source. In concrete

terms, the contributions of these theories are:

� Expected benefits of ASR system

� Technologies and processes necessary for ASR implementation

� Conception of store operations


First of all, this thesis benefits from the ARP literature and their methodologies for

measuring the performance of replenishment systems (research sub-question Q2).

Second, the hypotheses found in theoretical sources regarding the correlation

between IT capabilities, context of the organization and the outcome of automatic

replenishment system implementation are extremely helpful in creating the models in

the next chapter and in determining the IT requirements for each ASR level (see

Table 49). Finally, the examination of store operations literature brought a major

contribution for this thesis. As the field research in chapter 6 will show, the

implementation of an ASR system fundamentally changes some of the processes in

the stores as well as the organization structure. The best practices identified in the

literature are compared to the processes of the retailers in the sample. Therefore, this

theoretical perspective will be most helpful for the answering of research sub-

question Q5, recommendations on how to re-engineer a company to adapt to a new

ASR level.

Yet, it has to be said that the contribution of the research streams ECR and SCM for

this thesis is limited. The focus of this research project is on the replenishment of

goods to the point of sales of the retailers. For this research context, the distribution

systems along the supply chain, from the suppliers up to the retailers, play a

secondary role. Concepts that are mainly centred on collaboration and which aim to

reach a global inventory optimization across the supply chain are not considered in

this thesis. The only contribution of suppliers for the success of advanced ASR

systems is to ensure high inventory visibility. This is realized by having an

identification system (e.g. barcodes) at item level and by the use of EDI messages

such as dispatch advice. Yet for future research, the role of the supplier might

increase when automatic replenishment systems are implemented across several

supply chain tiers.

3.3. Business Information Systems Perspective

A stream of the business information systems research focuses in particular on the

impact of new information technologies on companies and the human agency. This

research branch has a longstanding tradition. It began as a stream of social research

on the influence of human behaviour and technology in general (e.g. Mumford 1934;

Noble 1984), focusing later on the impact of information technology (e.g. Zuboff

1988; Kling 1996). In this last section of the theoretical foundation, the role of

Enterprise Resource Planning (ERP) systems is examined. First of all, the

characteristics of ERP systems are analysed. Second, an overview of literature


regarding successful implementation is given before finally, the influence of ERP

packages on human agency is depicted.

3.3.1. Characteristics of ERP Systems

Watson and Schneider (1999) see ERP as a generic term for an integrated enterprise

computing system. In order to integrate the different functions of a company, a

common database is necessary (Gronau 2004). Today's ERP systems have

developed from systems used in production to determine the necessary material

quantities, so-called material requirements planning systems. By now these systems

are widespread in all industry sectors and have expanded their functionality to

include also human resources and finance elements. A recent paper on the influence

of ERP on companies is the work of Kallinikos (2004). For him, the widespread

introduction of ERP packages in organizations is radically changing human agency at

work. Kallinikos (2004, p. 9) states that a main characteristic of an ERP system is

"the reconstruction of the very micro-ecology of organizational tasks to which any

single transaction belongs." This means that in a company with advanced ERP

systems isolated tasks no longer exist. Even the smallest transaction is recorded and

thus must be accounted for. This has two effects.

First, the effects of one's actions become visible to others and to the individual

undertaking the activity. For instance, the connection of the SAP R/3 modules

"Financial Accounting" and "Warehouse Management" helps in recognizing the

financial effects of one choice on the rest of the organization (Bancroft, Seip et al.

1996; Soh, Kien et al. 2000). This interconnection is the reason why ERP systems

have had a more profound effect on human agency in organizations than other expert

systems (Kallinikos 2004). ERP systems are not only a structured rendering of

operations; they create a manageable organizational reality.

The second effect of ERP systems is that the order in which transactions are

correctly executed is stipulated by the system and thus makes the way tasks are

executed by employees homogeneous.

3.3.2. ERP Implementation and Selection

Integrated ERP systems are regarded by some authors to be of strategic importance

for the competitiveness of companies. Therefore, some years ago, Vossen (2000)

complained that German retailers were lagging behind the competition. He estimated

lost turnover due to antiquated systems at 3.5% and stressed the necessity for a

change of the IT system. Researchers have examined factors influencing the


successful selection and implementation of ERP packages (Kumar, Maheshwari et

al. 2001; Sarpola 2003) and the impact of organizational and cultural factors

(Krumbholz, Galliers et al. 2000; Soh, Kien et al. 2000). More practical managerial

guidelines (e.g. O'Leary 2000; Ptak and Schragenheim 2000; Merkel 2003) became

more popular in dealing with ERP choice and usage, as the implementation of an

ERP system can be extremely costly. The list of failed and abandoned ERP

introductions is long (Markus and Cornelius 2000), making the necessity for such

guidelines clear. A study from the year 1999 shows that, on average, ERP projects

are 178% over budget, take 2.5 times as long as intended and deliver only 30% of

the promised benefits (Densley 1999). This goes hand in hand with the existing

complaints of many practitioners that IT expenditure has failed to show significant

productivity gains. In the literature, this phenomenon is known under the term

"productivity paradox" (Stratopoulos and Dehning 2000). It was first described in

1987 by Steven Roach, the chief economist at Morgan Stanley (Brynjolfsson and Hitt

1998). The paradox is that although the amount of computing power used in the

service industry increased dramatically in the 1970's and 80's, the measured

productivity remained flat. Other studies have partly contradicted these findings

(Brynjolfsson and Hitt 1995; Dewan and Min 1997; Malone 1997). It is argued that

ERP as well as other complex IT systems, fail to deliver benefits because the

process of organizational change was not properly addressed (Hong and Kim 2002)

and working practices were not changed (Brynjolfsson and Hitt 1998). Therefore,

ERP implementation guidelines stress the need for a comprehensive re-engineering.

Chircu and Kaufmann (2000) speak of two types of barriers that have to be overcome

for a successful implementation: valuation barriers (how compatible is the technology

to industry standards?) and conversion barriers (which supporting resources have to

be considered to ensure the implementation succeeds?). The technical barriers are

rather low, as much of the IT and identification technology has become ready

available and comparatively inexpensive (Kuk 2004). The organizational barriers, on

the contrary, are much more difficult to surmount. Soh, Kien et al. (2000, p. 47) speak

of a "necessary disruptive organizational change." A change in the organization and

its processes is also necessary in the case of a misfit between the ERP system and

the existing company. A misfit occurs when the functionality offered by the ERP

system and the requirements of the company differ. In case of a misfit, a spectrum of

solutions is possible, requiring more or less change within the company (see Figure

9). An example for a greater required adaptation is when ASR systems presuppose

an electronic inventory record. The working habits of store employees can be

radically changed by this new process. Such electronic records need, for instance, an


accurate scanning of every product that is sold. The consequence is that a significant

part of employees' time will be dedicated to inventory counts and data administration.

Greater organizational change

Greater customisation of ERP

Accept shortfall

in ERP

functionality

Accept shortfall

in ERP

functionality

Adapt to the new

ERP functionality

Adapt to the new

ERP functionality

Customisation to

achieve the

required

functionality

Customisation to

achieve the

required

functionality

Workarounds to

provide the

needed

functionality

Workarounds to

provide the

needed

functionality

Figure 9: Spectrum of misfit resolution strategies39

3.3.3. ERP and Human Agency

Kallinikos (2004) notes that many sources dealing with the influence of ERP on the

organization have a strictly technical perspective. The behavioural assumptions

underlying ERP implementation are seldom examined in detail. For example, not

enough attention is paid to the construction of roles centred around new processes

(cf. Markus, Axline et al. 2000; Sheer and Habermann 2000). A detailed investigation

of the constitutive effects ERP packages have on work, human agency and

organizational action has not yet been systematically researched. The influence of

ERP on personnel that has been identified in theory is threefold:

� Change of working patterns

� Reduction of personal communication

� Retreat to own zone of duties, loss of interest in overall goals

The direct influence of ERP implementation on human working patterns has

already been stressed. Many ERP packages specify in detail every required work

process and have therefore a significant influence on human agency. The system

might constrain the way less structured human beings work by institutionalising

patterns of action. For instance, some higher ASR systems suggest an exactly

defined procedure for checking orders. For example, store employees may be

required to check the order suggested by the IT system during a small, defined time

window. In addition, the visibility of employees' actions increases. Increased visibility

means that actions (e.g. the placing of orders) can be monitored from every PC

39

Source: adapted from Soh, Kien et al. (2000, p. 50).


connected to the ERP system. At the same time, because transactions are stored

electronically, there is also the possibility to assess actions conducted in the past, a

convenience often not available in manual systems. For example, in an OOS

situation, it might be unclear in a manual system whether an order was placed in time

or not, and every party can blame another for the fault.

The second effect found in the literature describes the reduction of personal

communication between employees (cf. Fleck 1994; Sawyer and Southwick 2002).

In the case of advanced ASR systems, the contact between store employees and the

central office takes place by digital means. Johansson (2002) has made several

propositions on how the retailer-supplier relationship may change, when increasingly

quantities of information are interchanged via EDI, Internet and market places. He

examines the effects on three fields: relationship formation and development,

distribution of power and influence and the responsiveness and flexibility in supplier-

retailer relationships. The author states that co-ordination of activities is easier

through digital communication, at the cost of the richness of the communication and

the building of knowledge. Johansson further argues that with the use of digital

channels it is easier to handle continuous change in the supplier-retailer environment.

An example for continuous change is the change of consumers' consumption quantity

of a certain existing product. Yet for discontinuous changes, such as consumers

demanding completely new products, personal communication channels would be

the better choice. The findings of Johansson (2002) can only be partly transferred to

the head office-store relationship, as this is a relationship within an organization. The

distribution of power is another: there is no danger that the store will decide to

change the supplier due to, for example, lack of trust.

Finally, IT systems do not only reduce personal communication. In addition, typical

human behaviour, such as improvisation, exploration and playing, is often limited.

Kallinikos (2004) sees the danger of losing one of the ultimate goals of the company–

responding successfully to the demands of the market and customers–in a maze of

thousands of ERP transactions: "Concern with internal processes may obscure and

finally replace external adaptation" (Kallinikos 2004, p. 18). The ordinary user, when

overwhelmed by thousands of transactional possibilities, tends to retreat to his limited

zone of duties (Zuboff 1988; March and Olsen 1989). The wider purpose of their work

might be lost of sight. An aim of good ASR system design must therefore be to avoid

the loss of employee interest and motivation. When the retailer MYFOOD introduced

its ASR systems, one problem was that store employees lost any interest in the

placing of orders, as it was, in their eyes, no longer their duty. There were fewer

controls of the computer-generated ordering suggestions as wished by the central


office. The retailer had to make a significant effort to abolish the "if the goods are out-

of-stock, then it was the computer's fault" mentality. Therefore, by encouraging

employees to make a minimum number of interventions, they were made to feel as if

the orders placed were still "theirs". Employees should perceive the computer as a

mere assisting technology.

3.3.4. Contributions and Deficits of a Business Information Systems

Perspective

The discussion of ERP-related theoretical sources offers several contributions for this

thesis:

� ERP's major influence on business

� Pitfalls and success factors

� Change of human agency

The literature has shown the major impact ERP systems have on business. Several

authors stressed that to remain competitive, implementation of ERP systems has

become mandatory. However, the implementation of such systems is a challenging

task. ERP systems radically change the way employees work. Their effectiveness

depends to a great extent on the design of the organization and processes. The right

system can create an awareness of the effects of the actions taken by employees

(Kallinikos 2004). As higher ASR systems are usually integrated into ERP system,

the mentioned implication of the information business research can be transferred to

the ASR context. This research stream therefore provides valuable insights on how to

change the organization and organizational processes (sub-question Q5) and on

which system to take, considering the available technology (Q4).

The limits of the application of the ERP research to the subject of this thesis are that

traditional ERP systems only target procedures of action and execution. Modules with

real decision support logic are not common standard (Metaxiotis, Psarras et al.

2003).

Advanced ASR systems go a step further than traditional ERP packages in that they

implement knowledge aspects as well. Thus, additional considerations from the field

of expert systems have to be taken into consideration. Decision support systems

(DSS) are rule-based programs that often involve statistical or algorithmic analysis of

data. Their decisions are made in real-time after weighing all the data and rules for a

particular case (Davenport 2004). The implications of DSS systems for the business

community cannot yet be fully anticipated. For Davenport, the impact of this new


technology on the employment market will be much greater than the effects of

offshoring. From 2001 to 2004, 2.7 million US jobs were lost due to the increase of

productivity, compared to only 3,000 lost through offshoring (Davenport 2004).

Davenport recommends companies to incorporate DSS into their strategy to remain

competitive in today's markets.

3.4. Contingency Theory Perspective

Fred E. Fiedler is considered to be the founder of contingency theory. He carried out

research on the optimal leadership style. Fiedler (1967) examined the influence of

two factors–the leader's characteristics and the leading situation–on the performance

of employees. The result was that there is no optimal solution; the best leadership

style depends on the situation. This was the beginning of contingency theory. In

essence, contingency theory states that the effective level of an endogenous variable

depends on the level of an environmental (exogenous) variable (Staehle 1991;

Cadeaux 1994). In Fiedler's example, the contingency aspect is that the leadership

type (endogenous variable) has to fit to certain characteristics of the situation

(exogenous variable) in order to bring about the desired results. This theory can be

applied at different levels of abstraction (e.g. organization, process level) as

discussed in the following.

3.4.1. Contingency Theory at the Organizational Level

One of the first contexts in which contingency theory was applied was the

organization. Lawrence and Lorsch (1967) conducted empirical studies to formulate a

contingency theory of the organization. They argued that differences between types

of organizations are mainly due to the different environmental conditions they act in.

In their study, the authors characterize the environment with the pairs

secure-insecure and homogeneous-heterogeneous. The security dimension of an

environment can be measured with the help of following three criteria:

� Certainty and reliability of information (environmental stability)

� Frequency of changes of the information (environmental change)

� Duration of the feedback cycle (between system and environment)

Organizations reduce the complexity of their environments by creating sub-systems.

For instance, the complex environmental factor "supplier" leads to the creation of the

functional unit known as "purchasing department." Another example is provided by

Staehle (1991). He examined the plastics industry and discovered that it has a

heterogeneous environment: production is very secure, yet the basic research


department is classified as very insecure. Staehle then makes suggestions as to how

to organize a company depending on his environmental classification.

An application of contingency theory to logistics is made by Pfohl and Zöllner (1987).

In their theoretical work, they examine the influence of contingency factors on

organization design. They put into question the effectiveness of the popular strategy

of aggregating all logistical tasks into a major department. The authors look for

alternative organization forms. Pfohl and Zöllner argue that the choice of logistics

organization should no be limited to just quantitative aspects (such as the size of the

company). There are other important contingency factors that have to be considered

as well:

� The complexity and uncertainty of the criteria

� Requirements of the decision-making levels

� Relationships with other functional units

� The behaviour of the employees

The main conclusion of the authors is that the structures found in practice can only

be partly explained by contingency theory. Decisive roles are played by the

managers that set the overall strategy of the organization. Such managers choose

structures that coincide with their cognitive and motivational orientation (Bobitt and

Ford 1980). Different structures for logistics will emerge, depending on how the

importance of logistics in an organization has been assessed (Pfohl and Zöllner

1987). The authors cannot make a standard recommendation on how to implement

best logistics in an organization, because this will depend on the requirements of the

logistical task. For the type of companies in the focus of this thesis the consequence

is clear. Logistics is for retailers a pivotal task (Körber 2003). Pfohl and Zöllner (1987)

recommend that in this case, companies should determine the organizational

structure based strongly on logistical contingency factors.

A last example for contingency theory at the organizational level stems from Kahn

and Mentzer (1996). These authors suggest that the choice of the level of integration

and interaction between departments should depend on the situational context, in this

case the stability of the market.

3.4.2. Contingency Theory on Information Technology and Processes

Contingency theory also plays a role when assessing the appropriateness of

processes. Cadeaux (1994), for example, shows that the benefits from a

branch-flexible planning of the store assortment strongly depends on the volatility of


the product category. Another example is the contingency between supply chain

responsiveness and the need for forecasting abilities. It can be observed that

suppliers have invested more in forecasting capabilities than retailers (Småros,

Angerer et al. 2004b). An explanation given by these authors is that the need of

retailers for forecasting is different, as their supply chain is more responsive (i.e.

shorter lead times and higher service levels).

Another branch of the contingency research stream examines the impact of

information technology on business performance under the aspect of contingency

theory. A good overview can be found in Bergeron, Raymond et al. (2001). In this

paper, the authors list research executed on the study of specific impacts of IT,

among them studies on the IT impact on learning (Leidner and Jarvenpaa 1995), the

impact of IT problem-solving on task performance (Vessey 1991; Vessey and Galleta

1991), and the impact of IT on organizational performance (Chan and Huff 1993;

Bergeron, Raymond et al. 1998). A common proposition among these papers is that

a turbulent environment will induce firms to a more extensive use of information

systems (Pfeffer and Leblebici 1971; Lederer and Mendelow 1990). Chagué (1996)

argues that in risky environments, IT should be more flexible and managers more

alert to adapt information systems to external changes. In addition, information

acquisition should be more wide-ranging and continuous (Huber 1984). Generally

speaking, there are many papers including the environmental fit as a contingency

variable (Bergeron, Raymond et al. 2001) of IT performance. But very few show the

link between the fit of such systems on environmental uncertainty and performance.

An exception is, for example, the work of Sabherwal and Vijayasarathy (1994) which

examines telecommunications technology fits and performance.

Another application of contingency theory is the development of a contingency

approach for the selection of logistics performance measures as conducted by Chow,

Heaver et al. (1994). For example, the authors expect for a firm that has

concentrated its strategy on cost dimensions, to focus its performance measures on

cost dimensions as well. The transfer of these theories to the context of this research

project means that retailers that have more complex replenishment systems will have

at the same time a higher demand for elaborated performance measurements. If, for

example, a complex forecasting algorithm is in use that requires more care on data

accuracy, the company requires a more accurate image of the performance of this

algorithm. Therefore key performance indicators (KPIs) such as MAPE40 would

probably be introduced to test and control the algorithm.

40

Mean Absolute Percentage Error. For an explanation of MAPE see section 4.1.4.


3.4.3. Contributions and Deficits of a Contingency Perspective

The basic idea behind contingency theory is of great relevance for the answering of

research questions Q4 and Q5. The main contributions of the contingency theory for

this thesis are:

� Choice of the right ASR system for each product category

� Fit of organization and processes to ASR system

� Consideration of the influence of store and product characteristics on ASR

performance

One of the main goals in this thesis is to help retailers to choose the right

replenishment system. Yet it would be wrong to rely just on inventory management

research for this, as with this approach the contextual factors of retail are not taken

into consideration. A more differentiated approach is necessary. For example, a

possible implication drawn from the inventory literature is the hypothesis that for

turbulent products (i.e. products for which demand cannot be predicted) retailers

should choose higher ASR levels. Yet in practice there are retailers that nevertheless

prefer a manual approach for some high-demand-variance products such as fruit.

The reason for this is that these retailers see their green grocery category as a way

to differentiate themselves from the competition. As IT systems are not able to

assess the quality of the products, these retailers prefer to have specialised planers

making this decision manually.

As this example shows, it would be wrong to give any recommendations without

taking into consideration the context. This thesis therefore examines how the

organization and the replenishment processes influence the performance of ASR

systems. In section 6.5.3 technical and organizational requirements for each ASR

system are presented, while in section 6.5.4 recommended store operations are

proposed. A last implication of contingency theory is that ASR performance will also

depend on product and store characteristics, as illustrated in the explanation model in

section 4.3.

Nevertheless, one has to be aware of the limits of the contingency approach:

� Missing definition of term "fit"

� Explanation of "reality" limited

The basic statement of contingency theory, namely that context and structure have to

fit to each other, is at the same time the most critical part of this theory. The lack of


the definition of the term "fit" could alter the meaning and the relevance of the entire

theory, as methodological rigor is missing (Schoonhoven 1981; Drazin and van de

Ven 1985). As Bergeron, Raymond et al. (2001, p. 125) state:

"Researchers were not cautious enough in defining the concept of fit–which is

central to any contingency model–and in selecting the most suitable data analysis

approach to a given definition of fit."

A second drawback of contingency theory is that it is useful for giving normative

recommendations: in situation A, choose action B. Yet, it can be misleading to try to

use this theory to explain existing structures and practices found in industry. An

illustration of this statement is the paper by Ketokivi and Schroeder (2004) which

shows that contingency arguments do not adequately explain why certain companies

adopt innovative manufacturing practices such as TQM and lean management while

others do not. Another example is Tidd (1993), which demonstrates that in many

cases, organizational policies do not support technology strategies. This means that

the contingency between a market's technological complexity and firm organization

cannot be shown. In this example, there might be a tendency for companies to

display certain structures depending on environmental influences. But there is no

inevitability in this, other influences might be stronger, thus hiding the contingency

effect. This obscuring effect can be the reason why in this thesis certain theoretical

relationships do not correspond with observations made in reality.

3.5. Literature Research Overview

In the following table, the research streams analysed as well as exemplary sources

are listed.


Research

Stream

Research Focus Exemplary Sources

Importance of inventory

management for business

(Vollmann, Berry et al. 1992; Balachander and Farquhar 1994;

Silver, Pyke et al. 1998; Dubelaar, Chow et al. 2001; Gudehus 2001)

Mathematical optimization

of inventory

(Galliher, Morse et al. 1959; Dalrymple 1964; Chang 1967; Groote

1994; Inderfurth and Minner 1998; Bassok 1999; Ketzenberg,

Metters et al. 2000; Cachon 2001; Wagner 2002; Gudehus 2004)

Retail inventory control and

OOS

(Kotzan and Evanson 1969; Krueckenberg 1969; Cox 1970; Curhan

1972; Corstjens and Doyle 1981; Emmelhainz, Stock et al. 1991;

Drèze, Hoch et al. 1994; Andersen Consulting 1996; Taylor and

Fawcett 2001; Zinn and Liu 2001; Gruen, Corsten et al. 2002; Sloot,

Verhoef et al. 2002; Zinn, Mentzer et al. 2002; Roland Berger 2003b;

Broekmeulen, van Donselaar et al. 2004a; Stölzle and Placzek 2004)

Inventory

management

systems and

OOS

Organizational context of

inventory control

(Zomerdijk and Vries 2002)

SCM: optimization along the

supply chain

(Lee and Billington 1992; Fisher, Hammond et al. 1994; Womack

and Jones 1996; Feitzinger and Lee 1997; Lee, Padmanabhan et al.

1997b; Christopher 1998; Anupindi 1999; Chen, Drezner et al. 2000;

Hines, Holweg et al. 2000; Gudehus 2001; Duffy 2004; Pfohl 2004)

Demand-based supply

chains (Pull-SC)

(Lee and Billington 1993; Fisher, Hammond et al. 1994; Fiorito, May

et al. 1995; Bell, Davies et al. 1997; Cottrill 1997; Christopher 1998;

Closs, Roath et al. 1998; Lee 2001)

Automatic replenishment

programmes (e.g. VMI,

CRP, QR)

(Davis 1993; Daugherty, Myers et al. 1999; Ellinger, Taylor et al.

1999; Stank, Daugherty et al. 1999; Achabal, McIntyre et al. 2000;

Myers, Daugherty et al. 2000; Sabath, Autry et al. 2001; Kuk 2004)

ECR and information

sharing

(Kurt Salmon Associates 1993; Cachon and Fisher 2000; Corsten

2002)

Logistics

and

operations

management

Retail operations

management

(Raman, DeHoratius et al. 2001b; Wagner 2002; Fleisch and

Tellkamp 2004; Tellkamp, Angerer et al. 2004; DeHoratius and

Raman 2005)

Guidelines for successful

implementation of ERP

systems

(Dreyfus and Dreyfus 1986; Akkermans, Bogerd et al. 1999; Chircu

and Kauffman 2000; Markus, Axline et al. 2000; O'Leary 2000; Ptak

and Schragenheim 2000; Kumar, Maheshwari et al. 2001; Hong and

Kim 2002; Merkel 2003; Sarpola 2003)

Organizational implications (Mumford 1934; Noble 1984; Fleck 1994; Bancroft, Seip et al. 1996;

Ciborra 2000; Soh, Kien et al. 2000; Kallinikos 2004)

Implications for employees

and mode of work

(Zuboff 1988; March and Olsen 1989; Fleck 1994; Brynjolfsson and

Hitt 1998; Markus, Axline et al. 2000; Sheer and Habermann 2000;

Johansson 2002; Sawyer and Southwick 2002; Kallinikos 2004)

Business

information

systems

perspective

ERP and decision support

systems

(Metaxiotis, Psarras et al. 2003; Davenport 2004)

Contingency organizational

level

(Lawrence and Lorsch 1967; Perrow 1967; Bobitt and Ford 1980;

Pfohl and Zöllner 1987; Staehle 1991; Cadeaux 1994; Kahn and

Mentzer 1996)

Contingency

theory

Contingency at process

level

(Fiedler 1967; Pfeffer and Leblebici 1971; Huber 1984; Lederer and

Mendelow 1990; Vessey 1991; Vessey and Galleta 1991; Chan and

Huff 1993; Cadeaux 1994; Chow, Heaver et al. 1994; Sabherwal and

Vijayasarathy 1994; Leidner and Jarvenpaa 1995; Chagué 1996;

Bergeron, Raymond et al. 1998; Bergeron, Raymond et al. 2001)

Table 6: Summary of research streams perspectives

4. Development of Models 53


4.1. A Descriptive Model of Replenishment Systems

The first milestone in this work is the creation of a descriptive model of ASR systems.

It provides an answer to the first research question Q1: how can automatic store

replenishment systems be classified? This model does not claim to include all

possible kinds of replenishment systems, as new, revolutionary, decentralized

systems may be developed in the future.41 Yet it provides a helpful structure to

examine the replenishment systems of today's retailers. As it is created on an

abstract level, its core results can be applied to other industries dealing with piece

goods replenishment processes as well.

Regarded from a more abstract OR perspective, the store and the supplier are nodes

in a network (cf. Carter and Price 2001). The store is herein viewed as a material

sink, i.e. the place where the products are consumed.42 As stated before, material for

the store can be either sourced from the retailer's DC or directly from a supplier.

Obviously, supplier and retailer are only a small part of the entire distribution network.

The initial source would be the raw material producer (e.g. a wheat supplier), the final

sink would be the consumer (in contrast to the shopper43). In terms of information

flows, stores and suppliers can be sinks and sources at the same time. In some

systems, for example, the store sends an order to the supplier, the latter sends a

dispatch advice back with the expected delivery time and quantity.44

The basic function of a replenishment system is to decide, when to order which

amount (Wagner 2002). Characteristics of replenishment systems with make-to-stock

policy (i.e. the product is already built when the order comes in) can therefore be

captured with four elements (see Figure 10). To be able to decide which quantity to

order, it is obviously necessary to know how many items are on stock. This is called

inventory visibility, the ability to track the status of inventory in a supply chain tier or

41

For example, research has been conducted on transport networks where no central control is required, as the

system controls itself by a non-centralized network of intelligent items (e.g. Gausemeier and Gehnen 1998). 42

As illustrated in Figure 5, the store can also be a material source, as products are sometimes sent back to the

supplier. However, these quantities are negligible. 43

To illustrate the difference between shopper and consumer, here is an example: over 80% of all Gillette Mach-3

razor blades are purchased by women. However, over 90% of all Gillette Mach-3 razor blades are used by

men. In this example, women are the shoppers and men are the consumers (Hofstetter 2004). In this thesis,

though, no exact differentiation will be made between the two terms. 44

Cash flow between the parties, the third typical flow considered in logistics, does not play a role in the research

framework of this project.

54 4. Development of Models

even across the entire supply chain (Woods 2002). The replenishment logic defines

the business rules that are followed in order to decide when to order (Silver, Pyke et

al. 1998). Some systems take into account order restrictions as well. For example,

if the system can only order items in six-packs, an order of 10 units must not be

placed. The last element is the forecasting part. Some systems try to anticipate

future consumer demand by computing forecasts.

Fore-casts

Fore-casts

Order restrictions

Order restrictions

Inventory

visibility

Replenishment

logic

Figure 10: Descriptive model of replenishment systems

Depending on the replenishment system, these four modules can be either IT-based

or not. The sophistication of each of the four modules varies greatly between different

systems. When automating replenishment processes, the question of which decision

can be handled by the system autonomously and which not has to be examined. All

replenishment systems have a replenishment logic module and require inventory

visibility.45 The other two modules are facultative. For example, a Kanban-system

functions without forecasts or explicit restrictions.46

In the following sections, the notations outlined in Table 7 will be used. The notation

is based on Gudehus (2002), extended with elements of Silver and Pyke (1998) and

the author's own contributions.

45

In theory, it is possible to have a replenishment system without a module for inventory visibility. Instead, the

replenishment is triggered only by sales. Yet, the high shrinkage rates of grocery retailers would make this

system very inefficient in practice. 46

Kanbans are cards attached to units or containers that trigger replenishment. For a detailed description of

Kanban systems see Christopher (1998, p. 185) and Gudehus (2004, p. 261).


C: Cost function

Ci: Inventory carrying costs

Cs: Ordering costs

Coos: OOS costs

D(i): Demand rate of item in period i in a store

i: Time period

iS: Time period with an order

IA: Mean inventory accuracy

In, system: Inventory record of SKU n in the system

In, system: Real inventory stock quantity of SKU n in store

L: Replenishment lead time

m(i): Inventory position in store

mO(i): Inventory on stock

mS(i): Inventory ordered in store, not yet arrived

n: SKU

N: Number of SKUs

Q: Fixed order quantity

Q(i): Order quantity in period i

Q*(i): Optimal order quantity for period i

Qmax: Restriction, maximal order quantity

Qmin: Restriction, minimal order quantity

p: Period length

s: Order point

S: Order-up-to-level

SS: Safety stock

T: Order frequency

z: Safety factor

α: Service level

σD: Standard deviation of demand 1

,µ σ−Φ : Inverse of standard normal distribution

Table 7: Inventory notations


4.1.1. Inventory Visibility

As stated before, inventory visibility is the ability to track the status of

inventory across the supply chain (Woods 2002). This can be realized by

defining "buckets" for every location and status of inventory. Simple systems do this

with pen and paper, more sophisticated systems use identification technologies (e.g.

barcode, RFID) and electronic systems (inventory management systems).

For this research project, only the inventory in the store and in the pipeline towards

the store is of interest. The physical stock level in a store is called the on hand stock

mO(i). The inventory on the way to the store is called net stock mS(i). Both quantities

have to be taken in to account before reordering. Therefore, the inventory position

m(i) is defined as the sum of the on hand stock and net stock (see Figure 11).

m(i)= mO(i)+ mS(i)

Inventory level

Time

Q(i)

iS

L

s

SS

-D(i)

mO(i)

m(i)

mS(i)

iS

p

Figure 11: Exemplary time dependent course of the inventory stock level47

Another important descriptive parameter that is closely related to inventory visibility is

inventory accuracy IA. The accuracy of a store with N SKUs can be measured as the

mean difference between recorded inventory levels and real inventory levels.

47

Adapted from Silver, Pyke et al. (1998, p. 250) and Gudehus (2004, p. 371).


IA =, ,

1

| |N

n system n real

N

I I−∑

The inventory accuracy has not yet received the attention it deserves, though

practitioners stress the importance of maintaining high data quality. There are very

few papers concerning physical audits of retailers' inaccuracy levels; an exception is

a study by DeHoratius and Raman (2005). The authors found, after having examined

a US-retailer, that for 65% of the SKUs the inventory records in the systems do not

equal the real physical inventory. An older empirical study shows that the distribution

centres of a logistics operator had an inaccuracy rate of 36% (Millet 1994).

4.1.2. Replenishment Logic

The store has to have an on hand stock mO(i) at the beginning of the

period i that is bigger than the demand D(i), otherwise an OOS occurs

and the demand cannot be completely filled.

A replenishment system has to decide for every SKU whether it is time to place an

order or not. Formally, it means that it has to decide whether i=iS. How does the

system know that it is time to place the order? There are several sources dealing with

the issue of describing the decision rules of inventory systems (e.g. Vollmann, Berry

et al. 1992; Silver, Pyke et al. 1998; Arnold, Isermann et al. 2004; Stölzle, Heusler et

al. 2004). The decision rules determines, when and in what quantity a replenishment

order is made. The basic inventory decision rules are shown in

Table 8.

Order Quantity

Order Frequency Fixed Variable

Fixed (T,Q) (T,S)

Variable (s,Q) (s,S)

Table 8: Basic inventory decision rules48

The four basic decision rules are therefore:

� (T,Q) Order every T period the fixed quantity Q

� (T,S) Order every T period, fill up to level S

� (s,Q) Whenever inventory drops below s, order Q

� (s,S) Whenever inventory drops below s, fill up to level S

48

Adapted from Vollmann, Berry et al. (1992, p. 701).


For a discussion of the advantages of each decision rule see e.g. Silver, Pyke et al.

(1998, section 7.3).

Order Frequency

(s,Q) and (s,S) rules can have either a periodic review, making them (T,s,Q) and

(T,s,S) or a continuous review (T=0). Most grocery retailers have a periodic review

once per day (T=p=1 day).

Systems that do not forecast use simple heuristics, such as the following:

IF(m(i)<s; i=iS; 0)49

(An order will be placed in period i if the inventory position is below the reorder point).

More sophisticated systems calculate whether there is a chance of an OOS in the

case that the order is not placed until the next period. This means that the system

compares whether the inventory position is larger than the demand at the time p+L

(taking into account a certain desired service level). Consequently, the decision rule

is

IF(mO(i)<D(i+p+L); i=iS ; 0)

Order Quantity

If the system decides to order, then the system orders the quantity Q(i):

IF(i= iS; Q(i); 0)

Which amount is to be ordered? Simple systems have a fixed quantity Q set or just fill

up to a certain level S. More complex systems calculate the quantity Q(i) taking into

account an optimal order quantity Q* and some other restrictions.50 This quantity is

the result of optimizing a cost function C:

C= CS+ CI+ COOS51

49

The function IF(A;B;C) stands for: If the condition A is true, than do B, otherwise do C. "0" stands for "do

nothing." 50

For an overview of existing restrictions see section 4.1.3. 51

Cf. Gudehus (2004).


The cost function takes into account several parameters such as:

� CS: ordering costs

- Cost of creating and placing orders

This includes costs for creating the order, costs for sending it to the supplier

and also costs for the supplier caused by the handling of the order.

- Transport costs

The cost of the physical transport of the goods, such as truck costs, road

tolls and customs.

� CI: inventory carrying costs

- Place-in storage costs

These are costs that are created when the items are moved from the store

gate to the back room. The time taken for reshelving is responsible for the

major part of these costs. Additional costs may occur for bulky or heavy

items that require special transport methods.

- Unit variable cost

Not the selling price of the unit, more its value. It is necessary together with

the

- Carrying charge

to calculate the inventory carrying costs. The latter is the cost of having one

money unit of the item tied up in inventory.

- Backroom space cost

The cost of having one unit in one backroom space.

� COOS: OOS costs

These are probably the costs that are most hard to calculate. OOS costs can

be split into lost sales, backorder and lost customers. Therefore, the

computation needs not only to take into account the lost sales, but also the

reduction in customer satisfaction and the risk of losing her for good. To

reduce complexity, most retailers set a desired availability level and then

calculate the optimal order quantity, without explicitly calculating the OOS

costs.

Safety Stock

It is worth noting that because the demand function has a certain distribution and

variance, it is necessary to implement a safety stock to cope with this uncertainty.

The lead time and the delivered quantity might have high volatility as well, therefore

creating the need for additional safety stock. It is self-evident that the more volatile

these factors are, the higher the safety stock will be to maintain a certain service


level. For instance, if the demand varies (in a normal standard distribution) with a

standard deviation of σD , then the safety stock will be

SS= zσD p L+

where z is the safety factor. For normal distributions, z is calculated as

z= 1

, ( )µ σ α−Φ

for a normal demand distribution N(µ,σ), where α is the service level the company

wants to maintain (Alicke 2003).

4.1.3. Order Restrictions

Inventory replenishment systems are forced by many restrictions to

deviate from an optimal calculated order quantity and replenishment

time. For example, if minimum and maximum restrictions exist for the order quantity,

the determination rule for the order quantity would be

Q(i)= MAX(Qmin; MIN(Qmax;Q*))52

These order restrictions are necessary as retailers face certain business and logistics

limitations. Many of these aspects were not taken into account in the optimization

formula, and so the result has to be adapted. One of the most comprehensive order

restrictions compilations was made by Bernard (1999) and it is seen in Table 9.

Restriction Type Explanation

Minimum quantity Quantity constraint Meet a specified minimum order quantity

Meet a contractually agreed minimum quantity

Maximum quantity Quantity constraint Meet a specified maximum order quantity

Meet physical storage restrictions

Meet physical shipping restrictions

Minimum value Cost constraint Meet an order minimum charge

Maximum value Cost constraint Limit orders to defined signature levels

Security limit to reduce system errors

Minimum days supply Time constraint Prevent multiple orders in a time period

Reduce handling associated with frequent orders

Maximum days

supply

Time constraint Constrain the order quantity to meet inventory turns limits

Limit excessive purchases of low value parts

Limit parts with a shelf life to defined days of supply

Price break quantity Qualifier Take advantage of volume discounts

Rounding quantity Qualifier Order in case pack multiples

Minimum demand

quantity

Qualifier Increase quantity to cover a minimum, average demand or period quantity

Order quantity

multiplier

Qualifier Adjust quantity to account for spoilage, processing, shrinkage, inspections

rejects, etc.

Table 9: Exemplary order restrictions53

52

This formula assures that the calculated optimum quantity Q* is bigger that the minimum quantity Qmin and at

the same time smaller than the maximum quantity Qmax.


Bernard (1999) differentiates between quantity restraints and qualifiers. The former

provide upper or lower limits for the ordered quantity. There are numerous reasons to

limit the order, among others transport restrictions, capacity restrictions, stock level at

supplier restrictions, etc. Order quantity qualifiers (if/then rules) provide a

micro-adjustment mechanism to fine-tune calculated quantities. This is undertaken to

match the order quantities with specified case pack multiples. Both restrictions can

be applied at SKU or category level.

In manual systems, the order planners have to keep these restrictions in mind when

ordering. They are also responsible for the internal consistency of the restrictions.

The more sophisticated ASR systems are, the more restrictions automatically will be

followed and less interventions will be necessary. Nevertheless, there will always be

exceptions or temporary restrictions that have to be implemented manually (such as

a strike at a supplier's plant).

4.1.4. Forecasts

Types of Forecasting Techniques

In this thesis, a forecast is regarded as a statement about the expected

future demand. The importance of accurate forecasting has been

stressed by several authors (e.g. Ritzman and King 1993; Moon, Mentzer et al.

1998)–the success of entire companies depends on reliable forecasts. In theory,

forecasts are not necessary if the probability distributions are already available. If this

is the case, a formula can be directly applied to calculate the reorder point s, based

on a service level the company wants to reach. Yet in practice, the demand function

is unknown and remains a theoretical construct, making forecasts necessary

(Kallinikos 2004).

Hanke and Wichern et al. (2005) differentiate between two basic types of forecasting

techniques: qualitative and quantitative (see Figure 12).

53

Source: adapted from Bernard (1999, p. 293).


Forecasting TechniquesForecasting Techniques

Qualitative TechniquesQualitative Techniques Quantitative TechniquesQuantitative Techniques

Based on the subjective expectations

and experiences of persons

Based on the subjective expectations

and experiences of personsBased on statistical analysis of historical dataBased on statistical analysis of historical data

One-dimensional

Time series based forecasts

One-dimensional

Time series based forecasts

Multidimensional

Causal forecasts

Multidimensional

Causal forecasts

Figure 12: Qualitative and quantitative forecasting techniques54

In practice, qualitative techniques, also known as judgemental techniques, are very

popular among managers (Chase 1995). Such forecasts are based on the subjective

expectations of a single person or group (Schneckenburger 2000). This means that

demand is estimated based on personal experience and knowledge, and forecasts of

this kind are often somewhat intuitive in character. The forecaster's judgement is a

result of the mental extrapolation of past historical data (Hanke, Wichern et al. 2005).

Popular techniques are (Mentzer and Kahn 1995):

� Estimates by the sales force ("sales force composite")

� Commissions of experts ("jury of executive opinion")

� Delphi method

On the other hand, quantitative techniques are purely mechanical procedures that

compute quantitative results and do not require any input of judgement. These

statistical techniques estimate future demand based on historic sales data. There is a

distinction made between one-dimensional forecasts (also called time series

forecasts) and multidimensional forecasts (also called causal forecasts). The

difference is that while time series techniques try to explain demand based on merely

one parameter (i.e. time), causal techniques try to improve forecast accuracy by

implementing more parameters that could have an influence on demand. Examples

for such causal variables are, among others, price, weather and promotional

activities. In the following, the focus is laid on quantitative techniques, as they are the

ones that are used by higher ASR systems.

Accuracy of Forecasts

A common statement in forecast literature is that every forecast is per se wrong (cf.

Alicke 2003), as it is impossible to perfectly anticipate future sales. Therefore, a point

54

Source: based on Schneckenburger (2000).


forecast is never to be believed blindly. The credibility of a forecast is related to the

strength of the theory underlying it (Wacker and Lummus 2002). The output is only

an estimate of mean future demand (Wagner 2002); its accuracy will strongly depend

on the variation of the demand. With formulas such as MAD (Mean Absolute

Deviation) and MAPE (Mean Absolute Percent Error), the variability of forecasts can

be measured:

MAD=1

1 *| |N

ii iND D

=

−∑

MAPE=1

* *| |1 N

i i

i i

N

D D

D=

−

∑

D* is the predicted demand, D the real demand.

Managers face the challenge to choose the right forecasting methods. If two different

forecasting methods are available, the one producing the lower MAD or MAPE is to

be chosen. A practical solution to this problem was found by Fischhoff (1994). He has

created a 4-level forecast classification enabling managers to decide how reliable a

forecast is. Such classifications can be used to decide, whether an article can be

ordered automatically or not (Toporowski and Herrmann 2003).

All replenishment systems that make quantitative forecasts rely heavily on the quality

of historical demand data. As in a typical store the demand cannot be observed

directly (customers do not place an order, they just buy what is on the shelves), the

actual sales data is regarded as an approximation to the true demand. It is not

difficult to see that this approach is somewhat problematic. For example, if an OOS

occurs and customers do not buy at all, then the sales data will underestimate the

true demand. Wagner (2002) has coined the phrase "dirty data" for this and other

similar problems. In Wagner's paper one can find a comprehensive list of data

challenges companies face in reality that are often neglected in mathematical

models.

The data aggregation level has a major influence on forecast accuracy. An

aggregation is possible regarding time dimensions (days-months-year), product

dimensions (SKU-category) and geographical dimensions (city-region-country). The

higher the aggregation level, the higher the forecast accuracy will be. However, at the

same time the lower the information-richness of the forecast (Jain 2003). An example

of aggregation levels found in retail is given by Achabal, McIntyre et al. (2000). The

authors suggest for categories such as fashion goods, where due to different sizes,

colours, etc. many SKUs exist, to pool four to ten articles within one category and to


use this to compute a common forecast. This clustering is made subjectively by

selecting products that are expected to appeal to similar customers (e.g. a cluster of

eight different styles of men's casual pants). For a stable forecast they recommend

having at least 100 SKUs sold per time period. The allocation on store and SKU level

is carried out based on historical fractions of sales. Achabal, McIntyre et al. claim that

this is a better way to come to a forecast compared to store level forecasts. This is a

contradiction for some software producers, who claim that only forecasts at SKU and

store level will lead to the desired result (e.g. Beringe 2002).

Forecasts in Automatic Replenishment Systems

Automatic replenishment systems that use forecasts have to decide at the time of

order placement, whether the expected demand in the review period plus lead time is

higher than the current inventory position. Some systems take into account in

addition the uncertainty in demand and lead time. The order is triggered if the

inventory position does not provide enough service protection; otherwise another

period is waited (Wagner 2002) :

IF(mk(i)> Dk(i+L+p) ; PLACE Order Qk(i); 0)

An example of a multidimensional forecast formula used in an replenishment system

is provided by Achabal, McIntyre et al. (2000). Their model is as follows:

Sales = Baseline_sales * Seasonal_effects * Merchandising_effects

This multidimensional model takes into account merchandising effects, effects from

the elasticity of markdown prices, promotional frequency and length, advertisement

size, among other events. However, the authors stress the importance of having as

few parameters as possible ("parsimonious forecasts") as too many parameters can

be counterproductive. In their experiment, with an increasing number of parameters

the adjusted R-square increased in the estimation phase. This means that the

models were quite good in explaining historical data. But these over-sophisticated

forecasts were less accurate in prediction of future periods.

4.2. Classification of Automatic Replenishment Systems

A classification of the different ASR systems was developed based on the

examination of dozens of retailers. Based on the degree of automation, the following

classification system was developed, as seen in Figure 13.


Sophistication

of the store

replenishment

system

Electronic inventory-

based ordering

Electronic inventory-

based ordering

ASR1ASR1

ManualorderingManualordering

ASR0ASR0

Simpleordering

heuristics

Simpleordering

heuristics

ASR2ASR2

Time series-based

forecasts

Time series-based

forecasts

ASR3ASR3

Causal model-based

forecasts

Causal model-based

forecasts

ASR4ASR4

Manual IT-supported Sales-based Demand-based

Figure 13: Classification of automatic replenishment systems

In the following, the differences between the single levels of the presented

classification will be discussed.55

ASR0: Manual Replenishment Systems

This is the level where all replenishment systems were before retailers introduced IT

systems. A manual replenishment system relies solely on the information and

intelligence of the employees. This kind of system is still in use nowadays; in grocery

retailers (e.g. VOLG), bakeries (Kamps), discounters (Denner) and opticians

(McOptic). But only three out of 12 major European grocery retailers still rely solely

on such manual ordering systems (Småros, Angerer et al. 2004a).

ASR1: Electronic Inventory-Based Replenishment Systems

As soon as retailers have their inventory levels administered by an IT system, one

can say that a state of semi-automation has been attained.56 In the descriptive model,

this step means that the first module "Inventory Visibility" is represented electronically

in the IT system. With this electronic information, it is for the first time possible to

move replenishment decisions away from the store. A centralized system is now

thinkable, where a central organizational unit monitors inventories at all the outlets

and makes decisions concerning deliveries. In order to do so, a certain degree of

inventory records accuracy has to be reached.

It is worth noting that decisions about the time and quantity of the order are still made

manually; therefore, the module "Replenishment Logic" is still not IT-based. Order

restrictions can be displayed on an IT systems, but the planner still has to take them

55

See Table 10 for an overview of ASR differences. 56

The term semi-automation is used because the inventory records are now automatically administered by the IT

system, yet the ordering itself still takes place manually.


into account "manually" when placing the order. Fresh products, such as fruits and

vegetables, are often ordered this way. Examples of companies using predominantly

ASR1 systems are consumer electronics retailers (e.g. Media Markt CH) and book

stores (Orell Füssli).

ASR2: Simple Replenishment Heuristics

Basically, this is the first level where true automation starts. To reach this level, only a

small step is necessary from ASR1 systems. At that level, the system already stores

electronically inventory quantities. Consequently, just by implementing a simple

heuristic it is possible to make the replenishment system work without any employee

intervention. One of the most common heuristics used in retail is the order-point

order-quantity (minimum-maximum) heuristic. The inventory management system is

updated after each transaction or at least before a new order is to be placed. The

ordered amount is either a fixed quantity (Q) or the difference between the actual

inventory level and a desired inventory level (S). These replenishment parameters

are set beforehand and are valid over a long period (months to years). This kind of

replenishment system is called consumption-based, as the consumption of the items

at the POS releases the order (pull-system). With such a replenishment logic no

forecasts are necessary. This ASR level is the first at which basic order restrictions

can be automatically accounted for (e.g. the system follows the quantity restrictions

when placing an order).

An example of companies with ASR2 systems are grocery retailers, such as Coop

and Migros, along with specialised retailers dealing with cosmetics (Body Shop),

mobile phones (Orange Zone), household electronics (Fust), among others. Later in

this thesis an ASR2 system with a special difference is examined. The replenishment

parameters are set at category level and not at SKU product level. The label ASR2*

is introduced to make the difference clear.57

ASR3: Time Series-Based Forecasts

At this level, for the first time the forecasting module is regularly in use. The system

makes an estimate of the next period's demand and orders according to it. This

system basically checks whether it is necessary to place an order in this period,

keeping the actual stock level in mind, or whether it suffices to place the order in the

next ordering cycle. As the demand is only a prediction, decisions are made taking

into account a defined risk of OOS. The order quantity is either a cost-optimal amount


Q* or determined by a preset order-up-to-level point S. The main difference to the

former levels is that the placing of the order becomes demand driven. The forecast is

created by observing the behaviour of demand data in the past and making

assumptions about future consumption. On this level, forecasts are computed with

time series methods (one-dimensional techniques). More sophisticated programs

take into account trends, cyclical variations, seasonal patterns and irregular random

fluctuations (cf. Silver, Pyke et al. 1998). However, even such complex algorithms

remain one-dimensional, as the only independent variable is time. As the IT systems

used in ASR3 systems are more advanced, it is theoretically possible for them to take

into account more order restrictions when ordering. Retailers such as Spar

Switzerland, Orange SA and Spengler use these systems for some of their products.

ASR4: Causal Model-Based Forecasts

The difference between ASR4 systems and the former level is the complexity of the

forecasts used. Causal-based forecasts are obtained by taking into account not only

past patterns, but also by anticipating the effects that future events will have. These

systems claim to be able to predict the effect of promotions, price reductions, actions

of the competition, the weather and more, on the demand. For this, the systems have

to understand and compute the underlying effects influencing consumer demand.

Demand is consequently treated as the dependent variable, and explained within a

causal model with several, independent variables. This kind of system represents at

the moment the most sophisticated software tools available on the market. These

causal models have been successfully implemented, for example, at retailers such as

dm-drogeriemarkt, Woolworth and Metro Group.

ASR5: Multi-Echelon Systems

This level somehow breaks with the linear order of Figure 13. It is possible to

enhance any of the ASR0–4 systems towards a multi-echelon replenishment system.

This means that a system simultaneously controls the inventory stock and makes

replenishment decisions on several levels of the supply chain. An example would be

an ASR system that automatically and simultaneously calculates the optimal orders

for retailers' stores and DCs. Such systems would need even more intelligence than

normal systems, as the complexity of a multi-echelon system increases. The

optimization becomes more difficult due to more restrictions. As stated on section

57

The difference between ASR2 and ASR2* is described in detail in section 6.2.2 in the case of MYFOOD.


2.1, multi-echelon systems are not included in the focus of this thesis, therefore they

will not be further examined here.

The following table summarizes the differences of ASR systems.

Multi-dimensional

(causal) forecasts

Uni-dimensional (time series-

based)forecasts

NoneNone or qualitative

techniques

Forecasts

Complex restrictions implemented into IT systems

Medium complex

restrictions implemented

into IT systems

Basic restrictions shown in IT

systems

Manual conside-ration of

restrictions

Order restrictions

(s,Q*) or (s,S)

Simple heuristics,

e.g. (T,Q) (T,S) (s,Q), (s,S)

Manual decision: time and quantity

Replenish-ment logic

Electronic inventory record systemManual records

Inventory visibility

ASR4ASR3ASR2ASR1ASR0

Multi-dimensional

(causal) forecasts

Uni-dimensional (time series-

based)forecasts

NoneNone or qualitative

techniques

Forecasts

Complex restrictions implemented into IT systems

Medium complex

restrictions implemented

into IT systems

Basic restrictions shown in IT

systems

Manual conside-ration of

restrictions

Order restrictions

(s,Q*) or (s,S)

Simple heuristics,

e.g. (T,Q) (T,S) (s,Q), (s,S)

Manual decision: time and quantity

Replenish-ment logic

Electronic inventory record systemManual records

Inventory visibility

ASR4ASR3ASR2ASR1ASR0

Necessary inventory records accuracy increases

Table 10: Characteristics of automatic replenishment levels

4.3. Explanatory Model

4.3.1. Purpose and Structure of the Explanatory Model

The explanatory model in this paper is intended to be a representation of predicted

relationships between factors that influence ASR performance. The explanatory

model helps to quantify ASR1–ASR4 system benefits (research question Q2) and to

find the adequate level of automation for each retailer (Q4). The definition of the unit

of analysis is critical for the further understanding of the model. In theory, retailers will

face an optimal automation level for each SKU in every single store. Yet for practical

reasons, a retailer can only have a certain number of ASR systems running in

parallel. Therefore, retailers will have to define product clusters of items with similar

characteristics that will be managed through a single system that fits the

characteristics of that cluster best. For instance, a cluster could comprise

slow-moving canned products with good demand predictability and low value. The

explanatory model is also the basis for giving recommendations as to how to change

the organization and replenishment processes (Q5).


A question that has occupied many practitioners is the question of reasons

underlying OOS incidents. In the literature, there are many sources dealing with this

question (eg.Gruen, Corsten et al. 2002; Roland Berger 2003b). Although these

sources examine the underlying reasons, they remain descriptive. Furthermore, a

question that has not been discussed in full detail in theory is the quantitative

correlation between OOS and other variables such as product, store and

replenishment system characteristics. In the model constructed in this thesis, it is

postulated that it is possible to explain, at least partly, the OOS and inventory level

performance of retailers by having the product, store and replenishment

characteristics as independent variables. Therefore, the additive model that is

explored in this thesis is:

Y= M + ASR + P + S + (ASR x P) + (ASR x S) + e

where

Y: OOS rate or inventory performance measure

M: Overall mean

P: Effect from the product characteristics

S: Effect from the store and personnel characteristics

ASR: Effect from the replenishment system in use

ASR x P: Interaction effect replenishment system and product

ASR x S: Interaction effect between replenishment system and store

e: Error term, unidentified effects

The model consists of three main effects and two interaction terms.

The first effect implemented is the replenishment system characteristics (ASR). It

is the main hypothesis of this thesis that the inventory performance and the OOS

rate58 can be influenced decisively by ASR system choice.

The second main effect in the model consists of product characteristics. There are

several statements found in literature and practice that product characteristics will

influence the OOS rate and inventory performance (e.g. Andersen Consulting 1996;

Grocery Manufacturers of America and Roland Berger 2002). For example, from the

58

In this thesis, the OOS definition formulated by Gruen, Corsten et al. (2002) is employed: the OOS rate is

measured as a percentage of SKUs that are out-of-stock on the retail store shelf at a particular moment in time

(i.e., the consumer expects to find the item but it is not available).


inventory management literature it is known that products with higher demand

uncertainty need higher stock levels to achieve the same availability percentage

(Alicke 2003).59

The third main effect implemented in the model consists of store characteristics. It

is self-evident that this variable can play an important role in inventory performance.

Several studies have stressed that OOS rates vary widely between stores (e.g.

Gruen, Corsten et al. 2002; Roland Berger 2003b). In the case of MYFOOD–the

retailer that will be studied in detail in the quantitative analysis–all stores have exactly

the same distribution system; therefore logistics should not play a role. But the stores

have different sizes, assortments, regions and managers. These differences most

probably influence store performance. Furthermore, another difference in the results

could stem from the personnel, as they still have a significant influence on the

ordering and the quality of store operations (Grocery Manufacturers of America and

Roland Berger 2002). Therefore, it is sensible to include this variable in the model, as

a significant influence on the dependent variable can be expected.

In addition to these three main effects, two interaction effects are included in the

model. If the characteristics of a product have an influence on replenishment system

performance, the ASR x P variable should be statistically significant. This would be

the case, for instance, if store employees are influenced by the product's price when

ordering while the ASR systems are not. One of the interviewees stressed that some

store managers tend to have higher availability rates for products which they

personally like. A machine doing the order is emotionless and is therefore less

influenced by product characteristics. A similar result can be expected from the

ASR x S interaction. It can be assumed that for retailers, such as MYFOOD, where

store employees have greater influence on replenishment parameters, ASR

performance will differ from store to store, making this interaction variable significant.

In the following, hypotheses concerning possible interrelations between the

dependent variables OOS and inventory performance and other independent

variables are constructed. These hypotheses are constructed based on results from

KLOG projects, interviews with practitioners and literature research. The hypotheses

are tested in chapter 5 with the help of data from the grocery retailer MYFOOD. As

59

Sometimes the correlation is not as intuitive. For instance, Kotler and Bliemel (1999) emphasise that only 5% of

unsatisfied customers complain to store employees. Products where consumers complain more often will

probably tend to have a higher availability, as the employees are aware of the importance of the problem. As

customers will tend to complain for certain products more than for others, there is clearly a strong relation

between the product characteristics and the OOS rate.


this company has mostly ASR0 and ASR2 systems in use, the hypotheses are based

on a comparison of these two systems. The quantitative analysis of the hypotheses

will be carried out with the help of different types of analysis of variance.60

4.3.2. Hypothesis Development: Product Characteristics

Sales Variance

For a retailer it is more challenging to have the right amount of items on the shelves if

there is a large sales variance in a store. There are two problems that arise when

facing irregular selling behaviour. First, the replenishment system must be flexible

enough to cope with the increased complexity. For example, I must be able to order

on one day 50, on another only 5 items of a certain product. The case pack size

(CU/TU ratio) will play a role here as it determines the minimum possible order

quantity. Nevertheless, much more difficult to handle is the second effect. If, in

addition to the high sales variance of a certain product, there is also low predictability,

the replenishment becomes very complex.61 The amount of safety stock is strongly

influenced by this parameter: higher sales variance leads to higher inventory levels

(Krane 1994; Fafchamps, Gunning et al. 2000; Alicke 2003). The higher the demand

uncertainty, the higher the OOS rates will be. For store employees, it is very difficult

to keep track of inventory levels for products that have high sales variance. Besides,

some store managers prefer regular ordering patterns, i.e. they order every second

Monday a case pack (cf. Broekmeulen, van Donselaar et al. 2005). It is obvious that

the bigger the sales variance, the worse such simple ordering patterns will work. For

automatic systems, such as ASR2, inventory visibility is not affected by the sales rate

or variance, the system always knows the quantity in the stores (inventory records

accuracy aside). Besides, ASR2 systems decide every replenishment period (i.e.

every day in the case of MYFOOD) whether it is necessary to place the order. For

these reasons, one can expect that store personnel will have more difficulties

replenishing correctly high-sales-variance products compared to ASR2 systems.

Consequently, the following relationships are expected:

60

This procedure goes hand in hand with Backhaus, Erichson et al.'s (2003) recommendations, as the authors

emphasise the necessity of having a priori hypotheses about the effects of the independent variables on the

dependent variable before using ANOVAs. 61

For the products analysed in this thesis it is not possible to say whether a product has a good predictability or

not. To be able to compare the variance of slow and fast moving products the sales coefficient of variance is

used. This coefficient is calculated by dividing the daily sales' standard deviation of each product by its mean

sales.


H1a: ASR0 products62 with high sales variance have more OOSs than products

with a low sales variance.

H1b: For ASR2 products, the influence of sales variance on the OOS rate is

smaller than for ASR0 systems.

H1c: ASR0 products with a high sales variance have higher inventory levels than

products with a low sales variance.

H1d: For ASR2 products, the influence of sales variance on inventory level is

smaller than for ASR0 products.

Speed of Turnover

A product's purchasing frequency can have an influence on OOS rate. In the study by

Andersen Consulting (1996) the OOS rates were higher for fast-moving products.

Products that sell very often per day have to be replenished more often than those

with lower sales. Therefore, the danger of an OOS is far greater. On the other side,

very slow moving goods that are only seldom sold have the danger of being

overlooked when ordered manually. In the study by Stölzle and Placzek (2004) slow

moving products had the highest OOS rate. The authors explain this by the fact that

slow moving products receive less attention. This statement is confirmed in the study

by Andersen Consulting (1996), where the authors, in addition, stress the risk for

these products of remaining OOS for a long time. Therefore, for manual systems

(ASR0) a quadratic curve is expected, meaning that products with very low and very

high speed of turnover will have the most OOS incidents. As automatic systems do

not have the danger of overlooking orders, only a small influence of the speed of

turnover on the OOS rate should occur.

The inventory level in a store is determined by logistics aspects but also by marketing

aspects. Retailers want to avoid having too few products on the shelf. The reason for

this is explained by one interviewed store manager, who said, "we create demand by

having full shelves." Therefore, one can expect to see for slow moving SKUs more on

stock than is actually necessary from a logistical point of view. In addition, for some

slow moving units a single case pack size lasts for weeks, therefore increasing

average inventory levels. This means that the lower the turnover of a product, the

higher will be the inventory range of coverage. The situation is similar for ASR2

systems. Their inventory level will also depend on case pack size and turning speed:

the slow moving products will have higher inventory levels. Yet, there should be a

difference for very slow moving products. While store managers tend to have a

simple procedure for such items (e.g. order a case pack every third week) (cf.

62

The wording "ASRx product" is a short form for "a product that is ordered through an ASR level x system."


Broekmeulen, van Donselaar et al. 2005), MYFOOD's ASR2 systems check the

inventory position daily, therefore optimizing the ordering and thus reducing

inventory. Overall, it is hypothesised:

H2a: ASR0 products have higher OOS rates for very fast moving and very slow

moving articles than for the other products.

H2b: For ASR2 products, the influence of speed of turnover on the OOS rate is


H2c: ASR0 products with a low speed of turnover have higher inventory levels

than fast moving products.

H2d: For ASR2 products, the influence of speed of turnover on inventory levels is


Price

The effect of price on OOS and inventory level is ambiguous. DeHoratius and Raman

(2002) found that inexpensive goods were more likely to have inaccurate records

than expensive ones, as expensive SKUs receive more attention. According to this

fact, one would expect more expensive goods to be less often OOS, for both manual

and automatic systems. Normally, store managers would try to reduce the amount of

expensive goods on stock, as they are bounded capital. Yet, in the data set of this

thesis the most expensive goods are only worth 17 euro, 90% of all goods are

cheaper than six euro. This price range is very small compared to the range in

DeHoratius and Raman's (2002) work, where the most expensive goods had a value

of 2,800 euro. Yet, as Broekmeulen, van Donselaar et al. (2004b) show, the inventory

costs are secondary for typical grocery retailers, as they only represent 10% of

logistics costs. Much more important are the handling costs in the store, which are

five times as high. Therefore, it is reasonable to argue that for the observed grocery

retailer MYFOOD another relationship between price and inventory performance will

appear. Assuming that the more expensive goods have higher margins, the store

manager will tend to have a higher inventory level on the shelves to avoid any OOS

and consequently customer dissatisfaction. Yet, it is not clear if this strategy is

successful, as many sources from the just-in-time and lean management literature

have stressed the negative impact of high stock levels on availability (Schonberger

1982; Krafcik 1988; Fisher 2004). The higher inventory levels may even worsen the

availability rate of these products. For automatic systems, on the other hand, the

product price should not play such an important role, as the automatic system

reorders the product despite its price. Based on this research it is expected that:

H3a: ASR0 products with high prices have more OOSs than lower priced goods.


H3b: For ASR2 products, the influence of price on OOS is smaller than for ASR0

products.

H3c: ASR0 products with higher prices have higher inventory levels than

low-priced goods.

H3d: For ASR2 products, the influence of price on inventory level is smaller than

for ASR0 products.

Case Pack Size (CU/TU)

Trading units (TUs) are the cases used to transport the products to the stores and

contain several small consumer units (CUs)–the units that are finally bought by

shoppers. For a typical grocery retailer, the size of the case packs is the same for all

stores, as it is often determined by the supplier without taking into account the store's

individual demand. The consequence is high inventory levels. Broekmeulen, van

Donselaar et al. (2005) found in their research that 96% of the items in their sample

had a larger order case pack size than the stores needed in a reordering period.

Therefore, stores typically order a single case pack unit or wait a longer time to place

the order. The higher the CU/TU ratio, the stronger this effect and the more inventory

will be on stock. On the other hand, the smaller the CU/TU ratio is, the better a

store's replenishment system can work. It is not only manual systems that are

influenced by the CU/TU ratio. Falck (2005) has shown in theory that ASR3 systems

will only manage to reduce inventory compared to ASR2 systems if the CU/TU ratio

is small.63 Therefore, it is hypothesised that ASR2 systems will also be influenced by

the CU/TU ratio.

Big case packs force employees to place orders more seldom. Therefore, the danger

of forgetting to replenish the item is greater. As automatic systems do not forget any

order, one can assume that the OOS rate will be higher for manually ordered

products compared to ASR2 ordered products.

One effect that might cover the influence of the CU/TU ratio on OOS rate is shelf

space. Depending on the shelf space allocation, a certain case pack may or may not

be beneficial. Wegener (2002) hypothesises that the OOS rate will strongly be

63

It is worth noting that the resulting effect of the CU/TU ratio on performance depends on the store. Small stores

are more affected by the CU/TU ratio than bigger stores. For example, for some non-food products of

MYFOOD (e.g. shoe laces) the smallest case pack lasts in small stores for several months, while in a bigger

store one case pack is sold within days. Therefore, instead of looking at the absolute case pack value, all

analyses are made looking at the relative case pack size (case pack size divided by average daily sales),

analogous to Falck's procedure.


influenced by whether it is possible to place the whole case pack directly on the shelf

or whether part of its contents has to be stored in the backroom. The replenishment

from the backroom not only creates high handling costs, it is also one of the major

reasons for OOS incidents (Gruen, Corsten et al. 2002). Taking these studies into

consideration, it is hypothesised:

H4a: ASR0 products with a small CU/TU ratio have smaller OOS rates than

products with a bigger CU/TU ratio.

H4b: For ASR2 products, the influence of CU/TU on the OOS rate is smaller than

for ASR0 products.

H4c: Products with a small CU/TU ratio have lower inventory levels than products

with a bigger CU/TU ratio.

H4d: Products with a small CU/TU ratio have lower inventory levels than products

with a bigger CU/TU ratio.

Product Size

In the study by Andersen Consulting (1996) different findings on the effect of product

size on OOS are encountered. One could argue that bulky products need more

space on the shelf; therefore, retailers may opt to allocate them less shelf space than

would be advisable from a logistical point of view. Consequently, small products will

tend to have more facings to increase visibility to customers and therefore should be

comparatively more on stock in the store. Wegener's (2002) data analysis of four

product families from the beauty and household category showed that products with

bigger package size were indeed more often OOS than products with small package

sizes. The explanation for this effect given by an interviewed manufacturer was that

especially for bulky products that are not visually pleasing (such as toilet paper),

retailers tend to reduce the amount of facings on the shelves. Yet Wegener (2002)

relativises this result: for other product categories the correlation effect of product

size and OOS is likely to be smaller, as other product characteristics (such as speed

of turnover) are probably the predominant influence. As regards inventory level,

Broekmeulen, van Donselaar et al. (2004b) found in their analysis of two Dutch

retailers that smaller products had more shelf space than required from a logistical

point of view. Therefore, one can expect to see higher inventory levels for small

products. For automatic systems, on the other hand, the product size should not play

such an important role, as the automatic system reorders the product despite its size.

This is why it is predicted that:

H5a: ASR0 products with a small size have lower OOS rates than big-size

products.


H5b: For ASR2 products, the influence of product size on the OOS rate is smaller

than for ASR0 products.

H5c: ASR0 products with a big size have lower inventory levels than small

products.

H5d: For ASR2 systems, the influence of product size on inventory level is smaller


Shelf Life

Retailers are constantly aiming to offer products with longer remaining shelf life, as

consumers demand fresh products (Petrak 2005). Therefore, for products with a very

short shelf life (e.g. fresh pasta), store managers seek to reduce the amount of store

inventory. It is clear that the smaller the average inventory of any SKU (compared

relatively to its mean sales) the fresher the product will be due to a higher turnover.

Therefore, it is reasonable to expect low inventory levels for short-shelf-life products.

This finding is supported by physical audits such as that carried out by Broekmeulen,

van Donselaar et al. (2004b).

Small (safety) stocks reduce the danger of spoilage, but at the same time increase

the danger of OOS. On the other hand, fresh products such as green grocery are, in

terms of marketing, the most important goods for many grocery retailers. Therefore,

skilled store employees will pay extra attention to these products. In the study by

Stölzle and Placzek (2004), cooled products with a short shelf life had smaller OOS

rates. For products with a longer shelf life the retailer can afford to have bigger

quantities stored, as the danger of spoilage is less. Consequently, inventory levels

will increase as well as the time between the ordering thus increasing the danger of

OOS incidents. Overall, this leads to a quadratic effect of shelf life on OOS: products

with very short and very long shelf life will experience more OOS situations. Yet, the

effect of shelf life on OOS could be covered by other factors, such as sales variance,

that have probably a stronger influence on the inventory level. The reasons

mentioned for the influences of shelf life on inventory performance do not hold for

automatic systems. Systems such as ASR2 do not pay more or less attention to

products with a certain shelf life, consequently there should be no influencing effect

visible. Overall, it can be expected that:

H6a: ASR0 products with a very short shelf life have an increased OOS rate, as

well as products with a very long shelf life.


H6b: For ASR2 products, the influence of shelf life on the OOS rate is smaller than

for ASR0 products.

H6c: ASR0 products with a long shelf life have higher inventory levels than

products with a short shelf life.

H6d: For ASR2 products, the influence of shelf life on inventory levels is smaller


The following table summarizes the product characteristics hypotheses H1–H6:

Dependent Variables

Independent Variables

Out-of-Stock Inventory Level

+ (H1a, ASR0) + (H1c, ASR0) Sales variance

0 (H1b, ASR2) 0 (H1d, ASR2)

+ - + (H2a, ASR0) - (H2c, ASR0) Speed of turnover

0 (H2b, ASR2) 0 (H2d, ASR2)

+ (H3a, ASR0) + (H3c ASR0) Price

0 (H3b, ASR2) 0 (H3d, ASR2)

+ (H4a, ASR0) + (H4c, ASR0) CU/TU

0 (H4b, ASR2) + (H4d, ASR2)

+ (H5a, ASR0) - (H5c ASR0) Product size

0 (H5b, ASR2) 0 (H5d, ASR2)

+ - + (H6a, ASR0) + (H6c, ASR0) Shelf life

0 (H6b, ASR2) 0 (H6d, ASR2)

"+" Positive effect "-" Negative effect "0" Smaller effect of ASR2 system compared to ASR0

"+ - +" quadratic relationship

Table 11: Overview of hypotheses concerning product characteristics

4.3.3. Hypothesis Development: Store Characteristics

Several descriptive OOS studies have mentioned that there is a strong variance in

the OOS rates between countries and retailers studied (e.g. Gruen, Corsten et al.

2002; Roland Berger 2003b). The same sources also indicate that the OOS rate

differs from store to store, even though the stores' contexts (i.e. ASR system,

distribution network, assortment, marketing activities, etc.) are fairly similar. This

latter phenomenon will be analysed in the data of MYFOOD. In the following,

hypotheses concerning the influence of store characteristics on the OOS rate are

derived, as the understanding of these influences is important when assessing the

effect of ASR systems.


Different Performance Between Stores

As stated before, one of the common results found in previous availability studies is

that the OOS rate differs significantly between stores (e.g. Gruen, Corsten et al.

2002; Roland Berger 2003b; Stölzle and Placzek 2004). A transfer thought would be

that the same relationship holds true for inventory performance (i.e. inventory levels

and inventory variance). As MYFOOD managers are at liberty to change the

parameters of their replenishment system they have an important influence on the

inventory level. The hypotheses are therefore:

H7a: The mean OOS level of a store differs from store to store.

H7b: The mean inventory performance of a store differs from store to store.

Shrinkage and OOS

The pivotal question is how some stores manage to have lower OOS rates other

stores facing the same logistical context and using the same replenishment systems.

A possible answer could be that store managers decide willingly to reduce OOSs by

having more shrinkage. Yet, the interviewed practitioners from MYFOOD stressed

the importance of the capabilities of the store managers to the performance of the

store. The literature has often stressed the importance of good management for

performance (e.g. Fahrni 1998). In MYFOOD's interviewees' opinion, stores with

experienced mangers are able to reduce shrinkage and OOSs at the same time.

Consequently, the hypothesis is:

H8: There are some excellently-managed stores that are able to reduce OOSs and

shrinkage at the same time.

SKU Density

The more complex a store is, the more difficult it will be to manage. Raman,

DeHoratius et al. (2001a) report how difficult it is for employees ordering manually to

keep track of a product's availability. Yet it would be misleading to compare the

absolute number of products between stores. Bigger stores will tend to have higher

sales and a broader product range, but at the same time there will be more store staff

and more space on the shelves for the product. Therefore one cannot conclude that

bigger stores will have higher OOS rates. A way to compare different-sized stores

may be looking at the SKU density. The assumption is that the amount of products

per square metre is a good way of quantifying the complexity of a particular store.

This variable has also been used by DeHoratius and Raman (2002). The authors

were able to prove a positive correlation between the inaccuracy of records in a store

and the SKU density. The authors argue that in crowded stores it is more difficult to


track data inaccuracy, therefore increasing the danger of OOS incidents. Besides, the

more products a store has per m2, the less space there will be on the shelves for

each SKU, thus increasing the chances of an OOS. The hypothesis reflecting this

considerations is:

H9: Stores with high SKU density will have more OOSs than low-density stores.

Work Intensity

The economic theory of work states that there is a quadratic relationship of work

effort and efficiency (cf. Fairris 2004). Transferred to this context this means that the

number of employees in the store should also have a quadratic influence on the

quality of store operations. When there are very few employees in a store, every

single one has to work more; exhaustion and stress can set in, thus reducing the

efficiency of work. The replenishment of shelves and the accuracy of data will suffer.

Gruen, Corsten et al. (2002) found overworked staff to be one of the reasons for

OOSs. On the other hand, if there are too many employees, boredom can set in

(Fairris 2004), motivation can sink (Connell 2001), the responsibility for tasks can

become unclear, and store managers find it more difficult to coordinate tasks (Anon.

2004). Wegener (2002) states that the marginal rate of productivity sinks, therefore

she expects stores with an average personnel intensity to have the lowest OOS

rates. To be comparable, the number of staff should be seen relative to the size of

the store. The bigger a store the more the staff necessary to manage the shelves

accurately. Overall, the hypothesis can be formulated as:

H10: Stores with too many or too few employees per m2 sales area will have more

OOSs.

Store Managers' Experience

For retailers that have a very decentralized decision system, the single store can be

regarded as a little independent company. Therefore, the influence of the store

manager as an entrepreneur on the performance of the store will be significant, as

stated in classical entrepreneur theory (cf. Schumpeter 1935; Fahrni 1998). Stores

with good leadership will tend to have lower OOS rates. The more experience the

store manager has, the better the performance of the store will be as store operations

have a radical impact on the OOS rate (Gruen, Corsten et al. 2002). In the case of

MYFOOD, store operations are still within the responsibility of store managers; they

have a strong influence on replenishment parameters. A rather crude way to asses

the experience of a store manager is by looking at the number of years he or she has

worked in that particular store. The longer the store manager has been working in a


particular store, the better the performance should be. Already Raman, DeHoratius et

al. (2001a) confirm that store managers who have been working for a long time on a

particular store had a smaller rate of misplaced SKUs (i.e. items that were in the

backroom but not on the shelf). Another advantage of experienced managers is that

they have gained knowledge about the demand behaviour of their customers. For

instance, experienced managers know to quantify the influence of a soccer game in

the city on beverage consumption. However, an effect of diminishing returns will most

probably appear. This means that for managers that have been on site for a long

time, additional years in the store only result in small improvements. Based on these

statements it is hypothesised:

H11: Store managers that have been working in the same store for a long time

have lower OOS rates than other store managers.

Backroom

Authors such as Krafcik (1988) and Schonberger (1982) from the just-in-time and

lean management philosophy argue that inventory hides problems in manufacturing.

Transferred to this thesis, this means that having too much stock can be

counterproductive to the availability rate. First, in backrooms that are crammed with

goods, it will be very difficult to achieve a high visibility. And second, high inventory

impedes process improvement, as errors of suboptimal replenishment systems are

obscured by high inventories. Raman, DeHoratius et al. (2001a) report on a case in

which a certain shop suffered a substantial deterioration in performance after the

capacity of the backroom was increased. Fisher (2004, p. 14) therefore demands

from retailers "to get rid of the backroom." If this is really recommendable can be

tested by the following hypothesis:

H12: Stores with large backrooms have higher OOS rates than stores with small

backrooms.

Customer Satisfaction

Finally, a question asked by many retailers is, whether it is worth putting effort into

improving the OOS rate at all. Although a high availability is crucial for the

satisfaction of consumers (Sterns, Unger et al. 1981; Angerer 2004) one could argue

that by investing too much time in increasing availability (by having, for example,

store employees replenishing the shelves more frequently) the direct service to the

customer could suffer, as e.g. less check-out lanes are open. In addition accurate

scanning of goods takes longer, and is therefore not welcomed by customers.


Therefore, it is interesting to analyse whether there is indeed a positive correlation

between customer satisfaction and OOS rate:

H13: Stores with lower OOS rates have more satisfied customers than stores with

high OOS rates.

The following table summarizes the store characteristics hypotheses H7–H13:

OOS rate differs from store to store (H7a)

Inventory performance differs from store to store (H7b)

Reduction of OOSs and shrinkage is at the same time possible (H8)

The more SKUs per m2 the more OOSs (H9)

Too many and too few staff increases OOSs (H10)

More experienced store managers have fewer OOSs (H11)

Bigger backrooms lead to more OOSs (H12)

Stores with low OOS rates have higher customer satisfaction (H13)

Table 12: Overview of hypotheses concerning store characteristics

4.3.4. Hypothesis Development: ASR Characteristics

The last group of hypotheses test one of the main questions of this thesis (Q2): can

retailers achieve a better replenishment performance by using higher ASR systems?

From the theoretical research of ASR-related topics64 it is known that ASR systems

should be able to reduce OOSs (cf. Achabal, McIntyre et al. 2000), decrease

inventory level (cf. Ellinger, Taylor et al. 1999), have lower inventory variance and

show robust results compared to manual systems (cf. Closs, Roath et al. 1998).

Therefore, these performance measures have been chosen to test the capabilities of

higher ASR systems.

OOS Performance and ASR Level

Fisher, Raman et al. (2000) point out that many retailers still use very basic methods

for their ordering. According to the authors, rules of thumb and gut feelings are often

used when determining the quantities to be ordered. They see a great opportunity in

mixing "art and science" by using the vast possibilities of new technologies and data

availability to improve retailers' replenishment systems. Such technologies are the

ASR systems. It would be a major relief for retailers to have more reliable

replenishment system, as 72% of all OOSs are caused by faulty store ordering and

64

See research on ARPs in section 3.2.


replenishment practices (Gruen, Corsten et al. 2002). The reports from practitioners

(e.g. Beringe 2002) and the literature (e.g. Mau 2000) of the reduction of OOSs by

ASR systems are very courageous, yet there are no tests available in academia that

prove these statements. Therefore, this thesis aims to close this gap by showing that

such systems do indeed have the potential of reducing OOSs caused by

replenishment faults. As data for ASR2 systems is available, it is hypothesised:

H14: If the ASR level is chosen and parameterised correctly for a certain product

category, ASR2 products have less OOS incidents than ASR0 products.

Inventory Performance of ASR Systems

While for the OOS performance the situation is clear–the fewer OOS incidents in a

store the better–it is slightly more difficult to assess inventory performance. There are

various definitions of what a good performing replenishment system should optimize

regarding the stock level over time. Two possible perspectives on how to measure

inventory performance used in this thesis are:

� Cost side: to reduce inventory costs, there should be as low stock in the stores

as possible, therefore, the aim is to minimize the absolute inventory mean.

� Marketing side: there should be little variance between the stock levels each

day, as the customer expects full shelves at any time. Consequently, the

ordered quantity should be equal to the demand. The result of such a strategy

is a constant inventory level. Effective replenishment systems should minimize

inventory variance.65

Depending on the characteristics of the retailer, higher ASR systems should improve

inventory performance, as long as the adequate level of ASR is not reached.

The MYFOOD data includes data from three different automatic systems, which can

be compared to ASR0 systems. While ASR2 and ASR3 products should benefit from

the automation, an ASR2* automation is rather crude. ASR2* systems use the same

reorder parameter for many products. As products might exhibit different sales

patterns, the replenishment will probably be suboptimal. Overall, it is hypothesised:

H15a: ASR3 products will have a better inventory performance than ASR0

products.66

65

Whether the shelves will be constantly filled or not, will also strongly depend on store operations, as employees

have to refill the shelves from the backroom. However, a replenishment system that ensures that at any time

the same quantity of goods is in the store is the basis for a good refilling of the shelves. 66

The underlying assumption is that the ASR level was chosen and parameterised correctly for the product

category that is examined in the sample.


H15b: ASR2 products will have a better inventory performance than ASR0

products.

H15c: ASR2* systems will not perform better than manual systems.

The Figures 14–16 show an overview of the hypotheses.

OOSOOS

Product characteristics

Sales variance

Speed of turnover

Price

CU/TU

Package size

Shelf life

H1–6

Dataset1

Inventory

Performance

Inventory

Performance

Figure 14: Overview of hypotheses, product characteristics67

OOSOOS

Store characteristics

Shrinkage

SKU density

Personnel / m2

Store manager experience

Backroom size

H7–13

Dataset1

Figure 15: Overview of hypotheses, store characteristics

67

It is worth noting that these three figures are not meant to be a model, even if they have some visual similarities

to structural equation models. These figures are an illustration of the three different groups of hypotheses that

will be tested in chapter 5. On the left side, there are the independent variables, on the right side the dependent

ones. The tests of the hypotheses are carried out independently of each other. Furthermore, they are based

partly on different data bases.


OOSOOS

Inventory

Performance

Inventory

Performance

ASR characteristics

ASR system level

H15

Dataset2

H14

Dataset1

Figure 16: Overview of hypotheses, ASR characteristics

5. Quantitative Analysis 85


To be able to test the hypotheses formulated in the previous chapter, two different

data sets of the company MYFOOD, a major central European grocery retailer, are

analysed.68 One part of the data stems from a physical OOS audit project that the

KLOG conducted together with this retailer in summer and autumn 2004. In addition,

historical sales and POS data was extracted from the retailer's ERP system to

complement the findings. The quantitative analysis made on this dataset consists of

three parts, analogous to the three groups of hypotheses. First, correlations between

OOS rate and product characteristics are explored. Second, the influence of store

characteristics on the OOS rate is examined. Finally, overall replenishment

performance is tested. Table 13 gives an overview of the datasets that are used for

the hypothesis testing and which will be presented in this chapter.

Hypothesis Data Set Question Answered

H1–H6 Dataset1 Which product characteristics influence the OOS rate and

inventory level? Is there a difference between manual and

automatic systems?

H7–H13 Dataset1 Which store characteristics influence the OOS rate? Is there

a difference between manual and automatic systems?

H14–H15 Dataset1

and 2

Do ASR2 and ASR3 systems perform better than ASR0

systems?

Table 13: Overview of the utilization of the two datasets for hypothesis testing

5.1. Sample and Methodology

5.1.1. Dataset1: Testing of Out-of-Stock Hypotheses

Researchers who want to empirically test hypotheses concerning OOS incidents

require first of all a reliable data sample. To obtain the availability rate of products on

the shelves is, however, a difficult task, as today's IT systems only store the

quantities in the store, without making a difference between the backroom and the

sales area. To overcome this problem, an on-shelf availability project was launched

in summer 2004 together with the European retailer MYFOOD. In the context of this

project, 100 products were chosen across seven different categories and their

on-shelf availability was audited manually. The choice of the products ensured that

there was at least one article per each of the seven most important categories of the

68

For more information about the retailer MYFOOD, refer to chapter 6.

86 5. Quantitative Analysis

retailer.69 The top-selling products were selected in each category (e.g. bananas in

the category green grocery), as their availability has a major impact on the retailer's

financial results and customer satisfaction. In the sample are also products that are

very important for customers as they are difficult to substitute (e.g. yeast in the

category bakery products). In addition, care was taken to have all replenishment

channels represented in the sample. This means that products were chosen that are

distributed through regional and national DCs as well as products that are supplied

directly from the manufacturers. Finally, products in promotion were taken to check

for this influence as well. The final product sampling was determined by logistics and

marketing directors of MYFOOD together with KLOG project members.

The next step was to select the stores to be audited. Ten stores in a certain region of

MYFOOD's home market were chosen taking care to represent three parameters

that, according to the practitioners in the project, have most influence on current store

performance: store size (large vs. small), location (rural vs. urban location) and

historical performance (stores that had in the last years above and below average

performance). The judgement of the stores' historical performance was carried out by

marketing and logistics directors of MYFOOD, who took into account aspects such as

financial performance, sales growth, happiness of customers, etc.

Five students were sent during a period of two weeks in autumn 2004 to the stores to

count the OOS incidents. Their task was to check twice a day the availability of the

products, once in the morning and once in the evening. Therefore, this project

resulted in an OOS data base of 100[products] * 12[days] * 2[measures per day] *

10[stores] = 24,000 data points. Furthermore, for 15 selected products, inventory

levels were manually counted to check the accuracy of inventory records. At the end

of each day, the students handed a list with all OOS incidents to the store managers.

The managers' task was to check the underlying reasons for the missing products

from a provided checklist.70 Store managers chose from this list the reasons for the

OOS incidents for each SKU. The statements made by the store managers were sent

to headquarters and were checked for plausibility by logistics and marketing

representatives. The employees in the stores, as well as the store managers, were

69

The seven most important categories identified by MYFOOD managers are: packaged food (e.g. coffee), green

grocery (e.g. tomatoes), non-food (e.g. toothpaste), dairy products (e.g. Parmesan cheese), meat and fish (e.g.

minced meat), bakery products (e.g. white bread), frozen (e.g. French fries). The products in each of these

categories are all distributed and ordered through the same logistics system. 70

This checklist was analogous to the 13-items list ECR Europe has published (see Roland Berger 2003b).The

ECR-list had to be enhanced by one OOS root cause: "Problems with the replacing of an SKU." This means

that for some discharged SKUs the new replacing product was not yet available for ordering, therefore resulting

in stock-outs.


informed that students would be in the stores during two weeks making a physical

audit. But they were not informed what exactly was being measured. In personal

face-to-face meetings, store managers were informed that the aim of the project was

to measure the performance of the logistics system, not to check the performance of

store staff. The managers were requested not to let the daily business be in any way

influenced by the physical audit. This information strategy lead to the desired result,

as the OOS rate for the first week, compared to the OOS rate in the second week,

differed only by 0.1 percentage points.71 There were several cases where products

were OOS for several days in a row, although the store manager could have

intervened at any time. The results of other similar OOS audits undertaken before by

other researchers differ radically from these results. Stölzle and Placzek (2004) had

in their study a reduction of the OOS rate of 26% between the first, anonymous week

and the second week, where the employees were informed. To calculate the weekly

OOS rate of each product, the daily measures per product are converted into a

percentage value, the same method as used by Gruen, Corsten et al. (2002). For

instance, if an article was found to be absent from the shelves three times in the first

week, the OOS percentage is 25%.72

For the final analysis, some products had to be discarded from the 100 original

products available in Dataset1. These were products that were during any of the two

weeks on promotion or out-listed. The reason for this is that promotional articles are

not ordered regularly through the systems, instead the central office sets a fixed

starting amount for each store (push-replenishment). As this procedure is executed

manually, it would be not possible to compare the results of the ordering systems

with each other, as the promotional articles change ASR system during the

promotion. One article was OOS at all times in the store, as it was out-listed and the

new product was not available for order. Overall, the final sample consists of 84

products in 10 stores, as can be seen in Figure 17. The unit of analysis is in most of

the following each SKU in each store. Therefore, the sample population consists of

n=840 units.

71

This statement is only valid for non-promotional articles. The OOS rates of products in promotion differ radically

from one week to the other. 72

3 divided by 12, as during six days two daily measures were conducted.


Packaged

food

27

Green grocery

16Non food

16

Frozen

3Bread

3

Meat and fish

7

Dairy products

12

Figure 17: Distribution of the 84 products in Dataset1

During the OOS project additional information on each SKU was acquired: product

price, product size, shelf life and the CU/TU ratio. Another source of information was

MYFOOD's ERP system, which provided for each product the delivered quantity and

the daily sales. This data was used to calculate inventory performance (inventory

level and its variance), the mean sales variance and the speed of turnover of each

product.73

As the hypotheses H7–13 relate to the influence of store characteristics on the

performance, some additional store related variables were collected as well. These

are the yearly shrinkage rate (in percent of the turnover), the SKU density (number of

SKUs per m2 sales area), the personnel density (number of employees per 100 m2

sales area), the years the store manager has been working in the particular store,

and the size of the backroom in proportion to the sales area.

The products in the sample are classified according to the replenishment process.

The methods used are manually (ASR0: 31 products) and automatically (ASR2: 53

products). The latter group has to be split into two subgroups. For some ASR2

products, the store staff can only change the ordering parameters based on a

discrete index scale from 1 to 10. This means that store managers can only decide if

they want low replenishment parameters and stock levels ("1") or very high ones

("10") without specifying the exact quantity. Besides, the store manager can only do

this on a group level. This means, for example, that store mangers have to change

the replenishment parameters of all batteries, not only of one special battery type. As

one will see, this procedure changes completely the performance of the products.

Consequently, the term ASR2* level is applied to make a differentiation to the usual

ASR2 systems discussed in this dissertation.


Table 14 summarizes the available store, product and ASR variables in use.

Variable Operationalization Variable Name

Product Variables

Sales variance Coefficient of variance of the daily sales Sales_coefvar

Speed of turnover

(=sales per day)

Mean sales of an SKU per day: low, medium

and high category

Speed_of_ turnover

Price Price in local currency (for all stores the same) Price

CU/TU (case pack size) Relative CU/TU ratio: number of consumer units

per trading unit divided by mean sales

CU_TU_relative

Product size Size of the product: small products (up to the

volume of a closed fist) medium size products

(up to the size of a one litre milk pack) and big

products (for all stores the same)

Package_size

Shelf life Shelf life in days Shelf_life

Shelf life2 Shelf life squared Shelf_life_squared

Product sales Daily sales per SKU and store Product_sales

Product deliveries Daily delivered quantity to the stores per SKU Product_deliveries

Store Variables

Store identification Number (1–10) to identify the stores Store_ID

Store size Size category of the store (as defined by the

retailer): small, medium, large

Store_size

Store location Rural or urban location Store_location

Shrinkage rate Yearly shrinkage in % of turnover Shrinkage

SKU density Amount of SKUs in a store divided by sales area SKU_density

Personnel density Number of employees in a store divided by

sales area

Personnel_density

Store manager experience Number of years the store manager has been

working in the store

Store_manager

Backroom size Ratio backroom size to sales room Backroom_size

ASR Variable

Type of ASR system ASR0: manual

ASR2, ASR3: automatic

ASR2*: automatic, but crude parameterised

ASR_level

73

In the appendix, the method for the calculation of inventory levels from the ERP-data is presented in detail.


Performance Variables

Inventory level Inventory range of coverage74

Mean of the stock level divided by average daily

sales

Inv_range_of_coverage

Inventory variance Inventory coefficient of variance Inventory_coefvar

OOS All out-of-stocks OOS

Order-related OOS Only OOS incidents are considered, where the

underlying reason is an ordering fault.75

Weekly

mean

OOS_only_order

Table 14: Overview of variables used in the analysis76

The descriptive statistics of the product characteristics by ASR group can be seen in

Table 15. Although the manual group has much higher selling quantities, the sales

coefficient of variance is very similar for all three groups. The average CU/TU ratio is

higher for the ASR2 group than for the ASR0 group, meaning that the latter products

have more flexibility when ordering. A clear difference in shelf life exists between the

groups; the manual products tend to have the shortest shelf life.

74

Instead of using absolute inventory levels, in the analysis the inventory range of coverage is computed. The

inventory range of coverage is the absolute inventory level divided by the mean sales. This relative measure

allows the comparison of products with different sales behaviour. As an example: if a product has a mean daily

inventory level of 100, is this a lot or not? It depends; if the store sells 50 units per day, than the inventory is

rather low as the amount is sufficient to meet demand for two days (100 units divided by 50 units per day

equals 2 days of inventory). If the product is sold on average four times a day, than the store has a substantial

amount of inventory. The inventory range of coverage is in this case 25 days (100/4). 75

The possible OOS causes that are directly related to replenishing behaviour are: order was created too late, the

last order quantity was not sufficient, forecasts were not accurate, during the creation of the order an error

occurred, fault with transmission to the DC, other order-related faults. In the case of MYFOOD, 60% of all OOS

incidents are order-related. In the following, the wording "OOS (order-related)" is used to point out that only

these kind of OOS incidents are meant. 76

Some of these variables, like price, are originally metric. To make it possible to use them in ANOVAs, they are

banded before with the help of 33.33% quantiles. The result is three equal size groups that are called "low,"

"med" (medium) and "high." Whenever a confusion of the metric and the ordinal variable is possible, the suffix

"(banded)" is used.


ASR Group

N Minimum Maximum Mean Std.

Deviation

Sales per day [units] 482 .25 299.75 41.16 57.82

Sales coefficient of

variance 481 .04 2.83 .53 .26

Price (local currency) 490 .0 27.5 3.94 4.71

Product size (category) 490 1 3 1.84 .74

CU/TU relative [days] 482 .03 40.00 1.61 3.10

ASR0

Shelf life [days] 490 1 700 48.51 127.58

Sales per day [units] 295 .50 161.92 17.72 23.82


variance 294 .00 2.50 .57 .27

Price (local currency) 310 .5 23.0 3.41 4.18



ASR2

Shelf life [days] 310 60 700 460.00 259.34

Sales per day [units] 39 1.00 26.67 6.82 6.40


variance 39 .28 1.12 .53 .18

Price (local currency) 40 2.1 4.9 3.09 1.09



ASR2*

Shelf life [days] 40 700 700 700.00 .000

Table 15: Product characteristics of Dataset1 by replenishment system

In the two week period there was in average a 3.0% rate of order-related OOS. A first

evidence of the performance differences between ASR systems can be obtained by

regarding the OOS rate separated by replenishment group, as depicted on Table 16.

The products ordered manually had an OOS rate of 4.7% and a standard deviation of

11.7%. This contrasts to the much lower values of the ASR2 and ASR2* groups.


N Minimum Maximum Mean Std. Deviation

OOS (order-related) 840 0.0% 91.7% 3.0% 9.4%

By ordering system

ASR0 310 0.0% 91.7% 4.7% 11.7%

ASR2 490 0.0% 29.2% 0.6% 3.1%

ASR2* 40 0.0% 20.8% 1.3% 4.0%

Table 16: OOS rates (order-related)of the sample

This finding is consistent in almost all stores. As depicted on Figure 18 in all stores

but in store 10, the ASR2 systems perform better than the ASR0 systems. Store 10

has overall very low OOS values, therefore, the absolute OOS rate difference

between the ASR systems is minimal. Another finding depicted in Figure 18 is that

the good performance of the ASR2 systems is not repeated with in the case of

ASR2* products. Theses products have in all cases higher OOS rates than the ASR2

ordered products; for two stores, the ASR2* products perform even worse than the

manual ordered products.

0%

2%

4%

6%

8%

10%

12%

14%

1 2 3 4 5 6 7 8 9 10

Store ID

OOS (only order)

ASR0

ASR2

ASR2*

Figure 18: Comparison of the replenishment systems by store77

As with the OOS performance, a first indication on the inventory performance

differences depending on the ASR system can be obtained by regarding the stock

levels by replenishment group, as depicted on Table 17. The products ordered

manually have larger inventory range of coverage and standard variation than the

ASR2 and ASR2* groups. This difference is the more striking as the manual products

77

The stores 1-3 are the smallest stores in the sample. Their assortment do not include ASR2* ordered products,

therefore there is no value for these products in the figure.


are mostly from fresh categories. These products should also have by far smaller

inventory levels compared to the canned goods to keep the products fresh.

ASR Group

N Minimum Maximum Mean Std. Deviation

ASR0 Inventory range of coverage

442 .00 45.01 10.89 9.07

ASR2 Inventory range of

coverage 287 .00 50.88 9.46 9.99

ASR2* Inventory range of

coverage 36 .33 45.09 10.73 8.44

Table 17: Inventory range of coverage of Dataset1

5.1.2. Dataset2: Pretest/Posttest Analysis

As valuable as Dataset1 is, there is a small deficit in its analysis, namely that a true

pretest/posttest comparison is not possible. When assessing the performance of the

ASR systems, one is comparing different product categories with each other. As seen

before, the mean characteristics of the products (e.g. shelf life) are slightly different

between the ASR groups. One could argue that these differences are responsible for

the findings seen before, namely that higher ASR levels tend to perform better. To be

able to make final conclusions, it is necessary to have a true pretest/posttest

methodology as described by Sheeber, Sorensen et al. (1996) to underline the

findings. This means comparing the performance of the same products in the same

stores before and after the introduction of a new ordering system and having a

control group where the replenishment system is not changed. This method has the

highest control possibility for biasing influences. The goal of this comparison is to find

out whether there is a significant benefit in implementing automatic systems or not.

Therefore, a second dataset was extracted from the EPR system. As no OOS data is

available from the ERP system, the dependent variables in these tests are inventory

level and variance.

For the pretest/posttest, data from the same 10 stores as in the OOS-audit were

chosen. A period of 180 working days was defined, 15 weeks before and 15 weeks

after the implementation of the new ASR system. This comparison was only possible

for categories that experienced a change in the replenishment system in the last

three years, namely dairy, beauty/household and non-food products. Only products

that were included in the OOS study were chosen for this study, to profit from the

comprehensive product information already collected in this first study. In order to

increase the validity of the comparison it is important to reduce the effects of


non-controlled variables. One of these variables is seasonality. Therefore, when

choosing the periods to compare, the same weeks in the year were taken to minimize

the effect of holidays and seasonality. Otherwise, comparing the stock levels in

summer with stock levels in winter could bias the result. For example, dairy products

were automated in November 2004. Consequently, the weeks chosen for the test

were the first 15 weeks of 2004 and 2005, respectively. Both data sets contain thus

the weeks after Christmas and the Easter holidays. Another aspect taken into

account is that there is normally a time period after the introduction of new ASR

systems with strong irregularities in performance, as staff get used to the system.

ASR systems need some time until performance stabilises. Therefore, the time

period was chosen in such a way that there was at least a two months gap between

ASR introduction and the performance measurement. Table 18 summarizes the

periods chosen.

Category Number of Products

Data Period "Before"

Change of ASR System

Data Period "After"

Dairy products 5 Week 1–15, 2004 November 2004 (ASR0�ASR3)

Week 1–15, 2005

Control1

6 Week 1–15, 2004 None (ASR0)

Week 1–15, 2005

Beauty and household

12 Week 30–44, 2003 May 2004 (ASR0�ASR2)

Week 30–44, 2004

Non-food 4 Week 30–44, 2003 May 2004 (ASR0�ASR2*)

Week 30–44, 2004

Control2

14 Week 30–44, 2003 None (ASR0)

Week 30–44, 2004

Table 18: Dataset for the pretest/posttest

For each product the data from the inventory systems was extracted in analogy to

Dataset1 supplying in this way per day and store the delivered quantities and the

sales. This procedure results in 41[products] * 10[stores] * 180[days] * 2[measures] =

147,600 data points. In addition, information about whether and when a product was

in promotion was provided. There was no logistical change for any of the groups

during the period under examination; an unbiased comparison of the replenishment

systems was thus possible.

Comparison 1: Dairy Products (ASR0��ASR3)

In November 2004, a new ASR3 system was introduced for dairy products. Based on

the sales of the preceding four weeks, the new system creates a forecast for the

following days and dynamically calculates and places the new order. From the 100

products in Dataset1, the dairy products where chosen for further analysis as they


experienced a change from ASR0 to ASR3. Only products without promotional

activity were included in the analysis, resulting in a sample of five products. The

choice of non-promotional SKUs is of most importance, as during promotions the

inventory levels sometimes increased by a factor of 20. Having these products in the

analysis would distort the results enormously. Besides, promotions are still handled

manually. The products studied were yoghurts, cheese and eggs. As a control group

(Control1), six other randomly selected products were taken: packaged (e.g.

convenience food, frozen goods) and non-packaged food (vegetables, fruit). The

control group products were ordered in the same way during the entire period,

namely manually. As the data set consists of measures during 180 days in ten stores,

this results in 9,000 data points for the ASR3 and 10,800 data points for the ASR0

Control1 group. These figures can be considered large enough to make significant

comparisons. Descriptive statistics from these two categories can be seen in Table

19.

N78 Minimum Maximum Mean Std. Deviation

ASR0 Control1

ASR3 Dairy

ASR0 Control1

ASR3 Dairy

ASR0 Control1

ASR3 Dairy

ASR0 Control

1

ASR3 Dairy

ASR0 Control

1

ASR3 Dairy

Sales coefficient of variance 120 100 .27 .24 2.35 1.02 .82 .54 .38 .15

Price 120 100 1.00 .60 5.60 3.87 3.28 2.21 1.84 1.37

Package size [category]

120 100 1.00 1.00 3.00 2.00 2.33 1.20 .75 .40

Shelf life [days] 120 100 2.00 10.00 350.00 12.00 63.67 11.60 128.62 .80

Speed of turnover [units sold per day] 120 100 1.14 1.80 317.44 349.60 41.55 53.80 66.38 80.78

Table 19: Descriptive statistics of the dairy products (ASR3) and Control1 group (ASR0)

When the dairy products data is split by stores the result is that on average, before

the introduction of the ASR3 system, in four out of ten stores the dairy products had a

bigger inventory range of coverage than the control group. After the change, this was

still the case in only one store.

Comparison 2: Beauty and Household Products (ASR0 �� ASR2)

Typical products in this category are beauty and hygiene articles such as

handkerchiefs, toothpaste, nappies and toilet paper, as well as household articles

such as washing powder. A change in the replenishment system for these 12

78

The unit of analysis is again the SKUs per store and time period. The 90 days periods for each product are

condensed into one mean value. Therefore, the number of n=120 for the ASR0 group results from the

computation: 6 products * 10 stores * 2 time periods (before/after) =120.


products was made in May 2004, changing from manual ordering (ASR0) to a

minimun-maximun system (ASR2), in which a fixed order-up-to quantity exists for

every product/store combination. These replenishment parameters are normally

stable during the course of the year (no seasonality is taken into account) but can be

changed by store managers at any time without involving the retail central office. The

control group (Control2) consists of 14 randomly chosen ASR0 products including

fruit and vegetables, convenience food, meat and fruit juice.

Analogous to the analysis of the ASR3 group in the last section, promotional activities

are a major problem when analysing inventory performance. In contrast to dairy

products, promotions are in this category much more frequent. Virtually all products

were on promotion for at least a week during the period. Therefore, it was not

possible, as before, to reduce the analysis sample to products without any

promotional activity at all. Instead, the weeks with promotional activities were ignored

when calculating stock levels and variances.79 It was observed that the effect of a

promotion lasts sometimes a couple of days longer than the promotion itself. Stores

that had ordered manually too many SKUs still had above-average inventory some

days after the end of the promotion. Therefore, the week following the promotion was

taken out as well. The same procedure was applied to the control group, thus

eliminating the promotional effect for these products as well. With this procedure,

approximately 15% of the daily data is lost, but the gain in accuracy far outweighs

this loss. The same method of dealing with promotions is also applied to the ASR2*

group in the next section.

As depicted in Table 20, the product groups have exactly the same sales variance.

ASR2 products tend to be larger and have a lower rate of turnover than ASR0

products.

79

This is the same method as used by the ordering system of MYFOOD to compute forecasts for ASR3 articles:

dates with promotional activities are ignored.


N Minimum Maximum Mean Std. Deviation ASR0

Control2 ASR2 B&H

ASR0 Control2

ASR2 B&H

ASR0 Control2

ASR2 B&H

ASR0 Control2

ASR2 B&H

ASR0 Control

2

ASR2 B&H

Sales coefficient of variance 253 243 .16 .00 1.69 2.21 .63 .63 .26 .32

Package size [category]

266 243 1 2 3.00 3.00 1.93 2.46 .46 .50

Price 266 243 1 1 2.00 2.00 1.22 1.23 .41 .42

Shelf life [category] 266 243 1 2 2.00 2.00 1.50 2.00 .50 .00


B&H: Beauty and household products

Table 20: Descriptive statistics of the beauty and household group (ASR2) and Control2 (ASR0)

Comparison 3: Non-Food Products (ASR0 �� ASR2*)

With just four articles, this is the smallest category studied. The products examined

were batteries, baking foil, glue and stickers. The replenishment system for this

category was automated at the same time (May 2004) as that for the beauty and

household articles category. After May 2004, the non-food products have been

ordered through an ASR2* system. As before, the main difference between the non-

food products and the control group (Control2) is the very low rate of turnover of the

ASR2* products (cf. Table 21).

N Minimum Maximum Mean Std. Deviation ASR0

Control2

ASR2* Non-food

ASR0 Control2

ASR2* Non-food

ASR0 Control2

ASR2* Non-food

ASR0 Control

2

ASR2* Non-food

ASR0 Control

2

ASR2* Non-food

Sales coefficient of variance 253 74 .16 .36 1.69 .95 .63 .59 .26 .13

Package size [Category]

266 74 1 1 3 2 1.93 1.51 .46 .50

Price 266 74 1 1 2 1 1.22 1.00 .41 .00

Shelf life [category] 266 74 1 2 2 2 1.50 2.00 .50 .00


Table 21: Descriptive statistics of the non-food group (ASR2*) and Control2 (ASR0)

Methodology of Statistical Analysis

The datasets presented were utilized to test the three groups of hypotheses:

products, store and ASR characteristics influence.80 In the following, the

methodologies of the statistical analysis are presented.

80

See Figures 14–16.


First, the relationships between product characteristics and OOS rate/inventory

performance are examined. This analysis is conducted with the help of Dataset1, as

there the true OOS rates for each product are available. The OOS rate of a product is

the average rate of the two weeks physical audit. As this analysis aims to test the role

of the replenishment systems, examining all kinds of OOS incidents would lead to

distortions. A missing article on the shelf caused by a strike at the manufacturer

cannot be influenced by the retailer's ASR system. Yet, with the analysed dataset,

there is the unique opportunity to consider only OOS incidents caused by ordering

behaviour (OOS order-related). Therefore, other OOS root causes do not distort the

analysis of the hypothesis dealing with ASR performance. To establish statistical

relationships between the variables correlations, linear regressions and above all

ANOVAs are used.81 To prove that the effect of product variables on automatic

replenishment systems is not as strong as for ASR0 systems, the interaction variable

ASR x Product is considered. If this variable is significant, then the performance of

the ASR system depends on the characteristics of the product. Furthermore, post hoc

tests show whether there is a difference between the individual levels of product

characteristics. For example, post hoc analysis tests whether the OOS rate of low-

priced products that are ordered through ASR2 systems is different from the OOS

rate of medium- and high-priced products, respectively.

Second, with Dataset1 and the same statistical tools it is also possible to test the

influence of store characteristics on the OOS rate. In order to do this, a mean

weekly OOS was calculated for each of the 10 stores, resulting thus in a sample size

of n=20.82 The evidence from this rather small sample are intended to illustrate the

possible existing correlations rather than being a final statistical proof.

Third, the results of both these analyses are a necessary prerequisite for answering

research question Q2: the benefits of ASR systems. To prove that ASR2 products

perform better than ASR0, Dataset1 is examined with the help of ANOVAs. Yet, the

final proof of the performance of the ASR systems is given with the help of the

second dataset. As no OOS rates are available in Dataset2, the tests concentrate on

81

The significance level used in this thesis is 10%. As the ANOVA method is the primary method being used, the

most important requirements for this methodology are discussed in the appendix in section 8.1.2. 82

For this second analysis, the mean weekly OOS is calculated for the whole range of 100 products, therefore

also including the products in promotion. This procedure was chosen because the promotions are the same for

all stores and have the same duration. The performance of a store is therefore also measured by the

availability to handle promotional goods. For the same reason all the OOS causes are taken into consideration,

in contrast to the former hypothesis block, where only order-related OOS causes were considered. Therefore,

in this second analysis reasons like faulty replenishment of shelves from the backroom are also taken into

consideration.


the inventory levels and variance. With a controlled pretest/posttest methodology as

described by Sheeber, Sorensen et al. (1996) it is possible to prove that regardless of

the products chosen, higher ASR systems have significantly better results than

manually ordered products. The statistical method used are so-called linear mixed

models.83

5.2. Hypothesis Testing: Dataset1

5.2.1. Influence of Product Characteristics

In this section, two questions are covered. First, the influence of product

characteristics on OOS rates and inventory levels will be examined. If there are any

substantial correlations, retailers would have an instrument to know for which

products the danger of OOS is eminently high and which products tend to have high

inventory levels. Second, the interaction between product characteristics and ASR

system are tested. These tests make it possible to confirm whether the influence of

product characteristics on ASR performance is stronger for ASR0 than for ASR2

systems.84

Test of Hypothesis H1: Sales Coefficient of Variance

The variable sales coefficient of variance is banded into three categories (low,

medium and high)85 and an ANOVA conducted. The results are significant for all

variables but the interaction effect.

83

The basic model underlying the analysis of variance and multiple regression is the general linear model. One of

the extensions of this model is the linear mixed model, the main tool for testing the hypothesis in the

pretest/posttest methodology. The difference to the general linear model is that as the same subject is

measured several times the assumption of independence of the error terms is relaxed. The linear mixed model

allows the modelling of the values of a dependent scale variables (e.g. OOS) measured at multiple time periods

based on its relationships to category (e.g. package size) and scale (e.g. price) predictors and the time at which

they were measured (SPSS Inc. 2003). 84

For all tests in this section, the ASR2* products are omitted, as their sample size is very small (n=40) compared

to the other two groups (nASR0=490, nASR2=310). ANOVAs results would be strongly biased by this large group

size difference. 85

As stated before, the banding of the variable to create three groups is made by using three quantiles (33.33%

percentiles). The result of this is three groups with nearly the same number of units in each subgroup, thus

making the ANOVA analysis much more reliable.


Dependent variable: OOS (order-related)

Source Type III Sum of Squares

df Mean Square F Sig. Partial Eta Squared

Corrected model 3195.433 5 639.087 10.321 .000 .063

Intercept 4034.514 1 4034.514 65.157 .000 .078

ASR_level 2526.136 1 2526.136 40.797 .000 .050

Sales_coefvar 463.234 2 231.617 3.741 .024 .010

ASR_level x Sales_coefvar 185.062 2 92.531 1.494 .225 .004

Error 47616.619 769 61.920

Total 56753.472 775

Corrected total 50812.052 774

Table 22: ASR level and sales coefficient of variance ANOVA on OOS (order-related)

On the first look, Figure 19 shows a quadratic relationship between sales variance

and OOS. Yet, a Tamhane's T2 post hoc test shows that for the ASR0 products only

the difference between the medium and the high sales variance group is significant

(p=.071). This means that the ANOVA cannot confirm a significant difference

between the low and medium ASR0 group. For this, hypothesis H1a can only be

partly confirmed, i.e. higher sales variance will lead to higher OOS rates for ASR0

products.

low med high

Sales coefficient of variance (Banded)

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

Estimated Marginal Means

ASR Group

ASR0

ASR2

Sales Coefficient of Variance (Banded)

Figure 19: Estimated OOS (order-related) rate by sales coefficient of variance

The interaction variable ASR_level x Sales_coefvar is not significant (p>.225). This

means, that the forms of the two ASR lines, as depicted in Figure 19, do not

significantly differ from each other. Put in another words, the missing significance of


the interaction variable means that one cannot reject the hypothesis that these two

lines are parallel. Yet, a post hoc analysis of the ASR2 group shows that the

individual variance levels are not statistically different from each other (all p>.16).

Therefore, H1b is supported, namely that sales variance has a lesser impact on

ASR2 products than on the manually ordered ones

Overall, given that, in the opinion of experts, this is the most important parameter

influencing replenishment systems, it is surprising that the results above are not

stronger. The model estimates that the ASR0 low-variance group has a 4% and 6%

OOS rate for the low and high-variance group, respectively. This difference of only

two percentage points is rather low. A possible explanation is that replenishment

systems cope quite well with a certain demand variance. Yet there will be a certain

point at which this variance becomes so strong that performance will start to decline.

This would explain that there is no significant difference between the low and medium

variance group for the ASR0 system. It is worth noting that the mean sales coefficient

of variance in this dataset is slightly smaller for the ASR0 group than for the ASR2

group (53% compared to 57%). This means that although the automatic systems

have to deal with slightly higher variance, they perform better than their manual

counterparts.

For the average inventory level as the dependent variable, the results are clearer.

The products of the ASR0 group that have a higher sales variance tend to have

higher stocks (all post hoc tests are p<.002), supporting H1c.

Dependent variable: inventory range of coverage



Corrected model 40506.130 5 8101.226 16.193 .000 .096

Intercept 159995.245 1 159995.245 319.795 .000 .295

ASR_level 8865.787 1 8865.787 17.721 .000 .023

Sales_coefvar 22270.375 2 11135.188 22.257 .000 .055

ASR_level x Sales_coefvar 4483.293 2 2241.647 4.481 .012 .012

Error 383233.775 766 500.305

Total 603923.142 772


Table 23: ASR level and sales coefficient of variance ANOVA on inventory range of coverage

The interaction variable is significant (p=.012) and, as can be seen in Figure 20, the

ASR2 replenished products do not react as strongly on the increase of sales variance

(H1d). The only post hoc test that is significant for the ASR2 group is the one


between the low and high variance group (p=.010), while for the ASR0 group all post

hoc tests show a significant difference between the variance levels (p<.022).

low med high

Sales coefficient of variance (Banded)

5.00

10.00

15.00

20.00

25.00

30.00


ASR Group

ASR0

ASR2

Sales Coefficient of Variance (Banded)

Figure 20: Estimated inventory range of coverage by sales coefficient of variance

Test of Hypothesis H2: Speed of Turnover

As expected in hypothesis H2a, the best curve that fits the scatter plot of speed of

turnover and OOS is a quadratic one. Products with low speed of turnover and with

very high speed of turnover have the highest OOS rates. Yet, the explained variance

(R2) is, with 1%, rather low.

When the average sales volume per product and day is regarded, the ASR0 group

has a higher mean volume than the ASR2 products (41.2 units compared to 17.7

units). This could bias an ANOVA. Therefore, to neutralise the effect of the unequal

distribution of speed of turnover, this metric variable is made into a category variable.

An ANOVA shows that all the effects are significant (p<.001).





Corrected model 5280.535 5 1056.107 15.450 .000 .091

Intercept 4541.636 1 4541.636 66.439 .000 .079

Speed_of_turnover 1245.637 2 622.818 9.111 .000 .023

ASR_level 2868.753 1 2868.753 41.966 .000 .052

Speed_of_turnover x ASR_level 900.493 2 450.247 6.587 .001 .017

Error 52704.280 771 68.358

Total 64548.611 777


Table 24: ASR level and speed of turnover ANOVA on OOS (order-related)

Post hoc tests of the ASR0 group show that the products with the highest speed of

turnover (=high sales per day) have significantly lower OOS rates than the slow

turning products (8.0% compared to 2.4%; p<.001). The ASR2 products only show a

difference between the medium and high group (post hoc tests: p=.08), but not

between the medium and low group (p>.99). As depicted in Figure 21, the absolute

influence of the speed of turnover on the ASR2 products is very small, therefore

confirming hypothesis H2b.

low med high

Speed of turnover (Banded)

0.00%

2.00%

4.00%

6.00%

8.00%


ASR Group

ASR0

ASR2

Speed of Turnover (Banded)

Figure 21: Estimated OOS (order-related) rate by speed of turnover

The testing of hypothesis H2c confirms that the higher the speed of turnover, the

lower the stock levels are. The differences are significant for the ASR0 group (all post

hoc tests: p<.003). For the ASR2 products, there is no significant difference between


the medium and high group (p=.23). As can be seen in Figure 22, the effect of speed

of turnover on the inventory level is stronger for the ASR2 group, as the line for this

group is steeper. Because of this, hypothesis H2d can be regarded as confirmed.




Corrected model 50320.073 5 10064.015 20.673 .000 .119

Intercept 149988.612 1 149988.612 308.092 .000 .286

Speed_of_turnover 31316.425 2 15658.213 32.164 .000 .077

ASR_level 11667.890 1 11667.890 23.967 .000 .030

Speed_of_turnover x ASR_level 2633.599 2 1316.799 2.705 .068 .007

Error 373885.421 768 486.830

Total 603923.142 774


Table 25: ASR level and speed of turnover ANOVA on inventory range of coverage

low med high

Speed of turnover (Banded)

5.00

10.00

15.00

20.00

25.00

30.00


ASR Group

ASR0

ASR2

Speed of Turnover (Banded)

Figure 22: Estimated inventory range of coverage by speed of turnover

Test of Hypothesis H3: Price

As hypothesised (H3a), there is a slight trend towards higher price products having

higher stock outs (linear regression: p<.001), yet the explanation portion is very small

(R2=.039). In an ANOVA with the ASR level as category factor, the interaction

between price and ASR is significant (p=.029), as well as the entire model (p<.001).


A post hoc analysis shows that only for the ASR0 group is there a significant

difference between the high price subgroup and the other subgroups (p<.02).



Df Mean Square F Sig. Partial Eta Squared

Corrected model 4931.502 5 986.300 11.537 .000 .068

Intercept 5169.567 1 5169.567 60.469 .000 .071

ASR_level 3024.956 1 3024.956 35.383 .000 .043

Price_category 649.854 2 324.927 3.801 .023 .009

ASR_level x Price_category

607.124 2 303.562 3.551 .029 .009

Error 67879.609 794 85.491

Total 80416.667 800


Table 26: ASR level and price ANOVA on OOS (order-related)

Looking at the estimated means by this model one sees that the manual system

reacts much more strongly to price level than the ASR2 system. The latter has no

significant differences between any of the price groups (post hoc: all p>.46). The

automatic system is not influenced by the price when ordering, in contrast to human

beings (H3b).

low med high

Price (Banded)

0.00%

2.00%

4.00%

6.00%

8.00%


ASR Group

ASR0

ASR2

Price (Banded)

Figure 23: Estimated OOS (order-related) rate by price

An ANOVA with inventory range of coverage as the dependent variable shows in a

post hoc test clearly that the price levels differ from each other (p<.001). Again,


hypothesis H3c is confirmed, the more expensive the goods the higher the stock

levels are.




Corrected model 87031.665 5 17406.333 39.647 .000 .205

Intercept 169851.557 1 169851.557 386.881 .000 .335

ASR_level 4334.233 1 4334.233 9.872 .002 .013

Price 57741.651 2 28870.825 65.761 .000 .146

ASR_level x Price 5582.179 2 2791.089 6.357 .002 .016

Error 337173.830 768 439.028

Total 603923.142 774


Table 27: ASR level and price ANOVA on inventory range of coverage

For the ASR0 products, all post hoc tests are significant (p<.001). The post hoc test

of the low price and medium price of the ASR2 products is the only non-significant

(p=.110). As the significant interaction variable shows, ASR2 and ASR0 products

react differently to price influence. The two lines on Figure 24 cross between the low

price and medium price group, confirming that manually ordered groups react more

strongly on the price influence (H3d).

low med high

Price (Banded)

0.00

10.00

20.00

30.00


ASR Group

ASR0

ASR2

Figure 24: Estimated inventory range of coverage by price


Test of Hypothesis H4: Case pack size (CU/TU)

As stated before, the analyses of the case pack are conducted with the quotient case

pack size divided by mean sales (CU_TU_relative). The result is that all the main

effects as well as the interaction effect are significant (all p<.001)




Corrected model 5412.554 5 1082.511 15.876 .000 .093

Intercept 4629.639 1 4629.639 67.896 .000 .081

ASR_level 2873.172 1 2873.172 42.137 .000 .052

CU_TU_relative 1197.892 2 598.946 8.784 .000 .022

ASR_level x CU_TU_relative 1136.784 2 568.392 8.336 .000 .021

Error 52572.261 771 68.187

Total 64548.611 777


Table 28: ASR level and CU/TU ANOVA on OOS (order-related)

As Figure 25 depicts, the OOS rate of manual order products is strongly influenced

by the relative case pack size. The bigger the ASR0 case packs, the greater the OOS

rate, as hypothesised in H4a. Post hoc tests show a significant difference between

the low and high group (p<.001) and the medium and high group (p=.01). In contrast,

the automatically ordered products do not show a difference between any of the

groups (all p>.59), confirming H4b.

low med high

CU/TU relative (Banded)

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%


ASR Group

ASR0

ASR2

CU/TU Relative (Banded)

Figure 25: Estimated OOS (order-related) rate by CU/TU group


As expected in H4c and in H4d, bigger case packs lead indeed to higher inventories

(p<.001). For both manually and automatically ordered products, the SKUs with the

highest CU/TU value have at the same time the highest inventory levels. The

interaction effect is not significant (p=.13); all the post hoc tests for the ASR0 and

ASR2 products show significant differences between the case pack size (all p<.02).

This means that both replenishment systems are equally influenced by the case pack

variable.




Corrected model 37077.842 5 7415.568 14.711 .000 .087

Intercept 137012.570 1 137012.570 271.811 .000 .261

ASR_level 12754.988 1 12754.988 25.304 .000 .032

CU_TU_relative 22483.296 2 11241.648 22.302 .000 .055

ASR_level x CU_TU_relative 2061.403 2 1030.702 2.045 .130 .005

Error 387127.652 768 504.072

Total 603923.142 774


Table 29: ASR level and CU/TU on inventory range of coverage

low med high

CU/TU relative (Banded)

5.00

10.00

15.00

20.00

25.00

30.00


ASR Group

ASR0

ASR2

CU/TU Relative (Banded)

Figure 26: Estimated Inventory range of coverage by case pack


Test of Hypothesis H5: Product Size

An ANOVA analysis shows that product size has a significant (p=.001) influence on

the OOS rate. All other effects are significant as well.




Corrected model 6496.079 5 1299.216 15.556 .000 .089

Intercept 4666.274 1 4666.274 55.870 .000 .066

ASR_level 2812.039 1 2812.039 33.669 .000 .041

Product_size 1098.617 2 549.308 6.577 .001 .016

ASR_level x Package_size 1453.912 2 726.956 8.704 .000 .021

Error 66315.033 794 83.520

Total 80416.667 800


Table 30: ASR level and product size ANOVA on OOS (order-related)

For the ASR0 products, the differences between the small–medium (p<.001) and

small–large products (p=.002) are significant. Larger products have indeed more

OOS incidents as hypothesised (H5a). For the ASR2 systems, on the other hand,

there is no significant effect of product size on OOS (all p>.37), as predicted in H5b.

small medium large

Product Size

0.00%

2.00%

4.00%

6.00%

8.00%


ASR Group

ASR0

ASR2

Product Size (Banded)

Figure 27: Estimated OOS (order-related) rate by product size

The ANOVA on the inventory range of coverage shows that all factors are significant

(p<.04). Yet, as can be seen in Figure 28, the direction of the effect is not clear.



Source Type III Sum of

Squares df Mean Square F Sig.

Partial Eta Squared

Corrected model 25963.724 5 5192.745 10.014 .000 .061

Intercept 147353.369 1 147353.369 284.168 .000 .270

ASR_level 2263.971 1 2263.971 4.366 .037 .006

Product_size 10526.892 2 5263.446 10.150 .000 .026

ASR_level * Product_size

6644.695 2 3322.348 6.407 .002 .016

Error 398241.770 768 518.544

Total 603923.142 774


Table 31: ASR level and product size ANOVA on inventory range of coverage

Post hoc analyses demonstrate for the ASR0 group significant differences between

the small–medium size group (p<.001) and medium–large group (p=.005). The result

of this ANOVA is that medium-size products have the highest inventory levels,

contrary to hypothesis (H5c). For ASR2 products, the largest products have the

highest inventory on stock (small–large post hoc test: p=.009). The interaction effect

is significant, however, as both groups are strongly influenced by the product's size,

hypothesis H5d cannot be supported.

small med big

Product size (banded)

10.00

15.00

20.00

25.00


ASR Group

ASR0

ASR2

Product Size (Banded)

Figure 28: Estimated inventory range of coverage by product size

Test of Hypothesis H6: Shelf Life

To test hypothesis H6a, a simple quadratic regression was made separately for every

store. The result is that for all stores but one a quadratic function fits the data better


than a linear one. As expected, products with a low or very long shelf life have a

tendency to have higher OOS than products with a medium time shelf life (R2 in

mean 7.9%).

The ASR2 group contains only products with a shelf life longer than 60 days, while

85% of the ASR0 products have a shelf life shorter than 12 days. Therefore, the

results of the ANOVA have to be taken with caution. Figure 29 shows the estimated

results of an ANOVA analysis. For ASR0 products, there is a clear difference

between the low and medium group (post hoc test: p<.001), while the ASR2 group is

not influenced by shelf life (p>.79).

low med high

Shelf life (Banded)

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%


ASR Group

ASR0

ASR2

Shelf Life (Banded)

Figure 29: Estimated OOS (order-related) rate by shelf life

Next, two independent regressions were made, splitting the group by ASR classes

and making a regression analysis on Shelf_life and Shelf_life_square. The result is

that for the ASR0 group the shorter the shelf life, the more OOS it had (p=.046). The

quadratic variable is not significant (R2=11%). For the ASR2 group there was no

significant correlation at all. Also the insertion of the variable as a covariate to an

ANOVA does not give significant results. Overall, one can confirm that shelf life has

an effect on OOS for the manual systems, but not for the ASR2 systems. As the

distribution of shelf life is not even, hypothesis H6b can only be partially supported.



ASR Group

Unstandardized Coefficients

Standardized Coefficients

T Sig.

B Std. Error Beta

(Constant) 5.312 .586 9.059 .000

Shelf life -.024 .012 -.263 -1.966 .050

ASR0

Shelf_life_ square

2.819E-05 .000 .179 1.340 .181

(Constant) .541 .735 .737 .462

Shelf life -8.355E-05 .005 .007 -.017 .987

ASR2

Shelf_life_ square

2.697E-07 .000 .019 .046 .963

Table 32: Regression of shelf life and shelf life squared on OOS

Figure 30 shows that, basically, the same results as before apply to the test of the

inventory levels. The ASR0 products show a significant difference between the low

and medium shelf life group (p=.033). This means that hypothesis H6c cannot be

supported, as products with short shelf life have higher inventory levels than the other

products. For the ASR2 products, on the other hand, the variable shelf life is

significant neither in an ANOVA nor in a regression (p>.79 and p>.94, respectively).

This could be evidence of the smaller effect of shelf life on ASR2 systems, yet due to

the known restrictions of these tests, H6d is only partially supported.

low med high

Shelf life (Banded)

10.00

12.50

15.00

17.50

20.00

22.50

25.00


ASR Group

ASR0

ASR2

Shelf Life (Banded)

Figure 30: Estimated inventory range of coverage by shelf life


5.2.2. Influence of Store Characteristics

In the last section, the influence of product characteristics on OOS rate and inventory

performance was shown. Yet, this alone cannot explain the results from other studies

showing that there is a high variance between the stores. Therefore, the correlation

between store characteristics and the OOS quote is examined in the following by

conducting analyses on Dataset1. A correlation matrix of the store characteristics can

be seen in Table 33.

OOS per Week

Yearly Shrink-age in %

SKU Density

# Personnel per 100m

2

Store Manager- Years in Store

Ratio Sales-room to Backroom

Customer Satisfaction Index

Pearson correlation

1 .654(**) .167 .018 -.322 .706(**) -.581(**) OOS per week Sig. (2-tailed) .002 .483 .940 .166 .001 .007

Pearson correlation

.654(**) 1 .351 -.334 -.597(**) .318 -.437 Yearly shrinkage in % Sig. (2-tailed) .002 .129 .150 .005 .172 .054

Pearson correlation

.167 .351 1 .010 -.353 .132 -.292 SKU density

Sig. (2-tailed) .483 .129 .966 .127 .579 .212

Pearson correlation

.018 -.334 .010 1 .212 .006 -.547(*) # Personnel per 100 m

2

Sig. (2-tailed) .940 .150 .966 .369 .980 .012

Pearson correlation

-.322 -.597(**) -.353 .212 1 -.326 .598(**) Store manager– years in store

Sig. (2-tailed) .166 .005 .127 .369 .161 .005

Pearson correlation

.706(**) .318 .132 .006 -.326 1 -.478(*) Ratio salesroom to backroom

Sig. (2-tailed) .001 .172 .579 .980 .161 .033

Pearson correlation

-.581(**) -.437 -.292 -.547(*) .598(**) -.478(*) 1 Customer satisfaction index

Sig. (2-tailed) .007 .054 .212 .012 .005 .033

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

Table 33: Correlation between OOS per week and store characteristics (Dataset1)

Test of Hypotheses 7a,b: OOS Rate/Inventory Performance per Store

As hypothesised before, there is indeed a strong correlation of the store variable on

the OOS rate (H7a), meaning that the OOS (order-related) rate differs significantly

from store to store. An ANOVA with the store as a factor is highly significant (p<.001).

In all stores but one, the mean OOS rate of ASR2 products is smaller than those of

the ASR0 products. The interaction effect can be interpreted on how well the ASR2

systems perform compared to ASR0 in each store. As it differs significantly (p=.005),

one can assume that the store indeed has an influence on the ASR system. The

biggest absolute difference exists for store number 1. This store profits most from the

ASR2 implementation.


Dependent variable: OOS (order-related)86



Corrected model 9249.192 19 486.800 5.974 .000 .127

Intercept 5226.104 1 5226.104 64.132 .000 .076

Store_ID 2816.143 9 312.905 3.840 .000 .042

ASR_level 3176.885 1 3176.885 38.985 .000 .048

StoreID x ASR_level 1928.730 9 214.303 2.630 .005 .029

Error 63561.919 780 81.490

Total 80416.667 800


Table 34: ASR level and Store ANOVA on OOS (order-related)

1 2 3 4 5 6 7 8 9 10

Store ID

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%


ASR Group

ASR0

ASR2

Figure 31: Estimated OOS (order-related) rate by store

The results for the inventory ranges of coverage of each store are not that clear. An

ANOVA is neither significant for the factor Store_ID nor the interaction

Store_ID x ASR_level. Only the ASR_level variable is significant. In all but two stores

the ASR2 products have lower stocks than the products ordered manually. The range

of the inventory is estimated from 8.2 to 12.2 days.

86

One obtains similar results when making the ANOVA on all OOS reasons. Yet in this case, the interaction

variable ASRxStore_ID is no longer significant.





Corrected model 1428.936 19 75.207 .836 .664 .022

Intercept 72004.635 1 72004.635 800.619 .000 .530

Store_ID 744.789 9 82.754 .920 .507 .012

ASR_level 359.901 1 359.901 4.002 .046 .006

Store_ID x ASR_level 435.329 9 48.370 .538 .847 .007

Error 63764.766 709 89.936

Total 142943.244 729


Table 35: ASR level and Store ANOVA on inventory range of coverage

1 2 3 4 5 6 7 8 9 10

Store ID

6.00

7.00

8.00

9.00

10.00

11.00

12.00

13.00


ASR Group

ASR0

ASR2

Figure 32: Estimated inventory range of coverage by store

The variance of the stocks is significantly lower (p=.000) for the ASR2 group, as an

ANOVA shows. The result is consistent for each store, in all the stores the ASR0

products show a smaller coefficient of variance of the stocks compared to the ASR2

group. In this case, there is no interaction effect ASR x Store. Overall, hypothesis

H7b can only partially be supported, as there is a difference between the inventory

variance between the stores, yet no difference between the inventory levels.


Dependent variable: inventory coefficient of variance



Corrected model 1.949 19 .103 2.476 .000 .059

Intercept 375.430 1 375.430 9062.248 .000 .923

ASR_level .788 1 .788 19.014 .000 .025

Store_ID 1.079 9 .120 2.894 .002 .033

ASR_level x Store_ID .243 9 .027 .651 .754 .008

Error 31.154 752 .041

Total 422.729 772


Table 36: ASR level and store ANOVA on inventory coefficient of variance

1 2 3 4 5 6 7 8 9 10

Store ID

0.65

0.70

0.75

0.80

0.85

0.90


ASR Group

ASR0

ASR2

Figure 33: Estimated inventory coefficient of variance by store

Test of Hypothesis 8: OOS and Shrinkage

To test this hypothesis and the following, a simple linear regression is made. As on

Figure 34 depicted, the line fits rather well the data (p=.002) and explains 43% of the

variance. The higher the OOS rate in a store, the higher the shrinkage. There are

indeed some stores that manage to increase the availability without increasing

shrinkage, H8 is thus confirmed. A repeated measure ANOVA for all points shows

that the high shrinkage group has higher OOS rates (p=.08).


1.00 2.00 3.00 4.00 5.00 6.00 7.00

Yearly Shrinkage in %

0.000

0.020

0.040

0.060

0.080

0.100

0.120OOS per Week

R Sq Linear = 0.428

Figure 34: Relationship between shrinkage and OOS

Test of Hypothesis 9: OOS and SKU Density

There is a correlation between the store size and the weekly OOS rate, resulting in

larger stores having smaller OOS rates. This correlation is at the first glance counter

intuitive, as larger stores should be more difficult to handle. The explained variance is

with 19.5% rather small. The examining of the variable SKU density reveals another

picture. In Figure 35 the positive trend between SKU density and OOS rate is much

more visible. Yet, a repeated measure ANOVA for all points shows no significant

results.

A possible reason for this non-significance is that a single store has more than twice

the SKU density than the mean of the other stores, and nevertheless the lowest OOS

rates. When this exceptional store is not regarded, the explained variance of a simple

regression sky rockets to 49%. A repeated measure ANOVA without this outlier

shows that the high SKU density group has significantly more OOS incidents than the

other group (p<.011). The OOS difference is estimated to be 3.9 percentage points.

Overall, the hypothesis H9 can be regarded as partially supported.


5.00 7.50 10.00 12.50 15.00 17.50

SKU Density

0.000

0.020

0.040

0.060

0.080

0.100

0.120

OOS per Week

Figure 35: Relationship of OOS and SKU density

Test of Hypothesis 10: OOS and Personnel Density

To be able to compare the number of personnel between stores, the ratio employees

per 100m2 is calculated. A linear regression is not significant, as stores with very few

and many employees tend to have higher OOS rates. Consequently, a quadratic line

fits the data rather well (R2=0.39) and underlines the hypothesis H10. Yet, a repeated

measure ANOVA with the variable personnel (split in 3 categories) does not give a

significant difference between the three groups.

2.00 3.00 4.00 5.00 6.00 7.00

# Personnel per 100m2

0.000

0.020

0.040

0.060

0.080

0.100

0.120

OOS per Week

R Sq Quadratic =0.391

Figure 36: Relationship of OOS and number of personnel per m2


Test of Hypothesis 11: OOS and Store Manager

The relation between these variables is not as high as with the other store

characteristics variables. A linear regression explains only 10.4% of the variance.

Higher R2 values can be obtained by a logarithmical transforming of the years scale,

taking so into consideration the effect of diminishing returns. A repeated ANOVA with

three groups according to the years of experience is significant (p=.053). Again, the

Tamhane's T2 post hoc analysis show only a significant difference between the group

with the least years in the store to the medium group, confirming the theory of

diminishing returns. Overall, the hypothesis H12 can be supported, yet the effect is

not as clear as with the other store variables.

0.0 2.5 5.0 7.5 10.0 12.5

Store Manager- Years in Store

0.000

0.020

0.040

0.060

0.080

0.100

0.120

OOS per Week

R Sq Linear = 0.104

Figure 37: Relationship of OOS and number years of the store manager working in the store

Test of Hypothesis 12: OOS and Backroom Size

The hypothesis H12 can be fully supported, the larger the backroom in comparison to

the sales room, the higher the OOS rate. A simple regression model explains 49.8%

of the OOS variance, a repeated measure ANOVA analysis is significant (p=.028).


0.60 0.80 1.00 1.20 1.40 1.60 1.80

Ratio Salesroom to Backroom

0.000

0.020

0.040

0.060

0.080

0.100

0.120

OOS per Week

R Sq Linear = 0.498

Figure 38: Relationship of OOS and the size of the backroom

Test of Hypothesis 13: OOS and Customer Satisfaction

A strong correlation between customer satisfaction87 and OOS can bee seen in

Figure 39, as predicted in hypothesis H13. Stores with lower OOS rates tend to have

more satisfied customers, underlying the importance of high on-shelf availability. The

significance of the variable in a repeated measure ANOVA is p=.023. The explained

variance is 33.7%.

87

The satisfaction of the consumers was measured through a survey of MYFOOD. The highest satisfaction score

is 6.


4.80 5.00 5.20 5.40 5.60 5.80 6.00

Customer Satisfaction Index

0.000

0.020

0.040

0.060

0.080

0.100

0.120OOS per Week

R Sq Linear = 0.337

Figure 39: Relationship of OOS and customer satisfaction

Test of Hypothesis H14: Influence of ASR on OOS

In the descriptive statistics of Dataset1 one could see that the products ordered

manually have on average almost an 8-times higher OOS rate than those ordered

through the ASR2 system. The standard variation is also far smaller. In the section

5.2.2 the influence of product characteristics on OOS and inventory performance was

examined. Although this difference is very impressive, there could be a small chance

that this difference is the result of a random process. To test Hypothesis H14, that the

ASR2 system will perform better compared to manual ordering, a one way ANOVA is

made.




Corrected model 3304.928 2 1652.464 19.684 .000 .045

Intercept 1394.349 1 1394.349 16.609 .000 .019

ASR_level 3304.928 2 1652.464 19.684 .000 .045

Error 70266.170 837 83.950

Total 81111.111 840


Table 37: ASR level ANOVA on OOS (order-related)

As one can see, the differences between the different replenishment methods is

statistically significant (p<.001). One of the assumptions of ANOVA is that the group


variances should be equal. Yet the Levene's test of equality of error variances rejects

this assumption (p<.001). The ASR0 group has a higher variation (11.6%) than the

ASR2 group (3.13%). Nevertheless, as the ANOVA method is robust to this violation

Lindman (1992) does not recommend ignoring the F-test carried out above. Instead,

a Tanhame's T2 post hoc test is conducted, with the assumption that the variances

are not equal. The result is that the difference of 4.09% between ASR2 and ASR0 is

still significant (p<.001). The difference in the OOS rates between ASR2* and ASR0

is not significant (p=.678). Both a Welch and a Brown-Forsythe test, show a

significant difference between the ASR0 and ASR2 group (p<.001).

An ANOVA with ASR_level as the independent variable on inventory level shows that

there is indeed a significant difference between the ASR groups (p<.045). The ASR2

group has an estimated mean inventory range coverage of 9.5 days which is

significantly lower than the 10.9 days of the ASR0 group. The coefficient of variance

of the inventory, however, is smaller for the manual group: 69% compared with 75%

of the ASR2 group (ANOVA: p=.024).




Corrected model 359.102 1 359.102 4.027 .045 .006

Intercept 72055.366 1 72055.366 807.968 .000 .526

ASR_level 359.102 1 359.102 4.027 .045 .006

Error 64834.601 727 89.181

Total 142943.244 729


Table 38: ASR level ANOVA on inventory range of coverage

Therefore, the hypothesis H14 can be supported: the ASR2 ordered products have

on average lower OOS rates than ASR0 products. In addition, there exists strong

evidence that the same holds true for inventory performance. The final proof of this is

checked in the next section.

5.3. Dataset2: Pretest/Posttest

In this section, a comparison of ASR inventory performance is undertaken by

analysing Dataset2. For this, linear mixed models are employed in a pretest/posttest

methodology. Data from a period after the introduction of higher ASR systems is

compared to data from one year before, when the ordering was still done manually.


5.3.1. Dairy products: ASR3 performance (H15a)

Inventory Performance: Mean Inventory Levels

In order to compare the performance of the inventory between the categories, the

mean inventory range (i.e. the mean of the daily inventory divided by the sales mean)

is used as before. A first glance at the mean inventory range of coverage reveals that

comparing 2003 to 2004, the mean stock level of the ASR0 control group (Control1)

increased by 11.8% (from 2.8 days to 3.2 days) while for the ASR3 products the

inventory was reduced by 6.4% (from 2.6 to 2.5 days, see Figure 40).

2.85

3.18

2.652.48

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

2004 2005

Year

Inventory range of coverage

ASR0

ASR3

Figure 40: Mean inventory range of coverage in days of dairy products and Control1

When this reduction is spilt by store, one can see that six out of 10 stores had a

higher reduction for the ASR3 group (in percentage points) than for the control group.

For two stores that development of both groups was similar, and only for two stores

the ASR3 group developed considerably worse than the control group.


Store ID ASR Group Inventory Mean (days)

Inventory Mean (days)

Reduction in %

2004 2005

Performance of ASR3 Group Compared to Control Group

(percentage points)

1 ASR0 Control1 3.88 4.81 -24.01%

ASR3 Dairy 4.31 3.39 21.34% 45.3%

2 ASR0 Control1 2.41 2.43 -0.51%

ASR3 Dairy 2.71 2.12 21.80% 22.3%

3 ASR0 Control1 3.95 4.23 -7.13%

ASR3 Dairy 1.96 1.77 9.66% 16.8%

4 ASR0 Control1 2.96 2.48 16.26%

ASR3 Dairy 2.46 2.51 -2.02% -18.3%

5 ASR0 Control1 2.97 4.40 -48.52%

ASR3 Dairy 3.38 3.57 -5.46% 43.1%

6 ASR0 Control1 2.57 3.03 -17.63%

ASR3 Dairy 2.06 2.47 -19.94% -2.3%

7 ASR0 Control1 3.06 2.84 7.39%

ASR3 Dairy 2.80 2.67 4.55% -2.8%

8 ASR0 Control1 2.50 2.42 3.28%

ASR3 Dairy 1.52 2.08 -36.98% -40.3%

9 ASR0 Control1 2.17 2.32 -6.70%

ASR3 Dairy 3.50 2.09 40.29% 47.0%

10 ASR0 Control1 2.00 2.88 -44.41%

ASR3 Dairy 1.76 1.80 -2.22% 42.2%

Table 39: Performance of ASR3 group compared to the Control1 (ASR0)

To be able to statistically asses the effect of ASR3 introduction on inventory

performance, a linear mixed model analysis is made. The repeated factor is each

product in each store, as it is measured twice: before and after the introduction of the

new system. The result of such an analysis is that the introduction of the ASR3

systems did indeed have a significantly different impact for the dairy products

compared to the control group (p=.038). While both groups start 2004 at a similar

level (between 2.6 and 2.8 days inventory range of coverage), the ASR3 group has

the next year only in mean 2.45 days of inventory, while the control group has

increased its stock to 3.18 days. This confirms the first part of hypothesis H15a.


Source Numerator df Denominator

df F Sig.

Intercept 1 108 345.739 .000

Date 1 108 .287 .593

ASR_level 1 108 2.450 .120

Date x ASR_level 1 108 4.413 .038

Table 40: Repeated ANOVA on inventory range of coverage, ASR3 group


Adding the factors Store and Store x ASR_level to the model results in a slightly

better model fit. The Schwarz's Bayesian Criterion (BIC) is now 757.9 compared to a

BIC of 802.7 in the former model. The store factor is significant on a 10% level

(p=.073), the interaction between the store and the ASR group is not. The basic

result of this model is the same, the ASR3 group improves its mean stock level while

the control group worsens (p=.038). Higher degrees of interaction

(Store x ASR_Group x Date) are not significant.

Inventory Performance: Coefficient of Variance

A linear mixed model with the coefficient of variance of the stock as the dependent

variable supports the theory of the good performance of the ASR3 systems. In

analogy to the section before, the Date x ASR group interaction is significant

(p=.025). Again the ASR3 ordered products reduce their inventory coefficient of

variance (from 132% to 122%), while the control group worsens (from 160% to

191%), this confirming the second part of hypothesis H15a. This model results in a

BIC value of 573.0.

Dependent variable: Inventory coefficient of variance


df F Sig.

Intercept 1 108 330.969 .000

Date 1 108 1.252 .266

ASR_Group 1 108 8.522 .004

Date x ASR_Group 1 108 5.200 .025

Table 41: Repeated ANOVA on the coefficient of variance of the stock level, ASR3 group

1.60

1.91

1.321.22

0.0

0.5

1.0

1.5

2.0

2.5

2004 2005

Year

Inventory coefficient of variance

ASR0

ASR3

Figure 41: Means of the repeated ANOVA on the coefficient of variance of the stock level, ASR3 group

and Control1


5.3.2. Non-food Products: ASR2 and ASR2* Performance (H15b, c)

Inventory Performance: Mean Inventory Levels

The mean inventory range of coverage by year and ASR system can be seen in

Figure 42. Just by looking at this graph one can see that the ASR2 group showed a

very marked improvement, reducing their mean inventory range of coverage by 4.1

days. The ASR0 group (Control2) reduced their stock as well, but only by 0.9 days.

Contrary to this trend, the ASR2* inventory levels increased on average by 1.1 days

after the introduction of automatic ordering.

7.016.09

9.83

5.745.52

6.63

0

2

4

6

8

10

12

2003 2004

Year


ASR0

ASR2

ASR2*

Figure 42: Mean inventory range of coverage in days of non-food products and Control2

Two separated linear mixed models analyses were made to compare the ASR2 and

the ASR2* products with the control group. The interaction between the variable Date

and the ASR2 group is significant (p=.005), confirming H15b.

Dependent variable: inv_range_of_coverage


df F Sig.

Intercept 1 289.421 166.127 .000

Date 1 226.741 22.540 .000

ASR_level 1 289.421 .846 .358

Date x ASR_level 1 226.741 7.981 .005

Table 42: Repeated ANOVA on mean inventory range of coverage, ASR2 group

The estimated inventory means show that Control2 products reduced its stock from

6.8 days in 2003 to 6.0 in 2004 (-12%). But the reduction of the control group from

9.0 days to 5.9 days (-34%) is much more dramatic.


6.84

6.05

9.01

5.86

0

1

2

3

4

5

6

7

8

9

10

2003 2004

Year


ASR0

ASR2

Figure 43: Estimated means of the repeated ANOVA on the inventory range of coverage, ASR2 group

For the ASR2*, another picture results from the analysis. Here again, there is a

significant interaction between the Date and the replenishment type (p=.011). Yet the

difference is that while the control group managed to reduce its stocks, the ASR2*

showed an increase. The automatic replenishment system did not work as expected

on these products, confirming H15c.

6.85

6.03

5.41

6.63

0

1

2

3

4

5

6

7

8

2003 2004

Year


ASR0

ASR2*

Figure 44: Estimated means of the repeated ANOVA on the inventory range of coverage, ASR2*

group

Inventory Performance: Coefficient of Variance

The second performance indicator of stock is the coefficient of variance. As depicted

in Figure 45, the ASR2 group reduced its variance from 65% to 57%, while the

control group experienced and increase from 47% to 59%. In this case, the ASR2*

group also managed to reduce the coefficient of variance by 5.6 percentage points.


0.47

0.59 0.570.560.51

0.65

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

2003 2004

Year


ASR0

ASR2

ASR2*

Figure 45: Mean inventory coefficient of variance in days of ASR2, ASR2* group and Control2

Although the better performance of the ASR2 group seems clear, a linear mixed

model fails to prove a significance for the ASR x Date variable (p=.335). Because of

this, hypothesis H15b can only be partially supported. The same holds true for the

difference between ASR2* and the control group: the differences depicted between

the means could have happened by pure chance.

5.4. Quantitative Research–Conclusions

The quantitative analysis carried out provided answers to two research questions. On

the one hand, the influences of store and product characteristics on replenishment

performance could be identified (research question Q3). On the other hand, the

statistical analysis clearly showed that MYFOOD's replenishment performance has

benefited from the implementation of ASR2 and ASR3 systems (research question

Q2). In the following, a summary of the results is given.

The hypothesised direction of influences of product characteristics on the

performance of manual replenishment systems could be shown for most of the

product characteristics. The analysis demonstrates that products with higher prices,

larger case packs and with larger volume size will tend to be more often OOS when

ordered manually. The influence of sales variance, shelf life and speed of turnover

could be only partially proven. Furthermore, it was confirmed that products that have

higher sales variances, that are slow movers, that are expensive or that have large

case packs will tend to have higher inventory levels in the store. A major result of

these tests was to show the different reactions of automatic and manual systems on


different product characteristics. The data reveals for the ASR2 products a much

weaker influence of the product characteristics on the OOS and inventory

performance. None of the product variables had an influence on the OOS rate. The

inventory levels of ASR2 ordered products are only influenced by the case pack and

product size. This is a strong argument in favour of the use of automatic systems, as

their performance is much more robust.

Dependent Variables

Independent Variables

Out-of-Stock Inventory Level

+ (H1a, ASR0) part. sup. + (H1c, ASR0) sup. Sales variance

0 (H1b, ASR2) sup. 0 (H1d, ASR2) sup.

+ - + (H2a, ASR0) part. sup. - (H2c, ASR0) sup. Speed of turnover


+ (H3a, ASR0) sup. + (H3c ASR0) sup. Price


+ (H4a, ASR0) sup. + (H4c, ASR0) sup. CU/TU

0 (H4b, ASR2) sup. + (H4d, ASR2) sup.

+ (H5a, ASR0) sup. - (H5c ASR0) not sup. Product size

0 (H5b, ASR2) sup. 0 (H5d, ASR2) not sup.

+ - + (H6a, ASR0) part. sup. + (H6c, ASR0) not sup. Shelf life

0 (H6b, ASR2) part. sup. 0 (H6d, ASR2) part. sup.

"+" Positive effect; "-" Negative effect; "0" Smaller effect of ASR2 system compared to manual

system; "+ - +" quadratic relationship; sup.: supported; part. sup.: partially supported

Table 43: Results overview: product characteristics hypotheses

Five out of eight hypotheses concerning store characteristics could be fully

supported, three only partially. There is a marked difference in OOS performance

between the individual MYFOOD stores, despite their equal logistical context. Some

stores manage to have low OOS rates and at the same time low shrinkage rates.

Small backrooms might be one of the success factors for this performance, as

stressed by supporters of the lean management theory. Another success factor is

surely the store manager. To achieve good results experienced managers are

necessary that have been working in the store for a certain time. There exists strong

evidence for the fact that the right number of staff and the right number of SKUs per

m2 are also important for replenishment performance, although their influence in the

present data could not be fully proven.


(H7a) OOS rate differs from store to store Supported

(H7b) Inventory performance differs from store to store Partially supported

(H8) Reduction of OOS and shrinkage is at the same time possible Supported

(H9) The more SKU per m2 the more OOS Partially supported

(H10) Too many and too few staff increases OOS rate Partially supported

(H11) More experienced store managers have fewer OOSs Supported

(H12) Larger backrooms lead to more OOSs Supported

(H13) Stores with low OOS rates have higher customer satisfaction Supported

Table 44: Results: store characteristics hypotheses

Finally, the statistical analysis showed the expected results for the performance of

automatic systems. Products that are ordered through ASR3 systems show

significantly better inventory performance than ASR0 products; the automation

reduced the stocks of the ASR2 products by 6.4% while the control group increased

by 11.8%. The same holds true for ASR2 systems, after automation these products

had a 3.1 days lower inventory range of coverage, i.e. inventory could be reduced by

42% while the control group only reduced its inventory by 13%. The average ASR0

product has, with a 4.7% OOS rate, an almost 8-times higher OOS rate than ASR2

products. Yet, in this second case the improvement in inventory variance is not

significant. Last but not least, the quantitative analysis showed that a crude

automation will not lead to the desired results. The badly parameterised ASR2*

systems have a worse replenishment performance than manual systems.

(H14) ASR2 systems have lower OOS rates than ASR0 systems Supported

(H15a) Products changed from ASR0 to ASR3 systems improve their

inventory performance

Supported

(H15b) Products changed from ASR0 to ASR2 reduce inventory stocks,

however not inventory variance

Partially supported

(H15c) Products changed from ASR0 to ASR2* systems do not improve

their inventory performance

Supported

Table 45: Results: ASR hypotheses

A summary of the results of the hypotheses can be seen in Table 46.


Findings Method/

Significance Hypothesis Supported?

Product Characteristics

H1a ASR0: Products with high sales variance have more OOS incidents than products

with medium sales variance

ANOVA: p<.001

Part. supported

H1b ASR2: No influence of sales variance on OOS rate ANOVA post hoc Supported

H1c ASR0: Products with higher sales variance have higher inventory levels ANOVA: p<.002 Supported

H1d ASR2: Smaller influence of sales variance on inventory level ANOVA post hoc Supported

H2a ASR0: Slow-moving products have more OOS incidents ANOVA: p<.001 Part. supported

H2b ASR2: Smaller influence of speed of turnover on OOS rate ANOVA post hoc Supported

H2c ASR0: Slow turning units tend to have higher stock levels than fast movers ANOVA: p<.003 Supported

H2d ASR2: Smaller influence of speed of turnover variance on inventory level ANOVA post hoc Supported

H3a ASR0: Expensive products have more OOS incidents ANOVA: p<.02 Supported

H3b ASR2: No influence of price on OOS rate ANOVA post hoc Supported

H3c ASR0: Expensive products have higher inventory levels ANOVA: p<.001 Supported

H3d ASR2: Smaller influence of price on inventory level ANOVA post hoc Supported

H4a ASR0: Products with large CU/TU ratio have higher OOS rates ANOVA: p<.001 Supported

H4b ASR2: No influence of CU/TU ratio on OOS rate ANOVA post hoc Supported

H4c ASR0: Products with high CU/TU ratio have higher inventory levels ANOVA: p<.001 Supported

H4d ASR2: Same influence of CU/TU on inventory rate as ASR0 products ANOVA post hoc Supported

H5a ASR0: Large products have more OOS incidents ANOVA: p<.002 Supported

H5b ASR2: Product size has no influence on OOS ANOVA post hoc Supported

H5c ASR0: Medium-sized products have the lowest inventory level ANOVA: p<.005 Not supported

H5d ASR2: Large products have higher inventory levels ANOVA post hoc Not supported

H6a ASR0: Very short and very long shelf life leads to more OOS incidents Regression Part. supported

H6b ASR2: Smaller influence of shelf life on OOS, but data missing ANOVA post hoc Part. supported

H6c ASR0: Short shelf life products have higher stocks than medium shelf life products ANOVA: p=.033 Not supported

H6d ASR2: No difference between medium and high shelf life products, but data

missing

ANOVA post hoc Part. supported

Store Characteristics

H7a The OOS level and inventory performance varies strongly from store to store ANOVA: p<.001 Supported

H7b The inventory levels vary strongly from store to store, the inventory variance not ANOVA Part. supported

H8 Stores with high OOS rate have high shrinkage as well Rep ANOVA: p=.08 Supported

H9 Stores with high SKU density have higher OOS rates Regression Part. supported

H10 Stores with too little or too many employees per m2 have more OOS incidents Regression Part supported

H11 Stores with new store managers tend to have more OOS incidents Rep ANOVA: p=.053 Supported

H12 Stores with large backrooms tend to have more OOS incidents Rep ANOVA: p=.028 Supported

H13 Stores with low OOS rates tend to have more content customers Rep ANOVA: p=.023 Supported

ASR Characteristics

H14 ASR2 systems have lower OOS rates than ASR0 systems ANOVA: p<.001 Supported

H15a Products changed from ASR0 to ASR3 systems improve their inventory

performance

Rep ANOVA p<.05 Supported

H15b Products changed from ASR0 to ASR2 reduce inventory stocks, however not

inventory variance

Rep ANOVA p=.005 Part. supported

H15c Products changed from ASR0 to ASR2* systems worsen their inventory

performance

Rep ANOVA p=.011 Supported

Rep ANOVA: ANOVA with repeated measures; Part. supported: Hypothesis is partially supported

Table 46: Overview of the results of the hypotheses tested

132 6. Field Research and Managerial Implications


As seen in the quantitative analysis, higher ASR systems can significantly improve

retailers' inventory performance, reduce OOS incidents, and show more constant

results. Yet it was also found that badly or crudely parameterised systems will not

deliver the desired results. As stated by the contingency theory, the context of the

company is decisive in determining the success of an automatic replenishment

system. Therefore, in order to be able to give recommendations to practitioners it is

necessary to examine in detail how retailers successfully introduced ASR systems

and in which fields they can still improve their operations. In this chapter, the case

studies of four retailers are depicted. For each company, a description of their

replenishment processes, the organizational changes and personnel issues linked to

the ASR introduction are highlighted as well as the outcome of ASR system's

introduction.

Next, recommendations for management are derived from these findings. First,

advice is given to retailers on which ASR systems they should implement, depending

on product characteristics (research question Q4). Second, a cost-benefit analysis

assesses the effect of automation. Third, practitioners learn the necessary

organizational and technical prerequisites for each ASR level; the answer to research

question Q5.

6.1. Research Sample

6.1.1. Company Selection

As Eisenhardt (1989) states, the selection of cases is a most important aspect when

carrying out field research. Given the limited number of cases which can usually be

studied, she does not promote a random selection (statistical sampling). Instead,

Eisenhardt recommends the use of cases with polar types, filling each of the

categories that are relevant for the research.88 The cases are selected to highlight

theoretical issues and to refute or challenge the theory being tested (Bansal and Roth

2000). The main companies chosen for this research project are presented in Table

47.

88

This process is called theoretical sampling by Denzin (1989).

6. Field Research and Managerial Implications 133

Retailer name and type

ASR Systems in use Experience with

system89

A: MYFOOD

Grocery retailer

ASR0: fruit and vegetables

ASR2: packaged food and non-food

ASR3: dairy products

Several years

2 years

3/4 of a year

B: GROCO

Grocery retailer

ASR0: most of items

ASR4: goal 80% of goods

Several years

Pilots

C: NORTHY

Grocery retailer

ASR2: non-food

ASR3: most packaged goods

ASR3: fruit and vegetables

Several years

3 years

3/4 of a year

D: DRUCKS

Over-the-counter chemist retailer

ASR4: most products Several years

Table 47: Selected companies for the field research

The choice of these four retailers is illustrated in the following. The first retailer

chosen was MYFOOD. The quantitative analysis in the former chapter is based on

data from this grocery retailer. The richest insights into the replenishment systems

comes from this case study. The next retailer chosen was GROCO. This retailer is

piloting ASR4 systems. These systems are rarely found in European grocery

retailing; for this reason the studying of GROCO provided a most interesting case

study. The third grocery retailer, NORTHY, is known as one of the most innovative

retailers in northern Europe. No other grocery retailer known by the author has

acquired more experience with ASR3 systems in the last few years. The company

has already adapted its logistics, operational processes and organization to best

support the automatic systems. The last case study examines the over-the-counter

chemist retailer DRUCKS. Although it is not a grocery retailer, it is the company with

the most experience with ASR4 systems known to the author. DRUCKS has a very

broad product range, selling not only pharmaceuticals, beauty and health articles but

also packaged food and near-food products (2003: 11,000 SKUs). Furthermore, the

distribution channels and delivery frequencies to DRUCKS's stores are very similar to

the other three cases. For these reasons the company is increasingly comparable to

a regular grocery retailer. The only differences one can find between DRUCKS and

other grocery retailers is the absence of very large stores (>1500 m2) and the

absence of fresh goods such as green grocery. The similarities support the trend

89

As of August 2005.


found by Eichen and Eichen (2004) that the assortments of chemists and grocery

retailers are increasingly overlapping.

To sum it up, these companies were chosen because they represent the most

advanced ASR levels one can find at this point in time in retail, namely ASR3 and

ASR4. At the same time, the chosen retailers are still also ordering some products

through lower-sophistication levels, making thus valuable comparisons possible.

6.1.2. Market Characteristics

The four retailers come from two European regions: the Nordic countries and central

continental Europe. In terms of market share, they are among the top three in their

respective core markets. The four companies are under the influence of the same

market developments that can be observed in many western European countries.

First, the markets the sample companies are in are oligopolies; a consequence of

several consolidations of smaller retailers in the last few years. Second, as stated

before, a trend observed is the increasing overlap of the assortments between the

classic grocery retailers and chemists. The result is fiercer competition between both

retail types. The third trend observed is the power situation between the suppliers

and retailers. According to Eichen and Eichen (2004), manufacturers tend to dictate

prices and quantities, their power resulting from their branded products and category

share. However, retailers are becoming more powerful through their increasing

market share.90 The big players in the market in particular are profiting from the

consolidations that are taking place (Aalto-Setälä 2001).

6.1.3. Supply Chain Structure

The retailers MYFOOD and GROCO have both basically the same supply chain

structure. They distribute slow movers and special goods (such as frozen products,

wine) through central warehouses and regional distribution centres. Direct store

delivery (DSD) quantities are negligible.

This is different at NORTHY. DSD is used by NORTHY for many categories such as

brewery products (e.g. beer and soft drinks), fresh bread, dairy products and ice

creams. Measured in turnover, DSD accounts for about 30% of NORTHY's

distribution volume. The remaining products are transported through a main

90

This power shift opinion is shared by many researchers and practitioners, even if its quantitative verification is

somewhat difficult. See Ailawadi, Borin, et al. (1995).


distribution centre as well as smaller regional distribution centres and cross-docking

terminals.

DRUCKS changed its distribution system in the 1980's from a DSD philosophy

towards a system with 3 distribution centres: one central and two regional. With this

new system came a shift of power and responsibility away from the stores.

Nowadays there is centralized control of the logistics, which has led to major

improvements in the lead time and full truck loads due to bundled transport

possibilities. The national DC handles 80% of the SKUs. The national DC takes care

of all small products where the case packs have to be opened before being

transported to the stores.

Småros, Angerer et al. (Småros, Angerer et al. 2004a) found in their analysis of the

major European retailers two trends that can also be observed in the present sample:

the increasing employment of cross-docking and a move towards centralized storage

of slow moving goods (Småros, Angerer et al. 2004a). Retailers employ cross-

docking for a part of their products, typically for fast moving goods such as fruit and

vegetables or meat products. One-third of the companies examined by Småros and

Angerer (2004) have centralized storage of slow movers, just like MYFOOD and

GROCO in this sample.

Overall, as depicted in Figure 46, all retailers have a two-level distribution structure.

All four retailers have national DCs and heavily use cross-docking and regional

distribution centres. This eases the comparison between the ASR replenishment

systems, as all retailers have a comparable logistics context.

SupplierSupplier

Retail StoresRetail Stores

National DCNational DC

Regional DCs/ CDRegional DCs/ CD

SupplierSupplier




SupplierSupplier



MYFOOD/ GROCO NORTHY DRUCKS


Figure 46: Supply chain structure of sample


6.1.4. Chains and Store Formats

Along with supermarkets, GROCO's business also includes a variety of non-food

stores such as chemists and non-food specialities. The non-food sector accounts for

a major part of its turnover. In this thesis, only the classic grocery stores are studied

that have a size between 200 and 8,000 m2 and that carry on average 10,000 SKUs.

The retailer MYFOOD has a similar format channel concept ranging from small stores

with 200 m2 up to hypermarkets with up to 9,000 m2. The MYFOOD group also owns

some non-food chains. The total assortment has a size of over 300,000 SKUs.

NORTHY has three different retail chains. The first type of stores consists of small

neighbourhood shops. The stores are between 150 and 300 m2 in size and have

assortments of about 2,000 products. The next group consists of supermarkets that

are typically relatively small, although they come in a wide range of sizes, from 400 to

1500 m2. The stores carry between 3,000 and 5,000 SKUs. The third type of stores

consists of hypermarkets located in cities with slightly trendier products than the other

supermarkets. Their size is between 4,000 and 10,000 m2 and have assortments of

about 18,000–25,000 products.

DRUCKS has several hundred store outlets in its home market in central Europe. A

typical store has about 500 m2 and carries 10,000 SKUs. The biggest stores have

around 1000 m2. In the last few years, an expansion away from its home market

towards other European countries can be observed.

6.1.5. Delivery Frequency and Order-to-Deliver Lead Times

MYFOOD and GROCO have a very frequent delivery to their stores. Both stores

have daily delivery for most of their products, i.e. six times per week. Smaller stores

on rural areas are have slightly less frequent deliveries, that is, between three and

five times a week. For some very fresh products such as bread, MYFOOD even has

several deliveries per day. The order-to-deliver time, depending on the product

category, is typically around 1–2 days.

NORTHY delivers non-foods, packaged goods, fresh goods and green grocery on

average 5 times a week. Frozen goods are brought to the stores 2 or 3 times a week

on average; larger stores receive replenishment 5 times per week.

DRUCKS has a delivery frequency of between 6 times per week to only once a week.

The replenishment frequency depends mainly on the store turnover and in second


place on the store size. If two stores have the same turnover but one is much smaller

than the other, then the smaller store will have a more frequent replenishment

because the storage space is smaller. The entire assortment can be ordered at any

of the defined delivery days.

Delivery

Frequency

Delivery

Frequency

� 6 times a week� Rural stores:3- 5

times� Bread: twice a

day

� 6 times a week� Rural stores:3- 5

times� Bread: twice a

day

� 6 times a week� Rural areas and

small stores: 3- 5 times

� 6 times a week� Rural areas and

small stores: 3- 5 times

� Large stores: 5 times a week

� Frozen goods: 2- 5 times a week


� Frozen goods: 2- 5 times a week


� Very small stores: Once a week


� Very small stores: Once a week

MYFOODMYFOOD GROCOGROCO NORTHYNORTHY DRUCKSDRUCKS

Figure 47: Delivery frequency of sample

Overall, one can see that all retailers have a very frequent delivery frequency. In

addition, all the stores experience a high delivery service quality from their

distribution centres. For the bulk of products there are typically daily deliveries,

combined with a very fast response time. The order-to-delivery lead times between

distribution centres and stores are usually between 24 and 36 hours. This result is

analogous to the findings in the study by Småros, Angerer et al. (2004a). In this

European study the average delivery lead time of fresh goods to the stores is 24

hours, with a range of 14 to 32 hours. For other goods, the median value is about 34

hours, with a range of about 15 to 48 hours. The companies studied have an order-

to-delivery lead time from manufacturers to retailers' distribution centres of typically

less than 48 hours. Many fresh and green grocery products can be even replenished

within 24 hours. Therefore, the authors conclude that the supply chain of typical

European groceries has improved significantly over the last few years and has now

reached a very high level, when compared, for example, to American retailers (c.f.

Roland Berger 2003a). The four companies in this field research are no exception to

these European trends.

6.2. Replenishment Processes

This section takes a closer look at the four modules a replenishment system consists

of: inventory visibility, forecasting, replenishment logic and ordering restrictions.91

91

For more details on these modules see section 4.1 with the descriptive model.


6.2.1. Inventory Visibility

The importance of having extremely accurate inventory records

increases with the automation level in use. As the companies in this

thesis' sample order products with the help high ASR levels, the need for

high accuracy grows accordingly. In the following, the different processes that have

an influence on inventory visibility are described. To compute the stores' inventory

levels, the input and output product streams have to be considered. The basic idea

behind an inventory holding system is relatively simple. The inventory in the store

equals the initial stocks plus the amount delivered by suppliers minus the cash-out,

returns (e.g. wrongly delivered goods) and write offs (e.g. spoilage of fresh goods). It

is clear that it is not possible with such a system to know how many products are on

the shelf and how many in the backroom, as shelf replenishment is not monitored

separately. However, it makes a great difference for shoppers whether the products

are on the shelf or in the backroom. Figure 48 depicts the elements that have an

influence on inventory.

Backroom

inventory

Backroom

inventoryShelf

inventory

Shelf

inventory

Replenishmentfrom the

DC/supplier

Cash out/write off

Shelf replenishment

Shrinkage

Returns

Store Inventory

Figure 48: Inventory storage places and product flow processes

Inventory record books can be either administered on paper or electronically. The

four companies in the sample have for most of their products ASR2–4 systems,

therefore the use of electronic inventory management systems is mandatory. The

method for transferring information into the system is the barcode at trading unit and

SKU level. For instance, a single toothpaste SKU arrives at MYFOOD stores in case

packs of 24; each toothpaste tube and each case pack has a barcode on it. As stated

by a NORTHY logistics manager, such barcodes are "the backbone of any automatic

replenishment system."

Non-food products are an enormous challenge regarding one-to-one SKU

identification, as they tend have a huge variety. An example are apparel products; the


same T-shirt cut can have several colours and sizes. But also in the food sector there

are some products that are difficult to track. The GROCO ASR project leaders

reported that they had considered having products from the served cheese counter

also in the inventory recording system. This idea was abandoned rather quickly as

the only way of having an exact inventory record was to weigh each product in the

evening. As the larger cheese counters might carry more than a hundred different

products, the weighing of every SKU would have been very time consuming and thus

very expensive. Consequently, GROCO still orders these products manually.92

For an automatic system to work properly, one-to-one identification is necessary.

This means that every SKU needs its own barcode number. The technology is not

the problem, there are in theory enough digits available for every SKU. Yet several of

the small suppliers often do not comply with EAN-standards93, thus making individual

identification very difficult. In all four cases, the electronic inventory record system is

an integrated part of the ERP system of the company. Therefore, in the following no

difference will be made between the inventory record system and the ERP–they are

regarded as a single entity. To transfer the inventory level-information to the system,

the goods are scanned at different points in the supply chain; every transaction in the

supply chain is registered. Ideally, this scanning should happen after leaving and

before entering any of the storage places along the SC. However, there are in

practice several deviations from this procedure. For example, there is no retailer

known to the author that tracks the movement from the backroom to the store

shelves.94 In the following, the two main flow processes in the store are considered,

the replenishment from the supplier and the check-out.

Store Replenishment

The input stream of a store consists of the goods that are delivered from a supplier.

Here lies the first possible source of inaccuracy, as a GROCO distribution centre

director points out:

"In our system the sales automatically initiate the replenishment process. If any

picking errors are made at the distribution centre, then this has a dramatic

influence on inventory records, distorting the entire replenishment system."

92

Most Spanish retailers have found another solution for this problem: they abandoned any served counters,

fresh products such as meat, cheese, etc. are nowadays only offered in pre-packed portions. 93

EAN: European Article Numbering. 94

The Metro shop of the future is piloting RFID labels and smart shelves that promise to track the inventory on the

shelf.


Companies such as MYFOOD and GROCO have stopped internal checks on

deliveries from the DCs to the stores at the store gates. These retailers trust in the

quality of their own pickers and scanners and feel that the increase in accuracy is not

worth the cost of the physical audits. Consequently, an error that happens during

picking at the DC can no longer be detected at store level. Deliveries that arrive

directly from the supplier are still normally checked, as any delivery difference is

reclaimed and refunded. Some consultants (e.g. Arthur Andersen LLP 1997) highlight

the possibility of skipping checks for suppliers with good track records, as the

operations costs are higher than the refunds.

A logistics director from MYFOOD stressed the importance of having synchronized

the DC's deliveries to the processes at store level. In his opinion, it is not sufficient to

deliver the required quantities at the right day at the store. The exact hour and timing

of the deliveries is just as important. If all the deliveries from the different suppliers

arrive at the same time at the store, chaos would ensue. Store staff are not able to

cope with such high delivery quantities at one time. Therefore, MYFOOD, just like

many other retailers, has defined exact delivery windows for all the transports to the

store.

Check-out

MYFOOD has identified the importance of high POS data accuracy for ASR

performance. To improve the personnel's awareness of accurate scanning, the store

employees are trained intensively. A former problem was that there existed a generic

scanning number that was used at the check-out every time a product could not be

scanned. This source of inaccuracy was abolished, as it was used too frequently by

staff. To be able to introduce new ASR systems, it was necessary for MYFOOD to

install new tills. The problem was not missing functionality in the old machines but

rather that the stores did not have the same standardized systems, making a

centralized system connecting all tills impossible. The average scanning accuracy is

currently 99.4%.

GROCO's plan to improve check-out accuracy relies on a new function of their tills.

By examining individual scanning processes the retailer found out that in some stores

the repeated scanning button was unusually frequently used. The problem was that

some cashiers thought they could save the time of the shoppers by scanning fast, but

wrong. Instead of scanning five different yoghurts flavoured separately, only one

yoghurt was scanned and then the "repeat" key was pressed five times. The result

was that all these products had a wrong inventory record. To inhibit this behaviour in


the future, GROCO's new check-out system allows configuring the tills individually for

each store and cashier so that this key can be blocked.

NORTHY, on the other hand, already had modern equipment at the time of the ASR

introduction. The challenge was in this case not the usage of a new technology but

moreover to educate the people on the importance of using the tills right. As

NORTHY receives many direct deliveries from smaller companies it frequently faces

the problem of suppliers using the wrong barcodes. Consequently, suppliers had to

be instructed on the role of exact and reliable barcodes for the functioning of the

entire system.

At DRUCKS, modern check-out systems and PCs at store level were already in use.

In theory, no process had to be changed. Nevertheless, as the accuracy of the

inventory records had to increase after the introduction of the ASR4 system, a special

emphasis was placed on check-out accuracy.

Shelf Inventory Visibility

As stated before, the most important storage space for goods, the shelf, is at the

same time the most difficult place to control the inventory levels, at least for stores

with a backroom. The quantitative results in the former chapter showed the

counter-productive effect that excess backroom space can have (see Figure 38).

Some retailers have abolished these backrooms, for example Manor in Switzerland

or Globus in Germany. For these retailers, the inventory on the shelf equals the total

inventory in the store. The four retailers in the sample still use backrooms, just like

most European grocery retailers. A precondition to measure the availability rate in a

store is to know the quantity of goods on the shelf. From the research by Småros,

Angerer et al. (2004a) it is known that most retailers do not have systems or

processes to measure whether there is at least one SKU left on the shelf. Of the 12

major European retailers in the sample, only six companies have a process for

regular shelf availability checks; four companies check availability on a daily level,

two regularly but less frequently. In addition, three companies have commissioned

one-time studies to examine their availability in selected product categories. Three

out of 12 companies do not measure on-shelf availability at all. However, on-shelf

availability and approaches for measuring it are considered extremely important

development areas by the majority of the interviewees. The four retailers in this thesis

sample have had physical audits of OOS in pilots, yet no regular control has been

implemented. Nevertheless, all four companies expressed the need for a tool that is

able to measure OOS situations automatically from the ERP systems.


Processes to Increase Inventory Records Accuracy

There exist many factors that can make inventory records inaccurate. Among others

there are shrinkage, returns to the suppliers that are not recorded, and wrong

scanning at the check-out. The four companies in the sample check overall inventory

accuracy only during annual stock takes, as demanded by commercial law.

Consequently, it is not surprising that the interviewees had only a rough guess of

their inventory accuracy; in their opinion it lies between 97–99%. This figure is much

better than that reported in the study by Raman, DeHoratius et al. (2001b). And

indeed, a physical audit of inventory records accuracy of MYFOOD revealed a high

inaccuracy in the results. For many products, the quantities shown in the system

were different from the real physical inventory. An example of a product (baking

powder) that was manually counted during 14 days is depicted in Figure 49. As one

can see, the real inventory deviates constantly by 13 SKUs.

Inventoryrange of coverage in days

Units in store

Inventoryrange of coverage in days

Units in store

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7 8 9 10 11 12

Inventory according to the system Real inventory in store

10

0

5

Figure 49: Comparison of inventory records and real inventory in one store

Even by counting manually the products in the store, an exact measure of inventory

accuracy is rather difficult for two reasons. First, problems occur for inventory levels

that are not updated in real time. In the case of MYFOOD, the inventories are only

calculated once a day, when the regional sales system (i.e. how many items were

sold at the store?) communicates with the nationwide logistics system (i.e. how many

items were delivered to the store?). Only during this small time window, in the early

morning before the stores are replenished, the physical inventory in the store can be

compared with the records in the IT system. The second major problem is simply the

amount of goods in a store. For instance, MYFOOD has in an average sized store

around 200 packages of a nut cookies SKU. When there are that many products of a

single SKU on the shelf, the manual counting of the products is not only

time-consuming, but also error-prone. Already Millet (1994) reports on how in some

cases manual counting can actually worsen the accuracy of the inventory records.


Therefore, several retailers such as DRUCKS have introduced a counting process

that is exception-based: whenever an inventory record is zero or even negative,

employees receive an alert and make a counting check. A drawback of this

procedure is that some inventory inaccuracies will remain undetected. For example, if

the records show positive stock levels even though there are no goods in the store,

no alert will be given. As the ASR believes that there is still inventory left it will place

no new order. As no items can be sold, the quantity in the inventory system will never

decrease until by chance someone detects this error. A possible solution for this

dilemma is an intelligent alert system that also takes into consideration sales data (cf.

Corsten and Gruen 2004). The system compares the sales of historical articles with

previous data. If an article is not sold for a certain period at all, the employees receive

an alert.

GROCO's plan to increase inventory accuracy is called "evening-walks." At the end

of each day, employees are supposed to walk along the aisles and check for

"0–1–2–3 products." This means that the store staff look for products that have three

or fewer items left on the shelf. With this method out-of-stocks can be detected as

well as products which are in danger of becoming OOS in near future. For the

products with such a low inventory level the employees compare the quantity on the

shelves with the books and check, whether any orders have been placed. The idea

behind this procedure is that it is unproblematic to check the inventory records for

products that are almost sold out, as there are very few items to be counted. Another

advantage of this system is that in the long run, all SKUs are checked. Another

advantage of this procedure is that SKUs with a high risk of becoming OOS will also

be checked as well more frequently, as they often have near zero stock levels. Not

only are products with very low inventory levels checked, but the employees are also

asked to pay attention to products with unusually large stocks.

NORTHY has introduced a similar method, but not as structured as GROCO. The

store employees are required to check "unusual" amounts of SKUs, without

specifying what this means. This checking is not undertaken on a regular basis;

employees are asked to always keep an eye open for such anomalies.

DRUCKS's store managers are free to decide whether an inventory record accuracy

process is necessary. Central office produces a handbook which contains

recommendations on how to increase inventory accuracy. The processes described

in this operations-handbook are just suggestions and not at all compulsory. For

example, it is recommended to manually count the products that have an unusually

low sales ratio compared to the inventory records. The employees are responsible for


finding such anomalies independently, as there are no alerts provided automatically

by the IT systems. As described above, the only exceptions are products with a zero

or negative inventory; these products are specially marked by the IT systems.

Inventory accuracy plays an important role for DRUCKS, as without any exception all

SKUs have electronic inventory records. DRUCKS demands from every one of its

suppliers an electronic dispatch advice; this is something many retailers are still

fighting for, among them NORTHY.

All the retailers interviewed stated that with the introduction of higher ASR systems

training sessions were organized to increase the awareness of check-out personnel.

The problem is to find the right balance between accurate checking and customer

service. Many customers do not understand why it is necessary to scan every article

even though they have the same price. Therefore, cashiers are also instructed on

how to behave when facing complaints from customers. The high turnover of

cashiers and the large number of part-time employees makes it difficult and costly to

train every one appropriately.

To check and correct inventory records, all the four retailers have mobile PDAs in

use. This allows employees to check directly on the shelf or in the backroom the

accuracy of the inventory records, at least roughly if there is no real time updating of

the inventory records. Store employees are also allowed to change the records. On

the one hand, this is in the opinion of most interviewees the best method for

increasing the quality of the inventory records. On the other hand, some interviewees

see the danger that this ability might be misused to influence the automatic

replenishment system. One MYFOOD logistician reported a case where the

correction of the inventory records was misused to influence the automatic ordering.

By making the system believe that there were too few articles in the store, the

employee steered indirectly the delivery quantities. By now, MYFOOD has restricted

the ability of staff to change records. For its new ASR4 ordered products GROCO

has plans to introduce a central control mechanism to prevent misuse of this feature.

Employees will still be allowed to change the inventories. But this information is

reported to headquarters, where central planners watch for anomalies.

Incentives Related to Inventory Accuracy

The wages of MYFOOD's store managers consist of a basic salary and some

additional bonuses. One of the bonuses depends on inventory accuracy. The

problem with this incentive is that inventory accuracy is only checked manually once

or twice a year due to the high level of effort required. OOS rates and inventory levels

are not part of the incentive system.


GROCO also offers a salary incentive for the accuracy of inventory records at store

level. The maximal difference must be no higher than 0.3%. Yet, this incentive

system takes into account exceptions for special products with high shrinkage rates

such as razor blades or condoms. In a three-month test, GROCO found that only

32% of the razor blades that were delivered to the stores were sold regularly through

the check-out. The rest (68%) were stolen. Razor blades are therefore nowadays

only sold at the checkout. The biggest change concerning incentives came with the

introduction of the new ERP system. This new technology increased inventory

visibility and makes it now possible to check whether a store shows unusually high

inventory levels. In such a case, the central office would warn the store manager. At

DC level, there are monetary incentives for the accuracy of picking. According to a

GROCO logistics director, the new incentive scheme has shown excellent results.

NORTHY has adopted another approach. For this retailer it is of the utmost interest

to have a standardized assortment on its stores across the country. Consequently, it

has introduced negative incentives (i.e. punishments) for stores that do not stick to

the standardized planograms.

Finally, DRUCKS has decided not to implement any incentive system for their store

managers. The logistics director of DRUCKS stated that employees already have a

great interest in high inventory accuracy, as the stores' replenishment performance is

dependent on accurate values. This manager stated:

"Accurate records are like the foundations of a building. If the foundations are

skewed, the entire building will be skewed as well."

In his opinion, the low inaccuracy level of well below 3% (measured in the annual

stocktake) shows that no incentive system is required.

Inventory Records Accuracy and Performance

In spite of all the difficulties mentioned in this section about record accuracy, all

companies in the sample report very good results from their ASR implementation for

most of their goods. There are two possible explanations for this. On the one hand,

the ASR systems in use could be robust against inventory accuracy. The safety

stocks could have been set very generously, making the systems robust. This is an

open question that has to be solved in further research. Another explanation could be

that if at any time inaccuracy reaches a high level, the processes implemented to

detect this such as the "0–1–2–3 walk" seem to work pretty well. An exception is

NORTHY, as this retailer is rather unhappy with the performance of its ASR3 system


with perishable goods (products with a shelf life shorter than 10 days). Further

research is required to determine if poor inventory accuracy is the underlying problem

for this.

6.2.2. Forecasts and Replenishment Logic

At MYFOOD, an ASR3 system with integrated forecasts has been

implemented for dairy products. The forecasts on store level are rather

simple, on every weekday a forecast for the next day is calculated taking into

consideration the sales of the last four same weekdays (e.g. on a Monday the last

four Tuesdays are taken into account). Any days with special events (such as

Christmas) or with promotions are skipped. If the current inventory plus the goods on

the way are lower than the estimated required quantity for the next day, an order is

placed. To be on the safe side, the quantity necessary for the second day after the

current day is also ordered (e.g. on a Monday an order is placed to have enough for

the Tuesday and the Wednesday). For packaged products and non-food SKUs, a

simple ASR2 system is in use. This means concretely that there are minimum and

maximum levels for each product's inventory that are not to be under or over scored.

These parameters are constant throughout the year. The shop is responsible for the

setting and updating of the ASR2 system parameters. An exception is non-food

articles. As there are so many SKUs in this category, it would be too time-consuming

for each store to set the parameters on item level. Therefore, the store has a

restriction on changing these parameters. Employees can only change the quantity

based on a discrete scale ranging from 1–10. Furthermore, they can only do this on a

group level, meaning, e.g., that they have to change the replenishment parameters

for all coffee machines, not only of one special SKU.95

An logistics manager interviewed expressed the opinion that it would make no sense

to have a forecasting system for packaged and non-food goods, as there is a daily

delivery service to the stores. The parameters for non-food and packaged food are

set in such a way that the stores have an inventory range of coverage of

approximately three days. This rule is not applicable to slow-moving articles as they

tend to have much longer inventory ranges of coverage. The exact inventory level

depends on the size of the trading unit and the shelf space. MYFOOD tries to set the

parameters in such a way that an entire case pack can be put directly on the shelf to

avoid inventory in the backroom. Still, there are situations where this procedure does

not work due to the formula that is used for the replenishment. The parameters used

95

This system was called ASR2* in chapter 5, where the performance of such systems was examined.


for the replenishment are based on average sales. To make this clear, imagine an

SKU that sells on average 10 units per day. If the shelf holds 35 units and the

replenishment takes only one day the parameter will be set in such a way that as

soon as the inventory drops below 25 units, a case pack with 20 SKUs will be

ordered. The system calculates that within the lead time of one day, 10 articles will be

sold making hence a complete refilling possible. Obviously, there can be problems for

articles with high sales variance. An OOS incident is not very probable, as the safety

stock is quite large. The main problem occurs if sales are unusually low. In this case,

there are additional handling costs of having a half-empty case pack stored in the

backroom. The author has seen in many stores "creative" ways of stacking the last

units left of an open case pack on the shelf to avoid any additional reshelving effort.

Another unsolved problem is small stores, where the number of SKUs in a case pack

will be always too large for the shelves.

The order suggestions made by the systems are controlled via PDA (personal digital

assistant). Employees look at the automatically-generated orders and compare them

to the existing inventory. In the beginning, store employees were able to alter any

generated proposition. As too many suggestions were altered, the correction

possibilities were later restricted by the central office. The logistics director of

MYFOOD defends this procedure by saying:

"Either you do automatic replenishment the right way, or you better do nothing at

all."

One of the last influences MYFOOD's store managers have on ordering is that they

are able to change the reorder point levels. Yet there are thoughts in the central

office of abolishing this right as well. Goods in promotion and new product

introductions are not handled through this automatic system; the orders are placed

manually.

For fresh products there used to be a very simple automatic ordering system for

many years. Employees received sheets with tables where they wrote with a pen the

new orders. The tables were not empty, the last order was already pre-printed on

them. This way, if an employee forgot to place the new order, the last quantity was

ordered automatically.

The retailer GROCO still has at the moment an ASR0 system in use for most of its

products. This means in practice that employees walk along the aisles and decide

subjectively the replenishing quantity by looking at the inventory levels. The only

support is a PDA to check the electronic inventory records. Yet, in the past months,

an ASR4 system has been in pilot in two stores for two categories. In the near future

it is planned to introduce this system throughout the country for many categories. The

new ASR4 system will use the forecasting engine of a popular ERP package to


compute the forecasts. For each article in each individual store, a separate forecast

is calculated based on the sales data of the last 2 years. Any promotions in that time

are ignored in order to avoid biasing the result. For the employees at the stores, as

well as for the employees in the central office, the forecast is a "black box" as they do

not know the exact algorithms behind it. The retailer's central office feeds the system

with so-called events to make the forecast more precise. The variables that are used

at the moment to improve the causal forecasts are calendar events, opening times,

holidays, regional festivals and actions of the competition. The interviewed ASR

project member said that he is thinking of implementing weather data and

substitution-products in the future as well. Every day, the forecast is calculated for

each day of the following four weeks. For new products without sales history,

comparable products are taken as the basis of the calculation. The results of the

forecasting are two groups of order suggestions. In the one group there are the SKUs

where the software has a high confidence on the results. In the second group, the

software has some doubts about its own calculations and requests the manager to

check the results for plausibility. Approximately 5% of all computed orders are

classified as insecure. The system classifies products into the insecure cluster if the

inventory levels are negative, the order size calculated has an unusual size, or if it is

an article that is required to be counted in the formerly described "evening walks."

The software has up to 650 predefined reasons that can raise an alert. This is a

theoretical number, only a small proportion of them are practically implemented. The

store manager sees only about 5–10 exceptions, the central headquarters slightly

more. For example, if an article does not sell at all in a day, this is reported directly to

the replenishment group in the central office. The planners there have to decide if

any action is required and thus whether the store manager has to be informed.

Independently from this alert system, the store manager still has complete rights to

change any order suggested by the ASR system.

NORTHY's ASR3 system has a very simple forecasting algorithm. It uses exponential

smoothing to create the forecast for the next weekday. The exponential parameter is

the same for all products and with a value of 0.2 rather low. Such a low value means

that more weight is put on the sales history than on recent sales. Therefore, unusual

sales influence the forecast only slightly. NORTHY has experimented with several

other values and found this one to be the most appropriate. The retailer's actual

forecasting method does not take seasonality effects, promotions or holidays into

account. It is hence one-dimensional in the classification terms of section 4.1.4. The

parameters are fixed for the whole year. Nevertheless, sometimes the central

planners change the parameters for entire product groups when they expect high

demand changes. The overall accuracy of the forecasting is rather low, but as stated


by a logistics manager, a better forecast is not necessary for their company. The

interviewee shares the opinion of MYFOOD managers: when the replenishment

supply chain is fast and responsive then high forecast accuracy is not necessary. A

view that is shared by many retailers and researchers (cf. Alicke 2003; Småros,

Angerer et al. 2004b). Some of the articles are ordered through ASR2 systems and

thus no forecasts at all are required. Six parameters are necessary to make the

ASR3 system work: sales data, lead time, safety parameters, shelf space, minimum

shelf space and CU/TU ratio.96 The sales data is collected for each store, product

and day. Based on these numbers, the inventory position is calculated and together

with the lead time the schedule for the next order is determined. The system requires

manual information about how big the safety stocks should be. For each SKU there

exists one safety stock parameter (in days) that is the same across all stores. Yet,

sometimes the safety parameters are determined at category level. The next required

parameter for the logic is shelf space. Shelf space is not determined for each store,

moreover there exist six predefined assortment types depending on store size. For

each one of these assortment types the shelf space is strictly defined as well as a

minimum shelf filling parameter. The last parameter is a percentage value of the total

shelf space and defines a minimum shelf stock amount that should never be reached;

otherwise, the shelf would look too empty and thus would deter sales. Finally, the

CU/TU ratio plays an important role; this parameter is mostly set by the supplier. It is

worth noticing that if any of these parameters changes by more than 30% (e.g. when

the case pack becomes bigger or an SKU sells less than 70% of the average

quantity) an alert appears and the order has to be checked manually. For special

occasions with increased demand (e.g. Christmas or sports events), employees are

requested to place additional manual orders.

DRUCKS's ASR4 systems use a forecasting engine that was programmed by the

same company as the one from GROCO. As described before, the forecast is made

for each product in each store on the basis of the sales of the last two years.

DRUCKS has an every-day-low-price policy, therefore promotions do not have to be

omitted from this database. Overall, there are about 50 parameters and event types

that can be implemented in theory to improve the forecast. Replenishment is

calculated taking into account a minimum level at each store (for aesthetic reasons),

a desired availability level, seasonal effects, public holidays, new stores opened by

the competition and price changes. There are six different classes of forecasting

algorithms, while the slow moving products receive a very basic forecast, the fast

moving products receive the most attention and have forecasts where the entire

96

See also section 4.1 for a description of the elements of an ASR system.


bandwidth of events is taken into account. The classification of an SKU to a certain

forecast type is fixed for all the stores, even if in a store a product is sold more often

than in another one. The forecast for a single product requires only milliseconds and

is created by a PC in the store. This way, the forecast for all the 10,000 SKU's in a

typical outlet is finished within a few minutes. Overall, the forecasting quality of the

system is described by the DRUCKS logistics director as fantastic, yet there are no

KPIs in place assessing its quality. The interviewees could not name any regular

product where a manual forecast would be better than the ones calculated by PC.

Only for articles that are listed for the very first time, a manual ordering and

forecasting is made until the system has enough data to make a reliable forecast. As

with GROCO, the system automatically detects products where the forecast is

insecure and asks the personnel for a manual counter-check. The system displays

the reason for the alert, such as a zero stock level. About 5–10% of all articles are

counter-checked every day. The final replenishment decision is still in the

responsibility of the store manager. He or she can change any order at any time. It is

thus possible to fill an article with zeros to delist a product without the approval of the

central office.

Småros, Angerer et al. (2004a) found in their research that the majority of the

observed European grocery retailers have sophisticated forecasting capabilities only

at central warehouse level. At this level, forecasts are made from the aggregated

stores demand in order to place the orders to the supplier. Yet, the trend found in the

four retailers examined in this thesis is that more and more complex forecasts are

also made at store level. Retailers decide from category to category whether it is

worth investing in better forecasts or not. It is not always clear whether the decision

that lead to the choice of a certain ASR level was based purely on objective

reasoning. For some categories the choice is obvious. For example, for very fresh

products (e.g. bakery products) no forecasts are computed as there are not even

electronic inventory records available. For other categories the "importance" of the

product for the success of the retailer is the driver behind the choice of a higher ASR

category. MYFOOD, for example, has introduced ASR3 systems for dairy products

and in the future will do so also for green grocery, as this category is in their opinion

crucial to customer satisfaction. The GROCO ASR project leader estimates that it

makes economic sense for a typical retailer to have around 80% of all products

replenished with complex ASR4 systems, while 20% of the goods are better served

with manual replenishment. In section 6.5.1 a systematic look at the appropriateness

of a product category for a certain ASR level is made.


6.2.3. Order Restrictions

The most common restriction taken into account is the case pack size

restriction (CU/TU ratio). In all the observed companies the system

orders only multiples of the TU size. A second restriction has been

implemented by MYFOOD and takes into consideration the shelf size. As described

before, the ASR system is parameterised to place the order in such way that the case

pack units fit completely into the shelf. The aim of this restriction is to reduce the

number of items stored in the backroom. No other restrictions were observed.

NORTHY, for example, does not have a minimum order quantity, ensuring that

minimal orders are not placed with its suppliers. In the past, this has led to goods

being delivered in near-empty trucks, making deliveries very costly. But this is an

exceptional case; there are no plans to implement additional restrictions as it would

be too costly compared to the potential benefits. In addition, NORTHY has set aside

the idea of having maximum order caps, as all stores have a backroom in which to

store excess deliveries. Neither GROCO nor DRUCKS see the necessity of

introducing any additional restrictions at store level. An optimization of the logistics,

e.g. by introducing order caps to ensure full truck loads, is not an option at the

moment.

6.3. Organizational Changes and Personnel Issues

The implementation of ASR systems is often a small part of major ERP system

introduction. The change of ERP systems has been in turn seen as an opportunity to

centralize and homogenize IT systems. In some cases, the entire logistics system

had to be changed. The first part of this section describes how the retailers in the

sample changed their organization. As one will see in the following, the organization

in the four cases has become more centralized. In addition, there are insights as to

how the ramp up phase of the new systems was realized. In the second part changes

in the processes that influence the way employees work are illustrated. The retailers

approach differently the question of how great an influence employees should have

on automatically generated orders.

6.3.1. Structural Changes and Setup

MYFOOD and GROCO were some years ago very decentralized companies. Their

regional organization units had extensive decision freedoms. The main problem with

this organization structure was that several IT systems existed without compatibility

among them. It was therefore not possible to interchange information and cooperate

with other sub-organizations. The result was the loss of economies of scale and

scope. MYFOOD hence decided to start a centralization programme. As the basic


independence of the subdivisions should remain, the programme was limited in

scope. Nevertheless, a fundamental change took place in both logistics and

marketing. The first step was to introduce centralized logistics for slow moving goods.

In order to do so, a new central DC was built and a central logistics services group

was created that works as a separate profit centre. Delivery frequency for some of

the small stores was reduced, as automatic systems need fewer deliveries to achieve

the same service level compared to manual ordering. Next, the logistics as well as

the IT infrastructure were adapted. In order to maintain good service for its

customers–the regional units–the central unit had to harmonise the regional IT

infrastructures. Another goal of the new central unit was to maintain the master data

of all SKUs up to date. Within this major project that changed IT and distribution

strategy, the implementation of an advanced replenishment system at DC level was

also realized. As a second step, MYFOOD began to introduce advanced ASR

systems at store level as well. This top-down procedure is in contrast to the strategy

of GROCO, which is implementing its ASR systems at store level before changing

and centralizing its national distribution systems. For the manager in charge

interviewed at MYFOOD, this is the logical way to introduce an ASR system, but

because of the strong influence of the individual regional sub-divisions, it was not

possible for MYFOOD work in this way.

GROCO will have an even more radical change to its structure. The interviewed ASR

project members stressed that the introduction of the new IT systems together with

centralization will have an effect on almost everyone in the company. Eighty per cent

of employees will experience a substantial change in the way they work and, for this

reason, they will require training. In regard to the ASR system itself, a new

organization has been created. There will be ASR-dedicated planners in charge at

national level as well as regional planners. The exact distribution of tasks between

the two organization types still has to be decided. The ASR-planners will be

responsible for the setting and updating the parameters in the stores. They also will

check the computer-generated orders made by store employees and will decide

whether they are appropriate. Further, they will decide which products can be

ordered automatically and which not. They will be responsible for most of the

exceptions alerts created by the systems.

For NORTHY's ASR2 and ASR3 systems, the organizational change was not so

significant. In the stores no change in the number of employees was required. And at

head office, there was only a shift of responsibilities between existing positions, no

new function had to be created or personnel lost. The initial setup of the millions of

parameters was a rather simple project, as most of the values could be taken from


the existing systems, minimizing manual input. NORTHY had previously been

organized on strict centralized lines; most of the information was available at the

central office. Unlike MYFOOD and GROCO, the planograms were strictly followed

by the stores. Before the nationwide implementation of ASR3, different parameters

were tested in a number of stores. Also, comparable to the case of MYFOOD, the

product categories were automated one by one.

The DRUCKS case was similar, in that the implementation of ASR systems required

only minor changes because a central organizational structure was already in place.

The logistics did not experience any change, and the distribution channels and

replenishment frequency remained the same. Neither was it necessary to make any

changes to the existing technology. The same handhelds that were used before at

the shelf to place orders are now used to check inventory levels. The same holds true

for the tills and the utilized barcodes. The only process that was adapted was the one

regarding inventory records accuracy. A new KPI was introduced to check the

number of automatically generated orders that are accepted by the store managers:

the value is around 95%. The very first time the ASR system was tested, a pilot store

was chosen and a single category was automated. Then, each category was

implemented one after the other until all the goods (around 10,000 SKUs) were

ordered via the new system. Now, a new store gets a standard parameter set. This

parameter set is copied from a store with a similar size and sales volume. The effort

for this setup is very low, after 2–3 hours the parameters for the new store are

implemented. For every new store a set of performance goals is elaborated. In the

following months, store turnover and other KPIs are compared to the planned figures;

if there is a strong misfit the replenishment parameters are adapted.

6.3.2. Personnel and Change Management

Maybe the most radical change resulting from the implementation of higher ASR

systems is the change in organization and the role of employees. The change in

human agency is most visible in the standard manual ordering process. The main

intellectual task of an employee whose store orders manually is to take into account

many information sources at the same time. When determining the new

replenishment quantity at the shelves, employees have to take into consideration the

inventory levels in the backroom, how many goods are on the way to the shop, and

they have to estimate the demand for the next days. Furthermore, before creating the

final order the store staff has to take into account the lead time and other restrictions

such as shelf space or minimum order quantities. This task requires a lot of

experience so that only employees working for a long time for a certain retailer


achieve good replenishment results.97 With the new ASR systems, all this information

is stored in a computer, and the main task is now to keep the records accurate.

Therefore, day-to-day operations change radically, and new intellectual challenges

arise.

For an IT director at MYFOOD, the change from an ASR0 system to an ASR3 system

for fresh food products meant a radical change in the philosophy of the store staff:

"We have changed our philosophy from an 'order culture' to an 'inventory

management culture'. In seminars we tell our store staff how important it now is to

keep the inventory records accurate. We tell them 20 times a day until they cannot

hear it any more."

The new ASR system relieves employees of the ordering procedure. During training,

people are told to invest this freed-up time and energy in updating the inventory

records. For ASR2 systems, there is still the intellectual task of determining the right

replenishment parameters. The right setting of the order point decides whether it is

possible to place the complete TU on the shelf and has thus a major influence on the

effort required for reshelving. Another task that still requires intellectual effort is the

case of exceptional demand and upcoming holidays. MYFOOD's employees are able

to alter the suggestions for these cases, meaning that skilled labour is still required.

The same holds true for promotions and new product introductions.

The first main change for the employees connected with the installation of ASR

systems was the introduction of computers and PDAs. A couple of years ago, all the

orders were made on pieces of paper. Nowadays the use of computers is compulsory

for every employee, as the inventory records have to be updated electronically, the

automatic orders checked, and exceptional orders have to be placed. Consequently,

MYFOOD's employees first receive basic training on the use of computers. They

learn how to use a mouse and the basic functions of the operating system

(Windows). The next step is to get acquainted with the ERP programme as this

software is used for all the orders. This first steps are often the hardest, as many

employees have been working for many years without using any IT at all. According

to one IT manager, one of the goals of personnel training is to promote personal

responsibility. Consequently, voluntary e-learning programmes were offered to

employees. As an additional incentive, the employees receive a voucher worth 500

euro for an adult education centre where additional computer courses can be taken.

97

See the analysis of the influence of the time a store manager has been in a store on the OOS rate on Figure

37.


The company does not control what courses the vouchers are spent on as this would

not be part of its company philosophy, and employees could spend the vouchers on

language courses instead. The interviewed MYFOOD ASR expert estimates that

about five years are required for the introduction of such systems into all stores. The

employees need around 2–3 years until their mind-set has completely adapted to the

new philosophy.

The ASR project leader at GROCO stresses the importance of the human factor for

the success of an ASR project:

"80% of our [ASR4] project concerns the human factor."

As for the previous retailer, the human factor is considered to be one of the main

reasons for the length of such projects. According to the responsible ASR manager, it

takes up to 5 years until such a system can be used nationwide. The store staff

needs a significant part of this time to get used to new system. It was stressed by the

interviewees that the role of store employees as entrepreneurs should remain in the

new system. Therefore, store managers will be allowed to override orders made by

the ASR4 system. Yet, new KPIs will be introduced to check the amount of altered

suggestions. Another education activity will be targeted at the employees in the new

central replenishment organization. They require special training, as these jobs are

created from the scratch. Finally, the interviewed managers also stressed the

importance of superior management support, as such a fundamental change creates

much uproar in the organization.

The managers interviewed at NORTHY's confirmed the dramatic changes in the role

of store staff described before. Almost every process they were used to changed due

to the implementation of the ASR systems. The loss of the right to order and the

focus on inventory records accuracy brought the biggest changes. With this retailer,

store employees face the strongest limitation of the influence on the automatic

generated order in the sample. It is not possible for NORTHY's store employees to

cancel an order. The suggestion cannot be reduced, but it is possible to order

additional quantities. These strong limitations were partially the consequence of

misuse of the systems. There were cases where the staff tried to influence the

system indirectly. For example, inventory level records on the computer were

changed to show incredibly high levels to make the system stop deliveries of

undesired products. However, this problem was an exception; one year after the

introduction of ASR systems the new processes and rules became standard. This

can be partly attributed to the comprehensive educational courses the store

managers received. They were afterwards responsible for teaching their employees

the new processes. At head office, less than six logisticians are responsible for the


ASR systems in all stores. Such a small number of planners is only possible because

the product managers are in charge of updating the parameters of the ASR systems

in each store. This way, the central planners can concentrate on exceptions.

The DRUCKS interviewee confirmed the radical change for the staff, as suddenly

inventory management became the major task. But the introduction was not as

problematic as with the other retailers because DRUCKS' personnel already had

experience with computers in the stores. Neither the check-out process nor goods

reception had to be adapted. The ordering procedure is basically the same, as the

same PDAs still are used to confirm the order suggestions made by the ASR system.

The regional managers were convinced of the automatic replenishment by showing

them the successful results of the store pilots. The main argument in favour of ASR

systems was that the time savings realized by the store personnel could be used to

increase service to the customer. One of the major strategies of DRUCKS was the

communication strategy. The words "automatic ordering" were never used, instead

the management spoke of "PC-supported ordering." The word "automatic" was

avoided to never give the employees the feeling that their work could be replaced by

a computer.

Overall, all the interviewees agree on the importance of dispelling the main concerns

of employees. The first concern is that the ASR system will result in logistical chaos.

This fear can be overcome by having successful pilots. Second, many employees

expressed the fear that jobs could be cut due to the automation. The four companies

stress that nobody lost his job due to the automation. This is not always the case, as

the example of the retailer Globus in Germany shows. After an ASR introduction at

DC level this retailer made several employees occupied full time with ordering tasks

redundant (Shalla 2005). And third, employees face with automation the loss of a

fundamental part of their current work. Being relieved of the ordering task is not

always seen as something positive, as one logistics director points up:

"It is more fun to buy then to sell."98

The satisfaction of employees can dramatically suffer with the introduction of ASR

systems. One controversial topic is what intervention rights the personnel should

have as regards the IT-calculated replenishment orders. DRUCKS and GROCO

stress the importance of still having entrepreneurs in the stores making their own

decisions, and so store managers are able to ignore the computer-generated

98

Source: Dr. Michael Krings, director Logistics Douglas GmbH, 24.6.2005.


suggestions completely. This is the opposite of MYFOOD's and NORTHY's policy in

that both retailers have limited the influence of their store employees.

6.4. ASR Performance

One of the most interesting questions for practitioners is the question about the

performance of the systems. As seen in the quantitative analysis, it is at the same

time one of the most difficult questions to answer. The introduction of ASR systems

often goes hand-in-hand with a change of the distribution channels or the introduction

of a new ERP system. Consequently, very few retailers are able to quantify the

overall effect. In this section, first a hypothesis derived from the contingency theory

concerning performance measurement is verified. Later, the benefits of ASR

implementations on stock levels, OOS incidents and other effects for the four retailers

will be depicted.

6.4.1. Performance Measurement

One of the assumptions made in the contingency theory section 3.4.2 was that the

more sophisticated the system, the more performance measure variables would be in

use by the company. This statement can only be partly supported. In favour of this

theory is the fact that with new ASR systems new KPIs were introduced. An example

is the rate of computer-generated orders that were accepted by store employees.

Another example is GROCO: this retailer now checks the inventory levels of its stores

through a new KPI, something that was not possible before ASR introduction. On the

other hand, the two retailers with the most advanced systems (DRUCKS and

GROCO) do not measure any special logistics KPIs such as the MAPE or the

inventory accuracy that are not in use by the other two companies. Therefore, it is

difficult to support or decline this hypothesis.

6.4.2. Inventory Level Performance

When asked about inventory levels at the distribution centres, the interviewees in the

sample were quite content with their stock levels. The retailers are still trying to

reduce their inventory levels, but they do not expect any radical improvement in the

near future. The picture changes radically when the interviewees are asked about

inventory quantities in the stores. The interviewed retailers stated that a high

proportion of the goods in the supply chain they control used to be located at the

stores. These findings concur with the situation described in the study by Småros,


Angerer et al. (2004a). Their average inventory levels of 12 major European grocery

retailers are depicted in Table 48.

Distribution centre Store Inventory

Fresh Other Overall (incl. fresh and other goods)

Median 2 days 13 days 12 days

Minimum 0 days* 7 days 4 days

Maximum 10 days 20 days 16 days

*These goods are cross-docked

Table 48: Inventory range of coverage of European grocery retailers in days99

The retailer NORTHY is very satisfied with the development of its inventory levels

after the ASR introduction. As a logistics manager said, the inventory level could be

"considerably" reduced. The same statement comes from the retailers MYFOOD and

GROCO. Concrete numbers could not be delivered by the interviewees. Yet, in

GROCO's pilot the stock reductions were in part so strong that the central planners

had to intervene and artificially raise the parameters. Too low stock levels are, from

an aesthetic point of view, not desirable. One of the categories profiting most from

the ASR systems are very fresh products, as the mean remaining shelf life of these

products could be increased. Only DRUCKS is able to report a concrete figure. With

an ASR4 introduction, this retailer has reduced its store stocks by about 10–20%.

The quantitative analysis of this thesis shows, that a reduction of this magnitude is

also possible for grocery retailers.

6.4.3. OOS Reduction and Overall Performance

MYFOOD is very pleased with the results from the ASR2 and ASR3 system

implementations. The logistics managers report a reduction in OOS incidents, without

been able to quantify it. Another benefit is that the time necessary for the daily orders

could be reduced dramatically. A positive side-effect of the pull system is that the

orders from the stores to the DCs are now more stable and thus more predictable.

One of the highest financial impacts might have a last effect: the delivery frequency

of some small stores could be reduced to three times a week. All these positive

results have encouraged MYFOOD to introduce higher ASR systems for almost all

categories across the country.

99

Source: Småros, Angerer et al. (2004a).


The results of GROCO's ASR4 pilot are rated very positive by the ASR project

members. The first benefit is again the time savings from the lost ordering procedure.

The savings depend very strongly on the size of the store, for larger stores up to four

working hours are saved, while the smallest stores only experience a reduction of

half an hour. As in the previous case, the overall distribution system profited, as the

DCs experience nowadays less variance in the stores' orders. Overall, GROCO is

very pleased with the new ERP system, as the IT creates a transparency that is a

requisite for any further improvement of the supply chain. For example, it is now for

the first time possible to calculate the impact of a reduction of stores replenishment

frequencies, as the ERP systems have created a new visibility level. This would in

turn lead to greater cost savings. Another positive effect is described by one logistics

manager:

"After the introduction of the ASR and ERP system, our eyes were opened. For

example, it was thought before the introduction that the shrinkage values in the

distribution centres were minimal. Now, every little transaction is recorded

minutely. This has created a new awareness about what logistics really costs. It is

just amazing, how much information can be drawn out of these systems."

The retailer NORTHY is very satisfied with the performance of its systems, as in

addition to a reduction in inventory levels, the OOS rate could be dropped as well.

Furthermore, the time spent by store employees in placing orders was dramatically

reduced. The new spare time is used for a better control of inventory records and to

provide greater service to the customers. In addition, the part-time contract with some

employees has been reduced. Yet, an interviewed logistics manager stressed that

after the implementation nobody was forced to quit the job. Another side benefit was

that with the new system NORTHY's stores tend to stick more to the planograms and

assortment decisions made by central office. The new IT systems improved the

control of the stores due to increased inventory visibility. Consequently, new KPIs

and mechanisms to control and measure the flow of products are in pilot, for

example, a system that detects OOS incidents by analysing sales data. This system

produces at the moment very crude estimations and is not currently working for

slow-movers. More side benefits are expected from new electronically available

information that is obtainable now for the first time. Based on this data, the next

project will be to improve the amount of shelf space allocated to each SKU. As

product managers are now responsible for the parameters and hence in closer

contact with each store, they are now able to determine the shelf space based on

facts. As successful as the implementation of ASR ordering was for packaged goods,

the results from the automation of perishable goods are still not satisfactory. The

employees are rather unhappy with the system, and this is most probably not going


to change in the near future. Therefore, the management is thinking about returning

to the old manual system. A possible reason for this underperformance might be the

direct deliveries to the stores. Fruit and vegetables are delivered mostly by small

suppliers, and this group is known to have major problems fulfilling EAN barcodes

standards.

For DRUCKS, the reduction of costs was in the opinion of the interviewee only a

secondary reason for the introduction of the new ASR system. The main goal was to

increase availability in the stores and to increase service to customers. The OOS rate

was reduced by 70–80% and is now between 1 and 1.5%. An average store had to

spend between 3 and 5 hours to make an order of the complete assortment.

Sometimes to reduce complexity, on certain days only part of the assortment was

ordered to save time. The consequences were higher than necessary inventory

levels. Today, only between 15 and 30 minutes is necessary for the ordering of the

entire assortment. The time saved can now be used to increase inventory accuracy

or, more importantly, to deliver better service to the customers. With the newer

systems it is possible to have more SKUs per store, increasing thus the productivity

of each sales m2. Also, logistics has profited, as the orders to the DS have become

predictable.

6.5. Lessons Learned and Recommendations for Management

There can be no doubt that the automation of store replenishment is on the agenda

of today's retailers. Four of the five biggest grocery retailers in Switzerland have

implemented or at least piloted ASR systems. In a survey conducted in 2004 with

more than 30 Swiss, German and Austrian retailers, around two thirds already had or

were at least thinking about implementing higher ASR systems.100 The main reason

why retailers had not yet started with the implementation was that a certain level of

technology is necessary before the automation can start. For example, it does not

make sense to have sophisticated ordering algorithms if the retailer has not even

introduced a barcode system. In the following, the results from the quantitative and

the qualitative analysis are merged. First, the adequate ASR level depending on the

product and retailer's context is determined. Next, methods for successful ASR

introduction are presented. The technical and organizational requirements are also

discussed as best-practice store operations. This section ends with a cost-benefit

analysis.

100

These short telephone interviews were conducted in summer 2004 by the author and involved retailers in the

sectors electronics, apparel, grocery, jewellery and drugs.


6.5.1. The Adequate Automation Level: Recommendations

This thesis does not claim that every retailer should have the highest level of

automation that exists. One of the aims of the thesis is to create a guide aiding

retailers to decide for each product category the level of automation best suited to

them (question Q4). The combination of the information gathered from the interviews

and the quantitative analysis is shown in Figure 50. With such a decision tree, each

retailer is able to find the adequate ASR system for each product category. As stated

before, a company might opt to choose the best suited ASR level for each product

category so that it may ultimately decide to run different systems in parallel, as seen

with the examined retailers.

Which System to Choose for a Product Category?Which System to Choose for a Product Category?

Is electronic

inventory possible?

Is electronic

inventory possible?

Manualordering

Manualordering

Electronic inventory-based ordering

Electronic inventory-based ordering

Simpleordering heuristics

Simpleordering heuristics

Time series-basedforecasts

Time series-basedforecasts

Causal model-based forecasts

Causal model-based forecasts

Can

causal influences be

quantified?

Can

causal influences be

quantified?

Is quantitative

forecast better than

qualitative?

Is quantitative

forecast better than

qualitative?

Stable demand,

low value?

Stable demand,

low value?

yes

no

yes

no

no

ASR1ASR0 ASR2 ASR3 ASR4

yes

no

yes

Ultra fresh (bakery, cheese counter)

Fashion;Slow moving, high value items(jewellery)

Low value items(clothes pegs)

Non-food(shoe-cream)Dairy products(milk)

DrinksBarbecueFlowers

Figure 50: Decision tree for practitioners

In the following, the logic behind this decision tree is explained. The first node on the

ASR level decision tree tests the basic possibility of having electronic inventory

records for the product. Electronic inventory management is the basis of any

automatic replenishment system, and so at this point SKUs that have to be ordered

manually (ASR0) are separated from the others. For some products, electronic


inventory management is not possible or economically feasible. The case of

GROCO's served cheese counter discussed previously is an example of such

problematic SKUs, and this retailer has decided to keep this part of the product

assortment in the manual ordering scheme.

The second node in the tree separates the products for which a forecast is sensible

from those where it is not possible to make any forecast or where it does not make

economic sense. An example for products where forecasts are nearly impossible is

apparel products with a strong fashion influence. These products tend to have such

unpredictable sales behaviour that any system would have difficulties predicting their

sales from historical data. As every season new products appear or are trendy, there

it is often no comparable data because the products are too different from each other.

Other products where forecasts are not possible are unique SKUs that are only

ordered once (e.g. in the case of promotions or special events). For the products

where forecasting is not recommendable, retailers can choose between a semi-

manual (ASR1) and an automatic replenishment system with simple heuristics

(ASR2). There are three reasons for choosing the manual method instead of the

heuristics found in practice. First, some companies have very expensive products

and a small assortment. An example for such retailers is a jeweller. The speed of

turnover of such luxury goods is limited, hence manual ordering is not too complex. A

second reason for retailers not to chose an ASR2 system is when their products have

very high demand volatility or short cycle life, such as fashion articles. In the opinion

of one interviewee, simple systems such as the minimum-maximum algorithms work

fine until volatility reaches a certain point. The third argument for ASR1 systems is

that for products where the quality strongly depends on the season or on the supplier,

it is desirable to have the full control over ordering. In the field study, several retailers

still ordered fruits such as strawberries manually. A human planner was still

responsible for these products, and it was his or her task to decide when to order

which quantity from which supplier.101 Although these products are ordered

practically manually like ASR0 products, it still makes sense in many cases to have

stock levels stored electronically. A high inventory visibility is reached with such

electronic inventory records; these systems allow all direct and indirect benefits

described in the previous chapter.

101

These statements only hold true for products that are delivered directly to the store. In a central distribution

system it is possible to have part automation. The store orders automatically at the central office (ASR2 and

higher) in the stores, there a manual planner determines the supplier and quantity to be ordered.


The second group of products for which forecasts do not make economic sense can

be automated by having simple ordering algorithms (ASR2). As one could see in the

quantitative part of this thesis, many of the products on the ASR2 level had good

performance compared to the manual products, although the underlying logic was

rather simple. When the parameters are set well and stock level is not the

predominant issue, these systems deliver reasonable performance with a low

replenishment system effort. Products in these categories are all products that have a

low economical importance for the retailer (such as some non-food household

articles for a grocery retailer). Non-expensive articles with a long shelf life where a

reduction of the stock level is not mandatory (such as clothes pegs) can profit from

this level. Such simple rules can be beneficial also for products with very predictable

sales behaviour.102 As the ordering logic is very basic at this level, retailers with

low-performance IT systems might choose this level.

Consequently, the last two ASR levels only make sense for products where there is a

benefit from increased effort put into forecasting. The difference between ASR3 and

ASR4 is the sophistication of the forecasts. ASR4 systems seek to increase the

quality of forecasts using causal models. If the causal variables cannot be quantified

or are ambiguous, the ASR3 level might be the better option. For products where the

inclusion of simple casual variables (such as price) into the forecast is relatively easy

or for products that are very important for the retailer (due to marketing or financial

reasons) the effort made to improve forecasting might pay of. An interviewee from

GROCO recognized that on hot days the availability of drinks suffered. Because at

the same time, the sight of empty shelves had a negative impact on customers, the

company regarded the improvement of availability as paramount. As water crates are

very bulky, increasing the stock was not a feasible solution. Therefore, the inclusion

of the weather in the system's forecasts is a possibility that was taken into account.

This decision tree was based on empirical research carried out with dozens of

retailers. The problem again is that the decisions made in the nodes are based on

cost-benefit decisions. The case of DRUCKS showed, for example, that forecasts are

not very expensive in terms of required IT-power. Consequently, DRUCKS makes a

forecast for all its products. As it would be too time-costly to optimize every forecast,

a system was chosen that dynamically optimizes its own parameters. Other retailers,

for example MYFOOD, have for the majority of its automated products an ASR2

102

As can be seen in Figure 19, the ASR2 system at MYFOOD worked quite well for products with a demand

volatility up to a sales coefficient of variance of 250%. For products with a higher demand volatility, ASR2

systems might not work efficiently anymore.


system, although, according to the decision tree, ASR3 would also be adequate. To

assess a change of these products towards the new system really is economically

sensible a cost-benefit comparison between both systems would be necessary taking

into account the context of the retailer.

Overall the recommendation for retailers from the empirical analyses is that only for a

few products are completely manual systems recommendable. If there are no

restrictions regarding IT systems, the costs for automatic systems or other contextual

restrictions, the overall recommendation is to implement higher ASR level systems.

Automatic systems have the great advantage of levelling the influences of products

on replenishment performance, as shown in the quantitative analysis. For the

manually ordered products, the OOS rate was influenced by all the product

characteristics measured, namely sales variance, speed of turnover, the price,

CU/TU, product size and shelf life. On the other hand, ASR2 and ASR3 systems

showed much more stable behaviour, their OOS rate was in most cases not

influenced by product characteristics. Another result that was unambiguous is the

importance of the right setting of replenishment parameters. The moderate results of

MYFOOD's ASR2* systems show that wrongly or crudely parameterised systems will

not give the results expected.

The quantitative analysis showed another effect of higher ASR systems: performance

differences between stores are reduced. There is the danger that excellent stores will

worsen their performance by such automatic systems, as even the best IT system is

not able to outperform a good store manager who has many years experience in the

business and who knows exactly, how his or her customers behave.103 But in the final

analysis it can be seen that automatic systems tend to improve the average

performance of stores making them thus an attractive option for retailers.

6.5.2. ASR Introduction

The introduction of ASR systems was for all four retailers in the field study an internal

company decision and was mostly realized without the help of suppliers. The only

necessity for suppliers is to have a certain technology standard (e.g. EAN barcodes,

electronic dispatch advice) and a reliable service, as automatic ordering systems are

more sensitive to delivery errors than manual systems. In half of the cases, the ASR

systems were introduced together with new ERP systems and with major changes in

103

This is the opinion expressed by most practitioners interviewed. Yet there is no firm evidence for this

statement.


distribution logistics. In the other two cases, implementation was nevertheless a huge

challenge as well because millions of parameters had to be set correctly. The setting

of the parameters is, where possible, automated. One method would be to copy

parameters for a new store from an existing comparable store. After the ASR system

has run for a certain time, the fine tuning of the parameters begins. Another major

effect that has to be considered is the impact of new ASR systems on personnel, as

a fundamental duty of the store employees is cut. In three out of four cases,

comprehensive training was organized to teach the right usage of the new systems.

MYFOOD, GROCO and NORTHY created new organizational units as a

consequence of the introduction of ASR. All retailers began by testing the systems

with a small pilot. First, a single store with pilot product categories is observed during

a certain time. The starting categories contain non-critical goods such as products

with a stable demand, long shelf life or slow speed of turnover. Then, these

categories are introduced nationwide. More complex categories are then automated

on a rolling basis until the desired level of automation is reached. The retailers in this

study have achieved good results with this procedure, and it is recommended to any

retailer introducing ASR systems.

6.5.3. Technical and Organizational Requirements

In this section, the technical and organizational requirements are discussed by

automation level (research question Q5).

Companies have the opportunity to implement ASR1 systems as soon as an

IT-based inventory management system is available. The IT needs the capability of

handling enormous amounts of data, as daily stock levels of all SKUs in each store

have to be stored. New inventory systems also require a change in retailer processes

and equipment. Because consumption has to be measured in order to update

inventory levels, sales have to be tracked. For major grocery retailers it is not feasible

to make an inventory check every evening to track all the transactions made during

the day, in the same way as a number of small bakeries that were observed. In order

to do that, identification systems have to be implemented that store electronically

every sale at the cash desk. The most popular identification technology method used

nowadays is the barcode. In future it might be RFID tags. To be able to calculate the

inventory level in the store, it is obviously also necessary to track deliveries to the

stores. This can either be carried out at the gate of the store, or at the gate of the

supplier.


As inventory visibility plays such an important role for automatic systems, procedures

need to be put in place to ensure a high level of accuracy. Products that are not sold

through the regular channel, e.g. because of shrinkage or spoilage, have to be

somehow taken into account. This can be done with manual inventory checks where

the items are counted and the real numbers reconciled with those in the system.

In the field study, the described procedure seems to work rather well for packaged

products. But there are certain products that are much harder to control, e.g. fresh

products such as meat or cheese without packaging. For retailers selling such

products, it is perhaps not advisable to use ASR1 as tracking of the goods is too

time-consuming or too inaccurate.

As noted earlier, updating to ASR2 systems is straightforward from a technical

perspective. The simple heuristics do not require much IT-performance. The

challenge in these systems lies in setting the parameters. A grocery retailer with 1000

stores and about 10,000 SKUs has to set in theory 10 million parameters.

Furthermore, these parameters have to be checked and adapted on a regular basis,

as demand in an individual store might change. In practice, companies automate this

step by letting the computer decide on the parameters based on the past sales and

restrictions such as lead time. For products without data history, the data from similar

products is analysed. After some weeks of experience with the new system, OOS

incidents and high inventory cases are manually examined, to see if the parameters

have to be adjusted.

ASR3 systems require a forecasting engine that continuously estimates future

demand. To take again the example of the retailer before, it is now necessary to

make a forecast for each product, meaning the calculation of 10 million forecasts.

And in contrast to ASR2, this is not executed once but depending on the

replenishment frequency up to daily! Obviously, this is a challenge for any central IT

system. The example of DRUCKS showed that decentralizing– a PC in each store–

masters this task without any problem. The forecasts are based on past data–the

longer the history of the data, the higher the chance that regular variations (such as

seasonal effects) and trends can be predicted correctly. On the other hand, an

increase in the data requires an increase of storage capacity and computing power

for the forecasts. The cost of forecasts is very difficult to quantify, therefore in Figure

51 only a schematic cost-benefit sketch is depicted.


Decreasing

forecast errors

Increasing

costs Models thatnaively projectpast patterns

Models thatseek underlyingcauses

Should try to operate in thisregion

E (cost of operating aprocedure)

E (total cost)

E (cost of resultingforecast errors)

Figure 51: Cost of forecasting versus cost of inaccuracy104

The forecast method used in ASR4 systems–causal models–is the main

characteristic of this type of system. For causal models to work, it is not sufficient to

see the past history of sales. Events have to be introduced into the equation as well.

Basically, every event that has an influence on the demand can be included.

Examples are the weather, price changes and promotions (by the retailer and by its

competition), political events, holidays, among others. The challenge here is to

choose the right parameters. Every added event is an opportunity to improve the

quality of the forecast, but at the same time it inflicts higher costs. Most of these

events cannot be easily automated, such as the information about future actions of

the competition. There are few retailers with experience of this kind of system so that

it remains to be seen if the additional cost of implementing the events is justified by

increased forecast accuracy.

To be able to interact at Level 5, companies obviously need to have information

about each of the nodes of the network. Take, for example, a system designed to

optimize simultaneously the replenishment of the store, the DC of the retailer and the

DC of the supplier. ASR1–4 systems that want to develop to ASR5 need information

regarding inventory levels at each site. ASR2–4 systems require in addition

information on restrictions. ASR3 and 4 systems need the ingoing and outgoing data

104

Source: Silver, Pyke et al. (1998, p. 77).


of each of the nodes. And for ASR4 systems to become ASR5, the events that might

influence each of the nodes have to be implemented as well.

A final question that will arise at any level of replenishment automation is the right

choice of organization structure and power distribution. Obviously, this question is so

fundamental that it can not be decided only as a consequence of an implemented

ASR system. The examples in the field research showed that it is more easy to

introduce ASR systems in organizations where the decision-making power is

centralized. For higher systems, the power shifts even more towards central office.

For ASR3 and ASR4 systems it is recommended that central planners are introduced

to take care of the parameters in the forecasting engines and to intervene in

exceptional cases. Yet, even with the most sophisticated systems, retailers might

choose to let store employees have the final word on the replenishment system and

give them full rights to alter the computer-generated orders.

The results of this section are summarized in Table 49.

Level Technical Requirements and

Recommendations Recommendations on Operations

and Organization

ASR1

- Electronic inventory management system - SKU identification system (e.g. barcode) - Electronic checkout - Data storage capacity: few days–weeks

- Inventory records checks - Checkout processes - Goods entry processes

ASR2

- Setting of replenishment parameters - Electronic order transmissions (e.g. EDI

connection) - Electronic dispatch advice - PDAs

- Accuracy in checkout processes - Spoilage and returns control - Regular check of parameters and inventory

performance - Check of unusual sales or high inventories

ASR3

- Forecast engine (based on historic data) - Self-optimizing forecasting engine - Data storage capacity: some weeks–2 years

- Planners in central office - New products: comparison with similar products - Event system: check of unusual forecasts - Correction for promotions and special events

ASR4

- Technical implementation of events (such as weather)

- Event and historic data-based forecast engine - High central computing power or PCs in each store

- Past and future event data maintenance

ASR5 - Central IT system for coordination - Electronic connection to all nodes - Highest computing and data storage capacity

- One organization unit responsible for entire supply chain

Table 49: Technical requirements and recommendations on operations and organization structure


6.5.4. Store Operations: Recommended Action

Independent of the final ASR level chosen, this section lays out the lessons learned

from the case studies in four fields, implications of the quantitative analysis are

discussed. The aim is to increase the performance of automatic systems with the

right practices in different areas of store operations (see Figure 52).

Store Operations RecommendationsStore Operations Recommendations

Inventory Accuracy

� Training of processes� Regular physical audits� Random checks� Incentive programme

Inventory Accuracy

� Training of processes� Regular physical audits� Random checks� Incentive programme

OOS Measurement

� Initial physical audit� Regular combination of

physical and electronic checks

OOS Measurement

� Initial physical audit� Regular combination of

physical and electronic checks

Promotions

� Combination of qualitative and quantitative planning techniques

� Involvement of supplier and store managers

Promotions

� Combination of qualitative and quantitative planning techniques

� Involvement of supplier and store managers

Awareness and Training

� OOS impact on business� Customer reaction� Reshelving


� OOS impact on business� Customer reaction� Reshelving

Figure 52: Overview of store operations recommendations

Several of the practitioners interviewed mentioned that employees have no

awareness of the problem of OOS incidents. Consequently, this should be the first

action taken. A physical audit of stock availability in the stores could help to create

this awareness. Such an audit may show that causes for OOSs are poor inventory

records accuracy and the way promotions are handled. Recommendations on these

four topics are presented in the following.


Knowing that a certain problem exists is the first

step towards solving it. If employee awareness of

the OOS problem improves, then there is a good chance of an increase of product

availability. In one of their projects, Stölzle and Placzek (2004) measured the OOS

rate of a German retailer over two weeks. In the first week the auditing was

conducted without the employees being informed about the project, in contrast to the

second week. The result was that in the second week the OOS rate dropped by 26%.

Only by knowing that the OOS rate is being measured, the employees managed to

increase availability dramatically. Another example of the importance of having the

employees informed about the availability results from MYFOOD's OOS-project. One

of the most notable results is that regarding the top 10 articles, i.e. the articles that

are most sold in the stores. Two of them have an extraordinarily high OOS rate (12%

and 22% respectively). Managers have to ensure that the most important goods in

the stores in terms of turnover are paid the most attention by the personnel.

Store Operations Recommendations


Consequently, employees should be informed about which products are most critical

for business and for the satisfaction of customers. Second, it is necessary to inform

employees about the reactions of customers confronted with an OOS. Several

retailers interviewed expressed that they were not concerned about gaps in the

shelves, because, in an OOS situation customers would simply substitute the

product. However, this is only true for some categories and only to a certain extant.

By calculating the lost business due to such OOS incidents, the magnitude of the

problem becomes clear, as made by Gruen, Corsten et al. (2002). And third, as 25%

of all OOS incidents stem from reshelving practices, existing practices should be

reviewed in detail. Several retailers have introduced handbooks with well-defined

reshelving procedures. One English retailer has printed a bright star on the shelf floor

that becomes visible as soon the product is out of stock. As ASR systems are able to

relieve store employees, some of the spare time could be dedicated to accurate

reshelving. As was shown in the quantitative analysis, a reduction of the backroom

can be beneficial for the availability rate and should be considered by each retailer.

OOS Measurement

As was depicted in the last section, a physical

audit of the products' shelf availability is a good

way of creating awareness and gives a one-time picture of the OOS situation in a

store. But retailers are seeking a controlling to ol with which they can check

availability on a regular basis without the cost of manual audits. Therefore, the logical

next step after having introduced sophisticated systems to control and automate

replenishment is to have a controlling instrument for availability. During the KLOG

project undertaken for the MYFOOD, it was clearly showed how complicated this task

is. Even when high accuracy is guaranteed, there will be always OOS situations that

remain undetected. When availability suffers because shelf replenishment from the

backroom has not been executed in time, it is not possible to detect this problem by

just looking at the stock levels data. As the example in Figure 53 shows, the product

in this example was at all times available in the store. But, because the

replenishment from the backroom to the shelf did not work as expected, this SKU had

a shelf OOS rate of 21% during the two weeks of the study.



0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12

Inventory in store Shelf inventory

Figure 53: Comparison of inventory on shelf and total store inventory for a glue stick

Another possibility for calculating indirectly the availability rate is by analysing the

product sales data. In case of the sales of a product being unusually low for a day, an

availability problem might be the underlying cause. Obviously, this method is only

reliable for products with higher speeds of turnover.105 As the research on these

systems is in its infancy, it might be recommendable to have a combination of

physical and electronic audits to determine the OOS rate. A south-European Retailer

has introduced a threefold way of looking at OOSs, as described by Småros, Angerer

et al. (2004a):

1. One hour before the store is replenished, the store employees walk the store and

count the items that are OOS (i.e., they count the percentage of items available

before the store gets replenished). This metric is fairly conservative in that it

measures availability when the likelihood of stock-outs is relatively high. The metric

only measures the number of items OOS, without taking into account their selling

rates.

2. A second tool is used to estimate unfulfilled demand. This metric takes into

consideration the speed of turnover of products. In other words, the company not

only measures the percentage of items not available in the store but also estimates

the total sales that it might lose in the event that customers are not willing to

substitute. One issue faced when using this tool is the difficulty in estimating the

potential demand for items that are OOS. The tool basically provides statistical

estimates of how many items the product could have sold had it been available. It

also corrects for situations, such as wrong inventory records or defective items,

where the system thinks a few units are left on the shelf but either no unit is actually

105

According to ECR France (2002) about three or more sales per day are necessary for such methods.


there or just one unit that customers are not willing to take (as it is damaged) is

available.

3. Finally, the company takes into account the attractiveness of the shelf. In order for

the shelf to be attractive to the customer, it has to be filled to a given percentage of

its capacity at least. The key concept behind this metric is that inventory on the shelf

is not only necessary for meeting demand but also necessary to draw the consumers'

attention and create demand. The company does not only want to measure the ability

of the inventory position to fulfil demand but also its ability to create demand. The

company thus also measures the percentage of items with inventory positions below

a specified percentage of shelf capacity.

By combining the information of the manual and electronic methods, the company's

supply chain managers receive a very good sense of how the consumers experience

the quality of store operations.

Inventory Records Accuracy

The importance of inventory records accuracy

was shown in the chapter about field research.

For every automatic system high accuracy is beneficial. Yet for the higher ASR levels

it becomes even more important as the order is often placed without a second check

through human beings and the safety stocks are calculated more tightly. For this

reason, employees need to be made aware of the importance of accuracy when

working with ASR systems. Processes such as the check-out and the recording of

spoilage, returns and stolen goods have to be defined and trained intensively. To

control the results of these methods on records accuracy, the usual biannual

counting of the goods is not sufficient. A continuous manual audit process is

necessary that checks the accuracy of the records. This is feasible because store

employees should have more free time due to the automation of the ordering. But

without a defined procedure the retailer will not benefit from this spare time. One

solution seen in field research, the "0–1–2–3 walks," is a good example of such a

defined process (see section 6.2.1). This process has many of the characteristics a

good inventory auditing system should have: it is accurate (as counting errors with so

few units are not probable), it is easy to understand (guarantees acceptance by

personnel), it automatically concentrates on products with possible inventory

inaccuracies (as that might be the reason why the inventory level is so low), and in

the long run all products are checked at least once (as one can expect that all

products will have a low inventory at some point). From time to time, random samples



of products should be taken to check whether the inventory accuracy process works

as expected. Several of the examined retailers run an incentive programme to

promote inventory records accuracy. With a regular auditing mechanism in place, the

positive effect of this incentive system should increase. Nevertheless, one should be

aware that checking inventory accuracy too much might have some drawbacks as

well. Employees should not forget other tasks that are also important, such as the

tidiness of the sales area or customer service. To illustrate this, take the example of a

grocery retailer in the UK. This company had to stop an accuracy campaign it had

started a short time before; employees were requested to stop checking daily for

inventory accuracy. The problem was that the hundreds of corrections made in the

retailer's IT system created a commotion that distorted and confused the system,

worsening the replenishment process instead of improving it.

Promotions

An important lesson learned from the quantitative

analysis was the role of promotions on OOS, as

promotions dramatically change the inventory and sales level, thus increasing the

chance for OOS situations. How much attention promotion-related OOSs receive

depends very strongly on the policy of the retailer. Some retailers willingly take into

account that special goods in promotions run OOS after a certain time. Other

retailers, however, have a more consumer-oriented approach and want to offer the

product during the entire promotion period. It is obvious that for retailers with such an

approach it becomes most important to calculate the right quantity of SKUs in

promotions. In the case of MYFOOD, promotions are planned completely manually.

As the results are not entirely convincing and vary sharply from store to store,

managers should consider a mixed approach, where the technology supports the

employees in their planning decisions. A best-practice example is reported from a

European grocery retailer (Småros, Angerer et al. 2004a). This example is meant to

encourage retailers that are still handling promotions manually, to consider the usage

of ASR systems as well:

Before the promotion starts this best practice retailer uses a quite sophisticated

demand-forecasting tool to predict the number of customers that will enter the store

and the rise in sales per customer. The forecasts take into account variables such as

the promotion characteristics, the price cut and the weather, among others. This

initial forecast assumes that all stores are going to enjoy the same promotional lift.

However, it is well-known that the effectiveness of the promotion depends on many

store-specific variables, such as where the product is placed in the store, the number



of facings it has been allocated, etc., and that these variables can seldom be fully

controlled by the central organization. What the company does is to look at the first

few days of sales at each store and then update the initial estimates literally in real

time. Since this retailer benefits from a quick supply chain (both lead times from

distribution centre to store and from supplier to distribution centre are quite short), the

company can adjust its replenishment plans promptly.

The algorithm used behaves in a way like a human planner. At the beginning of the

promotion, the ASR overstocks the stores. It knows that even if the products do not

sell as expected, it is likely to be able to sell them by the end of the promotion.

Towards the end of the promotion, by contrast, the system is more conservative,

since it acknowledges the increased risk of being stuck with the product when the

promotion is over.

6.5.5. Cost-Benefit Analyses

The four retailers studied into detail have drawn a positive conclusion of their ASR

implementation. For the interviewees the advantages of higher ASR systems exceed

the possible drawbacks. The reported benefits are:

� higher availability on the shelves,

� higher turnover,

� time savings due to automatic ordering,

� lower stock levels in the stores,

� smaller stores require lower delivery frequency,

� more stable demand at the distribution centres, and

� inventory and cost transparency along the retailer's supply chain.

These benefits correspond to the ones reported in theoretical and practical sources

dealing with systems that improved forecasting accuracy. For example, Gudehus

(2002) and Duffy (2004) report improvement in operations such as decreasing

inventory levels, increasing service rates, fewer OOS, more on-time deliveries,

shorter order-to-deliver cycle times and more accurate KPIs for the management,

among others (see also Table 4).

The main initial impact of an ASR introduction is the investment necessary in new IT

systems. In all cases, the new replenishment systems were received with scepticism

on the part of employees. The interviewees stressed several times the importance of

taking serious the wishes and concerns of the workforce. Otherwise, the

implementation is likely to fail. Interestingly, in all four cases, after a couple of months

the uproar settled down and employees got used to the system.


But introducing ASR was not a success for all products. While DRUCKS, GROCO

and MYFOOD report fantastic results, NORTHY is not happy with the performance of

green grocery products ordered through their ASR3 system, even several after

months after introduction. A return to the old system is being considered. The

question is whether these sorts of products are not amenable to automation or

whether the chosen ASR3 system was not adequately parameterised. The fact that

other retailers have had good results with their systems for this kind of product

speaks in favour of the latter cause. NORTHY has some problems with the barcodes

and dispatch advices for these products, as they come mainly from small suppliers

and this is a possible reason for the suboptimal results. As seen with NORTHY's

case and the ASR2* systems at MYFOOD, there are many pitfalls in the

implementation of such systems; detailed planning is necessary to ensure the

success of the change.

A major point of criticism among opponents of ASR systems is that the most

sophisticated system will never cope with the best, long experienced store personnel.

And they might be right, as all the implicit information earned by good store

managers during a long life can never be adequately replaced by a computer system.

Unfortunately, the ordering skills of store staff vary a great deal. Therefore, with the

introduction of ASR systems, retailers aim to achieve constant ordering performance

at a reasonably high level that will be better than the average manual ordering

performance.

The decision as to which level to choose for each product should be based on a cost-

benefit analysis. In theory, the calculation of such an analysis is simple. On the one

hand, there are the benefits of having an automatic system as described in the last

section. This line can be interpreted as a negative cost line, as the costs, for example

OOS costs, are reduced (line CE in Figure 54). On the other hand, the more complex

the replenishment system, the higher the costs for the replenishment will be (CR

line). The optimum would be the point where the total cost line (TC) has its minimum.


Automation

level

Increasing

costs

CR: cost of replenishmentsystem

TC: Total costs

CE: cost of errorsof replenishmentsystem

minimun

ASR0 ASR1 ASR2 ASR3 ASR4

Figure 54: Costs in relation to replenishment level106

It is very difficult to transfer these rather theoretical thoughts to a real life cost

analysis. The benefits and costs connected to such an implementation are

summarized in Table 50.

Possible Benefits Possible Costs

� Fewer OOS incidents

� Higher product turnover in stores

� Fresher goods

� Less frequent store deliveries

� Lower inventory costs at store level

� Less personnel costs for order

setting

� Demand in DC easier to schedule

� Better service to the customers

� Inventory visibility

� IT investments

� IT running costs

� Personnel education

� Small, frequent deliveries towards

stores

� Product handling costs in store

� Additional personnel at headquarters

Table 50: Overview of possible benefits and costs following an ASR system introduction

The first difficulty is to quantify the benefits. As Fisher, Raman et al. (2000) state,

most retailers are not able to quantify the value of e.g. reduced OOS incidents and

markdowns. A rough estimate of the benefits of reducing OOSs is given by Gruen,

Corsten et al. (Gruen, Corsten et al.). In their estimation, for each percentage point

106 Source: adapted from Silver, Pyke et al. (1998, p. 77).


OOS reduction, an increase in turnover of 0.5 percentage points is to be expected.

Some companies have in lavish calculations evaluated the benefits of the reduction

of delivery frequency. Nestlé Germany, for instance, has found out that a change

from delivering every 24 hours to every 48 hours means a reduction in the costs by

the factor 5–6 (Weder 2005). Therefore, MYFOOD's initiative to reduce the

replenishing frequency has a major benefit potential. Other indirect benefits, such as

the new reached level of inventory visibility across retailer's storing places are much

more difficult to quantify.

As difficult to calculate are the cost side of such projects. The technology costs will

strongly depend on the technology already available on site as well as on the

organizational structure. It is hence impossible to make a general quantification of

costs. A centralized retailer changing from an ASR0 system to an automatic level

which needs to first implement barcodes and scanners may find here the biggest cost

block, making the change very expensive. As this retailer is already a centralized

organized, adapting the organization might not be necessary. On the other hand, a

decentralized retailer that already has a working ERP system might with just a

software upgrade change from an ASR2 system to an ASR4 system at negligible IT

costs. But this retailer might face strong opposition from the independent stores,

making it very costly to adapt the organization and train the personnel. The effect of

ASR on store delivery frequency and handling costs is not quite clear. As seen

before, MYFOOD sees the possibility to reduce deliveries to its stores. GROCO

logisticians reported frequent, low quantity orders created by an old ASR2 system.

This not only increases the transport cost, but at the same time store reshelving

costs. Obviously, it will depend on the parameterisation of the ASR systems whether

ASR will reduce or increase the logistics costs.

As stated before, many retailers combine the introduction of higher ASR systems with

an introduction of or change in ERP systems. This makes a final assessment of the

true benefits and costs very difficult. Instead of making a very imprecise estimate, in

the following the results from a comprehensive cost-benefit study of MYFOOD is

presented. This company changed its ASR system, its ERP system and its

distribution network all at the same time. Although this makes it difficult to attribute

real benefits to any single change, this case is a good example of how fast the

restructuring of replenishment systems can be amortised.

When ASR2 systems were introduced, MYFOOD lived simultaneously through a very

radical change of its organization. Its entire distribution strategy was changed,

namely from a decentral to a more central logistics system. Plans for the change of


the distribution system covered a 10-year time period. After five years, MYFOOD

drew its first cost-benefit conclusion. The goal of this analysis was to quantify the

benefits of the change from a decentral, manual system to a central system with

automatic replenishment. This task was described by one interviewee as

"back-breaking work," as a homogeneous definition on how to value costs and

benefits was necessary through the entire organization. The biggest benefit was an

increase in turnover (due to better, centralized category management) followed by a

reduction in purchasing costs and in stock. The increase in turnover adjusted by price

inflation, sales area increase and marketing was calculated at 0.7%. This increase

includes the effect of higher availability, fresher goods and a more attractive

assortment due to central category planners. The biggest benefit was seen for

seasonal, non-food products. The costs were split into three parts: IT, marketing and

logistics. Surprisingly, the IT costs in the long run did not increase. During the

implementation phase, there were indeed increased costs due to investments in

equipment and personnel education. But after 2–3 years, the annual IT costs were

again on the level of the former, decentral system. In logistics, a similar situation

happened. During the ramp-up phase of the system, inventory levels did increase

significantly, the central warehouse was loaded nearly to maximum capacity, and the

distribution running costs were very high. This was partly a consequence of a security

plan to ensure complete availability during the whole period. Yet after a very short

time, inventory stocks could be reduced. Two years after the introduction, stocks

were lower than before the change, although the turnover had increased.

Overall, this special retailer is more than happy with the restructuring and

implementation of the new logistics concept. As early as two years after the

introduction of the central system, the accumulated benefits were higher than the

costs. The yearly benefit for the retailer is a nine-digit euro number. Regarded from

the viewpoint of the customers, the benefits are, according to the interviewee, a

better assortment that is homogenized across the stores, a higher availability, and

better service due to the free time of the personnel. The cost reduction for the firm

will finally result in lower prices for the customers.

The conclusion of this section is that in a cost-benefit analysis it is very hard to isolate

the benefits from implementing a higher ASR, as only in a few cases the automation

of the replenishment was realized independently from the introduction of a new ERP

system or a change in logistics. Nevertheless, the quantitative and qualitative

examinations conducted in this thesis show what kinds of benefits one can expect.

The example of MYFOOD demonstrated how major investments in IT and logistics

have paid off after only two years. Retailers have to quantify as far as possible the


benefits and costs presented in this section before the final judgement on the

financial benefit of an ASR introduction can be pronounced.

180 7. Conclusion

7. Conclusion

In this final chapter, the contributions for theoretical research and practitioners are

summarized, before further research fields are depicted.

7.1. Theoretical Contributions

This thesis has some theoretical implications that are described in the following along

the four theoretical perspectives used in this thesis (see Figure 55).

Inventory managementresearch


Theoretical foundation forASR research






Business informationsystems research


ASR and human agency






Contingency theoryContingency theory

Explanatory modelExplanatory model

Contingency theoryContingency theory

Explanatory modelExplanatory model

Logistics & operations management


ASR benefitsStore processes






Figure 55: Theoretical contribution of thesis

The traditional inventory management research has concentrated on the

development of mathematical models of replenishment systems (e.g. Galliher, Morse

et al. 1959; Bassok 1999). This research stream is criticized by Wagner (2002) as too

theoretical and too far away from the challenges of real life. The main contribution of

this thesis is that a topic was chosen that is, on the one hand relevant for the

challenges retailers experience every day and, on the other hand, a field that has not

been in the focus of inventory research up to now. The novelty of this research field

demanded first of all the creation of a descriptive model. In chapter 4 such a model

was developed based on the traditional inventory holding models as described by

Silver, Pyke et al. (1998) and Gudehus (2002). The descriptive model was the basis

for the creation of the six-step classification system that is valuable for understanding

the main differences between manual and automatic systems (see Figure 13). The

classification of ASR systems facilitates the creation of hypotheses concerning

replenishment performance by each different system. A quantitative analysis proved

the significant performance differences between ASR levels. Consequently, the

enhancement of the existing inventory management research stream by the creation

of such an ASR classification system is appropriate. Another contribution of this

thesis concerns the theoretical sources that examine out-of-stocks in retail. Most of

OOS research has concentrated, on the one hand, on the extent of the OOS rate

and, on the other hand, on the consumer reaction. In contrast, this thesis has taken a

third aspect into focus, namely the influence of product and store characteristics on

7. Conclusion 181

the OOS rate. Again, the quantitative analysis showed that there are indeed several

significant relationships between these characteristics and inventory performance

that have not yet been studied in detail.

A special field of logistics and operations management research examines the

performance of automatic replenishment programmes. As predicted by Ellinger,

Taylor et al. (1999) and Götz (1999), the automatic replenishment systems reduced

OOS incidents as well as inventory holdings. For the first time in an academic

analysis, the size of this reduction was quantified in practice. Another most

noteworthy outcome is that the influence of product characteristics on replenishment

performance can be significantly reduced when ordering is automated. Sales

variance, speed of turnover, price, case pack size and product size are significant

influences on the OOS rate in the case of manual systems, but not for the automatic

systems. Similar results are obtained regarding the influencing factors of inventory

levels. A second contribution of this thesis regards store operations. A novelty is the

description of store operations in the context of ASR systems, something that very

few researchers have examined up to now.107

This thesis confirms some of the implications for business and human agency as

stated by theoretical business information sources. For example, several papers

predict the change of human working patterns due to the introduction of new

IT-based replenishment systems. The employees in the retailers studied clearly had

to radically change their working behaviour. The innovation in this thesis is to transfer

the statements about ERP systems to the context of ASR systems. As all ASR

systems examined in the sample are an integral part of superordinated ERP systems,

this procedure can be considered appropriate. The result is, as expected by

researchers such as Kallinikos (2004) that human agency is indeed deeply influenced

by new ASR systems. The way orders are executed within the retailers examined is

much more structured and transparent than it used to be before the introduction of

higher ASR systems. Personal communication and typical human behaviour such as

improvisation is strongly limited by the new systems. As recommended by Hong and

Kim (2002), the aspect of organizational change in the context of ASR

implementation was examined. The disruptive organizational change predicted by

Soh, Kien et al. (2000) could be partially seen for some of the retailers, where new

central organizational structures were created and a decision power shift occurred

from the stores to the central office.

107

One of the few exceptions is the work of Broekmeulen, van Donselaar et al. (2004b; 2005).

182 7. Conclusion

The last theoretical contribution concerns contingency theory. This theory states

that the effective level of some endogenous variable in a system depends on the

level of other environmental variables. This theory has been applied in many fields,

ranging from leadership style (Fiedler 1967) to organization structure (Lawrence and

Lorsch 1967; Staehle 1991) to logistics (Pfohl and Zöllner 1987; Chow, Heaver et al.

1994). This thesis applies the contingency aspect to retailers' replenishment systems.

In the field study, the statements of Zomerdijk and Vries (2002) concerning the vast

influence of retailers' context on the performance of replenishment systems were

confirmed. The necessity of having the right organization (task allocation design and

decision-making structure) and the influence of employees (the communication

processes and behaviour) on the performance of the system was confirmed by many

of the interviewees. The quantitative examination also showed the expected result.

Although all stores examined had the same logistical restrictions and used the same

replenishment system, their performance differed substantially from each other. The

data confirms that the influence of store managers, product characteristics and store

characteristics on replenishment performance are substantial. The typical answer of

contingency theory to the question of which ASR system to choose is "it depends."

This contingency aspect is reflected by the decision tree developed in this thesis, as

there the choice of the right replenishment system for a certain retailer will mainly

depend on product characteristics.

7.2. Contribution for Practitioners

The overall contribution to practice is that, with this thesis, managers have, for the

first time, a systematic analysis of automatic replenishment systems. The analyses in

this thesis were mainly based on grocery retailers, yet it is possible to transfer the

results to any make-to-stock industry where availability plays a crucial role and orders

are still set manually. This work is intended as a practical aid for managers to assess

and improve their replenishment systems. An overview of practitioners' questions in

the context of ASR systems answered in this work can be seen in Figure 56.

ASR classificationASR classification

Which types of ASR do exist?

Which types of ASR do exist?

Decision treeDecision tree

Do I have the right system for my products?

Do I have the right system for my products?

Quantitative analysisQuantitative analysis

Which benefits can I expect?

Which benefits can I expect?

Explanation modelExplanation model

Which products and stores are prone to

under-perform?

Which products and stores are prone to

under-perform?

Case study examplesCase study examples

How do I implement ASR systems?

How do I implement ASR systems?

Figure 56: Contribution for practitioners

7. Conclusion 183

First of all, with the help of the developed classification system, retailers are able to

gain an overview of the existing automatic systems. There are six different ASR

system types, the main difference between them being the ordering logic. While basic

systems calculate the order based on simple algorithms (e.g. minimum-maximum

logic), the most sophisticated ones use causal forecasting techniques.

Practitioners need to know the possible benefits of ASR systems. The analysis of a

grocery retailers' data showed the various advantages of automatic systems. The

retailer examined managed to reduce its inventory levels by 33% for non-food

products and by 16% for dairy products compared to a random control group. In

addition, the inventory levels of the dairy products were more stable when the

products were ordered automatically by ASR3 systems. A major benefit is also the

reduction of the number of OOS incidents. Products that were ordered manually had

on average an almost 8-times higher OOS rate than products ordered by ASR2

systems. An additional benefit is that automatic systems show robust results. While

the performance of manual replenishment is strongly influenced by product

characteristics such as price, speed of turnover, etc., automatic systems exhibit much

more consistent results. The time savings for each store are estimated between 0.5

and 5 man-hours, depending on the size of the store. Finally, the examined retailers

report secondary benefits of ASR implementation such as the possibility to reduce

logistics costs, for instance, by lowering store replenishment frequency.

Nevertheless, the success of ASR systems will depend on many factors, as the

explanatory model has shown. Understanding the influence of store and product

characteristics on inventory performance can enable retailers to identify products and

stores where the replenishment performance is probably not as desired. For

instance, it was demonstrated that stores having the biggest backrooms tend to have

at the same time the highest OOS rates. With this knowledge, necessary

counteractions can be initiated.

Practitioners want to know whether they have chosen the adequate system for their

products. For this reason, the fourth contribution of this thesis is the development of a

decision tree that helps managers to identify the right ASR level for each product

category. There are still many products where manual ordering (ASR0) is

appropriate. For instance, for very fresh products such as bread rolls, electronic

inventory records are not feasible. For other products that show very irregular

demand (e.g. fashion goods) or that are very expensive (e.g. jewellery)

semi-automation might not be economically sensible (ASR1). However, for most

articles of a typical grocery retailer automation should be strongly consideration.

184 7. Conclusion

Products with a stable demand or low value can achieve good results with simple

ASR2 systems. All other products might benefit most from automation based on a

forecasting engine, taking into consideration that computing power is nowadays not

the decisive constraint.

Retailers that have decided to change their replenishment system should be aware of

the wider consequences of their actions. The implications for the companies go far

beyond a mere improvement of the replenishment system. Retailers' technology,

organizational structure and processes have to be adapted to successfully introduce

ASR systems. Depending on the ASR level, different technical systems have to be

introduced, such as an electronic inventory records system, electronic data

interchange, forecasting engines, etc. However, retailers have seen the ASR

introduction as an opportunity to harmonise their entire IT and other technologies in

use, e.g. by introducing ERP systems or by installing the same type of tills in all

stores. Another change concerns the organization of some retailers, where a shift in

the decision-making power from the stores to the headquarters occurred. New central

planning functions have been created that control the flow of stores replenishment

and take care of the ASR parameters. Furthermore, ASR systems have dramatically

changed the working behaviour of many employees. Not every employee welcomes

the automation as a relief from the burden of placing orders, as this task is regarded

by many as an essential part of their work. Successful retailers have created an

acceptance among employees by showing them the benefits of the new systems.

IT-courses have dispelled the fear of many store employees concerning the new

technique. Other courses were used to show employees the new processes required

to support ASR effectiveness. One example of such new processes are the ones that

aim at increasing inventory records accuracy. As ASR systems are highly dependent

on records accuracy, definition and training of processes such as physical counts, the

check-out, the handling of shrinkage, among others, is crucial.

Overall, the implementation of ASR systems is a great opportunity for practitioners.

However, this thesis not only highlighted the benefits but also the risks of automating

replenishment. Practitioners have to be careful, because the introduction of wrongly

parameterised ASR systems will be counterproductive. At the end of the day,

managers' final question will be: Is introducing ASR worth all the effort? This is a

difficult question, and the answer needs to be decided by every practitioner in each

individual context. The change from a push to a pull system requires a significant

effort. The case study of MYFOOD has shown that only two years after the

introduction of the new ASR2 system the investment had been fully amortised.

Depending on the retailer's starting point, this period could be even shorter. The four

7. Conclusion 185

case studies of retailers which strongly benefit from ASR systems can be a major

help for any practitioner planning to automate store replenishment.

7.3. Further Research Fields

Considering the novelty of this research field, the present thesis can only be the

beginning of research projects on this topic. Six research fields have been identified

that could be examined in future research to gain more detailed insights into the ASR

topic.

Further Research Fields

� Quantitative analysis with larger empirical basis (several retailers)

� Research on non-grocery retailers

� Research on ASR4 systems

� ASR5: multi-tier systems

� ASR influence on human agency

� Overall replenishment cost function

Table 51: Overview of further research opportunities

First of all, the quantitative examination in this thesis concentrates on a single

grocery retailer. It could be proven that the introduction of the replenishment systems

was indeed beneficial for this retailer, as long as the replenishment logic was set

correctly. The next logical step would be to have another empirical examination with

several retailers to prove that this was not just an exception but that indeed all

retailers can benefit from this approach. However, such a detailed examination as

was made for this thesis may not be possible when looking at several retailers at

once. Moreover, this approach would examine a broad sample of retailers, e.g. with

the help of surveys, to create a statistical proof of the benefits of ASR systems.

Second, the present research focused on grocery retailers and chemists. Yet from

several interviews it is known that this topic is also relevant for other types of retailer.

Further research could hence concentrate on the transfer of the present results to

other types of products. Further studies are required in particular for articles with a

high fashion influence and with long lead times (such as in the apparel industry).

Third, in this thesis only the change from manual to ASR2 and ASR3 systems could

be statistically tested. A most interesting question for retailers is whether an upgrade

in ASR system is worth the effort. This knowledge is necessary to make a

186 7. Conclusion

cost-benefit analysis. Statistical examinations of retailers that have made the jump

from ASR2 or ASR3 to an ASR4 system would be particularly useful to practitioners.

A fourth possibility for further research is the examination of ASR systems from an

ECR perspective. The main idea of ECR is collaboration along the supply chain in

order to obtain better results. The question of the contribution of suppliers to the

success of ASR systems is still to be answered, as most of the retailers examined in

this thesis carried the implementation on their own. The models developed in this

work only regard the last tiers of the supply chain. The final aim of such collaborative

supply chains would be the hypothesised ASR5 system. The development of

systems that optimize replenishment along the entire supply chain would be one of

the biggest challenges in this field for future research.

The next topic which could only be examined on the surface is the influence of ASR

introduction for human agency. One possible approach would be a social-

psychological one. It would be interesting to see how the satisfaction of the individual

store employee changes after the introduction of ASR systems and how long it takes

to return to its former level. Another focus would be research on the ordering

behaviour of store employees confronted with ASR systems, as conducted by

Broekmeulen, van Donselaar (2005), and on the optimal degree of influence store

personnel should have on the automatically generated orders.

Finally, a topic that also requires further research is the one of the multiple tradeoffs a

retailer is facing when dealing with the setting of the replenishment system. It was a

remarkable result to prove that ASR systems are able to reduce OOS rate and

inventory level at the same time. Yet, it is not clear what exactly the relationship is

between the two. At some point, the store manager will have to decide on a tradeoff:

further reducing inventory will raise the OOS level and vice versa. Besides, there are

many other parameters that have influence on the total replenishment costs and the

handling workload in the store, such as shelf space, case order size and data quality.

The role of inventory records inaccuracy has to be examined in detail, as the retailer

MYFOOD in this thesis had good performance results despite major inventory

records inaccuracies. Consequently, further research should take a closer look at

creating an overall cost function, where all the possible costs of store replenishment

are taken into account alongside their influence on performance. This thesis provided

a small contribution for the realization of this ambitious task.

8. Appendix and References 187

8. Appendix and References

8.1. Statistical Appendix

8.1.1. Calculation of the Inventory Level

The difficulty with the data extracted from MYFOOD's ERP system was that the

system does not record any absolute values of the stock level. This means that it is

not possible to determine ex post the quantity of goods there were for a certain

product on a specific day. In addition, MYFOOD interviewees stressed the inaccuracy

of the inventory records. This statement is supported by physical audits (see Figure

49) and by other researchers (e.g. Broekmeulen, van Donselaar et al. 2005). The

only available data was relative, i.e. showing how many units were delivered and how

many units left the store on a certain day. With this data it is possible to determine

the stock level curve, but without knowing at which height on the y-axis the curve is

(see Figure 57) .

19.0

7.2

004

02.0

8.2

004

16.0

8.2

004

30.0

8.2

004

13.0

9.2

004

27.0

9.2

004

11.1

0.2

004

25.1

0.2

004

Calculated Inventorywithout zero line

supposed zero stock-point

Figure 57: Relative inventory level curve without zero line

Nevertheless, additional information makes it possible to determine the zero line. It is

evident that the inventory levels in the store can never be negative. Consequently,

the relative stock curve can be set in such a way that its minimum equals zero. This

method seems adequate for several reasons. First, the only problem with this method

would be with products that are never OOS. Nevertheless, the chances of this

188 8. Appendix and References

happening is very rare, because in the sample a data period of 90 days is covered.108

The chances of a product of being always in stock taken an OOS rate of 5% equals

0.9590= 0.1%. Second, even if the product was never OOS, the minimum value is

likely to be close to zero, thus still delivering a good approximation. For 15 of the 100

articles in the sample, the actual inventory levels were measured in the store during

two weeks. Comparing both lines, as can be seen in Figure 58, show the accuracy of

this method.109 Third, the variance of the stock is independent of the height of the

level, therefore this KPI is not affected by this procedure.

0

10

20

30

40

50

19.0

7.2

004

02.0

8.2

004

16.0

8.2

004

30.0

8.2

004

13.0

9.2

004

27.0

9.2

004

11.1

0.2

004

25.1

0.2

004

Calculated inventoryafter correction

Manual countedinventory

Figure 58: Absolute inventory level curve after the correction

This method is not 100% correct. Especially for products with very low OOS rates,

the inventory levels might be underestimated. However, greater accuracy is not

necessary, as the main goal of this thesis was to compare different ASR methods

with each other. As the possible inaccuracy of this method impacts equally both

manually- and automatically-ordered goods, the comparison is still valid.

108

Therefore the analyses on inventory levels done with the two-weeks data (Dataset1) have to be viewed with

caution. Yet, in those analyses it was not important to show the absolute level of the inventory, but more how

the impact of an external variable (e.g. price) influenced the inventory level. For this purpose, this method is

appropriate. 109

The still existing difference between both lines can be explained by the fact that the inventories were counted

during the day. Sales happening after the count make the manual line stand above the calculated line. Another

possible explanation for the small gap could be that the minimum value on August 14th

was not zero but a little

higher (around 2 units).


8.1.2. ANOVA Considerations and Prerequisites

The main task of ANOVAS is to prove the existence of a correlation between the

variables, e.g. that for one group the OOS rate is higher than for another.

Consequently, one has to be cautious about interpreting the size of this correlation

(Backhaus, Erichson et al. 2003). The ANOVAs only prove that one group is

statistically different from another, yet the direction, i.e. if the one group is higher or

lower than the other, has to be seen from the context. Normally this is not a problem,

as the descriptive statistics clearly show which group had the higher value.

In the following, the data conditions that have to be fulfilled for an ANOVA to be

applied are discussed (cf. Backhaus, Erichson et al. 2003, pp. 150–152). First, the

dependent variable has to be a metric scaled variable. In this thesis either the OOS

rate or some stock-level-related variables are tested, this is fulfilled with the present

data.110 Second, ANOVA designs normally demand that the sample taken is

representative for the entire basic population. This can be assured, e.g. through a

random sample. In this study the products and stores were not randomly chosen. Yet

in the choosing process–as described in the section 5.1–it was taken care that

representatives of each product category or store type that are relevant for the

analysis of the performance were present. Therefore, no distortion of the analysis is

expected. Third, the values in the population should have a normal distribution (Rao

1998) and fourth, the population variances have to be equal. But the last two

restrictions are often relaxed in practice and nevertheless the results are significant

ANOVAs (Backhaus, Erichson et al. 2003). In the analysis of this thesis, to compare

the effects with each other, a Tamhane's T2 post hoc analysis is made where the

assumption of equal variance is not required.

110

To be precise, most of the data presented in this case is censored, as e.g. the OOS rate cannot be lower than

0% or higher than 100%. Yet, as long as the data is not very close to these limits, one does not expect

significant impacts on the results. In the marketing literature, this procedure is most common (cf. Katsikea and

Skarmeas 2003).


8.2. References

Aalto-Setälä, V. (2001). "The effect of concentration and market power on food

prices: evidence from Finland." Journal of Retailing 78(3): 207-216.

Accenture (2000). Science retailing: bringing science to the art of retail. White Paper.

Achabal, D. D., S. H. McIntyre, et al. (2000). "A decision support system for vendor

managed inventory." Journal of Retailing 76(4): 430–454.

Ailawadi, K. L., N. Borin, et al. (1995). "Market power and performance: a cross-

industry analysis of manufacturers and retailers." Journal of Retailing 71(3):

211-248.

Akkermans, H. A., P. Bogerd, et al. (1999). The impact of ERP on supply chain

management. Fontainebleau, INSEAD.

Alicke, K. (2003). Planung und Betrieb von Logistiknetzwerken. Berlin, Springer.

Andersen Consulting (1996). Where to look for incremental sales gain. The retail

problem of out-of-stock. Atlanta, The Coca-Cola Research Council.

Anderson, B. V. (2004). Best practices–integrated retail replenishment. The Post. 20.

Angerer, A. (2004). "Out of Stock: Ausmaß, Ursachen und Lösungen." Logistik Inside

7(2): 16.

Angerer, A. and D. Corsten (2004). Zum Nutzen der Kunden. Logistik als Motor des

Einzelhandels. Frankfurter Allgemeine Zeitung. Frankfurt.

Anon. (2004). "Organize for efficiency." Supervision 65(10): 25–26.

Anupindi, R. B., Yehuda (1999). "Centralization of stocks: retailers vs. manufacturer."

Management Science 45(2): 178-191.

Arnold, D., H. Isermann, et al. (2004). Handbuch Logistik. Berlin, Springer.

Arthur Andersen LLP (1997). Small store survival: success strategies for retailers.

New York, John Wiley & Sons.

Backhaus, K., B. Erichson, et al. (2003). Multivariate Analysemethoden. Berlin,

Springer.

Balachander, S. and P. H. Farquhar (1994). "Gaining more by stocking less: a

competitive analysis of product availability." Marketing Science 13(1): 3–22.

Bancroft, N. H., H. Seip, et al. (1996). Implementing SAP R/3. Greenwich, Manning.

Bansal, P. and K. Roth (2000). "Why companies go green: a model of ecological

responsiveness." Academy of Management Journal 43(4): 717–736.


Bassok, Y. A., Ravi (1999). "Single-period multiproduct inventory models with

substitution." Operations Research 47(4): 632-642.

Baumard, P. and J. Ibert (2001). What approach with which data? In Doing

management research. R.-A. Thiétart. London, SAGE.

Baumgarten, H. and J. Thoms (2002). Trends und Strategien in der Logistik – Supply

Chains im Wandel. Berlin, Technische Universität Berlin.

Baumgarten, H. and S. Wolf (1993). Perspektiven der Logistik: Trend-Analysen und

Unternehmensstrategien. Berlin.

Bearing Point (2003). "Inventory Management 2003: Communication and technology

bear renewed promise." Chain Store Age(Section 2).

Bell, R., R. Davies, et al. (1997). "The changing structure of food retailing in Europe:

the implications for strategy." Long Range Planning 30(6): 853–861.

Bergeron, F., L. Raymond, et al. (1998). The contribution of information technology to

the performance of SMEs: alignment of critical dimensions. Proceedings of the

6th European conference on information systems, Aix-en-Provence.

Bergeron, F., L. Raymond, et al. (2001). "Fit in strategic information technology

management research: an empirical comparison of perspectives." Omega

29(2): 125–142.

Beringe, A. v. (2002). The integrated supply and demand chain. Speech at Sapphire,

Lisbon.

Bernard, P. (1999). Integrated inventory management. New York, John Wiley &

Sons.

Bobitt, H. R. and J. D. Ford (1980). "Decision-maker-choice as a determinant of

organizational structure." Academy of Management Review 5(1): 13–23.

Broekmeulen, R., K. van Donselaar, et al. (2005). An empirical study of ordering

behavior of retail stores. Working paper of the School of Technology

Management, Eindhoven.

Broekmeulen, R., K. van Donselaar, et al. (2004a). Automated Store Ordering: an

enabler for new inventory replenishment strategies in supermarkets. Report of

the Faculteit Technologie Management, Eindhoven.

Broekmeulen, R., K. van Donselaar, et al. (2004b). Excess shelf space in retail

stores: an analytical model and empirical assessment. Beta working paper

series, Eindhoven.


Brynjolfsson, E. and L. M. Hitt (1995). "Computers as a factor of production: the role

of differences among firms." Economics of Innovation and New Technology

3(3-4): 183–199.

Brynjolfsson, E. and L. M. Hitt (1998). "Beyond the productivity paradox."

Communications of the ACM 41(8): 49–55.

Cachon, G. (2001). "Managing a retailer's shelf space, inventory and transportation."

Manufacturing & Service Operations Management 3(3): 211–219.

Cachon, G. and M. Fisher (2000). "Supply chain inventory management and the

value of shared information." Management Science 46(8): 1032–1048.

Cadeaux, J. M. (1994). "Flexibility and performance of branch store stock plans for a

manufacturer's product line." Journal of Business Research 29(3): 207–218.

Campbell, D. T. and D. W. Fiske (1959). "Convergent and discriminant validation by

the multitrait-multimethod matrix." Psychological Bulletin 56: 81-105.

Carter, M. W. and C. C. Price (2001). Operations research: a practical introduction.

Boca Raton, CRC Press.

Chagué, V. (1996). "Gérer la technologie dans les PME." Direction Gestion

Enterprises 1996(157): 13–21.

Chan, Y. and S. Huff (1993). Investigating information systems strategic alignment.

Proceedings of the 14th international conference on information systems,

Orlando.

Chang, Y. S. (1967). "Reorder point models with non-decreasing stock-out costs."

Journal of Industrial Engineering 18(6): 365–374.

Chase, C. (1995). "The realities of business forecasting." Journal of Business

Forecasting 14(1): 2 and 26.

Chen, F., Z. Drezner, et al. (2000). "Quantifying the bullwhip effect in a supply chain:

the impact of forecasting, lead time and information." Management Science

46(3): 436–443.

Chircu, A. M. and R. J. Kauffman (2000). "Limits to value in electronic commerce-

related IT investments." Journal of Management Information Services 17(2):

59–80.

Chow, G., T. D. Heaver, et al. (1994). "Logistics performance: definition and

measurement." International Journal of Physical Distribution & Logistics

Management 24(1): 17–28.


Chow, G. and L. E. Henriksson (1993). A critique of survey research in logistics.

Working paper 93, Faculty of Commerce and Business Administration,

University of British Columbia.

Christopher, M. (1998). Logistics and supply chain management. London, Pitman

Publishing.

Ciborra, C. U. (2000). From control to drift. Oxford, Oxford University Press.

Closs, D. J., A. S. Roath, et al. (1998). "An empirical comparison of anticipatory and

response-based supply chain strategies." International Journal of Logistics


Coca-Cola Retailing Research Council (1994). "Measured marketing: a tool to shape

food store strategy."

Connell, J. (2001). "Influence of firm size on organizational culture and employee

morale." Journal of Management Research 1(4): 220–232.

Corsten, D. (2002). Efficient Consumer Response adoption: theory, model and

empirical results. Habilitation, University of St. Gallen.

Corsten, D. and T. Gruen (2004). "Stock-outs cause walkouts." Harvard Business

Review 82(5): 26–27.

Corstjens, M. and P. Doyle (1981). "A model for optimizing retail space allocations."

Management Science 27(7): 822–833.

Cottrill, K. (1997). "Reforging the supply chain." The Journal of Business Strategy

18(6): 35–39.

Coughlan, P. and D. Coghlan (2002). "Action research for operations management."

International Journal of Operations & Production Management 22(2): 220–240.

Cox, K. K. (1970). "The effect of shelf space upon sales of branded products."

Journal of Marketing Research 7(February): 55–58.

Curhan, R. (1972). "The relationship between shelf space and unit sales." Journal of

Marketing Research 9(November): 406–412.

Dagnoli, J. (1987). "Impulse governs shoppers." Advertising Age(11): 93.

Dalrymple, D. J. (1964). "Controlling retail inventories." Journal of Retailing 40(1): 9–

16.

Daugherty, P. J., M. B. Myers, et al. (1999). "Automatic replenishment programs: an

empirical examination." Journal of Business Logistics 20(2): 63–72.

Davenport, T. (2004). "Decision evolution; Automated systems are helping

businesses make decisions more productively and consistently." CIO 18(1): 1.


Davis, T. (1993). "Effective supply chain management." Sloan Management Review

4(34): 35–47.

DeHoratius, N. and A. Raman (2002). Inventory record inaccuracy: an empirical

analysis. Working paper, Harvard Business School.

DeHoratius, N. and A. Raman (2005). "Retail performance improvement through

appropriate store manager incentive design: an empirical analysis."

Unpublished paper, University of Chicago.

Densley, B. (1999). The magnificent seven: getting the biggest bang from the ERP

buck. In Proceedings of the first international workshop EMRPS99. J. Eder, N.

Maiden and M. Missikoff. Rome, Istituto de Analisi dei Sistemi ed Informatica:

59–65.

Denzin, N. K. (1978). The research act. A theoretical introduction to sociological

methods. New York, McGraw-Hill Company.

Denzin, N. K. (1989). The research act: A theoretical introduction to sociological

methods. Englewoods Cliffs, NJ, Prentice Hall.

Dewan, S. and C. Min (1997). "The substitution of IT for other factors of production: a

firm-level analysis." Management Science 43(12): 1660–1675.

Dey, I. (1993). Qualitative data analysis: an user-friendly guide for social scientists.

London, Routledge.

Drazin, R. and A. H. van de Ven (1985). "Alternative forms of fit in contingency

theory." Administrative Science Quarterly 30(4): 514–539.

Dreyfus, H. and S. Dreyfus (1986). Mind over machine. New York, Free Press.

Drèze, X., S. J. Hoch, et al. (1994). "Shelf management and space elasticity." Journal

of Retailing 70(4): 301–326.

Dubelaar, C., G. Chow, et al. (2001). "Relationships between inventory, sales and

service in a retail chain store operation." International Journal of Physical

Distribution & Logistics Management. 31(2): 96-108.

Duffy, M. (2004). "How Gillette cleaned up its supply chain." Supply Chain

Management Review 8(3): 20–27.

ECR Europe (2000). Day-to-day category management. Facilitated by Andersen

Consulting, Brussels.

ECR France (2002). Optimal shelves availability. Presentation by the ECR group

Taux de service en linéaire. Glasgow.


Eichen, B. F. and S. F. Eichen (2004). dm-drogerie markt: Vendor Managed

Inventory. In Supply Chain Management erfolgreich umsetzen. Grundlagen,

Realisierung und Fallstudien. D. Corsten and C. Gabriel. Berlin, Springer:

175–195.

Eisenhardt, K. M. (1989). "Building theories from case study research." Academy of

Management Review 14(4): 532–550.

Ellinger, A. E., J. C. Taylor, et al. (1999). "Automatic replenishment programs and

level of involvement: performance implications." International Journal of

Logistics Management 10(1): 25–36.

Emmelhainz, M. A., J. R. Stock, et al. (1991). "Consumer responses to retail stock-

outs." Journal of Retailing 67(2): 138–147.

European Logistics Association and A.T. Kearney (1999). Insight to impact: results of

the fourth quinquennial European logistics study. Brussels.

Fafchamps, M., J. W. Gunning, et al. (2000). "Inventories, liquidity and contractual

risk in African manufacturing." The Economic Journal 110(466): 861–893.

Fahrni, F. (1998). TQM in einem technischen Konzern: Unternehmenskultur im

Wandel. In Qualitätsmanagement an der Schwelle zum 21. Jahrhundert. R.

Boutellier and W. Masing. München, Hanser.

Fairris, D. (2004). "Towards a theory of work intensity." Eastern Economic Journal

30(4): 587–602.

Falck, M. (2005). "Automated store-ordering dream can come true." Supply Chain

Technology Software(July/August): 10–12.

Feitzinger, E. and H. L. Lee (1997). "Mass customization art." Harvard Business

Review 75(1): 116–121.

Fiedler, F. E. (1967). A theory of leadership effectiveness. New York, McGraw Hill.

Fiorito, S. S., E. G. May, et al. (1995). "Quick response in retailing: components and

implementation." International Journal of Retail & Distribution Management

23(5): 12–21.

Fischhoff, B. (1994). "What forecasts (seem to) mean." International Journal of

Forecasting 10(3): 387–404.

Fisher, M. (2004). "To me it's a factory. To you it's a store." ECR Journal.

International Commerce Review 4(2): 8–18.

Fisher, M. L., J. H. Hammond, et al. (1994). "Making supply meet demand in an

uncertain world." Harvard Business Review 72(3): 83–93.


Fisher, M. L., A. Raman, et al. (2000). "Rocket science retailing is almost here–are

you ready?" Harvard Business Review 78(4): 115–124.

Fleck, J. (1994). "Learning by trying: the implementation of configurational

technology." Research Policy 23(6): 632–652.

Fleisch, E. and C. Tellkamp (2004). "Inventory inaccuracy and supply chain

performance: a simulation study of a retail supply chain." International Journal

of Production Economics 95(3): 373–385.

Friedli, T., S. Billinger, et al. (2005). "Combining action research, grounded theory

and case study research with long term projects." Unpublished paper,

University of St.Gallen.

Galliher, H. P., P. M. Morse, et al. (1959). "Dynamics of two classes of continuous-

review inventory." Operations Research 7(3): 362–384.

Gassmann, O. (1999). "Praxisnähe mit Fallstudienforschung."

Wissenschaftsmanagement 6(3): 11–16.

Gausemeier, J. and G. Gehnen (1998). "Intelligent material flow by decentralized

control networks." Journal of Intelligent Manufacturing 9(2): 141–146.

Glaser, B. and A. Strauss (1967). "The discovery of grounded theory: strategies of

qualitative research." 3(2): 269–270.

Götz, N. D. (1999). Automatische Dispositionssysteme für den Handel. Köln, Josef

Eul.

Grocery Manufacturers of America and Roland Berger (2002). "Full-shelf satisfaction.

Reducing out-of-stocks in the grocery channel. An in-depth look at DSD

categories."

Gronau, N. (2004). Enterprise resource planning und supply chain management.

München, Oldenbourg Wissenschaftsverlag.

Groote, X. d. (1994). Inventory theory: a road map. Unpublished INSEAD teaching

note.

Gruen, T., D. Corsten, et al. (2002). Retail out-of-stock. Research Report, Grocery

Manufacturers of America.

Gudehus, T. (2001). "Optimaler Nachschub in Versorgungsnetzen." Logistik

Management 3(2/3).

Gudehus, T. (2002). Dynamische Disposition. Berlin, Springer.

Gudehus, T. (2004). Logistik – Grundlagen, Strategien, Anwendungen. Heidelberg,

Springer Verlag.


Guptill, A. and J. L. J.L. Wilkens (2002). "Buying into the food system: trends in food

retailing in the US and implications for local foods." Agriculture and Human

Values 19(1): 39–51.

Gurbaxani, V. and S. Whang (1991). "The impact of information systems on

organizations and markets." Communications of the ACM 34(1): 59–73.

Hanke, J. E., D. W. Wichern, et al. (2005). Business forecasting. Upper Saddle River,

Prentice Hall.

Hines, P., M. Holweg, et al. (2000). "Waves, beaches, breakwaters and rip currents–

a three-dimensional view of supply chain dynamics." International Journal of

Physical Distribution & Logistics Management 30(10): 827–846.

Hollinger, R. C. and J. L. Davis (2002). National retail security survey 2001.

University of Florida, Department of Sociology and the Center for Studies in

Criminology and Law.

Hong, K. K. and Y. G. Kim (2002). "The critical success factors for ERP

implementation: an organizational fit perspective." Information & Management

40(1): 25–40.

Hopp, A. and G. Arminger (2005). Kundenorientiertes Demand Chain Management

bei Metro: Nutzung von Verkaufsdaten zur Steuerung der Hersteller.

Presentation at the seminar Innovatives Replenishment in der Konsumgüter-

Distribution, Horn.

Horst, F. (2005). "Weniger Verluste in 2004." Retail Technology Journal(3).

Hoyer, W. D. (1984). "An examination of consumer decision making for a common

repeat purchase product." Journal of Consumer Research 11(3): 822–831.

Huber, G. P. (1984). "The nature and design of the post-industrial organization."

Management Science 30(8): 928–951.

Hui, C., S. Lam, et al. (2001). "Can good citizens lead the way in providing quality

service? A field quasi experiment." Academy of Management Journal 44(5):

988–995.

Inderfurth, K. and S. Minner (1998). "Safety stocks in multi-stage inventory systems

under different service measures." European Journal of Operations Research

106(1): 57–73.

Jain, C. L. (2003). "Forecasting errors in the consumers products industry." The

Journal of Business Forecasting Methods & Systems 22(2): 2–4.


Jick, T. D. (1979). "Mixing qualitative and quantitative methods: triangulation in

action." Administrative Science Quarterly 24(4): 602-611.

Johansson, U. (2002). "Information technology in supplier-retailer relationships."

Working paper, Institute of economic research, Lund University.

Kahn, K. B. and J. T. Mentzer (1996). "Logistics and interdepartmental integration."

International Journal of Physical Distribution & Logistics Management 26(8):

6–14.

Kallinikos, J. (2004). "Deconstructing information packages. Organizational and

behavioural implications of ERP systems." Information Technology & People

17(1): 8–30.

Karrer, M., T. Placzek, et al. (2004). "Einsatz strategieorientierter

Steuerungsinstrumente in der Logistik. Die Logistik-BSC am Beispiel großer

Handelsunternehmen." Controlling 16(8-9): 503–510.

Katsikea, E. S. and D. A. Skarmeas (2003). "Organisational and managerial drivers

of effective export sales organisations. An empirical investigation." European

Journal of Marketing 37(11/12): 1723–1745.

Keh, H. T. and S. Y. Park (1997). "To market, to market: the changing face of grocery

retailing." Long Range Planning 30(6): 836–846.

Ketokivi, M. A. and R. G. Schroeder (2004). "Strategic, structural contingency and

institutional explanations in the adoption of innovative manufacturing

practices." Journal of Operations Management 22(1): 63–89.

Ketzenberg, M., R. Metters, et al. (2000). "Inventory police for dense retail outlets."

Journal of Operations Management 18(3): 303–316.

Kieser, A. (2002). Organisationstheorien. Stuttgart, W. Kohlhammer.

Klaus, P. (2003). Die wahren Kosten der Logistik. URL: http://www.logistik-

inside.de/sixcms/list.php?page=pg_archiv_heftuebersicht&skip=20, accessed

01.09.2005.

Kling, R. (1996). Computerization and controversy. San Diego, Academic Press.

Körber, H. J. (2003). Die Zukunft des Handels. Presentation at the 20th BVL

Congress, Berlin.

Kotler, P. and F. Biemel (1999). Marketing-Management. Analyse, Planung,

Umsetzung und Steuerung. Stuttgart, Schäffer-Poeschel.

Kotzan, J. A. and R. U. Evanson (1969). "Responsiveness of drug store sales to shelf

space allocations." Journal of Marketing Research 6(November): 465–469.


Kowalski, M. (1992). Qualität in der Logistik. Wiesbaden.

Krafcik, J. (1988). "Triumph of the lean production system." Sloan Management

Review 30(1): 41–52.

Krane, S. D. (1994). "The distinction between inventory holding and stockout costs:

implications for target inventories, asymmetric adjustment and the effect of

aggregation on production smoothing." International Economic Review 35(1):

117–136.

Kromrey, H. (2002). Empirische Sozialforschung – Modelle und Methoden der

Datenerhebung und Auswertung. Wiesbaden, Opladen: Leske und Budrich.

Krueckenberg, H. F. (1969). The significance of consumer response to display space

reallocation. Proceedings of the Fall Conference, American Marketing

Association.

Krumbholz, M., J. Galliers, et al. (2000). "Implementing enterprise resource planning

packages in different corporate and national cultures." Journal of Information

Technology 15(4): 267–280.

Kuk, G. (2004). "Effectiveness of vendor-managed inventory in the electronics

industry: determinants and outcomes." Information & Management 41(5): 645–

654.

Kumar, V., B. Maheshwari, et al. (2001). "An investigation of critical management

issues in ERP implementation: empirical evidence from Canadian

organizations." Technovation 23(10): 793–807.

Kurt Salmon Associates (1993). Efficient Consumer Response–enhancing consumer

value in the grocery industry. Washington.

Lamnek, S. (1993). Qualitative Sozialforschung. Weinheim, Beltz PsychologieVerlags

Union.

Lawrence, P. R. and J. W. Lorsch (1967). Organization and environment. Cambridge,

Homewood.

Lederer, A. L. and A. L. Mendelow (1990). "The impact of the environment on the

management of information systems." Information Systems Research 1(2):

205–222.

Lee, H. L. (2001). Ultimate enterprise value creation using demand based

management. Stanford Global Supply Chain Management Forum.

Lee, H. L. and C. Billington (1992). "Managing supply chain inventory: pitfalls and

opportunities." Sloan Management Review 33(3): 65–73.


Lee, H. L. and C. Billington (1993). "Material management in decentralized supply

chains." Operations Research 41(5): 835–847.

Lee, H. L., V. Padmanabhan, et al. (1997a). "The bullwhip effect in supply chains."

Sloan Management Review 38(3): 93–102.

Lee, H. L., V. Padmanabhan, et al. (1997b). "Information distortion in a supply chain:

the bullwhip effect." Management Science 43(4): 546–558.

Leidner, D. and S. Jarvenpaa (1995). "The use of information technology to enhance

management school education: a theoretical view." MIS Quarterly 19(3): 265–

291.

Lindman, R. H. (1992). Analysis of variance in experimental design. New York,

Springer.

Malone, T. (1997). "Is empowerment just a fad?" Sloan Management Review 38(2):

45–53.

March, J. G. and J. P. Olsen (1989). Rediscovering institutions. London, Free Press.

Markus, L. M., S. Axline, et al. (2000). "Learning from adopter's experiences with

ERP: Problems encountered and success achieved." Journal of Information

Technology 15(4): 245–265.

Markus, M. L. and T. Cornelius (2000). The enterprise systems experience–from

adoption to success. In Framing the domains of IT research: glimpsing the

future through the past. R. W. Zmud. Cincinnati, Pinnaflex Educational

Resources.

Marshall, C. and G. B. Rossmann (1995). Designing qualitative research. Newbury

Park, SAGE.

Mau, M. (2000). Supply Chain Management. Realisierung von

Wertschöpfungspotentialen durch ECR-Kooperation zwischen

mittelständischer Industrie und Handel im Lebensmittelsektor. Frankfurt

(Main), Fachverlag Moderner Wirtschaft.

McGrath, J. (1982). Dilemmatics: the study of research choices and dilemmas. In

Judgement calls in research. J. E. McGrath, J. Martin and R. A. Kulka.

Newbury Park, Sage: 69–102.

Mentzer, J. T. and K. B. Kahn (1995). "Forecasting technique familiarity, satisfaction,

usage, application." Journal of Forecasting 16(1): 465–476.

Merkel, H. (2003). "Standard-Warenwirtschaftssysteme für den mittelständischen

Einzelhandel." Lebensmittelzeitung 55(25): 95-100.


Metaxiotis, K. S., J. E. Psarras, et al. (2003). "Production scheduling in ERP systems.

An AI-based approach to face the gap." Business Process Management

Journal 9(2): 221–247.

Millet, I. (1994). "A novena to Saint Anthony, or how to find inventory by not looking."

Interfaces 24(4): 69–75.

Moon, M. A., J. T. Mentzer, et al. (1998). "Seven keys to better forecasting." Business

Horizons 41(5): 44–53.

Mumford, L. (1934). Technics and civilization. London, Harvest/HBJ.

Myers, M. B., P. J. Daugherty, et al. (2000). "The effectiveness of automatic inventory

replenishment in supply chain operations: antecedents and outcomes."

Journal of Retailing 76(4): 455-481.

Noble, D. (1984). Forces of production: a social history of industrial automation. New

York, Alfred A. Knopf.

O'Leary, D. E. (2000). Enterprise Resource Planning systems: systems, life cycle,

electronic commerce and risk. Cambridge, University Press.

Perrow, C. (1967). "A framework of comparative analysis of organizations." American

Sociological Review 32(2): 194–208.

Petrak, L. (2005). "Dating Game." National Provisioner 219(6): 78–52.

Pfeffer, J. and H. Leblebici (1971). "Information technology and organizational

structure." Pacific Sociological Review 20(2): 241–261.

Pfohl, H. C. (2004). Logistiksysteme. Berlin, Springer.

Pfohl, H.-C. and W. Zöllner (1987). "Organization for logistics: the contingency

approach." International Journal of Physical Distribution & Materials


Progressive Grocer (1968). "The out-of-stock study." 1–16.

Ptak, C. A. and E. Schragenheim (2000). ERP: tools, techniques and applications for

integrating the supply chain. London, St. Lucie Press/APICS.

Punch, K. F. (1998). Introduction to social research–quantitative and qualitative

approaches. London, SAGE Publications.

Raman, A. (2000). "Retail-data quality: evidence, causes, costs and fixes."

Technology in Society 22(1): 97–109.

Raman, A., DeHoratius, et al. (2001a). "The missing link in retail operations."

California Management Review 43(3): 136–152.


Raman, A., N. DeHoratius, et al. (2001b). "The Achilles' heel of supply chain

management." Harvard Business Review 79(5): 25-28.

Rao, P. V. (1998). Statistical research methods in the life sciences. Pacific Grove,

Brooks/Cole Publishing Company.

Ritzman, L. P. and B. E. King (1993). "The relative significance of forecast errors in

multistage manufacturing." Journal of Operations Management 11(1): 51–63.

Roland Berger (2003a). 2003 GMA Logistics Study. GMA publication.

Roland Berger (2003b). ECR–Optimal shelf availability. ECR Europe publication.

Sabath, R. E., C. W. Autry, et al. (2001). "Automatic replenishment programs: the

impact of organizational structure." Journal of Business Logistics 22(1): 91-

105.

Sabherwal, R. and L. Vijayasarathy (1994). "An empirical investigation of the

antecedents of telecommunication-based interorganizational systems."

European Journal of Information Systems 3(4): 268–284.

Sackett, P. R. and J. R. Larson (1990). Research strategies and tactics in industrial

and organizational psychology. In Handbook of industrial and organizational

psychology. M. D. Dunnette and L. M. Houghs. Palo Alto, Consulting

Psychology Press.

Sarpola, S. (2003). Enterprise Resource Planning (ERP) software selection and

success of acquisition process in wholesale companies. Working paper,

Helsinki School of Economics.

Sawyer, S. and R. Southwick (2002). "Temporal issues in information and

communication technology enables organizational change: evidence from an

enterprise systems implementation." Information Society 18(4): 263–280.

Scandura, T. A. and E. A. Williams (2000). "Research methodology in management:

current practices, trends and implications for future research." Academy of

Management Journal 43(6): 1248–1264.

Schneckenburger, T. (2000). Prognosen und Segmentierung in der Supply Chain.

Ein Vorgehensmodell zur Reduktion der Unsicherheit. Dissertation, University

of St.Gallen.

Schonberger, R. I. (1982). Japanese Manufacturing Techniques. New York, The Free

Press.


Schoonhoven, C. B. (1981). "Problems with contingency theory: testing assumptions

hidden within the language of contingency 'theory'." Administrative Science

Quarterly 26(3): 672–693.

Schumpeter, J. A. (1935). Theorie der wirtschaftlichen Entwicklung: eine

Untersuchung über Unternehmergewinn, Kapital, Kredit, Zins und den

Konjunkturzyklus. München, Duncker & Humblot.

Shalla, G. (2005). Supply Chain- und Beschaffungsmanagement im Handel am

Beispiel der Globus-Gruppe (WND). Presentation at the University of

St.Gallen.

Sheeber, L., E. Sorensen, et al. (1996). "Data analytic techniques for treatment

outcome studies with pretest/posttest measurements: an extensive primer."

Journal of Psychiatric Research 30(3): 185–199.

Sheer, A. W. and F. Habermann (2000). "Enterprise Resource Planning: Making ERP

a success." Communications of the ACM 43(4): 57–61.

Silver, E., D. Pyke, et al. (1998). Inventory management and production planning and

scheduling. New York, John Wiley & Sons.

Sloot, L., P. C. Verhoef, et al. (2002). The impact of brand and category

characteristics on consumer stock-out reactions. Report, Erasmus Research

Institute of Management.

Småros, J., A. Angerer, et al. (2004a). Logistics processes of European grocery

retailers. Unpublished research project report.

Småros, J., A. Angerer, et al. (2004b). Retailer views on forecasting collaboration.

Logistics Research Network (LRN), Dublin.

Smith, H. W. (1975). Strategies of social research: the methodological imagination.

Englewood Cliffs, Prentice-Hall.

Soh, K., S. S. Kien, et al. (2000). "Cultural fits and misfits: is ERP an universal

solution?" Communications of the ACM 43(4): 47–51.

SPSS Inc. (2003). SPSS Advance Models 12.0, SPSS Handbook.

Staehle, W. H. (1991). Management. München, Franz Vahlen.

Stank, T. P., P. J. Daugherty, et al. (1999). "Collaborative planning: supporting

automatic replenishment programs." Supply Chain Management 4(2): 77–85.

Sterns, K. M., L. S. Unger, et al. (1981). Intervening variables between

satisfaction/dissatisfaction and retail re-patronage intention. In An assessment


of marketing thought and practice: 1982 Educators' Conference Proceedings.

B. J. Walker. Chicago, American Marketing Association: 179–182.

Stölzle, W. (2004). "Supply Chain Controlling und Performance Management –

Konzeptionelle Herausforderungen für das Supply Chain Management."

Logistik Management 4(3): 10–21.

Stölzle, W., K. F. Heusler, et al. (2001). "Die Integration der Balanced Scorecard in

das Supply Chain Management- Konzept (BSCM)." Logistik Management 3(2–

3): 73–85.

Stölzle, W., K. F. Heusler, et al. (2004). Erfolgsfaktor Bestandsmanagement. Konzept

– Anwendung – Perspektiven. Zürich, Versus.

Stölzle, W. and T. Placzek (2004). Umsetzung von Optimal Shelf Availability –

Messkonzepte und Standardisierungspotenziale. Presentation at the BVL

congress.

Stratopoulos, T. and B. Dehning (2000). "Does successful investment in information

technology solve the productivity paradox?" Information & Management 38(2):

103–117.

Taylor, J. C. and S. E. Fawcett (2001). "Retail on shelf performance of advertised

items: an assessment of supply chain effectiveness at the point of purchase."

Journal of Business Logistics 22(1): 73–89.

Tellkamp, C., A. Angerer, et al. (2004). "Improving data quality and supply chain

performance with RFID: evidence from the retail industry." IT Cutter 17(9): 19–

24.

Tidd, J. (1993). "Technological innovation, organizational linkages and strategic

degree of freedom." Technology Analysis & Strategic Management 5(3): 273–

284.

Toporowski, W. and B. Herrmann (2003). "Neue Entwicklungen in der Logistik –

Konzepte der Praxis und Beiträge der Wissenschaft." Handel im Fokus, EHI

Newsletter(II).

Ulrich, H. (1981). Die Betriebswirtschaftslehre als anwendungsorientierte

Sozialwissenschaft. In Die Führung des Betriebes. M. N. Geist, R. Köhler and

C. Sandig. Stuttgart, Poeschel: 1–26.

Ulrich, H. (2001). Systemorientiertes Management – Das Werk von Hans Ulrich.

Bern, Paul Haupt.


van Donselaar, K., T. van Woensel, et al. (2004). Improvement opportunities in retail

logistics. In Consumer driven electronic transformation: applying new

technologies to enthuse consumers. G. I. Doukidis and A. P. Vrechopolous.

Berlin, Springer.

Vessey, I. (1991). "Cognitive fit: a theory based analysis of the graphs versus tables

literature." Decision Science 22(2): 219–240.

Vessey, I. and D. Galleta (1991). "Cognitive fit: an empirical study of information

acquisition." Information Systems Research 2(1): 63–84.

Vollmann, T. E., W. L. Berry, et al. (1992). Manufacturing planning and control

systems. Homewood, Irwin.

Vossen, M. (2000). Warenwirtschaft. Meilenweit vom Ziel entfernt. Der Handel. 05:

36.

Wacker, J. G. and R. R. Lummus (2002). "Sales forecasting for strategic resource

planning." International Journal of Operations & Production Management

22(9/10): 1014–1031.

Wagner, H. M. (2002). "And then there were none." Operations Research 50(1): 217–

226.

Watson, E. E. and H. Schneider (1999). "Using ERP in education." Communications

of the AIS 1(9): 1-48.

Webb, E. J., D. T. Campbell, et al. (1966). Unobtrusive measures: nonreactive

research in the social sciences. Chicago, Rand McNally.

Weder, M. (2005). Die Logistik der Nestlé Deutschland. Effiziente Replenishment-

Strategien zur Erfüllung der Kunden- und Produktanforderungen. Speech at

the conference "Innovatives Replenishment in der Konsumgüter-Distribution",

Bad Horn.

Wegener, D. (2002). On-shelf-availability. Extent, causes and consequences.

Unpublished diploma thesis, University of St.Gallen.

Womack, J. P. and D. T. Jones (1996). Lean thinking. New York, Simon and

Schuster.

Woods, J. (2002). "SCM5 will drive the next wave of supply chain advantage."

Gartner research note.

Yin, R. K. (1988). Case study research: design and methods. London, Newbury Park.

Zinn, W. and P. C. Liu (2001). "Consumer response to retail stockouts." Journal of

Business Logistics 22(1): 49–71.


Zinn, W., J. T. Mentzer, et al. (2002). "Customer-based measures of inventory

availability." Journal of Business Logistics 23(2): 19–44.

Zomerdijk, L. G. and J. d. Vries (2002). "An organizational perspective on inventory

control: theory and a case study." International Journal of Production

Economics 81/82(3): 173–183.

Zuboff, S. (1988). In the year of the smart machine: the future of work and power.

New York, Basic Books.


8.3. List of Interviews111

Company Name Position Date Place

Athleticum Sportmarkets

Markus Scheffler Director logistics 23.06.2003 Hünenberg


Rolf Engler Vice-store manager 01.07.2003 St.Gallen


Urs Wettstein Product manager 08.07.2003 Hünenberg

BonAppetitGroup Dr. Christian Kubik Director supplier relationship management.

15.09.2003 Winterthur

Camion Tranport Wil

Hanspeter Imbach Director logistics 13.12.2002 Wil

COOP Martin Badertscher Store manager 26.08.2003 Oberriet

COOP Hanspeter Dörig Director category management

12.09.2003 Gossau

COOP Stefan Gächter Director DC-Gossau 17.02.2004 Gossau

17.06.2004 St.Gallen

Denner Arthur Mathys Director logistics 27.02.2004 Zurich

Denner Arthur Mathys Director logistics 04.08.2004 Zurich

Denner Daniel Bucher Store manager 07.08.2004 Walenstadt

Gebrüder Weiss Wolfgang Brunner Information systems 12.12.2002 Altenrhein

Gebrüder Weiss Jürgen Glassner Head of purchasing, central Switzerland

12.12.2002 Altenrhein

Interdiscount Hansjörg Kohli Director item management

26.09.2003 Jegenstdorf

Interdiscount Urs Wilhelm Director communications/PR

26.09.2003 Jegenstdorf

Interdiscount Markus Fitzi Store manager 04.10.2003 St.Gallen

Intersport Germany

Klaus Bader Director logistics 07.07.2003 Heilbronn

Intersport International

Raider Magnus Director logistics 14.07.2003 Ostermundigen

Intersport Magdon Friedrich Magdon Director/store manager 09.07.2003 Kempten

Intersport SFS Ernst Enz Director/store manager 01.07.2003 Widnau

Kaisers Tengelmann

H. Warzecha Logistics manager 04.12.2003 München

Kaisers Tengelmann

H. Ushold Logistics manager 04.12.2003 München

Kaisers Tengelmann

H. Türkyilmaz Store manager 04.12.2003 München

M-Electronics Oliver Muggli Store manager 25.09.2003 St.Gallen

111

Some of the interviews with representatives from GROCO, MYFOOD, NORTHY and DRUCKS are omitted for

confidentiality reasons.


Metro Group H. Orbach Logistics manager 11.03.2004 Düsseldorf

Metro Group/Extra H. Rapp Store manager 04.11.2003 Tuttlingen

Migros René Meyer Director logistics 05.09.2003 Zurich

Migros Roland Studer Project-leader logistics 22.09.2003

02.07.2004 Zurich

Migros Guido Federspiel Director logistics (Supply)

18.03.2004 Zurich

Migros Robert Vuilleumier Supply manager hardware

22.09.2004 Zurich

PKZ group Friedhelm Lange Director logistics 28.07.2003 Urdorf

PKZ group Irène Manser Store manager 12.08.2003 St.Gallen

PKZ group Moreno Papes Director purchasing 20.08.2003 Urdorf

PKZ group Konrad Niederhäusern

Store manager 02.09.2003 Zurich

SAF AG Prof. Gerhard Arminger

Scientific advisor 26.02.2004 Tägerwilen

SAP AG Alexander Kunkel Consultant 09.03.2004 Suhr

18.03.2004 Regensdorf

Schild Thomas Herbert Head of purchasing/ material planning

29.08.2003 Luzern

Spar Schweiz Egon Baumgärtner Director logistics 11.09.2003 St.Gallen

Spar Schweiz Kurt Walser Store manager 15.09.2003 St.Gallen

Spar Schweiz Wolfgang Mähr Regional director IT 16.02.2004 Gossau

Valora Group Willy Schmid Director sourcing Switzerland

17.09.2003 Muttenz

Vögele Schweiz Bernhard Westphal Head of material planning

25.07.2003 Pfäffikon

Vögele Schweiz Stefan Widmer Store manager 18.08.2003 St.Gallen

Volg AG Vogler DC director 03.03.2004 Oberwinterthur

Volg AG S. Näf DC vice-director 05.03.2004 Oberwinterthur


8.4. Curriculum Vitae

Name Alfred Angerer

Born 19th September, 1974 in Puebla (Mexico)

Education

Oct. 2002 − Dec. 2005 University of St.Gallen (Switzerland)

Doctoral student

Oct. 1995 − Aug. 2001 Karlsruhe University of Technology (Germany)

“Diplom Wirtschaftsingenieurwesen“ (Master in Industrial

Engineering and Business Science)

Oct. 1999 − June 2000 University of Edinburgh (Scotland)

Honour courses in Department of Economics

Sept. 1986 − June 1995 Bodensee Gymnasium, Lindau (Germany)

„Abitur“ (equivalent to A-levels)

Employment

Since Jan. 2006 McKinsey & Company, Munich (Germany)

Supply Chain Management consultant

Oct. 2002 − Dec. 2005 University of St.Gallen (Switzerland)

Research associate

Jan. 2002 – Aug. 2002 Nestlé Germany, Biessenhofen (Germany)

Supply Chain Management trainee

July 2000 – Sept. 2000 Lycos Europe-Bertelsmann, Guetersloh (Germany)

Internship in the Business Development department

Oct. 1996 – Dec. 1999 WebDesign Angerer & Dolling GbR mbH, Karlsruhe

(Germany)

Independent management of an own internet company

May 1999 – June 1999 Broadgate Technology Services, Madras (India)

Internship at software and internet-service provider

July 1997 – Sept. 1997 Siemens AG, Karlsruhe (Germany)

Internship in the department "Sales of Communication

Systems and Networks"

the impact of automatic store replenishment systems on...

Documents