T.R.

MARMARA UNIVERSITY

Department of Industrial Engineering

SUPPLY CHAIN PERFORMANCE

MEASUREMENT AND MANAGEMENT

HAZAL KARABIYIK

June 2009

T.R.

MARMARA UNIVERSITY

Department of Industrial Engineering

SUPPLY CHAIN PERFORMANCE

MEASUREMENT AND MANAGEMENT

HAZAL KARABIYIK

June 2009

Asst. Prof. Dr. Bahar SENNAROĞLU Prof. Dr. Akif EYLER

Supervisor Jury Member

UNIVERSITÀ DEGLI STUDI DI PERUGIA

FACOLTÀ DI INGEGNERIA

Dipartimento di Ingegneria Industriale

SUPPLY CHAIN PERFORMANCE

MEASUREMENT AND MANAGEMENT

Tesi di laurea di: Relatore:

Hazal KARABIYIK Prof. Ing. Stefano Saetta

Correlatore:

Dr. Ing. Paolo Taticchi

Anno Accademico 2008-2009

ACKNOWLEDGMENT

This thesis was written in Italy, in the Università degli Studi di Perugia, during I was

student by Erasmus Exchange Program. I have been blessed by having many good advisors

and supporters with whom we could chew over different ideas that have gone into this thesis.

I am deeply grateful to my advisor, Dr. Ing. Paolo Taticchi, without his tremendous

effort in supporting and guiding me I could not finish this thesis at this level.

Finally, I would like to thank to my family and my friends for their continual support

during my studies.

TABLE OF CONTENTS

ABSTRACT................................................................................................. VI

ABBREVIATIONS...................................................................................... VII

LIST OF FIGURES...................................................................................... VIII

LIST OF TABLES ....................................................................................... IX

PART I. INTRODUCTION and OBJECTIVES ...............................................................1

I.1. INTRODUCTION .....................................................................................................1

I.2. OBJECTIVES ...........................................................................................................2

PART II. GENERAL BACKGROUND ....................................................... 3 II.1. PERFORMANCE MEASUREMENT ......................................................................3

II.2. SUPPLY CHAIN PERFORMANCE

MEASUREMENT...........................................................................................................4

II.3. CITATION and COTATION ANALYSIS ...............................................................5

II.4. RESEARCH METHODOLOGY..............................................................................7

PART III. THESIS ....................................................................................... 11 III.1. FRAMEWORKS IDENTIFICATION ....................................................................11

III.2. GROUPING of GOOD CHARACTERISTICS .......................................................16

III.3. DESIGN of a NEW FRAMEWORK .......................................................................19

PART IV. RESULTS ................................................................................... 26

PART V. DISCUSSIONS and EVALUATIONS ......................................... 27

REFERENCES ............................................................................................ 28

VI

ABSTRACT

SUPPLY CHAIN PERFORMANCE MEASUREMENT AND

MANAGEMET

This paper first gives the information about Performance Measurements and Supply

Chain Management. After introduction to these concepts, the objective of paper is defined as to

find a proper framework for measuring performance measurement in Supply Chain. Also, the

need for performance measurement in Supply Chain was explained.

By means of ISI Web Knowledge “Performance” and “Measurement” and “Supply

Chain” were searched as key words. By this research, 231 articles were found in literature.

Also, the data set about the research was taken. With this data many graphics about literature

were created and explained in this paper. This brings the idea of how the literature analysis like

which authors are interested in this subject, which journals published about this and which

years authors started to write more articles about this subject.

231 articles were investigated by reading each abstract and then 47 of them were

selected for this project. After reading 47 articles, performance models that have been created

by different authors were explained and strengths and weaknesses table were created in order to

understand each framework. Also, common characteristics that authors mentioned in their

articles were grouped.

In thesis part, while creating framework, common characteristics and the strengths of

each frameworks were considered. In the first framework performance attributes and

management level aspects were used. Also to identify Key Performance Indicators for new

framework, frameworks that have been applied in the past were used and some of Key

Performance Indicators were created for this framework. But it is not enough to reach the aim

of the project. To add Supply Chain aspects Supply Chain Operations Reference Model was

added to the first framework, the new framework was created according to performance

attributes, management levels, internal and external aspect and whole Supply Chain parts.

By means of these frameworks, managers can show the whole Supply Chain. Also, to

show that every part of supply chain is another company as themselves, same performance

attributes in the first framework was put in the other parts. Then linking the all performance

attributes, Global supply performance attributes were obtained. To consider this, managers can

measure own company performance and also the whole Supply Chain performance.

Furthermore, in discussion part, it is discussed that the creativeness of the framework

deeply. In Evaluation part, the positive and the negative side of the framework were explained.

Then, suggestions for the using the framework were defined and in order to improve the

framework some steps were suggested for future works like validation of the framework is

needed.

VII

ABBREVIATIONS

SC : Supply Chain

SCM : Supply Chain Management

KPI : Key Performance Indicator

SCP : Supply Chain Performance

PCTM : KPI Cost Transformation Matrix

JIT : Just in Time

TQM : Total Quality Management

CSF : Critical Success Factors

PBC : Performance Based Costing

CRM : Customer Relationship Management

QoS : Quality of Services

QLF : Quality Loss Function

DEA : Data Envelopment Analysis

SCOR : Supply Chain Operations Reference-model

SCC : Supply-Chain Council

GS : Global Supply

VIII

LIST OF FIGURES

PAGE NO

Figure II.1 The phases of Development of Performance Measurement ...........................4

Figure II.2 Research Methodology .................................................................................7

Figure II.3 Published Items per Year ..............................................................................8

Figure II.4 Number of Articles per Source Title..............................................................8

Figure II.5 Number of Articles per each Author..............................................................9

Figure II.6 Number of Articles per Document Type........................................................9

Figure II.7 Number of Articles per General Categories ........................................................... 10

Figure II.8 Number of Articles per Subject Area ....................................................................... 10

Figure III.1 Metrics for the Performance Evaluation of a Supply Chain ..........................12

Figure III.2 Supply Chain Performance Metrics Framework ...........................................13

Figure III.3 Measuring Performance in New Enterprise ..................................................13

Figure III.4 Measurement of Quality of Service in Supply Chain ...................................14

Figure III.5 Risk Management Framework .....................................................................15

Figure III.6 Performance Attributes at Level 1 ...............................................................19

Figure III.7 First framework ...........................................................................................20

Figure III.8 The SCOR Model described at different level of details ..............................22

Figure III.9 SCOR model ...............................................................................................23

Figure III.10 Second Framework with SCOR model ......................................................25

IX

LIST OF TABLES

PAGE NO

Table II.1 Most Frequently cited performance measurement works ................................7

Table III.1 Frameworks for Performance Measurement in Supply Chain ........................11

Table III.2 Strengths and Weaknesses of Frameworks ....................................................15

1

PART I

INTRODUCTION AND OBJECTIVES

I.1. INTRODUCTION The interest in managing supply chains is growing rapidly among companies around

the world. Major forces behind this development and increasing competitive pressure and

belief that working cooperatively in supply chains (SCs) can create a competitive advantage.

Coordinating activities in supply chain, however, is difficult. The difficulties are partly due to

the complexity induced by the large number of related and independent activities in the

supply chain.

Understanding the interdependencies and the complex casual relationships in a supply

chain is therefore crucial to the successful management of activities. In many organizations

the problems show us that the use of system thinking is insufficiently developed although it

has been with us for several decades.

Because of the lack of system thinking, many firms approached to another important

area: the design of performance measurement systems with supply chain. A performance

measurement system plays an important role in managing a business as it provides the

information necessary for decision making and actions.

I.1.1. Supply Chain Management Supply chain management (SCM) is the integration of activities which starts with the

procurement materials, continues with the transformation of the material to semi-finished or

finished products and extends to the transportation of the product to the final customer. The

goal is to establish a chain which provides the greatest value to the customer and in the

meantime, to decrease waste considerably.

The object of SCM obviously is the supply chain which represents “a network of

organizations that are involved, through upstream and downstream linkages, in the different

processes ad activities that produce value in the form of products and services in the hands of

the ultimate customer”. In a broad sense a supply chain consists of two or more legally

separated organizations, being linked to material, information and financial flows. These

organizations may be firms producing parts, components and end products, logistic service

providers and even the (ultimate) customer himself.

A network usually will not only focus on flows within a (single) chain, but usually

will have to deal with divergent and convergent flows within a complex network resulting

from many different customer orders to be handled in parallel. In order to ease complexity, a

given organization may concentrate only on a portion of the overall supply chain.

The objective governing all endeavors within a supply chain is seen as increasing

competitiveness. This is because no single organizational unit now is solely responsible for

the competitiveness of its products and services in the eyes of the ultimate customer, but the

supply chain as a whole. Hence, competition has shifted from single companies to supply

chains. Obviously to convince an individual company to become a part of supply chain

requires a win-win situation for each participant in the long run, while this may not be the

case for all entities in the short term.

We are able to define the term Supply Chain Management as the task of integrating

organizational units along a supply chain and coordinating material, information and financial

2

flows in order to fulfill (ultimate) customer demands with the aim of improving

competitiveness of a supply chain as a whole.

I.1.2. Performance Measurement Performance measures have two central effects and work in two directions. First of all

they can be used to describe the past and present of the process being considered. On the

other hand they can be used to set performance goals. This allows establishing a focus on the

future. By fixing a will-be value or target of a performance measure it is possible to watch the

progress in reaching the target and the success in achieving the target itself.

Organizations measure their performance in order to monitor their employees and

departments, to direct them, to provide feedback for being able to carry on their goals and to

assess the performance of the organization vis-à-vis the strategic and continuous

improvement goals. Organizations need both short term and long term performance

assessment. Traditional performance measurement systems rely on static metrics that are

easier to measure, to gather and to quantify. However, the focus in the contemporary business

environment has shifted from the present to the future. Thus dynamic metrics which show the

movement of a variable toward a target ratio are more meaningful.

I.2. OBJECTIVE The objective of this thesis is to explore the role of performance measurement in SCM

and develop a framework and a right set of Key Performance Indicators (KPIs) for SC

performance measurement. The literature analysis shows that there are not sufficient

frameworks for performance measurement. In addition to that, to measure performance

measurement is not standard, how can we measure? What KPIs do we need? This thesis also

answers these types of questions.

I.2.1. Relevance of Performance Measurement in SCM In recent years, a number of firms realized the potentials of SCM. However, they often

lack the insight for the development of effective performance measures and metrics needed to

achieve a fully integrated supply chain. Moreover, such measures and metrics are needed to

test and reveal the viability of strategies without which a clear direction for improvement and

realization of goals would be highly difficult. Lee and Billington, 1992 argue that discrete

sites in a supply chain do not lead to an improved productivity if each is to pursue its goals

independently, which has been the traditional practice. There is, however, a greater need to

study the measures and metrics in the context for the following two reasons: The first one is

that lack of a balanced approach and the second one is that lack of a clear distinction between

metrics at strategic, tactical, and operational levels.

The second reason is that lack of a clear distinction between metrics at strategic,

tactical, and operational levels. Metrics that are used in performance measurement influence

the decisions to be made at strategic, tactical, and operational levels. However, we fail to

come across any such classification for supply chain management. Using a classification

based on these three levels, each metric can be assigned to a level where it would be most

appropriate. Therefore, it is clear that for effective management in a supply chain,

measurement goals must consider the overall supply chain goals and the metrics to be used.

(Gunasekaran et all, 2001)

3

PART II

GENERAL BACKGROUND

II.1. PERFORMANCE MEASUREMENT It is suggested that the performance measurement system be designed as a part of the

strategy implementation plan, that each metric’s effect on the firm’s goal be determined and

that the performance measurement system be conformable with the firm’s cultural values.

Measurement systems should focus on information rather than control, and should be revised

continuously.

The goal of performance measurement is to improve business processes and that one

should be careful when selecting the performance metrics. Performance measurement is a

time consuming and costly process. One should determine how performance measurement

will improve business processes. In this way, it is possible to identify the minimum number of

performance metrics which will provide maximum benefit during implementation.

Organizations measure their performance in order to monitor their employees and

departments, to direct them, to provide feedback for being able to carry on their goals and to

assess the performance of the organization vis-à-vis the strategic and continuous improvement

goals. Organizations need both short term and long term performance assessment. Traditional

performance measurement systems rely on static metrics that are easier to measure, to gather

and to quantify. However, the focus in the contemporary business environment has shifted

from the present to the future. Thus, dynamic metrics which show the movement of a variable

toward a target ratio are more meaningful. (Tarr, 1996).

It is suggested that the performance measurement system be designed as a part of the

strategy implementation plan, that each metric’s effect on the firm’s goal be determined and

that the performance measurement system be conformable with the firm’s cultural values.

According to Tarr, 1996, measurement systems should focus on information rather than

control, and should be revised continuously.

Robson, 2004, underlines that the goal of performance measurement is to improve

business processes and that one should be careful when selecting the performance metrics.

Performance measurement is a time consuming and costly process. One should determine

how performance measurement will improve business processes. In this way, it is possible to

identify the minimum number of performance metrics which will provide maximum benefit

during implementation. (Muratoglu, 2008)

Also, there is a figure about the historical development of Performance Measurement.

Also, this history is shown in Figure II.1. Performance Measurement starts in 1450’s. These

start with the basic measurement of financial transactions, an element that is still in evidence

today and which is focused on the traditional “buy cheap – sell dear – make profit”

perspective. It can be reasonably argued that these internally focused perspectives pervaded

the thinking of management for a long time, perhaps until the end of the World War II, and

the subsequent steady rise of the “quality revolution”. Although starting at a very low-level in

the 1950s, by the 1970s and 1980s the quality revolution was in full swing. The fourth phase

of performance measurement emerged in which the financial measures began to be regarded

as part of an integrated performance measurement system.

The Balanced Scorecard, probably the most widely evaluated and discussed

performance measurement system of all time, was introduced to the world by Kaplan and

Norton, 1996. The final and current phase is one in which the importance of the supply chain

4

emerges. From a philosophical point of view this represents a significant shift away from the

unitary to the pluralist perspective. It recognizes that customer satisfaction can only come

from the supply chain functioning effectively in totality (both processes and process

interfaces). This closely follows the logic “Theory of Constraints” model as focused on intra-

organizational activities, and its subsequent extrapolation to a wider inter-organizational

perspective. However, success in meeting customers’ needs requires an increasing

international perspective from the supply network and this introduces a new vector of pan-

cultural into the performance measurement perspective. (Morgan, 2007)

Figure II.1 The phases of Development of Performance Measurement

II.2. SUPPLY CHAINPERFORMANCE MEASUREMENT Cook and Hagey, 2003 suggest that firms should first determine their strategy and

after that, they should design their supply chain strategy according to the requirements of the

corporate strategy. Tracking the performance of the whole supply chain as well as making use

of analyses instead of estimations when setting goals, is essential for effective supply chains.

Successful firms are those which align their operations with those of the customers, suppliers

and the other parties in the chain, and which know their own performance metrics as well as

the performance metrics of other members of the chain.

A supply chain performance measurement system should comprise the whole chain

and should be congruent with the actual performance measurement systems of the firm. This

system should consider the interests of all the business partners, contain financial and non-

financial metrics in a balanced way, not focus on functional departments but focus on

business processes. Performance metrics should be simple, clear and meaningful, and they

should be limited in number. The flexibility of the performance measurement system, the

adaptability of the metrics according to the goals is essential. The performance measurement

system should contain not only historical data, but it should also have a structure which is

capable of covering future and potential developments.

Caplice and Sheffi, 1995, mention that a good supply chain performance measurement

system should be comprehensive, focus on cause and effect, are both vertically and

horizontally integrated, be internally comparable and useful. A comprehensive supply chain

performance measurement system is described as having several dimensions such as internal

efficiency, customer satisfaction, financial data etc. Supply chain performance measurement

should be comparable and conformable to both the firm’s departments and to between the

5

other firms in the supply chain. They suggest metrics such as customer satisfaction/quality,

time, costs and assets, which can be followed both in terms of results attained and in terms of

diagnosis.

In general, traditional performance metrics are financial and they measure return on

investment, profitability, cash return etc. These data are absolute and objective. However,

these measures are criticized as showing a limited perspective of the firm, being retroactive

and not being able to provide strong forecasts about the future. (Parker, 2000).

For an efficient performance measurement system, Morgan, 2004 suggests that

strategy should drive production activities and determine the goals for the performance

measurement system. Performance measurement should lead performance improvement, that

if performance measurement is used as feed forward instead of feedback, it would help

management to take care of the matters which require strategic improvements.

Successful firms are those which have conformable competitive strategies and supply

chain strategies. When establishing supply chains, it is suggested to balance responsiveness

and efficiency in the best way to provide the competitive strategy. There are four factors

which affect supply chain performance in terms of responsiveness and efficiency; inventory,

transportation, facilities and information. In the meantime, these four factors determine the

conformability of the competitive strategy and supply chain strategy. (Muratoglu, 2008)

II.3. CITATION and CO-CITATION ANALYSIS The challenges posed by performance measurement are enduring. The first ever

edition of the Administrative Science Quarterly, published in 1956, contained a paper entitled

“Dysfunctional Consequences of Measurement” (Ridgway, 1956). In that paper, Ridgway

explored the relative strengths and weaknesses of single, multiple and aggregated

performance measures, bemoaning the “strong tendency to state numerically as many as

possible of the variables with which management must deal”. A few years earlier – in 1952 –

Chris Argyris, in his classic text The Impact of Budgets on People, reported that managers

claimed to “feed machines all the easy orders at the end of the month to meet [their] quota”

(Argyris, 1952). These two themes – the desire to quantify and the unanticipated

consequences of measurement lead that doyenne of management – Peter Drucker – to argue

that one potential solution was to introduce “balanced” sets of measures. “Market standing,

innovation, productivity, physical and financial resources, profitability, manager performance

and development, worker performance and attitude, and public responsibility” are appropriate

performance criteria says Drucker in his 1954 publication The Practice of Management

(Drucker, 1954).

If the clock is turned forward thirty years then we find that the same themes are still

being discussed. Power’s book The Audit Society: Rituals of Verification bemoans the rise of

the “Audit Society”, arguing that practitioners and policy makers have become obsessed with

measurement and regulation (Power, 1997) – the desire to quantify. Hayes and Abernathy

explore the unintended consequences of this obsession in “Managing our way to economic

decline”. They argue that inappropriate performance measures and poorly designed incentive

schemes were partly to blame for a short-term US business culture, which damaged the

country’s competitiveness and economic prospects (Hayes and Abernathy, 1980). Johnson

and Kaplan expanded these arguments, claiming that not only did measurement systems result

in unintended consequences, but also that the measurement systems many firms used were

woefully inadequate because they provided managers with redundant information as they

were based on assumptions that were grossly outdated given the changing nature of

organizational cost structures (Johnson and Kaplan,1987). Alfred Chandler made similar

points in The Visible Hand, which emphasized that many of the basic principles of accounting

6

had remained largely unchanged since they were first developed in the 1920s by the DuPont

cousins and Donaldson Brown (Chandler, 1977).

These recurring themes – the desire to quantify and the unanticipated consequences of

quantification – appear to have resulted in frequent “re-discoveries” of Drucker’s 1954

suggestion that balanced measurement systems should be developed (Drucker, 1954).

Throughout the 1980s and early 1990s, numerous authors suggested measurement

frameworks that might be appropriate – the performance pyramid (Lynch and Cross, 1991),

the results-determinants framework (Fitzgerald et al., 1991), the performance measurement

matrix (Keegan et al., 1989) and, of course, the balanced scorecard (Kaplan and Norton,

1992). The result was that a dominant research question in the mid-1990s, at least for the

operations management community with an interest in performance measurement, was how

can these so-called “balanced performance measurement systems” be developed and

deployed. There followed a rich stream of work on the design and deployment of performance

measurement systems, which reported on research to develop processes for designing

measurement systems and barriers to their successful implementation (Bourne et al., 2000;

Dixon et al., 1990; Neely et al., 1996).

To examine these developments more fully and the basis of empirical evidence a

citation/co-citation analysis of research on performance measurement was conducted. Recent

advances in information technology and online data storage have considerably eased the

process of citation/co-citation analysis. The dataset used in this paper was constructed using

the ISI Web of Science database. Every publication that contained the phrase “performance

measurement” in its title, keywords or abstract was identified and downloaded. This search

identified 1,352 papers published in 546 different journals. The earliest paper included in the

dataset was published in 1981 and the most recent in 2005 (84 per cent of publications

included in the dataset have been published since January 1995).

The data were downloaded using the Sitkis software. Before conducting the analysis a

substantive review of the generated dataset was undertaken. Every record that related to the

20 most cited authors was reviewed and confirmed (the top 5 per cent of citations) and the

title of every journal in the dataset was checked. Other obvious errors in the dataset were

corrected in line with current best practice for bibliometric analysis.

The 1,352 papers included in the dataset provide some 31,646 citations, covering

25,040 works and drawing on 16,697 different lead authors. The most frequently cited

authors were: Bob Kaplan (398 citations), Andy Neely (153 citations), Rajiv Banker (134

citations), Abraham Charnes (111) citations and Robin Cooper (70 citations). As can be seen

from these data, there were only four lead authors whose works were cited more than 100

times and interestingly these four lead authors have somewhat different disciplinary

backgrounds – accounting (Kaplan), operations management (Neely), accounting/operations

research and information systems (Banker) and mathematics/operations research (Charnes).

Of the remaining citations – twelve lead authors were cited between 50 and 100 times, 266

were cited between 10 and 49 times and 11,929 (71.4 per cent) were cited only once. The

spread of journals from which citations appeared is interesting. In total, the citations were

drawn from 11,443 different journals. The most frequently cited journals were the Harvard

Business Review (650 citations), the International Journal of Operations & Production

Management (552 citations) and the Journal of the American Medical Association (339

citations). Together these three journals accounted for some 4.9 per cent of citations, while

the top ten journals accounted for 10.2 per cent of citations and 73.6 per cent of journals

contained only paper that was cited in the dataset. This diversity of source materials – large

number of rarely cited Works and journals – is indicative of a widely distributed and

relatively immature field of academic study, which has relatively little consensus about its

core theoretical foundations.

7

II.3.1. Performance measurement research: analysis of citations At a more detailed level, it is possible to explore the frequency of citations for

individual pieces of work. Once again the pattern of citations is diverse, further supporting the

suggestion that the field of performance measurement is immature with little consensus. Only

10 works are cited more than 30 times (Table II.1). Eighty-seven per cent are cited only once

and 99 per cent are cited less than 5 times. The most striking observation about the data

included in Table I is the dominance of Bob Kaplan and David Norton and the balanced

scorecard. Given that research data suggest that between 30 and 60 per cent of firms have

adopted the balanced scorecard (Rigby, 2001; Silk, 1998; Williams, 2001; Speckbacher et al.,

2003, Marr et al., 2004), this dominance is not surprising, but it is interesting, especially when

one bears in mind the relative paucity of empirical research into the performance impact of

measurement frameworks, including the balanced scorecard. (Neely, 2005)

Table II.1 Most Frequently cited performance measurement works

II.4. RESEARCH METHODOLOGY Research Methodology of this work, is shown in the Figure II.2. After Literature

Review and analysis, frameworks about Performance Measurement and Supply Chain in

literature were investigated. Then, strengths and weaknesses of each framework were defined.

After this identification, common good characteristics that many authors mentioned about

were grouped. By considering strengths of frameworks and good characteristics, the new

framework for performance measurement in Supply Chain was created.

Figure II.2 Research Methodology

8

II.4.1. Performance Measurement Literature Review In literature review part, main journal and books debating Performance Measurement

and Supply Chain topics have been reviewed through the electronic libraries of Perugia

University and Bradford University. Large information is also available on the web.

II.4.2. Performance Measurement Literature Analysis In this section, the literature review carried out is analyzed different perspective. 3 key

words, “performance and measurement” and “supply chain”, were investigated through ISI

Web of Knowledge. By means of this research, 231 articles were found. With these 231

articles, citation and co-citation analysis were done and the next section will explain the

results of this analysis.

II.4.2.1. Citation / Co-Citation Analysis The earliest paper about Performance Measurement and Supply Chain included in the

data set was published 1998 and the most recent in 2009. On the other hand the most number

of papers were published in 2007 is shown in Figure II.3. From this figure, it is obvious that

in 2008 there is less articles were published then in 2008 did. Furthermore, from the figure it

is understandable after 2000; there is a really big increase especially in 2003. This means that

after 2000’s the importance of PM was understood by academicians, although they say that it

is still not enough for development of PM. This search identified 231 papers published in 10

different journals. In this research, the most frequently used article is International Journal of

Production Economies shown in Figure II.4. The next ones are International Journal of

Supply Chain Management and Production Planning and Control.

Figure II.3 Published Items per Year

Figure II.4 Number of Articles per Source Title

9

There are not so much authors who are interested in PM. Figure II.5 shows that the

most frequently used authors are Gunasekaran A. and Reiner G. The articles of Gunasekaran

A. will be used more frequently in this paper. His frameworks and his perspective helped me

to create new frameworks for measuring PM. The main problem is in PM that there are few

authors who write articles about PM. Therefore, all articles have similar point of view. On the

other hand, the most used document type of papers is meeting and article is shown in Figure

II.6.

Figure II.5 Number of Articles per each Author

Figure II.6 Number of Articles per Document Type

There are several possible explanations for why the field of performance measurement

has not professionalized from an academic perspective. The main explanation is that

performance measurement is not and never can be a field of academic study because its

diversity. From literature survey, it is obviously seen that authors come from a variety of

different disciplinary backgrounds. Figure II.7 shows us the main classes of articles are

Science and Technology and Social Science which are not so close to each other. Besides,

Figure II.8 shows us that this variety. The most frequently used articles based on Engineering,

Business and Economics, Computer Science and Operations Research and Management

Science. The main point is to combine these different disciplinarians under the Performance

Measurement title.

10

Figure II.7 Number of Articles per General Categories

Figure II.8 Number of Articles per Subject Area

II.4.3. Literature Review and Analysis Findings

The original set of 231 articles has been reviewed. About 50% of the papers have been

excluded after abstract reading. The remaining articles have been read and 47 articles has

been identified has determinants for this research.

11

PART III

THESIS

III.1. FRAMEWORKS IDENTIFICATION This section shows that the frameworks that were found in literature among 47

articles. Each framework is explained shortly and also there are figures of all them. At the

end, there is a compassion section between these frameworks according to their strengths

and weaknesses.

III.1.1. Frameworks in Literature This thesis will mention five frameworks which can be used for measuring SC

performance, will be analyzed in next part. These frameworks for performance

measurement of Supply Chain are shown in chronological order in Table III.1.

Table III.1 Frameworks for Performance Measurement in Supply Chain

Name of Framework Year

Metrics for the Performance Evaluation of a Supply Chain 2001

Supply Chain Performance Metrics Framework 2004

Measuring Performance in New Enterprise 2005

Measurement of Quality of Service in Supply Chain 2006

Risk Management Framework

2007

III.1.1.1. Metrics for the Performance Evaluation of a Supply Chain This framework established by Gunasekaran et. al., 2001, to measure the strategic,

tactical and operational level performance in a supply chain. This has been done so as to

assign them where they can be best dealt with by the appropriate management level, and for

fair decisions to be made. The metrics are also distinguished as financial and non-financial so

that a suitable costing method based on activity analysis can be applied. In some cases, a

metric is classified as both financial and non-financial. For example, the buyer-supplier

relationship can be qualified in terms of financial performance achieved, such as cost savings,

and in terms of tangible and intangible benefits, like quality, flexibility and deliverability.

And this framework is shown in Figure III.1.

III.1.1.2. Supply Chain Performance Metrics - Framework This framework is based in part of a theoretical framework discussed by Gunasekaran

et. al., 2004 which is first framework that analyzed in this thesis. A framework for

performance measures and metrics are presented considering the four major supply chain

activities/processes (plan, source, make/assemble and deliver) shown as in Figure III.2. These

metrics also are classified at strategic, tactical, and operational to clarify the appropriate level

of management authority and responsibility for performance. Measures are grouped in cells at

12

the intersection of the supply chain activity and planning level. The items in each cell are

listed in the order of importance based on percentage importance ratings.

Figure III.1 Metrics for the Performance Evaluation of a Supply Chain

Some measures appear in more than one cell, indicating that measures may be

appropriate at more than one management level. Measures used at different management

levels will most assuredly require adjustment to tailor them to planning and control needs of

the different levels. There is nothing novel about this approach, as it has been used for years

in management planning and control systems. This framework should be regarded as a

starting point for an assessment of the need for supply chain performance measurement.

III.1.1.3. Measuring Performance in New Enterprise Figure III.3 provides examples of value creation areas, critical success factors (CSF),

performance measures and CSF drivers. It is framework for relating value creating areas to

CSFs and CSF drivers, and to performance measures. This framework does not, and could not

possibly contain all value creating areas, CSFs, performance measures, or CSF drivers,

because they are likely to vary from firm to firm, and from business model to business model.

13

Organizations could develop their own matrices for relating the elements of Performance

Based Costing (PBC)-matrices based on their unique needs. Inter-organizational teams of

Virtual business partner employees or from supply chain partners could use this methodology

to develop PBCs for assessing value system performance from end to end. Such a system

wide approach to PBC could provide a starting place for the internal PBC of individual

partners. (Gunasekaran et al., 2005)

Figure III.2 Supply Chain Performance Metrics Framework

Figure III.3 Measuring Performance in New Enterprise

14

III.1.1.4. Measurement of Quality of Service in Supply Chain This frame work which is presented in Figure III.4 focuses on quality service in

Supply Chain. A framework for the modeling and measurement of Quality of Services (QoS)

in supply chain the framework is divided in two parts:

(1) Model development (Part A)

(2) Measurement methodology (Part B)

In part A, the conceptualization of the model is based on gap analysis. Gaps covered

in this framework are divided into two types. One gap type is forward which is defined as

basic supply chain direction (direction of movement of product). The other gap type is reverse

which is considered as reversed direction of the basic supply chain process (reverse to the

physical movement of the product) A typically supply chain is influenced by a variety of

external environmental factors such as economic, political, legal which may play important

role in the context of a global economy, and affect the supply chain sourcing, distribution,

plant location and other operational decisions.

In part B, the measurement of service quality of supply chain is considered as

quantitative and qualitative aspects. There is two phases in part B first one is data collection

and the second one is data analysis. Data collection is made by surveys, expert interviews and

field observation. Data analysis are made by statistical analysis, quality loss function (QLF),

and data envelopment analysis (DEA) (Seth et al., 2006)

Figure III.4 Measurement of Quality of Service in Supply Chain

III.1.1.5. Risk Management Framework The schematic representation which is shown in Figure III.5 identifies the five major

components of the framework. In many respects, this is a fairly generic framework that may

apply across a number of business settings. A number of typical elements have been identified

within each of the five major components. There are features of the framework which are

intended to suggest, that it is more than simply an aggregation of the five components. The

linear sequential process can be explained like this. Changes in desired performance outcomes

may trigger a sequence of actions to reconsider the perceptions of the risks involved, asses the

impact on the portfolio being managed while simultaneously reviewing the evidence of the

15

primary risk drivers involved. The essence of risk perceptiveness pervaded the whole

framework. This framework also gives different functions for each component which are

included own determinants. (Ritchie and Brindley, 2007)

Figure III.5 Risk Management Framework

III.1.2. Comparison This comparison part gives us table about the strengths and weaknesses of all

frameworks that we mentioned above. This table also shows the year of foundation of

frameworks and by whom. By using this table, the compassion among the frameworks of

Supply Chain Performance Measurement can be done easily. When building new framework,

strengths of each framework were

considered, we tried to avoid weaknesses.

For example, the first two

frameworks have the same strength feature

is having managerial aspects. From these,

it is obvious that to develop framework

having managerial aspects will be better.

Also, we can understand that to develop

framework which has changeable features

from business to business will not be so

efficient framework.

To sum up with, this table is like

our guide while creating new frameworks.

We will use this, to follow positive sides

of frameworks that crated in the past also

to avoid negative sides of them.

It is also impossible to add all

strengths into one framework and it is also

impossible to develop framework which

has no weaknesses. On the other hand, we

want to reach the aim of this work which is

to develop a framework and right sets pf

KPIs for SC performance measurement. It

is important to choose proper KPIs. While

selecting the KPIs, we consider the KPIs

that other authors used while creating their

frameworks.

Table III.2 Strengths and Weaknesses of Frameworks

16

III.2. GROUPING of GOOD CHARACTERISTICS In this section, there is a grouping about the key elements that different authors wrote

in their article about same subject. There are seven main key elements that in the past authors

has mentioned.

III.2.1. Lack of a balanced approach between financial and non-

financial indicators

Gunasekaran et. al., 2001

o Many companies have realized the importance of financial and non-financial

performance measures. They failed to understand them in a balanced framework.

o For a balanced approach, companies should bear in mind that, while financial

performance measurements are important for strategic decisions and external reporting,

day-to-day control of manufacturing and distribution operations is better handled with

non-financial measures

III.2.2. Clear distinction between levels Gunasekaran et. al., 2001 o Metrics that are used in performance measurement influence the decisions to be made

o at strategic, tactical and operational levels.

o However, we fail to come across any such classification for supply chain management.

o Using classification based on these three levels, each metric can be assigned to a level

where it would be most appropriate.

Cuthbertson and Piotrowicz, 2008 o A supply chain dimension was lacking. The cases concentrated mainly on internal

o issues at a company level, not on the whole supply chain

o Operational benefits dominated, while the strategic impact was often ignored

Angerhorfer et. al., 2006

o The decision on which level(s) collaboration is suitable and beneficial is determined by

the market environment and business strategy

o The operational level may take the form of a routine task such as transportation

scheduling

o At managerial level could lead to better planning and forecasting

o At strategic level may involve decisions that will have medium-to-long term effects.

III.2.3. Capacity Gunasekaran et. al., 2001 o The role of capacity in determining the level of all supply chain activities is clear. This

highlights the importance of measuring and controlling the capacity utilization

o By measuring capacity, gains flexibility, lead-time and deliverability

Chan and Qi, 2003 o The ability of one specific activity to fulfill a task or perform a required function

o This dimension mainly concerns the maximum amount of tasks that a process or an

activity can complete under the normal conditions. (Production and transport)

III.2.4. Flexibility Gunasekaran et. al., 2004

o Flexibility in meeting a particular customer delivery requirement at an agreed place,

agreed mode of delivery and with agreed upon customized packaging

o This type of flexibility can influence the decision of customers to place orders.

17

Theeranuphattana and Tang, 2007

o The agility of a SC in responding to marketplace changes to gain or maintain

competitive advantage

Chan and Qi, 2003 o The ability of one specific activity to adapt to the varying functional requirements or

respond to the changes.

o Flexibility is based on the range of a variable capacity of tasks, processes or activities

that can be completed in the specific period of time and at a reasonable cost.

Beamon, 1999

o Reductions in the number of backorders.

o Reductions in the number of lost sales.

o Reductions in the number of late orders.

o Increased customer satisfaction.

o Ability to respond to and accommodate demand variations, such as seasonality.

o Ability to respond to and accommodate periods of poor manufacturing performance

(machine breakdowns).

o Ability to respond to and accommodate periods of poor supplier performance.

o Ability to respond to and accommodate periods of poor delivery performance.

o Ability to respond to and accommodate new products, new markets, or new

competitors.

o Flexibility, which is seldom used in supply chain analysis, can measure a system's

ability to accommodate volume and schedule fluctuations from suppliers,

manufacturers, and customers.

III.2.5. Costing System Gunasekaran et al., 2004

o To deal with distribution cost, measuring individual cost elements together with their

impact on customer service encourages tradeoffs that lead to a more effective and

efficient distribution system

Gunasekaran et. al., 2005

o Activities are difficult to trace because of the distributed nature of the virtual enterprise

or supply chain environment

o Many indirect costs will become direct costs and many direct costs will become indirect

costs; Logistics costs are a major portion of the total cost

o Many costs are hidden, and thus difficult to measure

o Knowledge management and information technology costs will be major costs in the

virtual enterprise or supply chain environment

o A complex cost system will not likely work with the supply chain/virtual enterprise—a

cost system similar to back flush costing may be suitable for new enterprise models

III.2.6. Lacks and Limitations Cousins et. al., 2008

o A cross-sectional study is limited in its ability to study a concept, such as socialization

and socialization mechanisms, which involve multiple actors over time. The lack of a

longitudinal perspective means they cannot gain a detailed understanding of the effect

of these variables over the life cycle of the supply relationship.

o The focus of our research on strategic relationships between buyer-supplier firms

limits the extent to which the findings can be generalized to this context. Future

research could consider a broader relationship approach examining the interplay of

performances measures and socialization across a range of inter-firm relationships,

such as alliance partners.

18

Lai et. al., 2001

o The sample of respondents is all transport logistics service providers. The study

assesses information only from the perspectives of transport logistics service

providers. Consequently, it offers a self-reported, one-dimensional focus.

o Respondents are asked to report the perceived Supply Chain Performance (SCP) of

their companies as compared to the competition at a single point in time. Therefore,

SCP in transport logistics on a temporal dimension cannot be measured.

Cai J. et. al., 2009 o If the environment is changing drastically and frequently, mutually dependent

relationships of the KPIs accomplishment may change dramatically

and influence the accuracy of KPI cost transformation matrix (PCTM).

o The procedural framework and PCTM analysis approach are applied in enterprises

where supply chain management has already been actively deployed.

o Results from PCTM should not be adopted as direct decisions, but as supporting

information for decision-making.

o It cannot influence the details of mechanisms of KPIs accomplishment. x

Lockamy and McCormack, 2004

o It is not possible to make cross industry comparisons or to draw general

conclusions about this relationship for all supply chain populations based on the

presented results.

III.2.7. Supply Chain Metrics

Lambert and Pohlen, 2008 Problems

o Many measures identified as supply chain metrics are actually measures of internal

logistic operations as opposed to measures of supply chain management.

o The majority are single firm logistics measures such as fill rate, lead time on-time

performance of the supply chain.

WHY?

o The lack of measures that capture performance across the entire supply chain

o The requirement to go beyond internal metrics and take a supply chain perspective

o The need to determine the interrelationship between corporate and supply chain

performance

o The complexity of supply chain management

o The requirement to align activities and share joint information measurement

information to implement strategy that activities supply chain objectives.

o The desire to expand the line of sight with in supply chaiThe requirement to allocate

benefits and burdens resulting from functional shifts with in supply chain.

o The need to differentiate the supply chain to obtain a competitive advantage

o The goal of encouraging cooperative behavior across corporative functions and across

firms in the supply chain

Lohman et. al., 2004

o The scorecard does not anymore support the control of (a part of) the business––During

performance review sessions it can appear that business areas or current or new

challenges are not covered in the scorecard. Then additional requirements are

formulated for the next edition of the scorecard.

o The organizational objectives change––Since performance metrics are aimed at tracking

the performance towards the organizational objectives, a change in strategy hits the

heart of the scorecard.

19

Cuthbertson and Piotrowicz, 2008

o There was a lack of consensus regarding the measures used, so there was a lack of

common measures.

o Economic measures dominated, while social and environmental aspects were often

ignored.

Saad and Patel, 2006 Should be

o Based on organization’s strategy and needs

o Applicable to concepts such as Just in Time (JIT), Total Quality Management (TQM),

SCM and other approaches used by the organizations

o Intended for all employees

o Lead to employee satisfaction

o Flexible

o Vary between locations of the organizations

Otto and Kotzab, 2003

o Six complementary ways to measure

System Dynamics, Operations and Research Perspective, Logistics, Marketing,

Form an Organization point, Strategy

III.3. DESIGN of a NEW FRAMEWORK

III.3.1. First Framework The first framework was established by considering the five frameworks and good

common characteristics that we found in literature analysis. In addition to this information,

we add the Performance Attributes to this framework. In the following, we explained what

performance attributes are. There is a detailed explanation about how the first framework was

created and what its strengths and weaknesses are.

III.3.1.1. Performance Attributes There is a concept about “Performance Attributes” which were found in e-books that

are existed in www.suuply-chain.org Performance Attributes are used to benchmark with

competitors of companies. These are reliability, responsiveness, flexibility, cost and assets. In

Figure III.6 also there is a definition of each attributes; this was taken from SCOR 8.0

Strategic-Operational Metrics e-book.

Figure III.6 Performance Attributes at Level 1

20

III.3.1.2. First Framework Developed After literature view and compassion frameworks that have been created in the

literature, we wanted to take positive sides of frameworks. In addition to these, according to

literature grouping key elements, we want to add some characteristics to the framework.

To combine everything that we mentioned above, the first frameworks was created. In

this framework, the company was divided in to two groups, the first one is internal of the

company and the other one is external company. Also internal company was divided first

according to performance attributes then each performance attribute was divided according to

management levels which are shown in Figure III.7. Some of KPIs were taken from the other

frameworks that will be explained deeply above and some of them were created for this

framework.

In this framework, most of internal KPIs were taken from Metrics for the Performance

Evaluation of a Supply Chain (Gunasekaran et. al., 2001) and Supply Chain Performance

Metrics Framework (Gunasekaran et. al., 2004). According to articles, we created for external

KPIs.

Figure III.7 First framework

There is a detailed explanation of all internal KPIs:

The first internal KPIs, which are under Reliability attributes, are Order Fulfillment,

Delivery Reliability Performance and DriverReliability for Performance, respectively for

Strategic, Tactical and Operational Management Level. Order Fulfillment was mentioned in

SCOR model in Level 1. Gunasekaran et al., 2004 mentioned Delivery Reliability

Performance in his “Supply Chain Performance Metrics Framework”. The operational one

was mentioned in Metrics for the Performance Evaluation of a Supply Chain by Gunasekaran

et. al., 2001.

The second internal KPIs, which are under Responsiveness attributes, are Order

Fulfillment Cycle Time, Responsiveness to urgent deliveries and Efficiency of purchase order

cycle time, respectively Strategic, Tactical and Operational Management Level. Order

Fulfillment Cycle Time mentioned in SCOR model in Level 1. Responsiveness to urgent

deliveries and Efficiency of purchase order cycle time were mentioned in the article by

Gunasekaran et. al., 2001.

The third internal KPIs, which are under Flexibility attributes, are Upside Supply

Chain Flexibility, Flexibility of service system to meet customer needs and Frequency of

delivery, respectively Strategic, Tactical and Operational Management Level. Upside Supply

Chain Flexibility was mentioned in SCOR model in Level 1. Flexibility of service system to

21

meet customer needs was mentioned in figure by Gunasekaran et. al., 2004. The last

operational was mentioned in article by Gunasekaran et. al., 2001.

The forth internal KPIs, which are under Cost attributes, are Cost of Goods Sold,

Utilization of economic order quantity and Cost per operation hour, respectively Strategic,

Tactical and Operational. Cost of Goods Sold was mentioned in SCOR model in Level 1.

Utilization of economic order quantity was mentioned in figure by Gunasekaran et. al., 2004.

The last operational one was mentioned in article by Gunasekaran et. al., 2001.

The fifth internal KPIs, which are under Asset attributes, are Rate of return investment,

Percentage of defects and Inventory days of supply. Rate of return investment was mentioned

in article by Gunasekaran et. al., 2001. Percentage of defects was mentioned in article by

Gunasekaran et. al., 2004. The last operational one was created for this framework.

The external KPIs were created for this framework because in literature there is no

framework which is included external perspective. It was easier to find KPIs for Employers,

Customers and Suppliers rather than Investors, Partners and Regulations. KPI for Employers

is Employers Satisfaction, for Customers, it is Customer Satisfaction and Loyalty and for

Suppliers, it is Time to delivery product. After making brainstorming with my supervisor we

decided to KPIs for the rest. For Investors KPI is Condition shares in the market. The most

difficult one is for Partners because we could not decide whether the company is

manufacturing or service. Then, we decided to focus on normal manufacturing company.

Therefore, KPI of Partners is Number of product developed with partners. The last one is for

Regulators is Sustainability of business.

To sum up with, this framework has some positive sides such as: It considers

management levels which authors mentioned about it in the literature as a strong feature, also

external and internal perspective. The main difference between this framework and the other

which are existed in literature is that this framework has also Performance Attributes. On the

other hand, it can not give anything about SC; by this framework we can only consider our

company. We have no idea about the relationship with the other parts of SC. In addition to

that, a major holistic approach is needed. Because of these weaknesses, we deiced to add this

framework SC aspect.

III.3.2. Second Framework The aim of this work is to develop a framework for SC performance measurement.

However, the first framework has no issue about SC. Because of this, we wanted to add SC aspect

to the first framework. One of the frameworks in literature called “SC Performance Metrics

Frameworks” was built by Gunasekaran in 2004; he used the processes of Supply-Chain

Operations Reference (SCOR) Model, make, plan, source and deliver. To focus on SC,

following section gives the SCOR Model explanation.

III.3.2.1. SCOR Model

SCOR Model is the product of the Supply-Chain Council (SCC), an independent, not-

for-profit, global corporation with membership open to all companies and organizations

interested in applying and advancing the state-of-the-art in supply-chain management systems

and practices. The SCOR-model captures the Council’s consensus view of supply chain

management. While much of the underlying content of the Model has been used by

practitioners for many years, the SCOR-model provides a unique framework that links

business process, metrics, best practices and technology features into a unified structure to

support communication among supply chain partners and to improve the effectiveness of

supply chain management and related supply chain improvement activities.

22

The SCOR is a process reference model that has been developed and endorsed by the

Supply-Chain Council as the cross-industry standard diagnostic tool for supply-chain

management. SCOR enables users to address, improve and communicate supply-chain

management practices within and between all interested parties.

SCOR is a management tool. It is a process reference model for supply-chain

management, spanning from the supplier's supplier to the customer's customer. The SCOR-

model has been developed to describe the business activities associated with all phases of

satisfying a customer's demand. By describing supply chains using process building blocks,

the Model can be used to describe supply chains that are very simple or very complex using a

common set of definitions. As a result, disparate industries can be linked to describe the depth

and breadth of virtually any supply chain. The Model has been able to successfully describe

and provide a basis for supply chain improvement for global projects as well as site-specific

projects.

The process reference model integrates the well-known concepts of business process

re-engineering, benchmarking, and process measurements into a cross-functional framework.

Each of the four processes at the top level is successively divided into sub-processes, first at a

configuration level, then at a process element level as shown in Figure III.8. Finally, at the

fourth level and beyond the scope of the SCOR model, activities are defined by companies

individually. Measures are defined for all processes at the three top levels, and firms provide

information about how they perform while receiving a benchmark in return against which

they can compare their own performance. This model provides not only an opportunity to see

how the firm is doing, but also a common frame of reference and a common language across

the supply chain.

III.3.2.1.1. Features of SCOR 1.a SCOR Spans

o All customer interactions, from order entry through paid invoice

o All product (physical material and service) transactions, from your supplier’s supplier to

your customer’s customer, including equipment, supplies, spare parts, bulk product,

software, etc.

o All market interactions, from the understanding of aggregate demand to the fulfillment

of each order

Figure III.8 The SCOR Model described at different level of details

1.b SCOR does not attempt to describe every business process or activity, including

o Sales and marketing (demand generation)

23

o Research and technology development

o Product development

o Some elements of post-delivery customer support

Links can be made to processes not included within the model’s scope, such as

product development, and some are noted in SCOR.

1.c SCOR assumes but does not explicitly address

o Training

o Quality

o Information Technology (IT)

o Administration (non SCM)

III.3.2.1.2. Processes of SCOR

SCOR is based on Five Distinct Management Processes, PLAN, SOURCE, MAKE,

DELIVER and RETURN, as shown in Figure III.9.

Figure III.9 SCOR model

2.a. PLAN: Demand/Supply Planning and Management

PLAN can be processes that balance aggregate demand and supply to develop a course

of action which best meet sourcing, production and delivery requirements.

o Balance resources with requirements and establish/communicate plans for the whole

supply chain, including Return, and the execution processes of Source, Make, and

Deliver.

o Management of business rules, supply chain performance, data collection, inventory,

capital assets, transportation, planning configuration, regulatory requirements and

compliance, and supply chain risk.

o Align the supply chain unit plan with the financial plan.

2.b. SOURCE: Sourcing Stocked, Make-to-Order, and Engineer-to-Order Product

SOURCE can be processes that procure goods and services to meet planned or actual

demand

o Schedule deliveries; receive, verify, and transfer product; and authorize supplier

payments.

o Identify and select supply sources when not predetermined, as for engineer-to-order

product.

o Manage business rules, assess supplier performance, and maintain data.

o Manage inventory, capital assets, incoming product, supplier network, import/export

requirements, supplier agreements, and supply chain source risk.

24

2.c. MAKE: Make-to-Stock, Make-to-Order, and Engineer-to-Order Production

MAKE can be processes that transform product to a finished state to meet planned or

actual demand. (Execution)

o Schedule production activities, issue product, produce and test, package, stage product,

and release product to deliver. With the addition of Green to SCOR, there are now

processes specifically for Waste Disposal in MAKE.

o Finalize engineering for engineer-to-order product.

o Manage rules, performance, data, in-process products (WIP), equipment and facilities,

transportation, production network, regulatory compliance for production, and supply

chain make risk

2.d. DELIVER: Order, Warehouse, Transportation, and Installation Management for

DELIVER can be processes that provide finished goods and services to meet planned

or actual demand, typically including order management, transportation management, and

distribution management. (Stocked, Make-to-Order, and Engineer-to-Order Product)

o All order management steps from processing customer inquiries and quotes to routing

shipments and selecting carriers.

o Warehouse management from receiving and picking product to load and ship product.

o Receive and verify product at customer site and install, if necessary.

o Invoicing customer.

o Manage Deliver business rules, performance, information, finished product inventories,

capital assets, transportation, product life cycle, import/export requirements, and supply

chain deliver risk.

2.e. RETURN: Return of Raw Materials and Receipt of Returns of Finished Goods

RETURN can be processes associated with returning or receiving returned products

for any reason. These processes extend into post-delivery customer support.

o All Return Defective Product steps from source – identify product condition, disposition

product, request product return authorization, schedule product shipment, and return

defective product – and deliver – authorized product return, schedule return receipt,

receive product, and transfer defective product.

o All Return Maintenance, Repair, and Overhaul product steps from source – identify

product condition, disposition product, request product return authorization, schedule

product shipment, and return MRO product – and deliver – authorize product return,

schedule return receipt, receive product, and transfer MRO product.

o All Return Excess Product steps from source – identify product condition, disposition

product, request product return authorization, schedule product shipment, and return

excess product – and deliver – authorize product return, schedule return receipt, receive

product, and transfer excess product.

o Manage Return business rules, performance, and data collection, return inventory,

capital assets, transportation, network configuration, regulatory requirements and

compliance, and supply chain return risk.

III.3.2.2. Second Framework Developed By adding the first framework to SCOR Model, we got the second framework which is

shown in Figure III.10. In this framework, we put the first framework in the middle of SCOR

Model as representing the inside of company. It is seen that the performance Attributes,

management Levels and its KPIs inside the company. We also put the first framework to the

inside of all parts in SC to show that every part of SC is another company as themselves.

Make which is all process inside the company. Return and Source link the company to its

25

Supplier. Also, Deliver and Return link the company to its Customers. The last one is Plan

can combine every part of SC. These processes come from SCOR Model which is mentioned

above.

Outside the company there are external perspectives like Partners, Investors,

Employers and Regulators. Their KPIs are shown under them. When focusing side of

Suppliers, it is obviously seen Supplier’s KPI and its Performance Attributes. On the other

hand, it is understandable from its Make, Return, Source and Deliver processes that Supplier

is also another company inside. These are same for the Supplier’sSupplier.

When focusing on the side of Customers, it is obviously seen Customer’s KPI and its

Performance Attributes. Being another company in SC is the same for

Customer and Customer’s Customer. Even if every part of SC is own features inside the

companies, when looking the

big screen there is a huge

picture. PLAN considers the

whole SC because to reach the

most beneficial SC, it is better

to plan everything according

to every part of SC.

After considering the

picture, it is beneficial to link

each Performance Attributes

for each company under

Global Supply (GS) title. That

is why the collection each

attributes under Global leads

to see Global level of SC. For

increasing the efficiency of

framework, addition of GS

Reliability, GS

Responsiveness, GS

Flexibility, GS Cost and GS

Assets shows the condition of

whole supply chain for making

better benchmarking.

The main strength of

the second frameworks is

considering whole SC, which

we wanted to reach before

building framework. Also

with this framework we can

find Global Performance

Attributes which is really

creative when we compare

other frameworks. On the

other hand, the framework has

not been tested so validation

with case studies is needed.

Figure III.10 Second Framework with SCOR model

26

PART IV

RESULTS

Furthermore, in thesis part after reading articles common characteristics of articles

were grouped according to their authors. Then, literature analysis was made by analyzing five

main frameworks were created in different years. The table about the comparison of these

frameworks was created according to their strengths and weaknesses.

In conclusion, relevant literature on PM and SCM had been reviewed and analyzed.

By investigating each framework in literature, we created strengths and weaknesses of each

of them. After according to this table, by considering strengths at the same time avoiding

weaknesses, we created the first framework.

The first framework was created by using management level and internal and external

aspects. Also, we used performance attributes, Reliability, Responsiveness, Flexibility, Cost

and Asset in this framework. But this framework was not considering the SC parts.

Then we combined this framework with SCOR Model and we got the last framework

which can show us whole SC and Global Performance Attributes is shown in Figure III.8.

Also while building the frameworks, we found proper KPIs to measure SC performance. The

result of this paper is to have a new framework for general manufacturing company by using

internal and external aspects, management level, performance attributes and SCOR Model.

Also academic paper about this work was written.

27

PART V

DISCUSSONS AND EVALUATIONS

V.1. DISCUSSIONS When considering the final framework, mangers can see the big picture. For example,

Global supply Reliability, Responsiveness, Flexibility, Cost and Asset give the opportunity of

reaching whole aspects of supply chain. These attributes come from not only the own

company but also supplier and supplier’s supplier and customer and customer’s customer.

In addition to that, framework can give us not only internal aspects but also external

aspects like its partners, regulators, employers and investors. Also inside the company, it is

shown that KPIs were classified according to performance attributes and management levels.

In this framework there are many creative things. Firstly, in literature there is no framework

which considers management level at the same time performance attributes. Secondly, there

is no framework which considers external issues like Partners, Investors, Regulators and

Employers at the same time in SCOR Model. Finally, it is really extraordinary framework

that combines all SC parts at the same time measures each Performance Attributes for whole

supply chain called GS Performance Attributes.

V.2. EVALUATIONS In this part, there is an evaluation of the created framework in this paper by making

comparison with the other frameworks that have mentioned above. The main positive side of

this framework is to have many different features at the same time. For example, SCOR

Model is found by many companies as a useful tool. But they cannot measure Performance

Attributes when they use SCOR Model. In this model, it is easy to reach to all part of the SC

which is the aim of this paper to measure its performance.

On the other hand, when considering the other frameworks in literature, they focus on

some specific aspect such as financial and non-financial aspects at the same time management

levels. Or, one of them focused on management level and SCOR Model process. These are

simpler frameworks when making comparison. The only negative side of this framework is

that it has not been tested yet. Also, before using this framework, managers should pay more

attention in details. Because there is no validation of framework, this framework will not be

preferred firstly by managers. However, after using they can realize that this framework is

more useful than the others.

V.3. SUGGESTIONS The paper suggests that is better use the framework in general manufacturing

company. My suggestion is that for the first time using the frameworks in a small company

should be more effective

V.4. FUTURE WORKS For future works this framework will be used in general manufacturing company and

multiple case studies is need for validation of the framework. By means of using this in real

company, the convenience of framework will be considered. After case study results, some

changes can be done for making more convenient for real companies.

28

REFERENCES

REFERENCES

BOOKS 1. Argyris C.: “The Impact of Budgets on People, Controllership Foundation”, New York, NY.

(1952)

2. Chandler A.D.: “The Visible Hand - Managerial Revolution in American Business”,

Harvard University Press, Boston, MA. (1977)

3. Dixon J., Nanni A.; Vollmann T.: “The New Performance Challenge, Business

OneIrwin”, Burr Ridge, IL. (1990)

4. Drucker P.: “The Practice of Management, Harper”, New York, NY. (1954)

5. Fitzgerald L.; Johnston R.; Brignall S.; Silvestro R.; Voss C.: “Performance Measurement

in Service Business”, CIMA, London (1991)

6. Johnson H.T., Kaplan R.S.: “Relevance Lost – The Rise and Fall of Management

Accounting”, Harvard Business School Press, Boston, MA. (1987)

7. Lynch R.L.; Cross K.F.: “Measure up!”, Blackwell Publishers, Cambridge, MA. (1991)

8. Marr B.; Neely A.D.; Franco M.; Wilcox M.; Adams C.; Mason S.: “Business

performance measurement: what is the state of the art”, Proceedings of the 4th International

Conference on Performance Measurement, Edinburgh (2004)

9. Neely A.D.; Mills J.F.; Gregory M.J.; Richards A.H.; Platts K.W.; Bourne M.C.S.:

“Getting the Measure of your Business”, Findlay Publications, Horton Kirby. (1996)

10. Power M.: “The Audit Society: Rituals of Verification”, Oxford University Press, Oxford

(1997)

11. Stadtler H.; Kilger C.: “Supply Chain Management and Advanced Planning”, Springer –

Verlag Berlin, Germany (2002) 7-9

WEB SITES 1. www.supply-chain.org (April 2009)

E-BOOKS 1. SCOR 8.0 Strategic-Operational Metrics

2. SCOR 9.0 Overview Booklet

THESIS 1. Angerhofer J.B.; Angelides M.C.: “A model and a performance measurement system for

collaborative supply chains”, Decision Support System, Vol.42 Isuue.1 (2006) 238-301

2. Beamon B.M.: “Measuring Supply Chain Performance”, International Journal of

Operations and Production Management, Vol.19 Issue.3 (1999) 275-292

3. Bourne M.; Mills J.; Wilcox, M.; Neely A.; Platts K.: “Designing, implementing and

updating performance measurement systems”, International Journal of Operations and

Production Management, Vol.20 Issue.7 (2000) 754-771.

29

4. Cai J.; Liu X.; Xiao Z.; Liu J.: “Improving supply chain performance management:

A systematic approach to analyzing iterative KPI accomplishment”, Decision Support

Systems, Vol.46 Issue.2 (2009) 512-521

5. Caplice C.; Sheffi Y.: “A Review and Evaluation of Logistics Performance Measurement

Systems”, The International Journal of Logistics Management, Vol.6 Issue.1 (1995) 61-74

6. Chan F.T.S.; Qi H.J: “Feasibility of performance measurement system for supply chain: a

process-based approach and measures”, Integrated Manufacturing Systems, Vol.14

Isuue.3 (2003) 179-190

7. Cook M.; Hagey R.: “Why Companies Flunk Supply Chain 101: Only 33 Percent Correctly

Measure Supply Chain Performance; Few Use the Right Incentives”, Journal of Business

Strategy, Vol.24 Issue.4 (1998) 35-42

8. Cousins P.D.; Lawson B.; Squire B.: “Performance measurement in strategic buyer-

supplier relationships The mediating role of socialization mechanisms”, International

Journal of Operations and Production Management, Vol.3 Issue.28 (2008) 238-258

9. Cuthbertson R.; Piotrowicz W.: ”Supply chain best practices – identification and

categorization of measures and benefits”, International Journal of Productivity and

Performance Management, Vol.57 Issue.5 (2008) 389-404

10. Gunasekaran A.; Patel C.; Tirtiroglu E.: “Performance measures and metrics in a

supply chain environment”, International Journal of Operations and Production

Management, Vol.21 Issue.1/2 (2001) 71-87

11. Gunasekaran A.; Patel C.; Mc Gaughey R.E.: “A framework for supply chain performance

measurement”, International Journal of Production Economics, Vol.87 Issue.3 (2004)

333-347

12. Gunasekaran A.; Williams H.J; Mc Gaughey R.E.: “Performance measurement

and costing system in new enterprise”, Technovation, Vol.25 Isse.5 (2005) 523-533

13. Hayes R.H.; Abernathy W.J.: “Managing our way to economic decline”, Harvard

Business Review, July-August (1980) 67-77

14. Kaplan R.S.; Norton D.P.: “The balanced scorecard: measures that drive performance”,

Harvard Business Review, January-February, (1992) 71-79.

15. Kaplan R.S.; Nortan D.P.: “Using the balanced scorecard as a strategic management

system”, Harvard Business Review, Vol.74, Issue.1 (1996) 20-25

16. Keegan D.P.; Eiler R.G.; Jones C.R.: ”Are your performance measures obsolete?”,

Management Accounting, June, (1989) 45-50

17. Lai K.; Ngai E.W.T.; Cheng T.C.E.: “Measures for evaluating supply chain performance

in transport logistics”, European Journal of Operational Research, Vol.133 Issue.1

(2001) 439-456

18. Lambert M.D.; Pohlen T.L.: “Supply Chain Metrics”, International Journal of Logistic

Management, Vol.12 Issue.1 (2001) 1-19

19. Lee H.; Billington C.: “Managing supply chain inventory: pitfalls and opportunities”,

Sloan Management Review, Spring (1992) 65-73

20. Lockamy A.; McCormack K.: ”Linking SCOR planning practices to supply chain

performance: An exploratory study”, International Journal of Operations and Production

Management, Vol.24 Issue.12 (2004) 1192-1218

21. Lohman C.; Fortuin L.; Wouters M.,: “Designing a performance measurement system A

Case Study”, European Journal of Operational Research, Vol.156 Issue.2 (2004) 267-286

22. Morgan C.: “Structure, Speed and Salience: Performance Measurement in the Supply

Chain”, Journal of Business Process Measurement, Vol.10 Issue.5 (2004) 522-536

23. Morgan C.: “Supply network performance measurement: future challenges?”, International

Journal of Logistic Management, Vol.1 Issue.2 (2007) 255-273

30

24. Muratoglu K.: “Performance Measurement of Supply Chain Via Balanced Scorecard:

The Case of a Brewing Group”, The Business Review Cambridge, Vol.10 Issue.1 (2008)

330-337

25. Neely A: “The evolution of performance measurement research: Developments in the last

decade and research agenda for the next”, International Journal of Operations and

Production Management, Vol.25 Issue.12 (2005) 1264-1277

26. Otto A.; Kotzab H.: “Does supply chain management really pay? Six perspectives

to measure the performance of managing a supply chain”, European Journal of

Operational Research, Vol.114 Isuue.2 (2003) 306-320

27. Parker C.: “Performance Measurement”, Work Study, Vol.49 Issue.2 (2000) 63-66

28. Rigby D.; “Management tools and techniques: a survey”, California Management

Review, Vol.43 Issue. 2 (2001) 139-60.

29. Ritchie B.; Brindley C.: “An emergent framework for supply chain risk management”, The

Journal of the Operational Research Society, Vol.58 Issue.11 (2007) 1398-1411

30. Robson I.: “From Process Measurement to Performance Improvement”, Journal of

Business Process Management, Vol.10, Issue.5 (2004) 50-512

31. Ridgway V.F.: “Dysfunctional consequences of performance measurements”,

Administrative Science Quarterly, Vol.1 Issue.2 (1956) 240-247.

32. Saad M.; Patel B.: “An investigation of supply chain performance measurement in

the Indian automotive sector”, International Journal of Benchmarking, Vol.13 Isuue.1/2

(2006) 36-53

33. Seth N.; Deshmukh S.G.; Vrat P.: ”A framework for measurement of quality of service in

supply chains”, International Journal of Supply Chain Management, Vol.11 Issue.1

(2006) 82-94

34. Silk S.: “Automating the balanced scorecard”, Management Accounting, Vol.79 Issue.11,

(1998) 38-44.

35. Speckbacher G.; Bischof J.; Pfeiffer T.: “A descriptive analysis of the implementation of

balanced scorecards in German speaking countries”, Management Accounting Research,

Vol.14 (2003) 361-387

36. Tarr J.D: “Performance Measurements for a Continuous Improvement Strategy”,

Hospital Material Management Quarterly, Vol.18 Issue.2 (1996) 77-85

37. Theeranuphattana A.; Tang J.C.S.: “A conceptual model of performance measurement

for supply chains alternative considerations”, Journal of Manufacturing Technology

Management, Vol.19 Issue.1 (2007) 125-148

38. Williams M.S.; “Are intellectual capital performance and disclosure practices related?”,

Journal of Intellectual Capital, Vol. 2 Issue. 3, (2001) 192-203