an integrated supplier selection model with product life

The Pennsylvania State University

The Graduate School

The Harold and Inge Marcus

Department of Industrial and Manufacturing Engineering

An Integrated Supplier Selection Model with Product

Life Cycle Considerations

A Dissertation in

Industrial Engineering

by

Richard John Titus, Jr.

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

May 2019

ii

The dissertation of Richard John Titus, Jr. was reviewed and approved* by the following:

Ravi Ravindran

Professor Emeritus of Industrial Engineering

Dissertation Adviser

Chair of Committee

Felisa Preciado Higgins

Clinical Associate Professor of Supply Chain Management

Smeal College of Business

El-Amine Lehtihet

Professor of Industrial Engineering

Vittaldas Prabhu

Professor of Industrial Engineering

Janis Terpenny

Head of the Department of Industrial and Manufacturing Engineering

*Signatures are on file in the Graduate School

iii

Abstract

Cost of purchased materials account for up to 70% of the overall product cost and the

consequences of supplier delivery, quality performance problems and price fluctuations

can have profound negative effects for an organization. Product life cycles are increasing

in length for defense applications and shortening in consumer products, such as toys,

electronics, etc. These factors make the purchasing function a critical factor impacting the

long term health of companies.

In this dissertation, we begin with the investigation of the relationship between supplier

quality and delivery performance and supplier attributes as part of an empirical study using

an ordinal logistic regression. The results of this study are utilized to develop an integrated

supplier selection model with product life cycle considerations. Supplier selection is a

multiple criteria optimization problem with conflicting criteria, such as quality, delivery,

service, product safety and others. Several multiple criteria sourcing models exist in the

literature. Very rarely they have considered the fact that the relative importance of the

supplier attributes depends on the product life cycle phase. For example, during the

Introduction phase, companies may work with a single supplier emphasizing product

safety, quality and delivery. Revenue targets are more important than gross profit margins.

However, during the Growth phase, multiple suppliers may be used to meet surging

demand and to introduce price competition among the suppliers. In the Mature phase,

controlling procurement cost becomes important in order to boost the product gross profit

margin. In addition, many suppliers can deliver materials needed for multiple products

under various stages of the product life cycle phase. Companies may also limit the business

volume to new and existing suppliers. All these factors are integrated into a general model.

In this thesis, a multiple criteria, multiple products, supplier selection model that explicitly

considers the product life cycle phases of the products is developed. The Goal

Programming (GP) approach is used to solve the multiple criteria problem. The general

model objectives include price, lead-time, quality, delivery, product performance and

safety. Products from the introduction, growth, maturity and decline phases of the product

life cycle are included in the general model. An illustrative example is developed and a

iv

number of goal programming approaches, including preemptive, non-preemptive,

Tchebycheff’s min-max and fuzzy, are utilized to provide the optimal solutions. The Value

Path method is used to provide visual tradeoffs of the conflicting objectives across the

product life cycle phases.

This general model is applied to a real-world problem. The case study is focused on a U.S.

based consumer products company which utilizes a diverse global supply chain to design,

manufacture and deliver products throughout the world. Three key executive decision

makers are employed to identify and rank the key sourcing criteria attributes for products

representing the introduction, growth, mature and decline phases of the product life cycle.

Ranking methods included rating method, Borda count utilizing pairwise comparisons and

the Analytic Hierarchy Process. The decision makers (DMs) shared their feedback on the

cognitive burden for each of the ranking methods. Ranking results indicate that the DM’s

priorities change based on the product life cycle phase. A number of constraints based on

the company’s procurement practices were include in creating the supplier selection

models. The GP model results, for the preemptive, non-preemptive and Tchebycheff’s

min-max models, were presented to the decision makers utilizing the Value Path method.

The decision makers quickly understood the significance of Value Path graphs and focused

on choosing the best overall sourcing solutions based on their rankings of the supplier

attributes.

In order to test the effectiveness of the model, the actual orders used by the company were

compared to the GP model results. A number of factors impacted the actual order

allocations, such as unanticipated demand, supplier capacity constraints and supply chain

designs. The GP model results exceeded the actual order allocation’s criteria performance

for many of the products throughout the product life cycle phases. A comparison of the

actual procurement cost with those of the GP models showed substantial cost savings to

the company, while maintaining quality, delivery and safety performance. These results

were presented to the company’s Chief Operating Officer using the Value Path approach.

He provided extensive feedback and had questions on how the model results could be

blended to examine alternate solutions. He was able to identify opportunities for the

v

company to improve supplier performance in an effort to minimize supplier risk. This

reveals the strength of this integrated decision-making model, which allows “what if”

scenarios to be tried, including changing goal priority weights, priorities and relaxing

business constraints and thus provide the DM with alternative sourcing strategies to

consider.

vi

Table of Contents List of Figures .................................................................................................................... xi

List of Tables ................................................................................................................... xiii

Acknowledgments............................................................................................................ xix

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

1.1 Problem Statement ............................................................................................... 4

1.2 Motivation for the Thesis ..................................................................................... 5

1.3 Overview of the Thesis ........................................................................................ 7

2. Literature Review ......................................................................................................... 10

2.1 The Importance of Supplier Selection and Management Processes .................. 10

2.2 Supplier Criteria Development and Selection Methodologies ........................... 11

2.2.1 MCDM Models ........................................................................................... 12

2.2.2 Supplier Criteria Determination and Supplier Selection ............................ 23

2.3 Risk and Supplier Selection ............................................................................... 32

2.4 The Product Life Cycle and Supplier Selection ................................................. 35

3. Empirical Study of Supplier Attributes to Supplier Delivery and Quality Performance .

................................................................................................................................... 39

3.1 Hypothesis Development and Research Methodology ...................................... 40

3.2 Hypothesized Impact of Critical Attributes on Quality and Delivery

Performance ....................................................................................................... 42

3.3 Delivery and Quality Dependent Variables Defined.......................................... 44

3.4 Quality Ratings and Technical Capability as it Relates to Delivery and Quality

Performance ....................................................................................................... 47

3.5 Financial Condition and Supplier Payment Performance as it Relates to Delivery

and Quality Performance ................................................................................... 49

3.6 Product Complexity and Differentiation as it Relates to Delivery and Quality

Performance ....................................................................................................... 51

3.7 Number of Employees as it Relates to Delivery and Quality Performance ....... 53

3.8 Distance as it Relates to Delivery Performance ................................................. 55

3.9 Financial Leverage or Purchasing Power as it Relates to Delivery and Quality

Performance ....................................................................................................... 56

3.10 Case Study Model Selection and Results.............................................................. 58

3.10.1 Model Selection....................................................................................... 58

3.10.2 Variable Definitions for Number of Employees and Financial or

Purchasing Leverage ................................................................................. 60

3.10.3 Summary Statistics .................................................................................. 62

vii

3.10.4 Model Definitions and Results ................................................................ 63

3.10.5 Empirical Study Conclusions and Implications for the Supplier Selection

Process and MCDM Model Formulations ................................................ 65

4. General Model for Supplier Selection incorporating results of Empirical Study and

Product Life Cycle ........................................................................................................ 70

4.1 Notations used in the model ............................................................................... 71

4.2 Mathematical Formulation of the Order Allocation Problem ............................ 73

4.2.1 Objective Functions .................................................................................... 73

4.2.2 Constraints .................................................................................................. 76

4.3 Goal Programming (GP) Models ....................................................................... 78

4.3.1 Ideal Solutions ............................................................................................ 78

4.3.2 General Goal Programming Model ............................................................. 79

4.3.3 Preemptive Goal Programming Model ....................................................... 82

4.3.4 Non-preemptive Goal Programming Model ............................................... 83

4.3.5 Tchebycheff’s Min-Max Goal Programming Model .................................. 83

4.3.6 Fuzzy Goal Programming Model................................................................ 84

4.4 Illustrative Example ........................................................................................... 84

4.4.1 Ideal Solutions ............................................................................................ 84

4.4.2 Preemptive Goal Programming Results ...................................................... 87

4.4.3 Non-Preemptive Goal Programming Results.............................................. 94

4.4.3 Tchebycheff’s (Min-Max) Goal Programming ......................................... 104

4.4.3 Fuzzy Goal Programming ......................................................................... 108

4.4.4 Overall Model Results .............................................................................. 111

4.4.7 Chapter Conclusion ................................................................................... 117

5. Case Study: Global Supplier Selection Problem across Product Life Cycle – Supplier

Ranking Results .......................................................................................................... 118

5.1 Background of the Company and the Decision Makers ................................... 118

5.2 Description of Products and Suppliers ............................................................. 120

5.3 Key Supplier Selection Criteria........................................................................ 122

5.4 Ranking the Product Life Cycle Phases ........................................................... 125

5.5 Ranking the Supplier Selection Criteria by PLC Phase ................................... 130

5.5.1 Ranking the Supplier Selection Criteria by DM1 .................................... 130

5.5.2 Ranking the Supplier Selection Criteria by DM2 .................................... 132

5.5.3 Ranking the Supplier Selection Criteria by DM3 ..................................... 133

5.6 Final Ranking and Weights for the MCDM Supplier Selection Models ......... 135

viii

5.6.1 Ranking and Weights of PLC Phases ....................................................... 135

5.6.2 Final Criteria Weights by PLC Phase ....................................................... 136

5.6.3 Overall Weights for the Goal Programming Models ................................ 136

5.7 Minimum and Maximum Number of Suppliers by PLC Phase ....................... 138

6. Case Study: Global Supplier Selection Problem across Product Life Cycle – Supplier

Selection and Order Allocation .................................................................................. 140

6.1 Preemptive GP Model for the Case Study ....................................................... 140

6.1.1 Preemptive GP Priorities.......................................................................... 141

6.1.2 Preemptive GP Solution ........................................................................... 143

6.1.3 Introduction Phase Results (Product 1).................................................... 143


6.1.5 Growth Phase Results (Product 3) ........................................................... 145

6.1.6 Mature Phase Results (Product 4) ............................................................ 147


6.1.8 Decline Phase Results (Product 6) ........................................................... 149


6.1.10 Optimal Order Allocations to Suppliers (All Products) ........................ 151

6.2 Non-Preemptive GP Model for the Case Study ............................................... 153

6.2.1 Non-Preemptive GP Model Weights and Solution .................................. 153








6.2.9 Optimal Order Allocations to Suppliers (All Products) ........................... 161

6.3 Tchebycheff’s Min-Max GP Model for the Case Study ....................................... 162

6.3.1 Tchebycheff’s Min-Max Goals/Targets and Solution ............................. 162







ix



6.4 Fuzzy Min-Max GP Model for the Case Study .................................................... 172

6.4.1 Fuzzy Min-Max Ideals and Solution........................................................ 172









6.5 Value Path Results ................................................................................................ 182

6.5.1 Introduction Phase (Product 1) ................................................................ 182


6.5.3 Growth Phase (Product 3) ........................................................................ 188

6.5.4 Mature Phase (Product 4)......................................................................... 190


6.5.6 Decline Phase (Product 6) ........................................................................ 195


6.6 Managerial Implications ................................................................................... 199

6.6.1 Actual Order Allocations ......................................................................... 200



6.6.4 Growth Phase (Product 3) ........................................................................ 209





6.6.9 Impact on Procurement Cost .................................................................... 223

6.7 Chapter Summary ............................................................................................. 228

7. Conclusion and Future Research ................................................................................ 232

7.1 Summary of Model Contributions to Supplier Selection ................................. 232

7.2 Summary of Practical Significance .................................................................. 234

x

7.3 Future Research Opportunities ......................................................................... 237

References ....................................................................................................................... 239

xi

List of Figures

Figure 1.1 Supply Segmentation Model ..............................................................................3

Figure 2.1 Selecting and Managing Suppliers ...................................................................10

Figure 3.1 Supplier Attributes and Performance Relationship ..........................................43

Figure 3.2 Hypothesized Relationships with Delivery and Quality Performance .............44

Figure 4.1 Illustrative Weights for the Non-preemptive GP Models.................................97

Figure 4.2 Non-Preemptive GP Model Goal Weights vs. Target Achievements ............103

Figure 4.3 Value Path Model Results Comparison ..........................................................114

Figure 5.1 Product Life Cycle Responses from DM1......................................................126

Figure 6.1 Fuzzy GP Results ...........................................................................................181

Figure 6.2 Value Path Model Results Comparison for Introduction Phase Product 1 ....185

Figure 6.3 Value Path Model Results Comparison for Introduction Phase Product 2 ....187

Figure 6.4 Value Path Model Results Comparison for Growth Phase Product 3 ............190

Figure 6.5 Value Path Model Results Comparison for Mature Phase Product 4 .............192

Figure 6.6 Value Path Model Results Comparison for Mature Phase Product 5 .............195

Figure 6.7 Value Path Model Results Comparison for Decline Phase Product 6 ............197

Figure 6.8 Value Path Model Results Comparison for Decline Phase Product 7 ............199

Figure 6.9 DM Review of the Value Path Calculations for Introduction Phase Product 1

..............................................................................................................................204

Figure 6.10 Value Path Graph: Comparison of Actual Orders and Model Results for

Introduction Phase Product 1 ...............................................................................204

Figure 6.11 DM Review Actual Orders and GP Model Allocations and Value Path Graph

for Introduction Phase Product 1 ..........................................................................205


Introduction Phase Product 2 ...............................................................................207


for Introduction Phase Product 2 ..........................................................................208


Growth Phase Product 3 .......................................................................................210


for Mature Phase Product 4 ..................................................................................213

Figure 6.16 Value Path Graph: Comparison of Actual Orders and Model Results for Mature

Phase Product 4 ....................................................................................................214

Figure 6.17 Value Path Actual Orders and Model Results Comparison for Mature Phase

Product 5 ...............................................................................................................217

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Figure 6.18 Value Path Actual Orders and Model Results Comparison for Decline Phase

Product 6 ...............................................................................................................220

Figure 6.19 Value Path Actual Orders and Model Results Comparison for Decline Phase

Product 7 ...............................................................................................................222

Figure 6.20 Procurement Cost Comparison of Actual Orders and GP Models for

Introduction Phase Products 1 and 2 ....................................................................223

Figure 6.21 Procurement Cost Comparison of Actual Orders and GP Models for Growth

Phase Product 3 and Mature Phase Products 4 and 5 ...........................................224

Figure 6.22 Procurement Cost Comparison of Actual Orders and GP Models for Decline

Phase Products 6 and 7 .........................................................................................227

Figure 6.23 DM Review of the Price Achievements for Actual Order and GP Models..228

xiii

List of Tables

Table 2.1 Supplier Selection Criteria Comparison ............................................................11

Table 2.2 Supplier Criteria and Selection Literature Summary .........................................38

Table 3.1 Delivery Performance Rating ............................................................................45

Table 3.2 Quality Performance Rating ..............................................................................46

Table 3.3 Financial Condition Rating ................................................................................49

Table 3.4 Supplier Payment Performance Rating ..............................................................50

Table 3.5 Distance (Mileage) Rating Scale .......................................................................55

Table 3.6 Employee Rating Scale ......................................................................................60

Table 3.7 Financial Leverage or Purchasing Leverage Rating Scale ................................61

Table 3.8 Summary Statistics ............................................................................................62

Table 3.9 Summarized Results for the Supplier Quality and Delivery Models.................65

Table 3.10 Ordinal Logistic Regression Results for Supplier Quality Performance .........67

Table 3.11 Ordinal Logistic Regression Results for Supplier Delivery Performance .......68

Table 4.1 Supplier Data for Illustrative Example ..............................................................85

Table 4.2 Ideal Solutions for the Illustrative Example ......................................................86

Table 4.3 Preemptive Goal Priority Order .........................................................................88

Table 4.4 Preemptive GP Achievements with respect to Target Values ...........................92

Table 4.5 Preemptive GP Procurement Plan......................................................................93

Table 4.6 Allocation of Weights by Objective for Non-Preemptive GP Model ................96

Table 4.7 Non-preemptive GP Achievements with respect to Target Values .................100

Table 4.8 Non-preemptive GP Procurement Plan............................................................100

Table 4.9 Tchebycheff’s Min-Max GP Achievements with respect to Target Values ....105

Table 4.10 Tchebycheff’s Min-Max GP Procurement Plan ............................................105

Table 4.11 Fuzzy GP Achievements with respect to Ideal Values ..................................109

Table 4.12 Fuzzy GP Procurement Plan ..........................................................................109

Table 4.13 Model Results and Target Values ..................................................................112

Table 4.14 Value Path Results .........................................................................................113

Table 5.1 Products and Suppliers for Case Study ............................................................120

Table 5.2 Yearly Unit Product Demand ..........................................................................121

Table 5.3 Supplier Performance Ratings and Maximum Business Levels ......................124

Table 5.4 Ranking of PLC Phases by Different Methods for DM1.................................127



xiv

Table 5.7 DM1’s Ranking of Criteria for Introduction and Growth Phases by Different

Methods .................................................................................................................130

Table 5.8 DM1’s Ranking of Criteria for Mature and Decline Phases by Different Methods

...............................................................................................................................131


Methods .................................................................................................................132

Table 5.10 DM2’s Ranking of Criteria for Mature and Decline Phases by Different

Methods .................................................................................................................133


Methods .................................................................................................................134


Methods .................................................................................................................134

Table 5.13 Average AHP weights by PLC Phase ............................................................136

Table 5.14 Average AHP Weights by PLC Phase and Supplier Selection Criteria ........137

Table 5.15 Overall AHP Weights for Goal Programming ...............................................137

Table 5.16 Number of Suppliers by PLC Phase ..............................................................138

Table 6.1 Preemptive GP Model Characteristics for the Case Study ..............................140

Table 6.2 Preemptive GP Supplier Selection Priorities ...................................................142

Table 6.3 Preemptive GP Procurement Plan for Introduction Phase Product 1 ..............143

Table 6.4 Preemptive GP Achievements for Introduction Phase Product 1 with respect to

Target Values ........................................................................................................144

Table 6.5 Preemptive GP Procurement Plan for Introduction Phase Product 2 ..............144

Table 6.6 Preemptive GP Achievements for Introduction Phase Product 2 with respect to

Target Values .........................................................................................................145

Table 6.7 Preemptive GP Procurement Plan for Growth Phase Product 3 ......................146

Table 6.8 Preemptive GP Achievements for Growth Phase Product 3 with respect to Target

Values ....................................................................................................................146

Table 6.9 Preemptive GP Procurement Plan for Mature Phase Product 4 .......................147

Table 6.10 Preemptive GP Achievements for Mature Phase Product 4 with respect to

Target Values .........................................................................................................148

Table 6.11 Preemptive GP Procurement Plan for Mature Phase Product 5 .....................149

Table 6.12 Preemptive GP Achievements for Mature Phase Product 5 with respect to

Target Values .........................................................................................................149

Table 6.13 Preemptive GP Procurement Plan for Decline Phase Product 6 ....................149

Table 6.14 Preemptive GP Achievements for Decline Phase Product 6 with respect to

Target Values .........................................................................................................150

Table 6.15 Preemptive GP Procurement Plan for Decline Phase Product 7 ....................151

xv

Table 6.16 Preemptive GP Achievements for Decline Phase Product 7 with respect to

Target Values .........................................................................................................151

Table 6.17 Preemptive GP Procurement Plan (All Products) ..........................................152

Table 6.18 Non-preemptive GP Model Characteristics for the Case Study ....................153

Table 6.19 Non-preemptive GP Weights .........................................................................154

Table 6.20 Non-preemptive GP Procurement Plan for Introduction Phase Product 1 ....154

Table 6.21 Non-preemptive GP Achievements for Introduction Phase Product 1 with

respect to Target Values ........................................................................................155

Table 6.22 Non-preemptive GP Procurement Plan for Introduction Phase Product 2 ....155

Table 6.23 Non-preemptive GP Achievements for Introduction Phase Product 2 with

respect to Target Values ........................................................................................156

Table 6.24 Non-preemptive GP Procurement Plan for Growth Phase Product 3 ............156

Table 6.25 Non-preemptive GP Achievements for Growth Phase Product 3 with respect to

Target Values .........................................................................................................157

Table 6.26 Non-preemptive GP Procurement Plan for Mature Phase Product 4.............157

Table 6.27 Non-preemptive GP Achievements for Mature Phase Product 4 with respect to

Target Values .........................................................................................................158


Table 6.29 Non-preemptive GP Achievements for Mature Phase Product 5 with respect to

Target Values .........................................................................................................159


Table 6.31 Non-preemptive GP Achievements for Decline Phase Product 6 with respect to

Target Values .........................................................................................................160


Table 6.33 Non-preemptive GP Achievements for Decline Phase Product 7 with respect to

Target Values .........................................................................................................161

Table 6.34 Non-preemptive GP Procurement Plan (All Products) ..................................162

Table 6.35 Tchebycheff’s Min-Max GP Model Characteristics for the Case Study .......162

Table 6.36 Tchebycheff’s Min-Max GP Procurement Plan for Introduction Phase Product

1 .............................................................................................................................163

Table 6.37 Tchebycheff’s GP Achievements for Introduction Phase Product 1 with respect

to Target Values .....................................................................................................163

Table 6.38 Tchebycheff’s Min-Max GP Procurement Plan for Introduction Phase Product

2 ............................................................................................................................164

Table 6.39 Tchebycheff's GP Achievements for Introduction Phase Product 2 with respect

to Target Values .....................................................................................................164

Table 6.40 Tchebycheff’s Min-Max GP Procurement Plan for Growth Phase Product 3

...............................................................................................................................165

xvi

Table 6.41 Tchebycheff’s GP Achievements for Growth Phase Product 3 with respect to

Target Values .........................................................................................................165

Table 6.42 Tchebycheff’s Min-Max GP Procurement Plan for Mature Phase Product 4

...............................................................................................................................166

Table 6.43 Tchebycheff’s GP Achievements for Mature Phase Product 4 with respect to

Target Values .........................................................................................................167

Table 6.44 Tchebycheff’s Min-Max GP Procurement Plan for Mature Phase Product 5

...............................................................................................................................167

Table 6.45 Tchebycheff’s GP Achievements for Mature Phase Product 5 with respect to

Target Values .........................................................................................................168

Table 6.46 Tchebycheff’s Min-Max GP Procurement Plan for Decline Phase Product 6

...............................................................................................................................168

Table 6.47 Tchebycheff’s GP Achievements for Decline Phase Product 6 with respect to

Target Values .........................................................................................................169

Table 6.48 Tchebycheff’s Min-Max GP Procurement Plan for Decline Phase Product 7

...............................................................................................................................170

Table 6.49 Tchebycheff’s GP Achievements for Decline Phase Product 7 with respect to

Target Values .........................................................................................................170

Table 6.50 Tchebycheff’s Min-Max GP Procurement Plan (All Products) .....................171

Table 6.51 Fuzzy Min-Max GP Model Characteristics for the Case Study ....................172

Table 6.52 Fuzzy Min-Max GP Procurement Plan for Introduction Phase Product 1 ....173

Table 6.53 Fuzzy GP Achievements for Introduction Phase Product 1 with respect to Ideal

Values ....................................................................................................................173

Table 6.54 Fuzzy GP Procurement Plan for Introduction Phase Product 2 .....................174

Table 6.55 Fuzzy GP Achievements for Introduction Phase Product 2 with respect to Ideal

Values ....................................................................................................................174

Table 6.56 Fuzzy GP Procurement Plan for Growth Phase Product 3 ............................175

Table 6.57 Fuzzy GP Achievements for Growth Phase Product 3 with respect to Ideal

Values ....................................................................................................................175

Table 6.58 Fuzzy GP Procurement Plan for Mature Phase Product 4 .............................176

Table 6.59 Fuzzy GP Achievements for Mature Phase Product 4 with respect to Ideal

Values ....................................................................................................................176

Table 6.60 Fuzzy GP Procurement Plan for Mature Phase Product 5 .............................177


Values ....................................................................................................................178

Table 6.62 Fuzzy GP Procurement Plan for Decline Phase Product 6 ............................178


Values ....................................................................................................................179

xvii

Table 6.64 Fuzzy GP Procurement Plan for Decline Phase Product 7 ............................179

Table 6.65 Fuzzy GP Achievements for Decline Phase Product 7 with respect to Ideal

Values ....................................................................................................................180

Table 6.66 Fuzzy Min-Max GP Procurement Plan (All Products) ..................................181

Table 6.67 Procurement Plan by GP Model for Introduction Phase Product 1 ...............182

Table 6.68 Model Results and Target Values for Introduction Phase Product 1 .............183

Table 6.69 Value Path Results for Introduction Phase Product 1 ....................................184

Table 6.70 Procurement Plan by GP Model for Introduction Phase Product 2 ...............185

Table 6.71 Model Results and Target Values for Introduction Phase Product 2 .............186

Table 6.72 Value Path Results for Introduction Phase Product 2 ....................................186

Table 6.73 Procurement Plan by GP Model for Growth Phase Product 3 .......................188

Table 6.74 Model Results and Target Values for Growth Phase Product 3 ....................189

Table 6.75 Value Path Results for Growth Phase Product 3 ...........................................189

Table 6.76 Procurement Plan by GP Model for Mature Phase Product 4 .......................191

Table 6.77 Model Results and Target Values for Mature Phase Product 4 .....................191

Table 6.78 Value Path Results for Mature Phase Product 4 ............................................192

Table 6.79 Procurement Plan by GP Model for Mature Phase Product 5 .......................193

Table 6.80 Model Results and Target Values for Mature Phase Product 5 .....................194

Table 6.81 Value Path Results for Mature Phase Product 5 ............................................194

Table 6.82 Procurement Plan by GP Model for Decline Phase Product 6 ......................195

Table 6.83 Model Results and Target Values for Decline Phase Product 6 ....................196

Table 6.84 Value Path Results for Decline Phase Product 6 ...........................................196

Table 6.85 Procurement Plan by GP Model for Decline Phase Product 7 ......................197

Table 6.86 Model Results and Target Values for Decline Phase Product 7 ....................198

Table 6.87 Value Path Results for Decline Phase Product 7 ...........................................198

Table 6.88 Actual Procurement Plan All Products ..........................................................200

Table 6.89 Procurement Plan by GP Model and Actual Orders for Introduction Phase

Product 1 ................................................................................................................201

Table 6.90 Criteria Values for Targets, Actual Orders and Model Allocations for

Introduction Phase Product 1 .................................................................................202

Table 6.91 Normalized Criteria Values for Value Path Graph for Introduction Phase

Product 1 ................................................................................................................203

Table 6.92 Procurement Plan by GP Model and Actual Orders for Introduction Phase

Product 2 ................................................................................................................205


Introduction Phase Product 2 .................................................................................206

xviii

Table 6.94 Normalized Criteria Values for Value Path Graph for Introduction Phase

Product 2 ................................................................................................................206

Table 6.95 Criteria Values for Targets, Actual Orders and Model Allocations for Growth

Phase Product 3 ......................................................................................................209

Table 6.96 Procurement Plan by GP Model and Actual Orders for Growth Phase Product

3 .............................................................................................................................209

Table 6.97 Normalized Criteria Values for Value Path Graph for Growth Phase Product 3

...............................................................................................................................210

Table 6.98 Procurement Plan by GP Model and Actual Orders for Mature Phase Product 4

...............................................................................................................................212

Table 6.99 Criteria Values for Targets, Actual Orders and Model Allocations for Mature

Phase Product 4 ......................................................................................................213

Table 6.100 Normalized Criteria Values for Value Path Graph for Mature Phase Product 4

...............................................................................................................................214

Table 6.101 Procurement Plan by GP Model and Actual Orders for Mature Phase Product

5 .............................................................................................................................215

Table 6.102 Criteria Values for Targets, Actual Orders and Model Allocations for Mature

Phase Product 5 ......................................................................................................216

Table 6.103 Normalized Criteria Values for Value Path Graph for Mature Phase Product 5

...............................................................................................................................216

Table 6.104 Procurement Plan by GP Model and Actual Orders for Decline Phase Product

6 .............................................................................................................................218

Table 6.105 Criteria Values for Targets, Actual Orders and Model Allocations for Decline

Phase Product 6 ......................................................................................................218

Table 6.106 Normalized Criteria Values for Value Path Graph for Decline Phase Product

6 .............................................................................................................................219

Table 6.107 Procurement Plan by GP Model and Actual Orders for Decline Phase Product

7 .............................................................................................................................221

Table 6.108 Criteria Values for Targets, Actual Orders and Model Allocations for Decline

Phase Product 7 ......................................................................................................221

Table 6.109 Normalized Criteria Values for Value Path Graph for Decline Phase Product

7 .............................................................................................................................222

xix

Acknowledgments

I thank my wife, Lisa, for her support throughout this journey. The completion of this

degree was truly a team effort. I have the best life partner and support team starting at

home. Without Lisa’s support in handling all the day to day household operations, child

care, financial support in addition to her moral support, I would have not been able to

complete my course work and dissertation. Our children, Victoria and Madeleine, have

also been part of this team effort and I am grateful for their support and encouragement.

Thank you Lisa, Tori and Maddie!

I extend my deepest gratitude to Dr. Ravi Ravindran for his excellent instruction,

mentoring, patience, support and guidance throughout my graduate education and the

research and writing of this dissertation. Dr. Ravindran is truly an amazing educator with

an incredible depth of knowledge and an equal depth of patience. I am truly blessed to

have him as my dissertation adviser. I also thank my committee members: Dr. Felisa

Preciado Higgins, Dr. El-Amine Lehtihet and Dr. Vittaldas Prabhu for their support and

valuable suggestions.

Next, I thank the three senior executives who supported the case study included in my

dissertation. In order to maintain the anonymity of the company, I will refer to them by

their designations in the dissertation. I am grateful for the amount of time, insight, feedback

and efforts provided by these executives. DM1 (Decision Maker 1) is a truly a great leader

who sponsored the case study and provided insights into the decision-making process as

well as critiques and feedback on the results. His business experience and detailed

knowledge of the company’s operations, strategy and supply chain were incredibly helpful.

DM2 (Decision Maker 2) is an experienced senior procurement executive, whose world-

wide experience base and knowledge made valuable contributions to this research. DM3

helped get this case study off the ground. Her detailed knowledge of the suppliers’

capabilities, performance and products were invaluable.

xx

My parents fostered a respect of education at a young age. They knew this was the path to

a better life and encouraged my brothers and me to pursue higher education. My Mother

and Father made substantial sacrifices during lean economic times to make sure we had all

the financial and emotional support needed to start our academic journey. I will be never

be able to repay them for all they have done for me and I am confident my Dad would be

proud that I finished my lifelong goal of earning my PhD.

Professors Aronson, Groover, Kane, Richardson and Wilson were my role models during

my undergraduate and Masters degree study. Their ability to connect with their students

and foster an atmosphere of learning and interest in the subjects while having fun in the

classroom was truly an inspiration. I hope I can make an impact in my students’ lives as

they have done for me.

Lastly, thanks to all the friends, study partners, professors and staff of the IE department.

Special thanks to Lisa Fuoss for her amazing administrative skills and support throughout

this journey and to Professor Chandra for his guidance and care as graduate studies adviser.

1

1. Introduction

The supplier selection process and the resulting purchase of goods and services have a

significant impact on the operating results of organizations. There are inherent risks in

supplier selection process. Supplier delivery or quality performance problems and price

fluctuations can cause profound negative consequences for an organization. For example,

after 95 years as supplier of the Ford Motor Company, Firestone severed its relationship

with Ford in May of 2001 following Ford’s announcement that it would launch a $3 Billion

recall to replace an additional 10 to 13 million defective Firestone tires beyond the original

tire recalls started in the summer of 2000 (Warfield et. al 2002). Ford’s quality ratings as

reported by J.D. Power and Associates sank to last place among the world’s seven largest

automakers during 2001 (Shirouzu and White 2002). In addition to the quality problems,

the product launch of the redesigned 2002 Ford Explorer was delayed due to quality

concerns, costing Ford by some estimates over $1 Billion in revenue due to the delays in

the delivery of new products (Shirouzu 2000). Hendricks and Singhal (2005) reported

lower operating income, return on sales, return on assets along with increased costs, higher

inventories and lower sales growth for firms that experienced supply chain disruptions. In

addition to this disruption risk, product life cycles are increasing in length for defense

applications and shortening in consumer products, such as toys, electronics, etc. These

factors make the purchasing function a critical factor impacting the long term health of

companies. Clearly the supplier selection process is a critical factor which is related to the

overall performance of an organization. This process often requires input from multiple

decision makers with conflicting criteria. The purpose of this research is to develop an

integrated supplier selection methodology using multiple criteria decision making

(MCDM) models with product life cycle considerations to identify and assess risks

associated with this critical selection process.

Tang (2006A) defines supply chain risk management as “the management of supply chain

risks through coordination or collaboration among supply chain partners so as to ensure

profitability and continuity.” This risk is amplified by the proportional contribution of

purchased materials on the overall product cost; Ghodsypour and O’Brien (2001) report

purchased materials cost account for up to 70% of overall product cost. As firms seek to

2

manage their supply chains on a global basis while reducing delivery times, production

lead times and cost, while improving quality, the reliance on suppliers and the resulting

risks from this increased reliance are again further amplified (Tisminesky et. al 2007, Choi

and Hartley 1996). Dickson (1966) surveyed purchasing agents belonging to the National

Association of Purchasing Management (NAPM) in order to ascertain the major attributes

used in the supplier selection process. The major factors identified by this study included:

price, quality, service, delivery, geographic location, financial position, business volume,

technical capability and supplier management. A literature review of supplier selection

factors by Weber, Current and Benton (1991) completed nearly 25 years after Dickson’s

original publication found the critical measures and characteristics were respectively:

quality, delivery, performance history, net price, production facilities/capacity and

technical capability. A survey of automotive supplier selection factors by Choi and Hartley

(1996) found that even “28 years since Dickson’s study” delivery deadlines and quality

were significant factors in the supplier selection process. Given the critical importance of

supplier delivery and quality with respect to supplier performance, this research will

include these two key measurements and item price. In addition to investigating major

supplier selection factors identified by Dickson, product life cycle (Heizer and Render

2004) and Kraljic’s (1983) supply segmentation model will be considered in this thesis.

Procurement activities and emphasis should also change depending on the item or purchase

material classifications. The supply segmentation model, shown in Figure 1.1, separates

purchases based upon their respective profit impact and supply risk assessments.

Purchased items or services are separated into four distinct categories based on their supply

risk and profit impact. These categories in this supply segmentation model include:

▪ bottleneck;

▪ strategic;

▪ leverage and

▪ non-critical items.

For example, strategic items are purchased material which have both a high profit impact

and high risk for the buying organization. The determination of supply risk is based on the

availability of the product or service, the number of possible suppliers, the demand for the

product or service, the make or buy opportunities, the storage risks and the availability of

3

substitute products or services. Profit impact is based on the volume purchased, the

percentage of the total purchasing spend and impact of the product or service on overall

`product quality and business growth. Certainly, general office supplies should not be

given the same attention during the supplier selection process as unique, high value items

which can only be provided by a small number of suppliers. Therefore, purchase material

classifications will be considered along with the product life cycle stage. Product life

cycles consist of the following stages or phases:

▪ Introduction;

▪ Growth;

▪ Maturity and

▪ Decline.

Product life cycles are both increasing in length for defense and telecommunications

industries and decreasing in length for electronic consumer products (Stogdill 1999,

Carbone 2003). Suppliers of raw materials and parts are critical during the entire life cycle

of a product. Both of these product life cycle conditions can create numerous supply

problems and negatively impact an organization’s performance and therefore should be

considered as part of the supplier selection process.

Su

pp

ly R

isk

Profit Impact

Strategic ItemsBottleneck Items

Leverage ItemsNoncritical Items

Main Issues:

Insure Forecast Accuracy

and Quantity Availability,

Develop Long Term

Supplier Relationships,

Assess Risk and Develop

Contingency Plans

Main Issues:

Insure Quantity

Availability,

Assess Risk and

Develop Contingency

Plans

Main Tasks:

Exploit Full Purchasing

Power, Actively Manage

Supplier Selection

Process and Implement

Target Pricing and

Negotiations Strategies,

Substitute Products (if

possible)

Main Tasks:

Standardize

Products, Target

Cost Purchasing

Processing Costs

Figure 1.1 Supply Segmentation Model

4

1.1 Problem Statement

This thesis will develop integrated Multiple Criteria Decision Making (MCDM)

approaches to the supplier selection problem with product life cycle considerations.

Specifically, the focus of this research will be the development of an integrated multiple

criteria supplier selection optimization model and solution methodologies for items in each

phase of the product life cycle. The research plan is to investigate the use of Goal

Programming (GP) approaches and their extensions for solving MCDM problems. The

supplier selection process inherently includes risk in the selection and performance of

suppliers. First, an empirical study of key supplier attributes and their relationship to

supplier delivery and quality performance will be investigated. The industrial case study

examines the relationship between a select number of key supplier attributes and supplier

performance. The results of this empirical study will be integrated into a general MCDM

supplier selection model with product life cycle considerations.

The relative importance of the supplier attributes depends on the product life cycle phase.

For example, during the Introduction phase, companies may work with a single supplier

emphasizing product safety, quality and delivery. Revenue targets are more important than

gross profit margins. However, during the Growth phase, multiple suppliers may be used

to meet surging demand and to introduce price competition among the suppliers. In the

Mature phase, controlling procurement cost becomes important in order to boost the

product gross profit margin. In addition, many suppliers can deliver materials needed for

multiple products under various stages of the product life cycle phase. Companies may

also limit business volume to new and existing suppliers. All these factors are integrated

into a general model in this thesis.

5

A number of Goal Programming solution approaches to the MCDM models will be

examined, including:

▪ Preemptive GP;

▪ Non-preemptive GP;

▪ Tchebycheff (Min-Max) GP;

▪ Fuzzy GP.

A real-world problem will be solved using these goal programming models. The focal

company is a global consumer product company that utilizes a global supply chain to

support of 1,100 active products. Strategic, bottleneck and leverage products will be

selected from all phases of the product life cycle. Key executive decision makers will

identify and rank the key sourcing criteria attributes for products representing the

introduction, growth, mature and decline phases of the product life cycle. The rating

method, Borda count utilizing pairwise comparisons and the Analytic Hierarchy Process

will be utilized to rank the product life cycle phases and the supplier selection criteria. The

DMs will also be asked for feedback on the cognitive burden for each of the ranking

methods. Goal programming models will be created to solve the supplier selection and

order allocation problem. The results from the supplier selection process will be presented

to the DMs using the Value Path method, which provides visual tradeoffs of the conflicting

criteria for products across product life cycles. DM feedback on the best compromise

solution will be obtained. Finally, the model results will be compared to the actual order

allocations used by the company in order to measure the effectiveness of the model

solutions.

1.2 Motivation for the Thesis

The supplier selection risks, identified in this thesis, will be grouped into two major

categories:

▪ supplier performance risk: this category includes specific supplier performance

issues that relate to delivery, quality and price variances. Specific supplier

attributes, such as, supplier financial position, technical capabilities, lead-time,

service and capacity planning performance, location, etc. will be included in the

supplier performance risk category.

6

▪ product risk: this category will be related to the purchase material classifications

based on the supply segmentation model and the current product life cycle stage for

that specific material classification. Product attributes, such as the make or buy

opportunities, product performance, product safety, tooling development time and

the availability of substitute products or services will be included in the product

risk category.

This thesis is motivated by an earlier industrial case study done by the author on the

supplier quality and delivery performance with respect to several key supplier attributes.

The results of this industrial case study (Chapter 3), which are included in the supplier

performance risk category, will be examined with respect to a select number of critical

supplier attributes. This analysis will attempt to combine the descriptive models of

observed supplier performance, which examines past major attributes proposed to impact

supplier performance, and a predictive model. Results from the industrial case study can

be used to short-list the suppliers prior to the final supplier selection. The results of this

dissertation research are intended to provide an integrated framework for the supplier

selection and risk assessment processes with respect to supplier performance and product

risks. Ellram (1990) called for a combination of these descriptive and predictive models,

facilitating a better understanding of supplier performance and attributes related to

“favorable outcomes” which would certainly include the identification and management of

supplier performance risk in the supplier selection process. The predictive model results,

using the findings of this case study, will be utilized to provide key inputs for the MCDM

models presented in this research.

7

1.3 Overview of the Thesis

The mathematical modeling efforts in this thesis will begin with the formulation of

different single period MCDM models to solve multiple supplier, multiple item sourcing

problems. The MCDM models will be solved by goal programming. The goal programs

will include items from the supply segmentation classifications, shown in Figure 1.1, in

various stages of the product life cycle. Supplier performance attributes, such as quality

and delivery performance, price, product safety, past supplier performance, technical

capability, service and capacity planning performance and tooling development time will

be included in an effort to manage the risk associated with the supplier selection process.

Delivery and quality problems with items from the strategic category could have

devastating effects for a company. Numerous examples of the impact of quality and

delivery problems relating to the supply of strategic items exist, such as the fire at Phillips

Electronics cell phone chip plant which devastated Ericsson’s cell business resulting in a

$2.34 Billion loss (Hendricks and Singhal 2005, Hendricks and Singhal 2003,

Bartholomew 2006). Therefore, this thesis will focus on developing GP models to optimize

the supplier selection of strategic, bottleneck and leverage items. The GP model will also

incorporate the various stages of the product life cycle as part of the supplier selection

process. Given the importance of a strategic item, the greatest procurement risk for this

type of item would occur during the introductory and growth stages of the product life

cycle. Suppliers of strategic items must contend with significant demand variances in these

preliminary stages of the product life cycle while insuring the stable production of a quality

product with increasing production volumes. Therefore, in order to minimize the risk to

the buyer’s firm, the goal of achieving a high quality, product safety and delivery

performance during the introductory and growth stages of the product life for a strategic

item must be assigned a high priority.

These goals will be considered in conjunction with selecting performance goals for supplier

attributes such as quality and delivery performance, product safety, product performance,

lead-time performance, service and capacity planning performance, net price, financial

position, technical capability and location in order to complete the supplier selection

process. For example, the relative importance of goals for items will change as the product

8

moves from introduction, to growth and then to the maturity and decline phases of the

product life cycle possibly changing the selection of suppliers. Goals of achieving a high

quality and delivery performance during the introductory and growth stages of the product

life may be changed to reducing price and developing alternate sources as the product

reaches the maturity and decline phases of the product life cycle. The intent of determining

the appropriate supplier performance goals based on the item type and stage in the product

life cycle is to maximize the benefit to the buying organization with respect to quality,

delivery, product performance, product safety, lead-time and price performance, while

understanding and managing the supplier performance and product risks associated with

the buying process.

The multiple criteria, multiple products, integrated supplier selection model with product

life cycle considerations is developed in Chapter 4. It will serve as the foundation for

applying the general model to a real world supplier selection problem detailed in Chapters

5 and 6. This case study, which starts with short-listed suppliers, will demonstrate the

effectiveness of the integrated model, which includes key supplier criteria that are critical

to this global consumer products company.

In summary, the overall contribution of this research is to examine the supplier selection

process using MCDM models with the addition of the product life cycle and purchased

material classifications. Given the nature of the supplier selection decision making process

and the tradeoffs required by decision makers in choosing the most important criteria, goal

programming will be used to provide the alternative solutions. The Value Path method

will provide the DMs with the tradeoffs for the products and product life cycle phases.

Finally, the model results will be assessed against the actual order allocations used by the

company. This comparison will provide a measure the effectiveness of the MCDM models

developed in this thesis.

The thesis will be organized as follows: Chapter 2 provides a literature review of the

supplier selection and management areas. Chapter 3 discusses the results of the empirical

study on supplier quality and delivery results. Chapter 4 presents the general MCDM

9

model including the product life cycle. Chapter 5 presents the ranking results of the senior

decision makers of the company used in the case study. Chapter 6 presents the final

supplier selection results and compares them to the actual order allocations used by the

company. Concluding remarks are provided in Chapter 7.

10

2. Literature Review

In this section, the literature relating to the supplier selection, supplier risk management,

and product life cycle is reviewed. This material serves as the foundation for formulating

the MCDM models and testing supplier performance hypotheses presented in the following

chapters. A review of MCDM models used in the supplier selection process will also be

presented.

2.1 The Importance of Supplier Selection and Management Processes

Selecting and managing suppliers are critical components of the purchasing and supply

chain management function (Ravindran and Wadhwa 2009, Monczka et. al 2002, Lee et.

al 2001, Carter et. al 1998). Figure 2.1 summarizes the major steps involved in this

important process. As noted previously, Dickson (1966) surveyed purchasing agents

belonging to the National Association of Purchasing Management (NAPM) in order to

ascertain the major attributes or criteria used in the supplier selection and management

process. The major factors identified by this study included: price, quality, service,

delivery, geographic location, financial position, business volume, technical capability and

supplier management. Weber, Current and Benton (1991) reported that critical measures

and characteristics were respectively: quality, delivery, performance history, net price,

production facilities/capacity and technical capability. Table 2.1 provides a comparison

of the supplier selection ranking criteria between Dickson’s 1966 survey and Weber,

Current and Benton’s 1991 literature review. This comparison reveals that while many of

Figure 2.1 Selecting and Managing Suppliers

11

the criteria remain in the literature their rankings have clearly changed since Dickson’s

original survey. For example, bidding procedural compliance which was ranked 9th in the

Dickson survey now occupies the 16th position in the Weber et al. study. While the ranking

position of many supplier selection criteria have changed, Choi and Hartley’s (1996) study

of automotive suppliers found that Dickson’s primary goals of meeting delivery deadlines

and quality were still identified as critical factors in the supplier selection process.

Table 2.1 Supplier Selection Criteria Comparison

2.2 Supplier Criteria Development and Selection Methodologies

In this section and the following sections, the literature related to the development of the

supplier selection processes will be examined. de Boer et al. (2001) and Ravindran and

Wadhwa (2009) in their literature reviews of the supplier selection process expanded these

processes into four major steps and examined the decision methods used to accomplish

these tasks. de Boer et al.’s examination found that qualitative tools were primarily used

12

in the problem and criteria formulation steps, while quantitative tools were used in the

supplier qualification and final selection steps. This literature review will examine the

treatments used in the formulation of the supplier selection criteria, the supplier

qualification and the final selection of supplier processes in the following sections. Risk

management and the product life cycle’s impact on the supplier selection problem will be

reviewed as well. In the next section a review of the MCDM models will be presented. It

will provide a foundation for the general MCDM model presented in Chapter 4.

2.2.1 MCDM Models

The MCDM models, including ranking methods for finite alternatives and MCMP

(Multiple Criteria Mathematical Programming) methods for infinite alternatives, will be

discussed in this section. MCDM models are classified into to either selection problems

or mathematical programming problems. The multi-criteria selection problems (MCSP)

are focused on ranking or selecting the preferred or best alternatives from a finite set of

alternatives. The MCSP are also referred to as multiple criteria methods for finite

alternatives (MCMFA) and multiple attribute decision making (MADM). In contrast

MCMP problems have explicit constraints that result in an infinite number of feasible

solutions. A comprehensive review of these methods can be found in Ravindran and

Wadhwa (2009) and Masud and Ravindran (2008). The following MCSP ranking methods

will be reviewed in this section:

▪ Lp Metric;

▪ Rating or Scoring Method;

▪ Borda Count;

▪ Analytic Hierarchy Process (AHP).

13

Lp Metrics

The Lp metric corresponds to the distance between two vectors x and y. The general form

for the Lp metric where x, y Rn is shown in Equation 1.

The commonly used Lp metrics are L1, L2 and L∞. The L1 metric (p = 1) represents the

sum of the absolute values of the distances between the vectors. The L2 metric (p =2)

represents the Euclidean distance between the two vectors and is often called the Manhattan

distance. The L∞ metric (p = ∞) represents the maximum value of the absolute values of

the distances between the vectors. Suppliers are ranked based on a set of supplier criteria

and the ideal solution. The ideal solution represents the best possible values which can be

achieved for each supplier criterion ignoring the other criteria. Given that the ideal solution

is not achievable because of criteria conflicts, the Lp metric computes the overall distance

for each supplier from this target. Suppliers are then ranked based on this distance from

the smallest to the largest value. This process is often used to short list a set of suppliers

for further consideration in the supplier selection process. An example of using the L2

metric to short list suppliers is provided by Mendoza et al. (2008). For a detailed

explanation and example of the Lp rankings use in the supplier selection process, see

Ravindran and Wadhwa (2009).

The rating or scoring method is one of the most widely employed supplier ranking methods.

For example, a typical rating scale from 1 to 10 is employed with 1 having the lowest value

and 10 having the highest value. The DM rates each of the criteria. The rating for each of

the criterion is normalized providing a weight for the specific criterion. The weights for

each criterion are then multiplied by the specific supplier rating for that criterion and

summed for all criteria generating an overall score for each supplier. Suppliers are then

ranked based on the overall scores with the highest achieving the top position in the

selection process.

ppn

j

jjp yxyx

/1

1

−=−

=

(2.1)

14

The Borda count method begins with the ranking of the criteria from the most important to

the least important. Supposing there are n criteria, the most important criterion is assigned

n points, the next highest ranked criterion is assigned n – 1 points with the lowest or least

important criterion being assigned 1 point. The sum of all the points are then determined

(sum of points S = n (n+1)/2). Criteria weights are determined by dividing the respective

criterion points by the sum of the points S. The overall ranking for each supplier is

determined by computing the weighted scores as before. When the number of criteria

becomes large it is difficult for the DM to determine the criteria rankings. In this case, a

pair wise comparison of criterion is often used to create the rank order required by the

Borda count method.

The Analytic Hierarchy Process is widely used in the supplier selection process.

Introduced by Saaty (1980), the first step in the AHP method is to structure the problem in

the form of a hierarchy. The top level represents the overall objective or goal (aka select

the best supplier proposal). The next levels of the hierarchy represent criteria, sub-criteria

and this decomposition process continues until all the sub-criteria have been accounted for

in the hierarchy. Ravindran and Wadhwa (2009) summarize the basic principles of AHP

as follows:

▪ Create a hierarchy for the problem beginning with the top level hierarchy. This top

level hierarchy or top vertex represents the main objective of the selection problem.

The bottom level vertices represent the alternatives under consideration. The

intermediate vertices correspond to decision criteria and subcriteria, which are

aggregated into the top level hierarchy as move up in the hierarchy.

▪ Paired comparisons of criteria/subcriteria are completed at each level of the

hierarchy. “Contribution weights” are determined based on the paired

comparisons. The weights provide the decision maker with a value of the specific

criterion’s contribution to the next higher vertices that they are associated with.

▪ The pairwise comparisons are completed using a scale of 1-9 with 1 representing

equal importance and 9 being most important.

15

▪ Pairwise comparisons of the criterion and subcriterion are completed with respect

to the alternatives presented to the decision maker. A numerical score representing

a weight is determined for each criterion and subcriterion.

▪ Finally, a total weighted scored is determined for each alternative which also

provides the final ranking of the alternatives.

The completion of the pairwise comparisons includes mathematical computations which

check the decision maker’s consistency in making the pairwise comparisons. Inconsistent

decisions are identified and corrections must be made to the pairwise comparisons in order

to properly utilize the AHP method. A complete AHP example, including the problem

decomposition into the hierarchy, pairwise comparisons, mathematical formulas and

calculations can be found in Ravindran and Wadhwa [2009].

Next a review of the general multiple criteria mathematical programming model and

critical definitions will be presented. MCMP problems are usually solved by creating

explicit mathematical relationships using decision variables integrated within objectives

and constraints. The goal programming approach to solve an MCMP model will be

discussed. The following goal programming methods will also be discussed:

▪ preemptive GP;

▪ non-preemptive GP;

▪ Tchebycheff (Min-Max) GP and

▪ Fuzzy GP.

Other MCMP approaches, such as compromise programming and interactive methods, will

also be examined.

The general MCMP model presented can be found in Masud and Ravindran (2008) and

Ravindran and Wadhwa (2009). The general Multiple Criteria Mathematical Programming

problem is stated as follows:

Maximize F(x) = {f1(x), f2(x), …, fk(x)}

Subject to gj(x) ≤ 0 for j = 1, …, m

(2.2)

16

Where x is an n-vector of decision variables and fi(x), i= 1, …, k are the k criteria/objective

functions, and gj(x) are the constraints of the problem.

Let S = {x/ gj(x) ≤ 0, for all ‘j’}

Y = {y / F(x) = y for some x S}

S is called the decision space and Y is called the criteria or objective space in MCMP.

A superior solution for an MCMP model is one that is feasible and simultaneously

maximizes all the objectives. Superior solutions to MCMP models do not exist as the

objectives conflict with one another. An example of conflicting objectives for a supplier

selection problem may be selecting the supplier with the lowest price and highest quality

concurrently.

MCMP models also can be utilized to generate solutions which are compared to an ideal

solution. The ideal solution is the vector of the individual objective function’s optimal

values which are determined by optimizing each objective function separately while

ignoring all other objectives. Ideal values are used in Fuzzy goal programming as target

values with overall objective of minimizing the maximum normalized distance from the

ideal solution for each objective. Ideal values are also useful to set realistic targets on the

objectives.

In order to manage conflicting objectives in multi criteria mathematical programming

models the concept of a superior solution or optimality for all objectives, is replaced by the

concept of an efficient, non-dominated or Pareto optimal solution. A solution x0 S to the

MCMP problem is identified as being efficient if fk(x) > fk(x0) for some x S implies that

)(xfj

< )( 0xfj

for at least one other index j. Simply stated an improvement in any one

objective value will result in the degradation of at least one other objective’s value.

Solutions which are not efficient are identified as dominated solutions. The efficient set is

defined as the set of all efficient solutions. MCMP problems with continuous decision

variables can have an infinite number of efficient solutions. Given the infinite number of

efficient solutions, input from the decision maker is critical for comparing possible

17

solutions. Multi criteria mathematical programming models assume that the DM has a

preference function, which is based on the values of the objectives, but is not explicitly

known. Under this assumption, the primary purpose of the MCMP methodologies is to

find the best compromise solution. The best compromise solution represents a solution on

the efficient frontier which maximizes the DM’s unknown preference function. Multiple

criteria mathematical programming methods can be divided into the following categories

based on how and when the decision maker’s preferences are obtained:

1. Complete information regarding the preferences is available from the DM at the

beginning of the solution process.

2. No information is available from the DM.

3. Partial information is progressively obtained from the DM.

Goal programming is an MCMP methodology that falls under the first category. Objectives

in goal programming are assigned target levels for achievements as well as the priority for

achieving the objectives. These target values are treated as goals to aspire to. The optimal

solution for the GP is the one that minimizes the differences from the targets following the

order of the decision maker’s specified preferences. These target values are used to

formulate the goal constraints or goals in the goal programming model.

The general form of the goal programming model as presented by Masud and Ravindran

(2008) is shown below:

Minimize Z = =

−−k

iii

dw1

Subject to: i

f (x) + −

id -

+

id =

ib for all i = 1, …, k

j

g (x) ≤ 0 for j = 1, …, m

j

x , −

id ,

+

id ≥ 0 for all i and j

(2.3)

(2.4)

(2.5)

(2.6)

18

The objective function of the GP is shown in Equation 2.3. This GP objective function

minimizes the weighted sum of the deviational variable −

id , which is consistent with the

maximization of the objective function for the MCMP model shown in Equation 2.2. In

Equation 2.4, bi represents the target levels for achieving objective i

f (x). Deviational

variables are used to represent the under or overachievement of each goal. The deviational

variable −

id represents underachievement from the target bi and

+

id represents

overachievement of the target. Since the original objectivesi

f (x) are to maximize, the GP

objective is to minimize −

id the underachievement from the stated target. Equation 2.4

presents the system of equations representing the goal constraints which are related to the

multiple criteria and the respective goals/targets for the criteria. The deviational variables

and their respective weights shown in Equations 2.3 and 2.4, represent the DM’s

preferences with respect to under or over achieving the specific goals. Weights can take

two forms: cardinal or ordinal.

Non-preemptive GP

Non-preemptive goal programming utilizes cardinal weights which are pre-specified by

the DM. The rating or scoring method, Borda Count or Analytic Hierarchy Process (AHP)

which have been reviewed previously, can be employed by the decision maker to determine

the weights for the goals. It is imperative to properly scale the criteria values in order to

facilitate a fair comparison between the criteria. Once the weights are determined, the goal

programming model represented by Equations 2.3 to 2.6, is reduced to a single objective

optimization problem. Weights can be easily changed in the objective function facilitating

easy comparison of trade-off solutions. The use of a single objective function reduces the

computational efforts required to generate a solution. Non-preemptive goal programming

also assumes a linear utility function because of the use of the weights. Preemptive goal

programming, which is more commonly used in practice, uses an ordinal ranking of goals

providing a priority ranking of the goals which are ranked in the order in which the goals

should be achieved. That is discussed next.

19

Preemptive GP

Preemptive GP is represented by the following:

Minimize Z = =

k

ii

1

P −

id

where Pi corresponds to priority given to the achievement of the ith goal. For example, −

1d

may have a priority P1 = 2 and −

2d may have a priority P2 = 1. Here goal 2, which is

represented by P2, has a higher priority than goal 1. This ordering of priorities insures that

lower priority goals are considered only after higher priority goals have been achieved.

The ordering of priorities requires less cognitive effort by the DM as compared to

generating weights for goals which is required by non-preemptive goal programming.

Scaling and normalization of the goals are not required given this prioritization of goals.

Preemptive GP does not assume a linear utility function which is another requirement of

non-preemptive GP. Unfortunately given this achievement order of goal priorities,

preemptive goal programming is in effect a sequential single objective optimization

process which increases the computational efforts required to generate solutions.

Successive optimizations are completed on the alternate optimal solutions of the previously

optimized higher priority goals. Therefore, trade-off solutions cannot be presented to the

DM when using preemptive GP due to the sequential creation of solutions based on the

priority ordering of goals.

Tchebycheff’s (Min-Max) GP

The next GP model to be reviewed will be Tchebycheff’s (Min-Max) Goal Programming

method (Masud and Ravindran 2008). This model only requires the DM to specify the

goals/targets for each objective. The decision maker’s preferences are not required given

that the model minimizes the maximum deviation from the stated goals/targets.

Tchebycheff’s GP uses L∞ metric (p = ∞), shown in Equation 2.1, and the model

minimizes the maximum deviation from the stated goals/targets. Therefore the model

(2.7)

20

i= 1, …, k

i= 1, …, k

reduces to a single objective optimization model. The Tchebycheff’s GP model is

represented by the following

Minimize Maximum )( −

id

−

id ≥ 0 i

Subject to the constraints (2.4) – (2.6).

Equation 2.8 can be reformulated as a linear objective by setting

Maximum )( −

id = M ≥ 0

Then Equation 2.8 is equivalent to the following

Minimize Z = M

Subject to M ≥ −

id for all i = 1, …, k

The disadvantages of Tchebycheff’s GP method are the required scaling of goals, which is

similarly required for non-preemptive GP, and outliers can dominate the final solution

producing poor solutions.

Compromise programming

Compromise programming (CP), which is also known as the global criterion method

(Masud and Ravindran 2008), is a MCMP method which provides the decision maker with

a solution which minimizes the distance from the ideal solution. The ideal solution is the

best possible value that can be achieved for each objective while ignoring all other

objectives. Given the conflicting objectives, the ideal solution is generally not achievable;

compromise programming attempts to obtain a solution that comes as “close as possible”

to the ideal solution. The decision maker’s preferences are not required since CP provides

the decision maker with closest possible solution to the ideal. This “closeness” or distance

is defined by the Lp distance metric as follows:

Lp = p

k

i

p

ii

p

i ff

/1

1

*

−

=

for p = 1, 2, …, ∞.

(2.8)

(2.9)

(2.10)

(2.11)

21

Where f1, f2, …, fk are the different objectives and *

if = maximum )(

if ignoring the other

criteria, is the ideal value for the ith objective and λi are the weights given to the criteria.

In general, λi = */

iifw , where the

iw ’s are the relative weights. Any point that minimizes

the Lp distance for λi > 0, and 1= i and 1 ≤ p ≤ ∞ is called a compromise solution,

which is always non-dominated. It should be noted that geometrically, the distance

measures in compromise programming have different connotations which are dependent

on the value of p chosen (Masud and Ravindran 2008). For p =1, the Lp metric measures

the “city-block” or Manhattan block distance which is equal to the sum of the distances

along all axes, for p = 2, the Lp metric measures the Euclidian or straight line distance

from the ideal solution. For p = ∞, the Lp distance represents the minimum of the

maximum distance from the ideal solution. Therefore as p increases outliers have more

impact on the solution and like Tchebycheff’s GP method outliers can dominate the final

solution producing poor solutions.

If we set p = ∞, λi = */1i

f , then the CP problem is called Fuzzy goal programming.

Fuzzy GP

Fuzzy goal programming is represented by the following

Minimize Z = M

Subject to M ≥ *

* )(

i

ii

f

xff −for i = 1, …, k

jg (x) ≤ 0 for j = 1, …, m

x ≥ 0 for all i and j

Fuzzy goal programming is similar to Tchebycheff’s (Min-Max) GP method (Ravindran

and Wadhwa 2009), but instead of using the DM’s goals/targets for each objective, Fuzzy

GP utilizes the ideal values for objectives. Fuzzy GP generates an ideal solution for each

objective independently and then minimizes the maximum normalized distance from the

(2.12)

(2.13)

(2.14)

(2.15)

22

ideal solution. This GP method does not require targets or preferences from the DM, since

the ideal values are used. The scaling of goals is also not required to use this methodology.

Interactive Methods

Interactive methods rely on the progressive evaluation of preferences by the decision maker

(Shin and Ravindran 1991). The following represents the major steps involved in

interactive methods:

1. Find a solution which is preferably feasible and efficient.

2. Interact with the DM in order to obtain their inputs or response to the solution

obtained in step 1.

3. Repeat steps 1 and 2 until the DM is satisfied with the solution or some other

termination criterion is met.

This MCMP methodology represents a model where partial information is progressively

obtained from the DM. The most critical factor in the application of interactive methods

to real-world problems is the functional restrictions placed on the objective functions,

constraints and the DM’s preference function which is unknown at the start of this process.

See Shin and Ravindran (1991) for a detailed survey of interactive methods which includes

a classification scheme, a review of the methods in interactive method category and a rating

of the DM’s cognitive burden associated with each method.

In the next section, literature which combines the supplier criteria and selection processes

will be examined. As noted previously in de Boer et al.’s (2001) and Ravindran and

Wadhwa’s (2009) examination of the supplier selection processes (see Figure 2.1)

quantitative tools are used extensively in the supplier qualification and final selection steps.

This section will focus on research, which combines qualitative tools used in supplier

criteria selection as well as quantitative tools which are used in short-listing suppliers and

the final supplier selection processes. These studies attempt to bridge the gap between

supplier selection criteria and supplier selection models and will also be examined in detail

given the importance supplier selection criteria can have on supplier selection and

performance.

23

2.2.2 Supplier Criteria Determination and Supplier Selection

Muralidharan et al. (2002) described a 5 step process applying the Analytic Hierarchy

Process method to the supplier criteria and selection processes. Using a real-world

example for a bike manufacturer, the first step was to identify the decision makers which

was followed by the identification of the significant factors or criteria which would be used

in the supplier evaluation. The decision makers used nominal group technique to determine

the weights of the suppliers’ selection criteria, using the rankings the criteria weights were

determined by the AHP process. These final supplier criteria weights were used to develop

the final consensus ranking of the suppliers under consideration.

Mendoza et. al (2008) article spanned the supplier criteria and final supplier selection

processes. The authors created a three-phase supplier selection process which utilized a

supplier screening process using an Lp Metric, AHP to determine the decision maker’s

(DM) weights and Goal Programming for determining the supplier order allocations. In

phase 1, the L2 metric, which is a measurement of Euclidean distance between vectors, is

used to screen the initial set of 18 suppliers versus the ideal value for each of the seven (7)

criteria. This initial set of suppliers is screened or short-listed to the top seven (7) suppliers

for further consideration. The authors note the short list of suppliers should be limited to

ten in order to facilitate the use of the Analytic Hierarchy Process (AHP) in the second

phase of the supplier selection process. Phase 2 requires the use of AHP to complete a

pairwise comparison of suppliers versus the main criteria which includes: quality, delivery,

flexibility, service and price. Quality and delivery are further analyzed for each supplier

by specific sub-criteria. Applying the Analytic Hierarchy Process allows decision makers

to make solid choices based on both qualitative and quantitative criteria. In Phase 3, a non-

preemptive goal program was employed to allocate the orders between the short-listed

suppliers. A sensitivity analysis was performed based on eight (8) realistic scenarios

allowing the DMs to view a number of results based on varying priorities in the goal

program.

Ghodsypour and O’Brien (1998) combined AHP and linear programming (LP) to solve the

combined supplier criteria and selection problems. They present a decision support system

24

which is designed to include both tangible and intangible factors involved in the supplier

selection process.

Wang et. al (2004) developed a supplier selection model by combining AHP with

preemptive goal programming (PGP). The authors utilized the twelve (12) performance

metrics, which fall under four major categories defined by Supply Chain Operations

Reference (SCOR) Model as the supplier ranking criteria. The authors claim that one of

the strengths of their model is that tradeoffs between tangible and intangible criteria are

made possible through the application of AHP to the SCOR criteria creating a ranking of

suppliers based on both qualitative and quantitative attributes or criteria.

Wang, Huang and Dismukes’ preemptive goal program utilizes the suppliers’ final

rankings based on weights determined by AHP ranking process of the supplier’s SCOR

subcategories. The first priority of the preemptive goal program was to maximize total

value of purchasing while the second priority was to minimize total purchase cost. The

illustration created for the combined AHP and PGP model evaluates three (3) different

items (tires, electronics and peripherals) which are supplied by nine (9) different suppliers.

The authors claim the single period model includes product life cycle considerations. This

is a weak assertion given that product life cycle consideration is based on the item type.

For example, it is asserted that the tire purchases represent the maturity phase of the product

life cycle and an agile supply chain (AGS) strategy should be employed which changes the

AHP supplier rating weights accordingly. A more through treatment of product life cycle

considerations would include multiple periods and changing supplier criteria ratings or

rankings for the four phases of the product life cycle. Chapter 4 of this thesis will present

a supplier selection general model which will include multi-periods which will employed

to represent various stages in the product life cycle.

Sarkis and Talluri (2002) created a supplier selection model which utilizes the Analytic

Network Process (ANP) to rank the selection criteria and select the best supplier based on

an industry based case illustration. ANP is a generalized from of the Analytic Hierarchy

Process which considers interactions and interrelationships between criteria or factors. An

25

example of these interactions and interrelationships is the impact that cost and quality have

on supplier flexibility. The focus of their research was on developing a model for strategic

supplier selection and it included partnership factors proposed by Ellram (1990).

Lee et al. (2001) used AHP to select suppliers based on information from a Korean air

conditioner manufacturer. Eighteen (18) months of data, which represented three (3)

supplier performance evaluation periods, which are conducted every six (6) months, for

twelve (12) critical parts supplied by thirty-six (36) suppliers, was used to determine

weights for the supplier selection criteria. Weights for the supplier selection criteria were

created for each of twelve (12) critical high volume and high risk parts. The example,

provided by the authors, focused on a printed circuit board (PCB) which could be supplied

by two (2) suppliers.

Petroni and Braglia (2000) used information from a manufacturer of bottling and packaging

line machinery as the illustration for the application of the principal component analysis

(PCA) methodology to the criteria selection and supplier selection process. The research

evaluated six (6) supplier attributes or criteria. These criteria included: management

capabilities; production facilities and capacity; technological capabilities; price,

quality and delivery compliance. The first three (3) criteria, which are qualitative

measures of a supplier, were evaluated by purchasing personnel using a Likert scale, while

the remaining criteria are quantitative measures of supplier performance. PCA normalizes

the supplier attribute ratings and then selects suppliers based on “extreme observations that

lie away from the rest of the data.” The best supplier selected using this method represents

a statistical outlier, with the largest variance, from the remaining suppliers in the selection

pool. The top supplier selection criteria found in this industrial example were delivery,

quality and price respectively. The empirical study, found in Chapter 3 of this thesis, will

examine the relationship between supplier delivery and quality performance and critical

supplier attributes such as technological capabilities. Understanding these relationships

will provide valuable insights which can be utilized to determine supplier selection criteria

that are related to supplier performance. This understanding will provide decision makers

with valuable information linking supplier selection criteria with supplier performance.

26

Vokurka et al. (1996) created an expert system to be used in the evaluation and selection

of suppliers. The intent of their study was to create a system which would capture the

judgment and expertise of an expert purchasing user and combine this knowledge with the

formal supplier evaluation criteria found in the literature. This combined expert judgment

and formal supplier evaluation criteria were used to create the supplier selection criteria

and rules that would be utilized by the expert system. The system evaluated suppliers who

passed the initial pre-screening of a supplier and evaluation of sample products.

Bhutta and Huq (2002) combined the analytic hierarchy process and the total cost of

ownership (TCO) to solve the supplier criteria and selection problems. The authors note

the focus of the TCO process is pricing and determining all the purchase related costs

associated with the purchasing and materials management processes while qualitative

issues are ignored. AHP allows the inclusion of both quantitative and qualitative factors

in the decision making process. The authors provide examples of applications of both the

TCO and AHP methods and suggest that combining these methods would provide a more

“robust tool” for managers to select criteria and suppliers. Unfortunately, the authors do

not provide an illustration or example which combines these two powerful methodologies

to substantiate their assertions.

Narasimhan et. al (2001) link the supplier evaluation criteria, performance and selection

using data envelopment analysis (DEA). Their research links input variables to output

variables in an effort to link supplier performance to specific supplier attributes or criteria

that the authors identify as input variables. The authors evaluated purchasing data, which

included the input and output variables, for twenty-three (23) suppliers of a large multi-

national telecommunications company. The research presented by Narasimhan et al.

provides a useful model which links supplier selection criteria to supplier performance.

Another important contribution is the concept of taking action to reduce the risk associated

with poor supplier performance. Chapter 3 of this thesis will present results from an

empirical study of supplier performance which examines the links between key supplier

input variables or attributes and the output variables quality and delivery. These results

27

could be employed to take action on based on poor supplier performance reducing the risk

in the procurement process.

In the next section of this literature review, research which is focused on the supplier

selection process will be summarized. As noted previously by de Boer et al. (2001) and

Ravindran and Wadhwa (2009) in their literature reviews of the supplier selection process,

while qualitative tools were predominantly used in the problem and criteria formulation

steps, quantitative methods are the primary tools used in the supplier short-listing or

qualification and final selection steps.

Smytka and Clemens (1993) summarized a supplier selection process which was employed

by General Electric’s (GE) Wiring Devices operations. The model used total cost as key

decision variable for choosing suppliers but also used supplier risk and business desirability

factors to prescreen suppliers prior to the final decision which was based on total cost.

Twelve (12) risk factors and subcategories were utilized as a Go/No Go decision making

process in screening the pool of capable suppliers. These risk factors such as Financial

Stability included subcategories which were assessed for each potential supplier. This

three step process required substantial effort but in GE’s opinion provided the best

objective method for the supplier criteria and selection processes. In addition to the

defining the risk categories for screening potential suppliers, GE’s wiring devices

operations utilize specific measures of supplier performance combined with a range of

demand values to provide a more robust supplier criteria selection model. Chapter 3 in this

thesis will examine a number of risk factors, such as financial stability using Dunn &

Bradstreet ratings, in order to empirically test the impact of these factors on supplier quality

and delivery performance.

Buffa and Jackson (1983) employed preemptive goal programming to solve a multi-period

multiple supplier selection problem. The GP made selections from eight possible suppliers

for a twelve month time period. Their illustration attempted to achieve eight (8) goals set

at five (5) priority levels. The top priorities were focused on achieving a quality acceptance

28

rate, achieving an on-time arrival rate or delivery time and avoiding excess inventory

holding costs.

Bender et al. (1985) created a single objective mixed integer programming model which

was utilized at IBM’s Poughkeepsie, New York facility resulting in a savings ranging from

five to 20 percent of the total purchase cost, while achieving a four to 10 percent better

solution than experienced buyers solving the same problem. The mixed integer

programming model minimized the total purchase cost which included the “sum of

purchasing, transportation, and inventory costs,” without exceeding any vendor’s

production capacity and without violating any of the purchasing policy constraints.

Narasimhan and Stoynoff (1986) also used a single objective mixed integer program to

allocate the production of sixty-four (64) parts among five different suppliers which could

be produced in fifteen (15) supplier plant locations and shipped to twenty-eight (28) of the

manufacturing firm’s locations. Since all suppliers did not have quality approval to

manufacture all sixty-four (64) parts the possible combinations under modeling

consideration were reduced from 26,880 to 1,354. Initially the authors suggested

developing a multi-objective goal program but based on feedback from the manufacturer’s

management selected a mixed integer model which minimized the total cost of shipping

and penalty costs associated with the supplier capacity utilization while minimizing both

positive and negative deviations from the suppliers’ economic manufacturing quantities

(EMQ).

Weber and Ellram (1993) created a multi-objective mixed integer program to solve a single

period, single item supplier selection problem for a Fortune 500 pharmaceutical company

which was practicing a JIT (Just in Time) Philosophy. The objectives defined by the firm

included: minimizing total purchase price, minimizing late delivery and minimizing the

number of units rejected as a measure of supplier quality performance. The number of

suppliers selected to supply the items was varied to provide alternative solutions. The

29

Value Path methodology was utilized to graphically display the various solutions allowing

the decision makers to choose the best-compromise solution.

Weber and Current (1993) also employs a multi-objective mixed integer program to solve

a single period, single item supplier selection problem for a firm employing JIT. The

objectives are similar to Weber and Ellram (1993). The authors note that one of the

strengths of the multi-objective analysis is this methodology provides the decision maker

with a set of efficient or non-inferior solutions versus a single “optimal” solution

determined by single objective models.

Weber et. al (2000) extended the original weighted multi-objective mixed integer

programming model (Weber and Current 1993) by adding data envelopment analysis to

the supplier selection process. First the supplier selection process is solved using a multi-

objective programming model which creates the efficient solutions for the selection of

varying number of suppliers (e.g. the model is solved for four, five and six suppliers

providing the single item). The efficient solutions from the four, five and six supplier

models are compared to the efficient solution for the single supplier model. The DEA

efficiency is calculated for these solutions and analyzed versus multi-objective solution for

a single supplier. Statistically comparing the mean DEA efficiency and standard deviation

between the solutions allows the user to choose solutions offering the greatest difference

in results.

Karimi and Rezaeinia (2014) utilized a multi-segmented goal programming formulation

for supplier selection. Decision makers set multiple aspiration levels for the various

objectives. Based on these aspiration levels, the GP model was solved and the DMs were

presented with the deviations from each goal. The results presented the decision makers

with tradeoffs among the aspiration levels and goal achievements.

Wadhwa (2016) employs a goal programming to solve a multi-objective, multi-period

supplier selection problem with bundling for tactical or noncritical items. Cost, quality

rejects, lead-time and supplier risk are minimized. Preemptive, non-preemptive,

30

Tchebycheff’s (min-max) and Fuzzy goal programming are used to solve the problem. The

Value Path approach is utilized to present the multi-objective results.

Ghodsypour and O’Brien (2001) created a mixed integer weighted multi-objective non-

linear programming model to evaluate the selection of multiple suppliers at varying levels

of demand and projected supplier quality performance. The goal program was solved for

the optimum cost and quality levels.

Kumar et al. (2004) utilizes a Fuzzy mixed integer goal program to solve the supplier

selection process. The primary goals of this GP are to minimize price, the number of

rejected units as well as the number of late units.

Feng, Wang and Wang (2001) create a stochastic integer program to select suppliers and

tolerances based on the Taguchi quality loss function and process capability. The objective

function minimizes both the total supplier cost for an item and the Taguchi total quality.

The model results are presented at a number of quality loss function levels and process

capability levels.

Liu et al. (2000) utilized data envelopment analysis to solve the supplier selection process.

They applied the model to an agricultural and construction equipment manufacturer’s

twelve (12) commodity groups supplied by over 250 suppliers. The results sorted suppliers

into high performing suppliers, suppliers that needed performance improvement and

suppliers which should be eliminated from further business. The results also projected the

level of potential improvements in price, delivery and quality performance.

Wadhwa and Ravindran (2007) address the supplier selection problem by creating and

illustrating the application three multi criteria decision making. The methods included the

following:

▪ weighted objective;

▪ goal programming;

▪ compromise programming.

31

Minimizing price, quality defects and lead-time variations represent the major goals or

objectives for each of these methods. Constraints include the quality levels, lead-time and

production capacity of the potential suppliers. The results from the models are presented

using the Value Path method, which allows the DM to visualize the results of the various

methods versus the three goals.

Narasimhan et. al (2006) utilize compromise programming to minimize cost, which

includes direct and indirect components, minimize the number of supplier transactions as

a measure of transaction complexity, minimize quality deviations from the quality

standards for a specific product and minimize delivery deviations from the delivery

standards for a specific product. The authors’ illustration includes three products for three

time periods with the product life cycle stage changing as the time period changes. The

achievements of the goals are changed through the time periods for each of the products

by changing the allowed weighted deviations for the goals. Changing the weighted

deviations allows for changing the priorities for the various goals based on the current stage

the item in the product life cycle.

Wu and Olson (2008) employed chance constrained programming (CCP), data

envelopment analysis and multi-objective programming to solve a supplier selection

problem. Their illustration utilized ten (10) possible suppliers, one retailer which

represented the purchasing firm and twenty (20) customers supplying a single item for a

single period. A variety of distributions including normal, exponential and lognormal were

used to simulate demand, quality acceptance rates and late delivery rates respectively.

These distributions were employed in the CCP model to create a number of different

scenarios which including increasing levels of variance by changing the Z values in the

distributions. DEA was also employed to identify the best set of suppliers as well as

suppliers who are considered to be inefficient when compared to the best set. The weighted

multi-objective problem minimized purchase cost, the percentage of late deliveries and

percentage of rejected items. The weights were changed in order to generate a number of

solutions and the authors referenced the work of Narasimhan, Talluri and Mahapatra (2006)

as an example of how the weights could be changed to reflect changes in an item’s product

32

life cycle requirements. Simulations were run for each of the three models showing the

impact variation demand, quality acceptance rates and late deliveries had on the results.

These simulations were intended to demonstrate the variation in supply chain results and

the risks associated with these results which could be expected due to the inclusion of

stochastic methods.

In the next section literature which examines risk and the supplier selection process will be

reviewed.

2.3 Risk and Supplier Selection

In section 2.2.2 supplier criteria development and supplier selection methods were

examined. Companies have used a variety of the methods to reduce risk associated with

the supplier selection process. Texas Instruments’ used a weighted supplier criteria ranking

method in an effort to identify and screen potentially poorly performing suppliers prior to

supplier selection and split orders between suppliers to reduce risk (Gregory 1986).

Thompson (1990) also used a weighted point system for supplier selection but included a

Monte Carlo simulation of supplier performance. This simulation of expected supplier

performance added a stochastic aspect to supplier selection through the addition of supplier

performance variances. Users could examine the risks of selecting a supplier with excellent

average performance but with a wide performance variance. As noted previously, General

Electric’s (GE) Wiring Devices operations used a supplier criteria selection process based

on assessing supplier risk and business desirability factors in order to prescreen suppliers

prior to the final decision which was based on total cost (Smytka and Clemens 1993).

Twelve (12) risk factors and subcategories were utilized as a Go/No Go decision in the

initial supplier screening process. These risk factors included for example Financial

Stability with subcategories such as Dunn & Bradstreet Rating, Profit/Sales Trends, etc.

Section 2.2.2 which reviewed the combined supplier criteria determination and supplier

selection methods also includes research that addressed risk in the supplier criteria and

selection processes. Lee et al. (2001) used AHP to select suppliers of twelve (12) critical

high volume and high risk parts for a Korean air conditioner manufacturer. The top three

33

selection criteria were cost reduction, rejection rate in the incoming quality control and

time loss in the production line. The risks associated with these selection criteria could

cause significant disruption to production and financial results. Likewise, Narasimhan et

al. (2001) utilized data envelopment analysis to link important supplier input and output

variables in order to identify suppliers whose performance created a risk for the purchaser.

The authors work segregated suppliers by performance and identified companies whose

performance created a high risk for the purchaser. The objective of this research was to

identify and terminate poorly performing suppliers thereby improving the overall

performance of the supply base through the removal of “risky” suppliers. Wu and Olson

(2008) investigated risks associated with supplier selection and performance. The authors

used chance constrained programming (CCP), data envelopment analysis and multi-

objective programming to analyze the variation in supplier performance due to the

inclusion of a variety of distributions which were used to simulate demand, quality

acceptance rates and late delivery rates. Simulations using each of the three models

demonstrated the varied results in supplier performance and allowed users to access the

performance risks associated with this varied performance.

Chapman et al. (2002) report on an industry study of the current state of risk management

in supply networks noted that firms do not manage risks very well. The authors asserted

that a risk management process is required.

While Chapman et al. make the case for actively identifying and managing supply risks a

study by Computer Sciences Corporation in 2003 referenced by Tang (2006B) noted that

while 43% of 142 companies reported their supply chains were vulnerable to disruptions

nearly 55% of these companies had no documented contingency plans. Tang

recommended a number of strategies to build a “robust supply chain” in order for a firm to

continue operations should a major disruption occur.

Chopra and Sodhi (2004) identified a number of risk categories and mitigation strategies.

Companies add inventory, capacity and develop redundant suppliers in order to manage

procurement risks. The authors also suggest tailoring the risk response according to the

34

item volumes and type. For example, developing redundant suppliers is recommended for

high volume products while consolidating to a small number of flexible suppliers is

recommended for low volume products. It is recommended that cost be favored over

supplier responsiveness for commodity items while supplier responsiveness is favored over

cost for short life cycle products.

Chopra et al. (2007) examines the effects of supply disruption and the recurrence of

disruptions on varied supply sources. Their research examines the results of the disruption

and length on two supply sources, one being a cheaper, less reliable supplier and the other

being a more costly but more reliable supplier. The study findings indicated that as

disruption probabilities increased use of the more reliable but more expensive produced

better overall results for the purchaser.

Tomlin (2006) examined a number of contingency strategies for managing supplier chain

risks. Tomlin explored three supply disruption mitigation strategies including: carrying

additional inventory, single sourcing from a reliable but more expensive supplier and

passive acceptance of the disruption. Tomlin found that carrying additional inventory was

not a good strategy for rare but long disruptions. The author noted that in some cases, such

as labor strikes, advance notice could be employed and inventory could be added in

anticipation of a possible disruption. Tomlin determined the number of disruptions and

length of disruptions varied the recommended mitigation strategies available to the

purchaser.

Snyder and Shen (2006) examined demand and supply disruptions on a supply chain. They

determined that backup or alternate suppliers played a key role in supplying capacity for

supply disruptions or uncertainty. However backup suppliers would have “little value”

for dealing with demand uncertainty since they would only produce when demand

exceeded capacity which occurred on an infrequent basis.

Zsidisin et al. (2004) examined the risk assessment techniques employed by seven (7)

companies. The authors found that two of the companies had formal risk assessment

35

processes. Other firms used a variety of supplier audit techniques including checking for

quality award certifications such as the Malcolm Baldrige National Quality Award,

checking supplier’s financial results and health and monitoring the percentage of firm’s

business with a particular supplier. Other companies in the study modeled the supply

chain’s market fluctuations in an effort to prevent or reduce stockouts.

The supply risk literature includes a combination of qualitative and quantitative techniques.

The literature includes empirical studies of risk assessment techniques and

recommendations, supplier selection criteria models focused on reducing risks associated

with supplier selection and quantitative models used for supplier selection and risk

mitigation. Chapter 3 of this thesis will examine the relationship between a number of

supplier attributes or criteria and supplier performance. Understanding these relationships

will provide information which will provide inputs to the robust optimization supplier

selection general model, which will be developed in Chapter 6.

In the next section literature relating to the product life cycle and supplier selection will be

examined.

2.4 The Product Life Cycle and Supplier Selection

Dealing with product life cycle issues, especially product end of life (EOL) notices which

companies use to communicate the end of production for a specific item can create serious

supply chain management problems (Jorgensen 2005). Jorgensen reports that 150,000

electronics parts were designated end of life (EOL) in 2004 and 66% of designers reported

component obsolescence issues in past 6 months. In the electronics industry this problem

is further exasperated by the European Union’s (EU) ban of lead and several other

substances (RoHS program) used in integrated circuits (IC's) increasing the EOL

notifications. Carbone (2003) found that the main reasons for the increase in end of life

notices are “volume, margins, revenue and contribution activity” deficiencies.

Military systems face serious product life cycle issues as weapon systems life spans such

as the B-52 bomber, are extended well into the 21st century (Stogdill1999). As products

36

and required components reach the decline and end of life cycle stages, risk increases in

the supplier selection process as new supply sources are introduced, new replacement parts

are purchased and emergency orders to purchase the remaining supply on an EOL item are

issued.

The Electronics Industries Association or Electronics Industry Alliance uses a Gaussian or

Normal curve to approximate the length of the product life cycle (Solomon et al. 2000).

Based on this information, the “Zone of Obsolescence” can be calculated which

approximates the time in the product life cycle from the current date that the product will

be obsolete.

Handfield and Pannesi (1994) stated that product life cycles must be managed at the

individual component level. The authors also propose a model of the component life cycle

that includes procurement risk. For example, components in the introductory stages of the

PLC are considered to be high risk, high cost, low reliability and low volume. Contrasting

this high risk introductory stage would be the maturity stage, where the risk is low, price

is at the lowest, quality and volume levels are high. The authors suggest employing a

number of techniques in order to reduce the risks as items move through the product life

cycle.

Fandel and Stammen (2004) created a general single objective linear mixed integer model

to model an entire supply chain. The objective of this model was to maximize the global

after-tax profit for a firm. The researchers included product life cycle considerations by

creating constraints which focused on customer satisfaction for products in the introductory

stage of the product life cycle. This general model also included recycling centers where

products can be collected and disposed of in an appropriate manner. This recycling process

is essential given the fact that cars sold in the European Union after 2007 must have a

recycling plan insuring proper disposal of the vehicle.

Li, Wong and Kwong (2013) created a dynamic programming model which included

decision variables from a AHP process. This research was focused on the short product

37

life cycle apparel industry. This multi-objective problem focused on minimizing the risk

of material purchasing and total purchasing cost.

Narasimhan, Talluri and Mahapatra (2006) employed compromise programming to model

the supplier selection problem for three products for three time periods with the product

life cycle considerations changing as the time period changed.

Table 2.2 summarizes the supplier criteria and selection methods examined in this review.

It provides a comparison of the objectives of this thesis to this body of research. As noted

previously, this thesis will develop integrated MCDM models which include a variety of

item types identified in the supply segmentation model (shown in Figure 1.1) at various

stages in the product life cycle while incorporating risk components through empirical

testing results of critical supplier attributes to supplier performance. In the next section an

empirical study of key supplier attributes, conducted by the author and their relationship to

supplier quality and delivery performance will be examined. Gaining further

understanding of these critical relationships will provide increased understanding which

will be utilized in the formulation of MCDM models in this thesis.

38

Table 2.2 Supplier Criteria and Selection Literature Summary

Authors

Quantitative/

Qualitative

Real

Example

Used? Methodology

Deterministic/

Stochastic

Single/

Multiple

Sourcing

Multi-

Objective Multi-Period

Product Life

Cycle

Multi-Item

Classifications

Bender, Paul S.;

Brown, Richard W.;

Isaac, Michael H.

and Shapiro,

Jeremy F. (1985) Quantitative Yes

Mixed Integer

Programming Deterministic Multiple No-Single Multi-Period No Multi-Item

Bhutta, Khurrum S.

and Huq, Faizul

(2002)

Quantitative

and

Qualitative No

Total Cost of

Ownership (TCO)

and Analytic

Hierarchy Process

(AHP) Deterministic Single Yes Single Period No Single Item

Buffa, Frank P. and

Jackson, Wade M.

(1993) Quantitative No

Preemptive Goal

Program Deterministic Multiple Yes Multi-Period No Single Item

Ellram (1990) Qualitative No Interviews Not Applicable Not Applicable

Not

Applicable Not Applicable No Not Applicable

Fandel, G. and

Stammen, M.


Mixed Integer

Program Deterministic Multiple No-Single Multi-Period

Yes, constraints

created to insure

availability for

new products Multi-Item

Feng, Chang-Xue

(Jack); Wang, Jin

and Wang Jin-Song

(2001) Quantitative Yes

Stochastic Integer

Program using

Supplier Process

Capability Stochastic Multiple No-Single Single Period No

Single Item per

Example

Ghodsypour, S.H.

and O'Brien, C.

(1998)

Quantitative

and

Qualitative No

AHP and Non-

preemptive Goal

Program Deterministic Multiple Yes Single Period No Single Item

Ghodsypour, S.H.

and O'Brien, C.


Mixed Integer Non-

Linear Program Deterministic Multiple Yes Single Period No Single Item

Gregory, Robert E.

(1986)

Quantitative

and

Qualitative Yes

Weighted Ranking

Method Not Applicable Multiple Yes Single Period No Multi-Item

Kumar, Manoj;

Vrat, Prem and

Shankar, R. (2004) Quantitative No

Fuzzy Mixed

Integer Goal

Programming Deterministic Multiple Yes Single Period No Single Item

Lee, Eon-Kyung;

Ha, Sungdo and

Kim, Sheung-Kown

(2001)

Quantitative

and

Qualitative Yes

Analytic Hierarchy

Process (AHP) Deterministic Single Yes Multi-Period No Multi-Item

Lehmann, Donald

R. and

O'Shaughnessy,

John (1982)

Quantitative

and

Qualitative No User survey Not Applicable Not Applicable

Not

Applicable Not Applicable No Multi-Item

Liu, Jian; Ding,

Fong-Yuen and

Lall, Vinod (2000) Quantitative Yes

Data Envelopment

Analysis (DEA) Deterministic Multiple Yes Not Applicable No Multi-Item

Mandal, Anukul

and Deshmukh,

S.G. (1994)

Quantitative

and

Qualitative Yes

Interpretative

Structural Modeling

(ISM) Not Applicable Not Applicable No Not Applicable No Not Applicable

Muralidharan, C.;

Anantharaman, N.

and Deshmukh,

S.G. (2002)

Quantitative

and

Qualitative Yes

Analytic Hierarchy

Process (AHP) with

Nominal Group

Techniques for

Ranking and

Selection Deterministic Not Applicable Yes Not Applicable No Not Applicable

Mendoza, A.;

Santiago, E. and

Ravindran, A. Ravi

(2008)

Quantitative

and

Qualitative Yes

L2 Metric, AHP and

Preemptive Goal

Program Deterministic Multiple Yes Single Period No Single Item

Narasimhan, Ram;

Talluri, Srinivas and

Mahapatra,

Santosh K. (2006) Quantitative No

Compromise

Programming Deterministic Multiple Yes Multi-Period

Yes, weights

changed for item

stage in product

life cycle Multi-Item

Narasimhan, Ram;

Talluri, Srinivas and

Mendez, David

(2001) Quantitative Yes

Data Envelopment

Analysis (DEA) Deterministic Not Applicable

Not

Applicable Not Applicable No Not Applicable

Narasimhan, Ram

and Stoynoff, Linda

K. (1986) Quantitative Yes

Mixed Integer

Programming Deterministic Multiple Yes Single Period No Multi-Item

Petroni, Alberto

and Braglia,

Marcello (2000) Quantitative Yes

Principal

Component

Analysis Deterministic Multiple

Not

Applicable Single Period No Not Applicable

Sarkis, Joseph and

Talluri, Srinivas

(2002)

Quantitative

and

Qualitative Yes

Analytic Hierarchy

Process (AHP) Deterministic Single Yes Single Period No Single Item

Smytka, Daniel L.

and Clemens,

Michael W. (1993)

Quantitative

and

Qualitative Yes

Total Cost with

Assessment of

Risk Factors

(Go/No-Go) Deterministic Single

Not

Applicable Single Period No Single Item

Soukup, William R.

(1987)

Quantitative

and

Qualitative Yes

Weighted Ranking

Method w/

Expected Value

Calculation for Total

Cost by Supplier Stochastic Single/Multiple No Single Period No Single Item

Thompson,

Kenneth N. (1990)

Quantitative

and

Qualitative No

Weighted Ranking

Method w/ Monte

Carlo Simulation of

Vendor

Performance Range Stochastic Single Yes Not Applicable No Single Item

Vokurka, Robert J.;

Choobineh, Joobin

and Vadi, Lakshmi

(1996)

Quantitative

and

Qualitative No Expert System Deterministic Single Yes Single Period No Single Item

Wadhwa, Vijay and

Ravindran, A. Ravi

(2007)

Quantitative

and

Qualitative No

Weighted

Objective, Goal

Programming and

Compromise

Programming Deterministic Multiple Yes Single Period No Multi-Item

Wang, Ge; Huang,

Samuel H. and

Dismukes, John P.

(2004)

Quantitative

and

Qualitative No

AHP and

Preemptive Goal

Program Deterministic Multiple Yes Single Period

Yes, but by item

type Multi-Item

Weber, Charles A.

and Current, John

R. (1993) Quantitative Yes

Weighted Mixed

Integer Program Deterministic Multiple Yes Single Period No Single Item

Weber, Charles A.;

Current, John R.

and Desai, Anand

(2000) Quantitative

Fortune

500 JIT

Example

extended

from 1993

Paper

Mixed Integer

Program and Data

Envelopment

Analysis (DEA) Deterministic Multiple Yes Single Period No Single Item

Weber, Charles A.

and Ellram, Lisa M.

(1993) Quantitative

Fortune

500 JIT

Example

Weighted Mixed

Integer Program Deterministic Multiple Yes Single Period No Single Item

Wadhwa, Vijay

(2016) Quantitative No Goal Programming Stochastic Multiple Yes Multi-Period No Multi-Item

Wu, Desheng and

Olson, David L.


3 Methods: Chance

Constrained

Programming, Data

Envelopment

Analysis and Multi-

Objective

Programming Stochastic Multiple Yes Single Period No Single Item

This Thesis

Quantitative

and

Qualitative Yes Goal Programming Deterministic Multiple Yes Single Period Yes Multi-Item

39

3. Empirical Study of Supplier Attributes to Supplier

Delivery and Quality Performance

In this section an industrial case study testing the formal relationship between key

supplier’s attributes and quality and delivery performance will be presented. The

motivation for this chapter was to test the relationships between these supplier attributes

and performance in order to answer the question, is there an association between supplier

attributes and performance? The finding from this study of an industrial equipment

manufacturer will be included in the creation of robust optimization supplier selection

models. The literature review of supplier criteria determination and supplier selection

models (Petroni and Braglia 2000, Narasimhan et. al 2001, Smytka and Clemens 1993)

includes research which is focused on selecting supplier attributes which are related to

supplier performance. As it was noted previously, disruptions in a company’s supply chain

can lead to higher costs, higher inventories and lower sales (Hendricks and Singhal 2005).

Monitoring and controlling the supplier performance is an important activity which is

directly linked to company performance. Ittner et al. (1999) found that firms not making

use of supplier selection and monitoring techniques had lower profits and quality than those

firms using supplier selection and monitoring practices. Surveys have been utilized to

analyze supplier performance and how supplier management effects supplier performance

measures such as quality and delivery (Park, and Hartley 2002). Unfortunately, many of

these surveys rely on self reported supplier performance improvements and their links to

selected attributes or supplier improvement processes (Krause and Scannell 2002). A

number of commercial products and services are also available to assist in the supplier

selection, management and development (Dun and Bradstreet Supply Management

Solutions Website 2018, Rohbe Company Supplier Analysis System Website 2008). These

commercial packages generate scores and assessments of supplier risk based on financial

measures including credit worthiness, pending litigation, liens, judgments, quality and

delivery. This study will provide increased understanding of the supplier performance risk

by utilizing a number of statistical methods. These findings can then be used to short-list

suppliers. These suppliers can then be included as part of the final supplier selection for

products across the product life cycle using an MCDM model. This integrated MCDM

model, which combines descriptive and predictive models, will result in increased

40

understanding of supplier performance and the reduction of risks associated with the

supplier selection process.

As organizations seek to improve unsatisfactory supplier performance and reduce the risks

associated with the supplier selection, management and development processes, it can be

argued the firms are faced with limited alternatives. These include: (1) produce the items

internally, (2) change to a more capable supplier, (3) assist in the improvement of the

existing supplier’s performance or (4) innovate and enhance or replace existing products

or services (Handfield et al. 2000, Burt et al. 2003). How can organizations protect

themselves against poor supplier performance and reduce the risk in the supplier selection

process? Understanding these relationships could improve the ability of organizations to

model the supplier selection process by facilitating the choice of a better supplier, thereby

improving the company’s overall performance. In the next section, selected supplier

attributes and their hypothesized relationship to quality and delivery performance will be

examined.

3.1 Hypothesis Development and Research Methodology

This section will present relationships between supplier quality and delivery performance

against a select number of supplier attributes. The inductive research methodology used

to study these possible relationships will also be described.

This study will analyze supplier quality and delivery performance results versus a number

of factors in Dickson’s study (1966) including: quality ratings, geographic location, credit

rating, technical capabilities and operational controls such as quality control systems. The

literature review by Weber et al. (1991) established that quality and delivery performance

were top criteria for measuring supplier performance. Based on these studies and others

discussed in Chapter 2, the relationship of the dependent variables quality and delivery

performance with respect to a select number of independent variables (supplier attributes)

will be analyzed.

41

A division of a $9 billion industrial equipment manufacturer was the focal point of this

empirical study. The sales of this location were approximately $200 million and the supply

base for direct production material exceeded 400 suppliers during the time of this analysis.

A combination of supplier survey data, public information including company brochures

and web-sites, Dun and Bradstreet (D&B) Business Information Report (BIR) elements

and rankings, revenue information (Dow Jones Factiva Financial Database Search 2018)

and quantitative historical performance for delivery and quality (from division’s enterprise

resource planning (ERP) system) were employed as part of this analysis. This location was

ISO9000 certified, supplied mission critical equipment to the U.S. Navy and friendly naval

forces for both the surface and submarine fleets. ASME (American Society of Mechanical

Engineers) nuclear certified units and parts were produced for the worldwide nuclear power

industry at this facility. Commercial grade industrial products were also manufactured for

customers across the world and an Oracle based system ERP system was utilized to control

and monitor the design and manufacturing of all these various units and service parts. In

addition, nearly five years prior to this study, the company implemented a supplier

management program, which specifically defined the criteria for quantitative measurement

of delivery and quality performance.

Using the purchasing module within the ERP system, suppliers with over $5,000 of

purchase orders were selected to receive the survey. The preliminary survey was reviewed

by a supply chain management expert, the Quality and Purchasing Departments of the

company and selected suppliers. Changes were made based on this feedback. Over 300

suppliers received the survey and confidentiality was assured via a survey cover letter. The

primary manufacturing location was asked to complete the survey with assistance of the

appropriate sales office. Fortunately the overwhelming majority of suppliers utilized single

facilities for the products supplied to this location.

The survey was returned via traditional mail, e-mail and faxes. In order to increase the

number of returned surveys, purchasing agents and buyers made follow-up telephone calls

and sent e-mails encouraging suppliers to participate and to return the surveys. In addition

to mailed surveys, suppliers were called and, in some cases, visited by the primary

42

researcher to aid in the completion of the surveys. Not all surveys were completed in their

entirety due to the proprietary nature of a number of questions (e.g. major customers,

capital investments, etc.). Approximately 83 useful surveys were completed for a survey

return rate of 28%. Other sources of information such as quality department surveys and

public information were utilized to add six more suppliers to the database.

A Microsoft Access database was created and utilized to store the data for the surveys and

analysis. Supplier quality and delivery performance data, from the firm’s ERP system, was

merged with the supplier analysis tables creating a single table, which could be exported

into Minitab for statistical analysis.

3.2 Hypothesized Impact of Critical Attributes on Quality and

Delivery Performance

The process of selecting, managing and developing suppliers often involves rating

suppliers on a number of attributes. Attributes such as quality capability, financial

condition, number of employees, geographic location (distance between facilities) and

amount of business (financial leverage) are often listed as being critical to the selection,

management and development of suppliers (Landry 1998, Cox 2001A, Hendrick and

Ellram 1993). Trade journals such as Purchasing magazine have published articles, which

point out the inconsistent use of financial leverage in the procurement process (Porter

2001). Product complexity is another attribute, which has been used as part of the supply

selection process and its impact on supplier performance will be investigated as part of this

study (Novak and Eppinger 2001, Laios and Moschuris 1999).

The research question is what if any relationship do these key supplier attributes have with

quality and delivery performance? The following questions will be examined in this study.

1. Does a recognized quality standard and capability such as ISO9000 certification

correlate to quality performance? 2. Does the use and application of statistical quality

control techniques relate to quality performance? 3. Does having financial leverage

correlate to improved performance in either quality or delivery performance? Figure 3.1

illustrates the supplier performance relationship between these selected attributes and the

43

dependent variables quality and delivery performance that will be analyzed as part of this

study. The attributes are grouped by internal or external designations. Internal

representing attributes the supplier has control or influence over and external for attributes

beyond the control of the supplier.

These independent variables and their relationship to the dependent variables will be

examined for specific quality (QUAL) and delivery performance (ONTIME). The factors

or attributes that will be examined include quality organization certifications (QCERT),

quality technical capabilities (QTECH), financial condition (FINRATE) and supplier

payment performance (FINPAYRATE), using Dun & Bradstreet financial information,

number of employees (EMPSCAL), geographic location (DISRATE), financial leverage

(PURPOWER), product differentiation (PRODIFF) and product complexity

(PRODCPLX).

Figure 3.1 Supplier Attributes and Performance Relationship

44

Figure 3.2 depicts the a priori hypothesized relationships between these attributes and

delivery and quality performance. The indicators, which are positive (+), negative (-) or

question mark (?) for an uncertain impact, designates the hypothesized relationship these

independent variables are expected to have with the delivery and quality performance.

Both of the dependent variables (delivery and quality) in Figure 3.2 are shown as increasing

for the hypothesized relationships with the independent variables QCERT, QTECH,

FINRATE, FINPAYRATE, EMPSCAL, DISRATE, PURPOWER, PRODIFF and

PRODCPLX. In the next sections the independent and dependent variables will be defined,

the hypotheses will be stated for these variables.

Figure 3.2 Hypothesized Relationships with Delivery and Quality Performance

3.3 Delivery and Quality Dependent Variables Defined

The dependent variables for delivery and quality performance were scaled according to

values, which were relevant for this industry. The dependent variables used in this study

represent interval variables where the ratings of poor, fair, good and excellent identify

classes of performance (Agresti 2002). The overall objective is to have all suppliers

performing at the excellent level. These interval variables have numerical distances

between the values but like ratings for blood pressure level, there are threshold values

Figure 1.2

45

which represent performance levels along an “underlying continuum” of values (Cohen et

al. 2003). In lieu of excellent performance, an objective of the supplier selection and

management program would be to work with suppliers in order to improve performance to

the next highest performance level. These values can be found in Tables 3.1 and 3.2

respectively.

As noted previously, approximately five years prior to this study the company implemented

a supplier management program, which specifically defined the criteria for delivery and

quality performance. The supplier performance management system was developed to

support an ISO9000 process. As such the supplier performance monitoring system set the

minimum standards required to prevent a supplier from being put on probation or

restrictions such as 100% inspection of incoming material. In addition to statistical data

for quality and delivery performance, other criteria such as the complexity of the part and

the type of quality problem (major or minor) is to be taken into account along with the

quality and delivery performance as part of a supplier probation decision. Based on these

facts, it can be inferred that the supplier performance monitoring system provides the

purchasing staff with latitude, allowing other factors to be considered as part of the supplier

performance measurement process. Therefore, it would be inappropriate to utilize these

minimum threshold measures of supplier performance as the primary measures of supplier

performance for this study. Consequently, the delivery performance values in Table 3.1

will be used to rate supplier performance in this study.

Table 3.1 Delivery Performance Rating

46

Delivery performance for a specific supplier is calculated by dividing the number of

purchase orders received by the supplier’s promise date by the total number of receipts for

that supplier for the given period. The ranges are based on the impact of delivery

performance on this operation. Performance of less than 75% on-time delivery would

cause serious consequences such as missing end-customer shipment dates for units and

service parts for this manufacturing operation. Given this potential impact, the rating of

poor is assigned to values of 0 to 75% delivery performance. A rating of fair is assigned

to values greater than 75 and less than 85% delivery performance. Fair performance would

cause moderate to serious consequences such as rescheduling units and increased

expediting costs such as overtime to make up for this delivery performance. Good is

assigned to the ratings of 85 to less than 95% delivery performance. Finally, a delivery

rating of 95% and above was given an excellent rating in this scale. The scales were created

based on the knowledge and impact of delivery performance for suppliers within this

industry. The product lead-times for units in this location range from 16 weeks to 102

weeks, so there is some ability to adjust production schedules to compensate for variations

in supplier delivery performance. Grouping suppliers into performance categories of poor,

fair, good and excellent is based on the impact of these performance levels on the overall

operation. While an analysis based on a continuous scale could be conducted, it is more

appropriate to analyze supplier performance based on these groupings. These groups

provide thresholds for supplier performance ratings. Suppliers performing in the poor and

fair categories create ongoing and serious problems. Using a range and corresponding

levels for delivery performance also allows other industries to scale the delivery

performance based on the values which are appropriate for their industry and supply base.

Percentage of

Receipts with

Zero (0) Quality

Problems

Quality

Performance or

QUAL Rating

0- LT90 Poor (1)

90 - LT 95 Fair (2)

95 - LT 99 Good (3)

99 and above Excellent (4)

Table 3.2 Quality Performance Rating

47

The quality performance is calculated by the total number of quality deviations for received

items versus the total number of received items for that supplier for the given period.

Quality deviations are identified as any variance from the specifications of the purchase

order and part requirements. While countermeasures can be taken to mitigate delivery

performance such as overtime, rescheduling, etc., quality problems such as discarding

supplier material for quality deficiencies can have a profound impact on the overall

operation of the organization. Courses of action for replacing defective supplier material

could include making replacement items in house, finding alternative sources and

evaluating the root cause of the problem in the supplier’s processes. As Benton and

Krajewski (1990) determined using simulation, supplier quality problems had a greater

impact on completion and shipment of end products than supplier delivery problems.

Therefore the scale for quality performance has substantially higher threshold values for

the performance levels than the delivery performance rating scales. Quality levels of 95 %

to 98.99% are given a good performance rating. The excellent rating is only given to

suppliers achieving a rating of 99% or more (see Table 3.2). Using interval variables for

quality performance allows other industries to scale the quality performance based on the

values which are appropriate for their industry and supply base.

3.4 Quality Ratings and Technical Capability as it Relates to Delivery

and Quality Performance

Quality organization certifications are based on an assessment of a supplier’s adherence to

a number of possible quality systems (such as ASME nuclear certifications, military or

Department of Defense (DOD), ISO9000 programs and QS9000 programs). Quality

technical capabilities are based on the presence of processes such as an established formal

quality program, existence of a quality manual, use of quality management reports,

existence and use of a corrective action program and quality data including the use of

statistical process control. Quality certifications and technical capability variables are

coded as dummy variables (0 for does not exist and 1 for does exist) in this analysis. White

et al. (1999) used a similar approach to evaluate the effect of “Total Quality Control”

techniques on the implementation of Just In Time (JIT) manufacturing and the resulting

48

effects on throughput time, internal quality, external quality, labor productivity and

employee behavior. This quality capability and its impact on an organizations’ ability to

provide an excellent product or service have been listed as essential to the supplier

selection, management and development processes [Monczka et al. 2002, Krause 1997).

Tan et al. (1999) tested the impact of quality management techniques on overall

performance. Noted quality expert Philip Crosby estimated that suppliers account for

nearly 50% of firms’ quality problems (Monczka et al. 2002).

Hypothesis 1: Suppliers who have attained quality certifications will positively relate to

performance for delivery and quality performance.

Hypothesis 2: Suppliers utilizing quality techniques will positively relate to performance

for delivery and quality performance.

The introduction of recognition awards such as the Malcolm Baldrige National Quality

Award established in 1987 and international quality standards such as ISO9000 could be

expected to improve overall performance of organizations who achieved these (Evans and

Lindsey 2002). Such efforts should yield improved performance for delivery and quality

measures. The absence of positive correlation to delivery and quality performance would

raise doubts about the initial goals of achieving external quality certifications or developing

and implementing quality technical capabilities.

Given the emphasis on Total Quality Management (TQM) over the past 20 years and the

rapid increases in ISO9000 registrations, it is reasonable to assume that organizations

would not support such efforts without a resulting improvement in internal and external

quality measures (Ebrahimpour et al. 1997). In a 1999 survey by Tan (2002), 50.4% of

companies reported they were ISO9000 series certified. Ebrahimpour, Withers and Hikmet

found in their survey of organizations pursuing ISO9000 certification that while improving

process efficiency and product quality ranked 3rd and 4th, increasing market share and

customer requirements were the principal motivations. Another study of 649 firms by Rao,

Ragu-Nathan and Solis (1997) found that firms that had achieved ISO9000 certification

49

had improved results of quality management practices including quality assurance, supplier

relationships and quality results. Simmons and White (1990) determined there was a

difference in profitability for firms that were ISO9000 certified versus those that were not.

A successful implementation of TQM requires the use of a number of techniques and

policies (Walton 1986). The application of these methodologies should be positively

correlated with improvements in the overall quality measures including supplier

performance. Therefore, improvements in both delivery and quality performance are

expected with the use of these quality techniques.

3.5 Financial Condition and Supplier Payment Performance as it

Relates to Delivery and Quality Performance

The financial condition of suppliers’ organizations, as determined by the information with

the Dun and Bradstreet BIR report, was added to the empirical model. The standard D&B

scale of 1-4 was utilized to code the values for these suppliers (see Table 3.3 for the D&B

financial condition rating) while Li, Fun and Hung (1997) used a 1-10 scale for financial

stability as part of an overall measure of supplier performance. The D&B ratings assess

the organization’s financial strength (Dun and Bradstreet Business Information Report

(BIR) documentation 2018). The Dun and Bradstreet rating of one (1), the highest rating,

was converted to a four (4) in order to simplify the analysis.

The other D&B measure used is the supplier payment performance or PAYDEX® Rating.

The supplier payment performance rating provides a measure of whether a company has

been paying its bills within or beyond the agreed terms as reported to D&B. The

Table 3.3 Financial Condition

Rating

50

PAYDEX® rating has been scaled to three (3) levels based on feedback from several credit

professionals (see Table 3.4 for the Paydex or supplier payment performance scale).

Hypothesis 3: Suppliers who have attained higher financial rating will positively relate

to performance for delivery and quality performance.

Hypothesis 4: Suppliers who have attained higher PAYDEX® ratings will positively

relate to performance for delivery and quality performance.

Monczka et al. (2002) stated that the financial conditions of a supplier can impact their

performance with respect to quality and delivery. Included in the risks associated with

poor financial performance are: 1. the risk that a supplier may go out of business; 2.

suppliers with financial problems may not be able to afford investments in capital required

to improve performance; 3. the supplier may be too dependent on the customer for

continued revenues. Burt, Dobler and Starling (2003) claimed that a supplier may be too

“financially weak… to maintain quality” or “a financially unsound supplier” may not be

able “to work overtime to meet a promised delivery date (impacting delivery

performance)”. Therefore it might be hypothesized that as the financial condition improves

the supplier performance with respect to quality and delivery should also improve. Similar

reasoning applies to supplier payment performance (D&B’s PAYDEX® Rating).

Increases in the ability to pay suppliers in a timely fashion should be positively correlated

to an increase in both quality and delivery.

Table 3.4 Supplier Payment Performance

Rating

51

3.6 Product Complexity and Differentiation as it Relates to Delivery


Ellram and Billington (2001) listed several factors involved in the outsourcing decision.

The factors influencing this decision include supplier’s specialized knowledge, lower costs

of operations, better material pricing and economies of scale or scope. Chrysler

Corporation used this specialized supplier knowledge to have suppliers assume

responsibility for the design and production processes of selected components (Dyer 1996).

Dyer asserts that having suppliers design and manufacture complex parts and

subassemblies required a change in the relationship between Chrysler and these key

suppliers. As organizations broaden outsourcing efforts, product complexity of the

outsourced items can influence this process. Novak and Eppinger (2001) studied the

relationship between outsourcing complex parts versus the percentage of in-house

production. Their findings indicated that there was a positive and significant relationship

between in-house production and product complexity. Laios and Moschuris (1999) also

examined the association between product complexity and product differentiation and the

resulting outsourcing decision. They found that previously purchased items had a higher

degree of both product complexity and differentiation as compared to previously

manufactured items. A 1-5 (1-Low to 5-High) Likert scale was utilized for both product

complexity and differentiation. The use of a 5 point Likert scale allows for a variety of

products to be evaluated with respect to complexity and differentiation. The product

differentiation used in this study primarily focuses on form and feature differentiation

(Kotler 2003). Product complexity measures could be related to attributes such as the

number of total parts for a subassembly or end-item or the number of bill of material levels.

The difficulty with using these measures for complexity is that items without a large

number of component parts could still be extremely difficult to manufacture. The Likert

scale ratings allow this methodology to be used for a variety of industries for both product

differentiation and product complexity. Suppliers in this study filled out a survey which

identified the categories of product or services they produced for this specific location.

Using this information or supplier information from the firm’s ERP system, engineering

personnel from this firm rated the product complexity and product differentiation for the

suppliers using a 1-5 Likert scale.

52

Does product complexity and differentiation impact supplier quality and delivery

performance? As product complexity increases, there more opportunities for errors. Does

this translate into reduced performance in supplier quality and delivery? Some

organizations have attempted to capture these opportunities for errors through a Defect Per

Total Opportunities (DPO) or Defects per Million Opportunities (DPMO) calculation

(Breyfogle 1999). Instead of just recording the defects at the end of a process, the DPO or

DPMO calculation takes into account the number of potential problems for a specific

product or service. By taking into account the potential number of defects, additional

insight is provided regarding the quality level versus the total number of potential

problems. As product complexity increases so does the total potential defects. Given this

relationship one could assume that more complex products could have a higher overall

defect level thus decreasing overall quality performance. Certainly the potential defects in

an automobile greatly exceed the potential problems for manufacturing a standard

hardware item such as a nut or bolt.

As potential quality problems occur due to increasing opportunities for failure, product

delivery may be delayed to provide time to correct quality problems. As the potential

number of defects increases it may be difficult for organizations supplying complex

products to detect and fix all problems before the item is finished and shipped to the

customer. Therefore, examining the impact of product complexity on quality and delivery

performance may provide valuable insights into the supplier performance. In addition to

product complexity, is there a supplier performance link to highly differentiated products?

The combination of product complexity and product differentiation will be examined with

respect to supplier delivery and quality performance.

Hypothesis 5: As product complexity increases, it will negatively impact performance

for delivery and quality.

It is theorized that as product complexity increases, supplier performance with respect to

quality and delivery degrades. As product complexity increases so does the DPO and

53

DPMO values. Therefore, it might be expected that the final impact of these increasing

defect opportunities would probably be felt in decreasing supplier quality and delivery

performance. As organizations outsource more complex products and services, gaining

further understanding of the relationship between product complexity and supplier

performance could be beneficial. Previous studies have examined the relationship between

product complexity and the outsourcing decision; it seems that increased knowledge of the

impact on supplier quality and delivery could provide further understanding to be used as

part of the supplier selection process.

3.7 Number of Employees as it Relates to Delivery and Quality

Performance

Firm size, based on the number of employees, has been utilized to analyze performance

with respect to supplier selection, lot size and product complexity (Choi and Hartley 1996,

Watkins and Kelley 2001). A scale, which is hybrid of a scale employed by Choi and

Hartley and the U.S. Census Bureau for Employment Size (U.S. Census Bureau 2007), will

be utilized to scale values for number of employees. Simmons and White (1999) attempted

to capture firm “bigness” by utilizing total assets to test the effectiveness of ISO9000

registration on return on assets and the sales/stockholder equity. Carr and Smeltzer (1999)

used sales as a measure of firm size to test the effectiveness of benchmarking on firm

performance and strategic purchasing. Total assets and sales are effective measures of a

firms’ size, and are used in major rankings such as the Fortune 500 and 1000 company

listings (2003). If the relationship between number of employees and sales is examined for

Fortune 1000 equipment manufacturers, a strong positive correlation exists (2003). The

Industrial and Farm Equipment Manufacturers category represents similar companies to

the focal firm for this study. In addition, several of the firms on this list also function as

suppliers of the focal firm of this study. The correlation of revenue to number of employees

is .874 with P-value = .0001, suggesting a strong positive relationship between revenue

and employees for industrial and farm equipment manufacturers.

The employee scale used in this study will act as a proxy for firm size. As noted previously,

the overwhelming majority of suppliers used single facilities for the products supplied to

54

the study organization. This scale can create issues for distribution or warehouse facilities,

but it should be remembered that distribution centers are generally managing the

procurement, receipt and inventory management and as such are performing limited

manufacturing activities. There is also a strong correlation between the Fortune 1000

Diversified Wholesalers (2003) revenue and number of employees (.948 with P-Value =

.0001). This positive correlation suggests there is a relationship between revenue and

number of employees for wholesalers servicing this type of manufacturer. Products being

supplied by distribution or warehouse facilities are generally finished products or raw

materials available to be purchased according to customer delivery requirements. These

facilities may have different performance measures such as fill rates, etc. than traditional

manufacturing firms but delivery and quality measures remain as critical measures of the

performance of these organizations (Bowersox et al. 2002).

Hypothesis 6: Suppliers, who have a larger number of employees and therefore a larger

scale of operations, will negatively relate to performance for delivery and quality.

It is proposed that as the size of the firm increases, the difficulty to manage the efforts of

larger numbers of employees can result in decreases in ONTIME and QUAL performance.

White et al. (1999) examined the improvement in external and internal quality for smaller

firms and for their larger counterparts based on a variety of quality techniques. Their study

determined that internal quality was higher for smaller firms than larger firms. In addition,

increasing the number of employees increases the need to generate more revenue to cover

these labor costs; therefore, more product must be produced for an increasing number of

customers. All of these factors can result in a negative impact on delivery and quality.

55

3.8 Distance as it Relates to Delivery Performance

Distance is a factor that certainly influences the choice of transportation used to deliver the

product. Trucking dominates the transportation market in the United States with motor

carriers accounting for $509 billion or 80.0 % of the total U.S. transportation costs in 2004

(Wilson 2005). Motor carriers are the most cost effective mode of transportation for

distances of 500 miles or less (Coyle et al. 2000). The scale used in this analysis is similar

to scale used by Liu et al. (2000). This study used the Data Envelopment Analysis (DEA)

for supplier selection and performance improvement. A distance factor based on a time to

deliver the product was employed which is similar to the scale shown in Table 3.5. Li, Fun

and Hung (1997) also used proximity to plants as part of their supplier performance

evaluation. Wafa, Yasin and Swinehart (1996) tested the supplier proximity and Just-In-

Time (JIT) program success. They hypothesized that there was a negative relationship

between JIT success and increasing distance. But contrary to their expectations they found

that increasing distance positively related to program success. The scale used in this study

attempts to differentiate the transport time based on miles from the facility. Use of air

carriers would be limited due the weight and size of products procured by this company.

Primarily air carriers would be utilized to expedite the delivery of late shipments due the

expense of transporting these items. Therefore for mileage less than 2,400 it can be

assumed that motor carriers will be the primary transportation mode utilized and the ratings

scale attempts to group mileage based on motor carrier transit times. The types of carriers

(truckload versus less-than-truckload) utilized for each movement of freight will not be

analyzed as part of this study. Given the increasing competition in the trucking industry

Table 3.5 Distance (Mileage) Rating

Scale

56

over 2 and 3 day freight markets and the requirement to provide excellent service levels, it

is assumed that analyzing distance will provide some consistent and straightforward

measure of transportation time for this study.

Distances greater than 2,400 miles will probably require a combination of international

water carriers and motor carriers to move the freight from the port to the facility.

Transportation is a very important concern for suppliers delivering materials from off-shore

locations (Fraering and Prasad 1999). This mileage scale was created to test how this

distance impacts supplier delivery performance.

Hypothesis 7: Suppliers who are at a greater distance from a supplier will negatively

relate to delivery.

It is expected that as distance from the facility increases there will be a negative correlation

to ONTIME performance. This hypothesis contradicts the relationship found by Wafa et

al (1996). As distance increases it can be expected that transportation time and its

variability also increases which contradicts the findings of the Wafa et al study. At a

distance of less than 120 miles, transportation can be easily and quickly coordinated

insuring minimal disruptions. At greater distances, it can be expected that delivery

performance may be negatively impacted.

3.9 Financial Leverage or Purchasing Power as it Relates to Delivery


Emerson (1962) stated that “power to control or influence the other (party) resides in

control over the things (they) value” this power “resides implicitly in the other’s

dependency”. Analyzing power in the customer-supplier relationship has been largely

focused on a marketing channel viewpoint while limited research has been completed on

the supplier-manufacturer dyad (Maloni and Benton 2000). French and Raven (1959)

defined the bases of power as reward, coercion, expert, referent, legitimate and legal

legitimate. Maloni and Benton observed that reward and coercion remain the most widely

used of the power bases with the power source being able to furnish rewards for desired

57

performance and to provide punishment when the desired performance is not met. Gosman

and Kelly (2002) examined the effect of large retailer purchases, such as Wal-Mart, on the

business results of several suppliers. The gross profit margins of two of the three suppliers

declined as business levels increased with this large retailer. Andrew Cox (2001B) argues

that “power is at the heart of all business to business relationships”, but how can this

statement be correlated with actual financial leverage and quality or delivery performance

for a selected business enterprise? Is supplier quality and delivery performance affected

as a customer increases the percentage of business with that supplier? Can a supplier

become too dependent on a customer and will it negatively impact the supplier’s

operational performance?

Landry (1998) noted that there is a power balance which needs to be maintained in the

customer-supplier relationship. This power balancing targeted between 20 to 40% of the

business requirements for a specific part be given to a specific supplier. In Dyer’s (1996)

examination of purchasing practices at Chrysler he found that few of its suppliers relied on

Chrysler for a majority of its business, while nearly 50 of Toyota’s 310 suppliers depended

on Toyota for nearly sixty-six percent of their total business. This study will review the

impact of this purchasing power or financial leverage on the supplier quality and delivery

performance. The hypothesis is that as the percentage of business increases with a specific

supplier, the quality and delivery performance will be positively affected. As the

percentage of business increases it can be reasoned that the influence of the power bases

of reward and coercion is also enhanced.

Hypothesis 8: Financial power or purchasing leverage will positively impact

performance delivery and quality.

Testing the relationship between financial power or purchasing leverage on supplier

performance will add useful information relating to the manufacturer-supplier dyad. Is

there a point that relying too heavily on a single supplier is detrimental to the operational

performance of that supplier? It is assumed that reward and coercion power bases increase

with financial power or purchasing leverage. Following this reasoning, supplier quality

58

and delivery performance can be expected to improve as suppliers become increasingly

more reliant on a single customer. The counter argument would be that as the percentage

of business with a single supplier decreases so does the influence to impact quality and

delivery performances, given the reward and coercion power bases are diminished with a

decreasing percentage of overall supplier revenue.

3.10 Case Study Model Selection and Results

Results from the empirical model and hypothesis testing will be presented in this section.

First statistical model selection will be discussed, explaining the reasoning for the choice

of the specific methodology used in this study. The Variable Definition Section will

present the methodologies used for defining specific variables not discussed in the

Hypothesis Development and Research Methodology Section. Finally, summary statistics

and the model results for supplier quality and delivery performance will be presented and

discussed.

3.10.1 Model Selection

There were several techniques which were considered as statistical methods for analyzing

supplier quality and delivery performance versus internal and external supplier attributes.

One of these methods, multiple regression assumes the following: existence, independence,

linearity, homoscedasticity and normality (Kleinbaum et al. 1998). The linearity,

homoscedasticity and normality assumptions cannot be met for this dataset. Alternatively,

logistic regression allows for the analysis of one or more independent variables and their

relationship to the probability of one or more possible outcomes (Cohen et al. 2003). The

basic form of logistic regression for predicting the probability (πi) of fitting into a specific

outcome for a single predictor X is

(β1Xi + β0) πi

(β1Xi + β0)

= 1

1 + e -(β1Xi + β0)

= e

1 + e .

59

Another form of the logistic regression equation is to express the probabilities as a ratio of

the probability of a specific outcome (πi) divided by the probability of not having that

outcome (1 - πi). This second form of the logistic regression equation is

The third form is the natural logarithm of the previous form. This form presents a linear

equation, on the right hand side, which is similar to ordinary least squares (OLS)

regression. The third form of the logistic regression equation for a single predictor is

Using this form the logit, or logistic probability unit is shown as

Ordinal logistic regression (also referenced as the proportional odds model) is an extension

of the single predictor model which assumes movement along an underlying continuum of

values while crossing specific outcome thresholds (Cohen et al. 2003). These thresholds

represent the boundaries between the various outcome categories. Therefore, the results of

an ordinal logistic regression delineates the probability (πi) of belonging to a particular

category (i) for the dependent variable with up to I total categories. As such the ordinal

logistic model creates maximum likelihood estimation for the various predictors (x’s). The

general model of the ordinal logistic regression with adjacent categories (from 1 to I) is

where β is a shared coefficient. Adding I- i terms the categorical logit model is

e πi

1 - πi =

(β1Xi + β0) .

1 - πi .

πi

= β1Xi + β0 ( ) ln

. ) 1 - πi

πi

( ln logit =

+ αi log πi+1 (x)

πi (x)

= β΄x, i= 1,…., I-1

60

where αj is the jth level intercept on the dependent variable ordinal scale (Agresti 2002).

Ordinal logistic regression will be utilized for this study. The dataset includes categorical

data, non-linear relationships, heteroscedastic and non-normal data. This method will

provide probability estimates for the interval variables for supplier delivery and quality

performance as well as test the influence of dummy variables for quality certification and

technical capabilities.

3.10.2 Variable Definitions for Number of Employees and Financial

or Purchasing Leverage

Interval variables will be used for both number of employees (EMPSCAL) and financial

leverage (PURPOWER). Analysis of the continuous raw data for both of these

independent variables indicates both of these datasets are non-normal, exhibit high kurtosis

and are skewed to the right of the mean. Tables 3.6 and 3.7 show the interval values used

for EMPSCAL and PURPOWER respectively. The interval values allow grouping and

analysis of similar firms based on these levels. The employee scale is hybrid of a scale

employed by Choi and Hartley (1996) and the U.S. Census Bureau for Employment Size

(2007) will be utilized to scale values for number of employees.

= αj + log πI (x)

πi (x)

i= 1,…., I-1 Σ j=i

I-1

β΄(I-i) x,

Table 3.6 Employee Rating Scale

61

There are few research models which evaluate the effect of financial leverage or purchasing

power on supplier quality and delivery performance. Landry (1998) noted that a target of

20 to 40% of a supplier’s total revenue provided a power balance between the selling and

buying firms. The scale for financial leverage or purchasing power (shown in Table 3.7)

attempts to classify the various levels of the amount of business as a percentage of the total

company’s revenue. Ratings 1 and 2 on this scale represent relatively low levels of

business with the cutoff being 1% of total business for these classifications. A rating of 3

is assigned to a percentage of business which could certainly make the buying firm a top

100 customer. The rating of 4 represents a percentage of a supplier’s total revenue from 5

to 10 %, which would make the buying firm at least a top 20 customer. A rating of 5

represents between 10 and 20% of the supplier’s total revenues. At this level the customer

would at least be a top 10 customer and would approach the power balancing threshold

proposed by Landry. Level 6 represents suppliers in the power balancing range of above

20 % of total business levels. As noted by Dyer (1996) few of Chrysler’s suppliers relied

on Chrysler for the majority of their business while 16% of Toyota’s 310 suppliers relied

on Toyota for the majority of their business. Both the final leverage and employee scales

will be tested for their relationship with supplier delivery and quality performance.

Table 3.7 Financial Leverage or

Purchasing Leverage Rating Scale

62

3.10.3 Summary Statistics

Table 3.8 presents summary statistics for the variables used in this study. The sample size

for the independent variables ranged from 84 to 89 with a mean value of 87. Reviewing

the correlation coefficients shows a high degree of multicollinearity between the

independent variables. Multicollinearity occurs most frequently when independent

variables are defined using the same constructs (e.g., depression, anxiety) (Cohen et al.

2003). The objective of selection of the independent variables used in this study was

provide separate supplier attributes and test their relationship to supplier delivery and

quality performance.

The ordinal logistic regression model will test the heterogeneity of the independent

variables and their effects on the performance levels of the dependent variables quality and

delivery performance. It should be noted that the ordinal levels of the dependent variables

provide for improved testing of the strength of the association with the dependent variable’s

Table 3.8 Summary Statistics

63

levels and enhanced data analysis and output capabilities as compared to other logistic

regression models (Agresti 2002, Agresti 1984).

3.10.4 Model Definitions and Results

The model results for supplier quality and delivery performance versus various models will

presented in this section. Four models for each of the dependent variables were tested.

Model 1 (M1) represents the base line for testing of the supplier attributes versus the quality

organization certifications, quality technical capabilities, financial condition, supplier

payment performance, product differentiation and product complexity. This baseline

model presents a combination of independent variables that have been empirically tested

in conjunction with supplier performance. Other independent variables, such as financial

condition and supplier payment performance, and their relationship to supplier

performance have been hypothesized using deductive methods. The independent variable

product differentiation is included in the quality and delivery performance models. There

are no hypotheses, proposed in this research, which are associated with this variable.

Previous studies have tested the product differentiation and complexity pair versus supplier

performance. Therefore, in order to provide a consistent baseline model, the independent

variable product differentiation is included in Model 1. Model 2 (M2) adds the number of

employees to the baseline model. Model 3 (M3) adds the geographic location via the

mileage rating or distance variable. The distance variable is included in both the supplier

delivery and quality performance models, even though there is no specific hypothesis

included in the quality performance model for distance. Including the mileage rating in

both models facilitates consistent testing and presentation of results for both the supplier

quality and delivery performance models. Model 4 (M4) adds the financial power or

purchasing leverage to the final model.

The sample size of 82 meets the general approximation of 10 occurrences per predictor for

logistic regression (Agresti 2002). Tables 12 and 13 display the maximum likelihood

estimation results for the quality and delivery performance logits.

64

Table 3.9 provides summarized results for both the supplier quality and delivery models.

Reviewing the results for the supplier quality performance model testing, the χ2 and log-

likelihood diagnostic statistics are satisfactory for models M2 and M4 at a 5% significance

level while models M1 and M3 are significant at a 10% level. While the 5% significance

model is traditionally the cutoff used for presenting statistically significant results,

important information and relationships between supplier quality performance and key

attributes would be lost without including models M1 and M3. The following hypotheses

are rejected: H1 (Quality Organization Certification positively relates to improved

performance); H3 (Financial rating positively relates to performance); H4 (Supplier

Payment Performance positively relates to performance); H8 (Financial Leverage or

Purchasing Power will positively relate to performance). The following hypotheses are not

rejected: H2 (Quality Technical Capability will positively relate to performance); H5

(Product complexity will negatively relate to performance); H6 (Number of Employees will

negatively relate to performance). The financial leverage or purchasing power hypothesis

(H8) is rejected due to the unexpected negative relationship to quality. While the model

variable for financial leverage is reported to be significant at the 10% level, the negative

value for the coefficient does not support the a priori hypothesis.

The results for supplier delivery performance model testing reveal that all models (M1

through M4) are satisfactory for the χ2 and log-likelihood diagnostic statistics. The

following hypotheses are rejected: H1 (Quality Organization Certification positively relates

to improved performance); H2 (Quality Technical Capability will positively relate to

performance); H4 (Supplier Payment Performance positively relates to performance); H6

(Number of Employees will negatively relate to performance); H7 (Distance will negatively

relate to performance); H8 (Financial Leverage or Purchasing Power will positively relate

to performance). The following hypotheses are not rejected: H3 (Financial rating positively

relates to performance); H5 (Product complexity will negatively relate to performance).

65

Table 3.9 Summarized Results for the Supplier Quality and Delivery Models

3.10.5 Empirical Study Conclusions and Implications for the

Supplier Selection Process and MCDM Model Formulations

Empirical testing results from this case study of the relationship between supplier attributes

and quality and delivery performance will add to the inductive research findings in the

supplier selection and management areas. The testing of objective measures of these

attributes versus actual performance results provides insights relating to supplier

performance. These results can be incorporated into robust optimization supplier selection

models for further testing of the impact of these attributes on the supplier selection and

performance models.

Reviewing the results for quality attributes illustrates that quality technical capabilities can

be positively related to improved quality performance (Table 3.10). The odds ratio of 4.94

indicates the increased probability of improving performance with the use quality technical

capabilities. Quality certifications on the other hand, have not been shown to statistically

relate to improved quality performance. Several studies have shown that the motivation

for ISO900 certification was primarily marketing and customer requirement driven. Given

these driving factors it might be reasoned that actual quality performance may not be the

primarily objective of these efforts to attain quality certifications. The results for financial

66

leverage or purchasing power while significant point out an interesting relationship. While

much has been written about the purchasing power, few studies have analyzed how the

level of business with a particular supplier might influence quality or delivery performance

(Table 3.11). James A. Wier, CEO of Simplicity Manufacturing, a lawn-mower maker that

decided to stop selling to Wal-Mart. "When you drive the cost of a product down, you

really can't deliver the high-quality product like we have (Bianco et al. 2003)”. It might be

expected that as financial leverage increases likewise it could be reasoned that quality and

delivery results would improve since that supplier is more and more dependent on the

customer for sales and revenue. Instead this model indicates that there is a significant

negative relationship between increasing business levels and quality performance. While

this is a surprising result, it could be inferred that as the amount of business increases with

a single customer, cost pressures, clearly described by Mr. Wier, could cause suppliers to

revise processes or cut costs resulting in reduced quality levels.

Product complexity is significant in both the quality and delivery models. Increasing

product complexity is related to decreasing supplier performance. Defect opportunities

increase for complex products, providing an interesting argument that as products becomes

increasingly complex the overall quality and delivery performance degrades. Financial

rating was also found to significantly relate to delivery performance. It has been deduced

that a healthy financial rating allows supplier flexibilities to meet changing requirements

such as a need for overtime and facility and equipment improvements. Without a healthy

financial rating, researchers have conjectured that supplier performance would be

degraded. This case study supports these assertions that a financially healthy company

provides improved delivery performance as compared to companies with poor financial

ratings. Dun and Bradstreet provides alerts to its subscribers notifying them when a

company’s financial rating changes in order to make them aware of possible supplier

performance problems.

67

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69

While these results provide some interesting insights into attributes often included as part

of the supplier selection processes, clearly a great deal of research is required to add to this

important body of knowledge. This study presents a method for further investigation of

this critical process. The motivation for this examination of critical supplier attributes and

supplier delivery and quality performance was to utilize these results into an integrated

supplier selection model with product life cycle considerations. This integrated supplier

selection model and a real world case study will be presented in the following chapters.

70

4. General Model for Supplier Selection incorporating results

of Empirical Study and Product Life Cycle

This chapter will present a supplier selection model which incorporates both the findings

from the empirical model presented in Chapter 3 and product life cycle considerations. An

illustrative example, as well as a case study using industrial data, will be developed

using a number of goal programming solution approaches including: preemptive GP; non-

preemptive GP; Tchebycheff (Min-Max) GP and Fuzzy GP.

This model is intended for selecting suppliers who have been short-listed. This is typically

the second phase of the supplier selection where the final supplier selections are completed

using the decision maker’s criteria. The major distinguishing features of this general model

include the following:

1. Integration of the product life cycle into the supplier selection model, through the

inclusion of items representing the introduction, growth, maturity and decline

phases of the product life cycle.

2. Inclusion of model parameters, based on the results of the empirical study of key

supplier attributes versus supplier performance, including: the maximum amount

of business allowed for a particular supplier; the financial condition of a supplier,

the minimum allowed financial condition required for a supplier of a particular

item, the quality technical capability of the supplier and the minimum required

quality technical capability required for a supplier of a particular item.

71

3. The objectives of the supplier selection model include minimization of cost, lead-

time, quality defects and late deliveries.

4. In addition to the capacity constraints, the model includes constraints on the

maximum amount of business which can be placed on particular suppliers.

Additionally, the financial condition of a supplier is included as a constraint

insuring the supplier meets the minimum requirements for financial stability

required to supply an item in a specific stage of the product life cycle. Similarly, a

minimum quality technical capability of a supplier is included as a constraint

insuring the supplier meets the minimum requirements for quality technical

capability required to supply an item in a specific stage of the product life cycle. A

demand constraint is included in the model as well. The maximum and minimum

number of suppliers’ constraints for a particular item are also included as

constraints. These constraints will be varied based on the item’s stage in the

product life cycle. For example, multiple sourcing may be required for critical

products in the introduction stage while single sourcing may be employed for

products in the decline stage of the product life cycle. The amount of business

constraint, financial condition, quality technical capability constraints are

included in the general model based on the results from the empirical study.

4.1 Notations used in the model

Model Indices

I = Set of products to be purchased, which can be in any one of the different mutually

exclusive stages of product life cycle. The product life cycle includes Introduction

(I1), Growth (I2), Maturity (I3) and Decline (I4), i.e. 4321

IIIII = .

72

K Potential set of suppliers

Model Parameters

pik Cost of acquiring one unit of product i from supplier k

Fk Fixed ordering cost associated with purchasing any product from supplier k

Gik Tooling cost associated with acquiring product i from supplier k

di Demand of product i

lik Lead time of supplier k to produce and supply product i

qik Quality that supplier k maintains for product i, which is measured as percent yield

delik Delivery performance that supplier k provides for product i, which is measured as

percent on-time deliveries

mbk Maximum amount of business ($) for a particular supplier k for a given planning

horizon

finck The financial condition of a supplier k

rfinci The required minimum financial condition for a supplier to supply product i

qtk The quality technical capability of supplier k

rqti The required quality technical capability for a supplier to supply product i

CAPik Production capacity for supplier k for product i

Mi Maximum number of suppliers that can be selected for product i

Ni Minimum number of suppliers that should be selected for product i (e.g. multiple

sources for critical product or critical phase of product life cycle).

73

Decision Variables in the model

Xik Number of units of product i supplied by supplier k

δik Binary variable denoting if a particular supplier is chosen or not for product i. This

is a binary variable which takes a value 1 if a supplier is chosen to supply product

i and is zero, if the supplier is not chosen.

4.2 Mathematical Formulation of the Order Allocation Problem

4.2.1 Objective Functions

The four conflicting objectives used in the model are minimization of cost, lead-time,

rejects and delivery delays. It is relatively easy to add other objectives also. The

mathematical forms for these objectives are given below:

1. Procurement Cost (Z1n): Total cost of purchasing has two components; fixed and

the variable cost.

Total variable cost: The total variable cost is the cost of buying every additional

unit from the suppliers for each of the four phases of the product life cycle (Z1n for

n = 1,..,4) which represent the four phases of the product life cycle. At a specific

point in time each product i is known to be in a particular stage of the product life

cycle (introduction (I1), growth (I2), maturity (I3) and decline (I4)) and the variable

costs are assumed to be known. The total variable cost objective function is given

by:

ik

Ii k

ik Xpn

for n = 1, 2, 3, 4.

74

Fixed Costs: If a supplier k is used for product i, then there are fixed costs associated

with utilizing supplier k, as well as tooling costs associated with product i being

supplied by supplier k. The total fixed cost is given by:

k

ikkF + k

ikik

Ii

Gn

for n = 1, 2, 3, 4.

Where Fk is the fixed cost of using supplier k and Gik is the fixed tooling cost of

supplier k for supplying product i.

Hence the total Cost (Z1n) is

++

n

n n

Ii k

ik

Ii k k Ii k

ikikikkikik

X

GFXp

for n = 1, 2, 3, 4. (4.1)

In equation (4.1), the objectives Z1n (n = 1,..,4) correspond to the weighted

procurement costs of all products and suppliers in the life cycle phase n. The

denominator, which represents the sum of all demands for products in phase n, is a

constant and can be ignored in the optimization process. This results in linear

objective functions for the procurement cost corresponding to each product life

cycle phase. At any time, each product i is known to be in a particular stage of the

product life cycle (introduction (I1), growth (I2), maturity (I3) and decline (I4)) and

the value of the procurement costs are assumed to be known.

2. General Lead-time (Z2n):

n

n

Ii k

ik

Ii k

ikik

X

Xl

for n = 1, 2, 3, 4. (4.2)

Similarly the general lead-time objective is partitioned into four lead-time

objectives, Z21, Z22, Z23 and Z24, which correspond to the four phases of the product

life cycle. The lead-time objective function Z21 relates to products in the

75

introductory stage; Z22 relates to products in the growth stage; Z23 relates to

products in the maturity phase and Z24 relates to products in the decline phase. The

objectives Z2n (n = 1,2,..,4) correspond to the weighted lead-times of all products

and suppliers in life cycle phase n. The denominator, which represents the sum of

all demands for products in phase n, is a constant and can be ignored in the

optimization process. Hence, the general lead-time objectives (Eq. 4.2) are also

linear functions.

At any time, each product i is known to be in a particular stage of the product life

cycle (introduction (I1), growth (I2), maturity (I3) and decline (I4)) and the values of

the lead-times are also assumed to be known.

3. General Quality (Z3n):

n

n

Ii k

ik

Ii k

ikik

X

Xq

for n = 1, 2, 3, 4. (4.3)

The general quality objective is likewise partitioned into four quality objectives

(Z3n for n = 1,..,4), which correspond to the four phases of the product life cycle.

The objective Z3n corresponds to the weighted quality performance, based on

product reject rates, for all products and suppliers in phase n. The denominator,

which is identical to the denominator in the cost and lead-time objectives, also

represents the sum of all demands for the products in phase n, which is a constant

and can be ignored in the optimization process. Thus, the quality objectives (Eq.

4.3) are also linear functions.

76

4. General Delivery (Z4n):

n

n

Ii k

ik

Ii k

ikik

X

Xdel

for n = 1, 2, 3, 4. (4.4)

The general delivery objective is also partitioned into four delivery objectives (Z4n

for n = 1,..,4), which correspond to the four phases of the product life cycle. The

objective Z4n corresponds to the weighted delivery performance, measured in terms

of late deliveries, for all products and suppliers in phase n. The denominator is

utilized to represent the sum of all demands, which is a constant and can be ignored

in the optimization process. Thus, the delivery objectives are also linear functions

(Eq. 4.4).

Thus, we have 16 objective functions in the optimization model, four for each

product life cycle phase.

4.2.2 Constraints

The constraints in the model are as follows:

1. Capacity constraints: Each supplier k has a maximum capacity for product i,

CAPik. Total order placed with this supplier must be less than or equal to the

maximum capacity for product i. The capacity constraint is varied by product given

that a supplier’s capacity may vary by product. Hence the capacity constraint is

given by:

ikik

i

ik CAPX )( i,k.. (4.5)

The binary variable on the right hand side of the constraint implies that supplier k

cannot supply product i if they are not chosen, i.e., if δik is 0.

77

2. Business Volume Constraints: The total business volume for a set of products i

received from supplier k must be less than the maximum amount of business level

for the supplier. The business volume constraint for each supplier is given by:

kik

iik

mbXp k . (4.6)

3. Financial Condition Constraints: The minimum financial condition required to

do business for product i with supplier k is given by:

irfinc

ikkfinc ki, . (4.7)

4. Quality Technical Capability Constraints: Denotes the quality technical

capability condition for product i required to do business with a supplier k. They

are given by:

irqt

ikkqt ki, . (4.8)

5. Demand Constraints: The demand for product i has to be satisfied using a

combination of the suppliers. The demand constraints are given by:

idXi

kik

= . (4.9)

6. Maximum number of suppliers: The maximum number of suppliers chosen for

product i must be less than or equal to a specified number. It is given by:

k

iik M .

(4.10)

Where Mi is the specified maximum for product i.

7. Minimum number of suppliers: The minimum number of suppliers chosen for

product i must be greater than or equal to a specified number. It is given by:

78

k

iik N . Where Ni, is the specified minimum for product i. (4.11)

Note: If iN 2 then multiple sourcing is enforced.

8. Non- Negativity and Binary constraints:

(0,1) ;0X ikik . (4.12)

4.3 Goal Programming (GP) Models

Goal programming will be used to solve the multiple objective linear integer program

developed in Section 4.2. This section will briefly explain the ideal solutions, goal

constraints, formulation and objectives of the preemptive and non-preemptive goal

programming models as well as the formulation of the Tchebycheff’s min-max and Fuzzy

GP models.

Goal programming utilizes target levels for the achievement of the objective functions,

represented by equations 4.1 to 4.4. These GP objectives are assigned target levels and

priorities for achieving the stated targets. These target values are treated as goals to strive

for. Since not all the targets may be achievable, deviational variables are used to allow for

the under or overachievement of the targets. The target levels are established by first

determining the ideal solutions. Generation of the ideal solutions will be discussed next.

4.3.1 Ideal Solutions

The initial step required prior to solving the problem using the various goal programming

models is to obtain the ideal values, which will be used by all of the GP models. Ideal

values are the best values achievable for each objective, ignoring the other objectives. In

this case, single objective optimization models are created for the four objectives of quality,

delivery, cost and lead-time for each of the four products, yielding sixteen individual linear

integer programs, in order to determine the ideal values. The ideal values are calculated

by individually solving these sixteen linear programs, one for each of the objectives and

79

for each of the four products and their respective phases in the product life cycle. The ideal

values will then be utilized to set target values for the various goal programming models.

4.3.2 General Goal Programming Model

The multi-objective problem using goal programming is created by partitioning each of the

four objectives: cost (4.1), lead-time (4.2), quality (4.3) and delivery (4.4) into the four

phases of the product life cycle and their respective goal constraints. First the cost objective

will be explained.

The general cost objective (4.1) is partitioned into four cost objectives, Z11, Z12, Z13 and

Z14, which correspond to the four phases of the product life cycle. The objective function

Z11 relates to procurement cost in the introductory phase; Z12 relates to procurement cost

in the growth phase; Z13 relates to procurement cost in the maturity phase and Z14 relates

to procurement cost in the decline phase. Each of the four cost objectives have target

values, denoted by T1n (n = 1,..,4), for each of the product life cycle phases. At any

particular point in time each product i is known to be in a particular stage of the product

life cycle (introduction (I1), growth (I2), maturity (I3) and decline (I4)) and the values of the

fixed and variable costs are known.

T11 relates to the objective function Z11 and the introductory phase; T12 relates to the

objective function Z12 and the growth phase; T13 relates to the objective function Z13 and

the maturity phase and T14 relates to the objective function Z14 and the decline phase.

Deviational variables d1n- and d1n

+, (n = 1,..,4) for each of the product life cycle phases, are

also included in the objective function. The deviational variables allow the under (d1n-) or

overachievement (d1n+) of the goal constraints. Given the objective is to minimize cost,

the goal is to minimize the positive deviational variable d1n+ (n = 1,..,4) for each of the


The cost goal constraints are given below:

Z11 (Intro. phase) =

++1 1

iiiiIi k k Ii k

kkkkkki GFXp + d11- - d11

+ = T11; (4.13)

80

Z12 (Growth phase) =

++2 2

iiiiIi k k Ii k


+ = T12; (4.14)

Z13 (Maturity phase) =

++3 3

iiiiIi k k Ii k


+ = T13;

(4.15)

Z14 (Decline phase) =

++4 4

iiiiIi k k Ii k


+ = T14. (4.16)

Like the cost objectives, each of the four lead-time objectives have target values T2n (n =

1,..,4) for each of the product life cycle phases. T21 relates to the objective function Z21

and the introductory phase; T22 relates to the objective function Z22 and the growth phase;

T23 relates to the objective function Z23 and the maturity phase and T24 relates to the

objective function Z24 and the decline phase. Deviational variables d2n- and d2n

+, (n = 1,..,4)

are included to allow the underachievement (d2n-) or overachievement (d2n

+) of the goal

constraints for the product life cycle phases. Given the objective is to minimize lead-time,

the goal is to minimize the positive deviational variable d2n+ (n = 1,..,4) for each of the


The lead-time goal constraints are given below:

Z21 (Introduction phase) =

1Ii k

ikik Xl + d21- - d21

+ = T21; (4.17)


2Ii k


+ = T22; (4.18)


3Ii k


+ = T23; (4.19)


4Ii k


+ = T24. (4.20)

Similar to the cost and lead-time objectives, each of the four quality objectives have target

values T3n (n = 1,..,4) for each of the product life cycle phases. T31 relates to the objective

function Z31 and the introductory phase; T32 relates to the objective function Z32 and the

growth phase; T33 relates to the objective function Z33 and the maturity phase and T34

relates to the objective function Z34 and the decline phase. Deviational variables d3n- and

81

d3n+, (n = 1,..,4), corresponding to the product life cycle phases, are included allowing the

underachievement (d3n-) or overachievement (d3n

+) of the goal constraints. Given the

objective is to maximize quality performance, the goal is to minimize the negative

deviational variable d3n- (n = 1,..,4) for each of the product life cycle phases.

The quality goal constraints are given below:


1Ii k

ikik Xq + d31- - d31

+ = T31; (4.21)


2Ii k


+ = T32; (4.22)


3Ii k


+ = T33; (4.23)


4Ii k


+ = T34. (4.24)

In the same manner as the previously detailed objectives, the four delivery objectives have

target values T4n (n = 1,..,4) for each of the product life cycle phases. T41 relates to the

objective function Z41 and the introductory phase; T42 relates to the objective function Z42

and the growth phase; T43 relates to the objective function Z43 and the maturity phase and

T44 relates to the objective function Z44 and the decline phase. Deviational variables d4n-

and d4n+, (n = 1,..,4), relating to the product life cycle phases, are incorporated in the

objective function allowing the underachievement (d4n-) or overachievement (d4n

+) of the

goal constraints. Given the objective is to maximize delivery performance, the goal is to

minimize the negative deviational variable d4n- (n = 1,..,4) for each of the product life cycle

phases.

The delivery goal constraints are given below:


1Ii k

ikik Xdel + d41- - d41

+ = T41; (4.25)


2Ii k


+ = T42; (4.26)


3Ii k


+ = T43; (4.27)

82


4Ii k


+ = T44. (4.28)

Thus we have 16 goal constraints in this optimization model. The real constraints, which

are given by equations 4.5 to 4.12, represent the boundaries of the decision space for the

objectives and decision variables for all the GP models.

After setting the target levels for the goal constraints, the relative importance of achieving

the targets have to be specified. Different goal programming models are used to specify

the relative importance. These GP models include preemptive, non-preemptive,

Tchebycheff’s min-max and Fuzzy methods. A brief discussion of the GP models and the

application to solving the general model follows.

4.3.3 Preemptive Goal Programming Model

Preemptive GP requires the decision maker to identify the order in which goal constraints

will be achieved after setting the target levels (T11 to T44) for goal constraints. This requires

the decision maker to rank order the goal constraints (equations 4.13 to 4.28) from highest

to lowest priority. Priority order of the goal constraints is determined by the decision maker

(DM). No scaling of the goal constraints is required since higher goal constraint

achievement supersedes the attainment of the lower priority goal constraints. Therefore

preemptive GP does not require a great deal of cognitive effort by the DM since they are

simply ranking the goal constraints in priority order sequence and setting target levels. A

pair-wise comparison method can be used to get the rankings of the goals.

The target levels for the preemptive GP model are set using the ideal solutions, which

were determined by solving for the ideal values described in Section 4.3.1. For

illustration, the target levels are set at 90% of the ideal values for maximization, such as

quality and delivery performance. Target levels for cost and lead-time are set at 110% of

the ideal values given the objective of minimizing these goal constraints. Decision

makers ultimately will set the appropriate target levels. Setting the targets at 90% and

83

110% for maximization and minimization goal constraints for the illustrative example

allows for consistent comparisons among the various GP model results.

4.3.4 Non-preemptive Goal Programming Model

Non-preemptive GP requires the DM to determine the relative weights in achieving the

targets (T11 to T44) for the goal constraints. The goal constraints, deviational variables and

targets, given in equations 4.13 to 4.28, along with the real constraints, given in equations

4.5 to 4.12, are used in a single objective model. The relative weights (win) are set for each

of the deviational variables (din) in the single objective model. Similar to the preemptive

GP method, the target levels are set at 90% of the ideal values for the quality and delivery

objectives. Likewise the cost and lead-time minimization objectives are set at 110% of the

ideal values.

The Borda Count method or Analytic Hierarchy Process can be utilized by the DM to

determine the relative weights of the goal constraints. This increases the cognitive burden

on the DM given the 16 goal constraints. Scaling or normalization of the goal constraints

is also required to facilitate an equitable comparison among the objectives.

4.3.5 Tchebycheff’s Min-Max Goal Programming Model

The Tchebycheff’s goal programming methodology also uses goals/targets for each of the

objectives and deviational variables to represent violations from the targets, but it does not

use priorities or weights for the goals. Instead, it minimizes the maximum deviation from

the targets (T11 to T44), using the deviational variables (d11 to d44). Tchebycheff’s min-max

GP requires the decision maker to determine the targets for the goal constraints. The goal

constraints, deviational variables and targets, given in equations 4.13 to 4.28, along with

the real constraints, given in equations 4.5 to 4.12, are used in a single objective model.

The target levels, for the illustrative example, are set at 90% of the ideal values for the

maximization goal constraints for quality and delivery. The cost and lead-time

84

minimization objectives are set at 110% of the ideal values. This target level setting places

minimal analytical requirements on the DM. Scaling of the goal constraints is required.

4.3.6 Fuzzy Goal Programming Model

The Fuzzy GP model does not use targets for the objectives. Instead, it uses the ideal values

as the targets. Similar to the Tchebycheff GP model, it minimizes the maximum deviation

from the ideal values. Thus, Fuzzy GP neither requires target levels nor preferences among

the targets.

4.4 Illustrative Example

A realistic example problem is created to demonstrate the application of the goal

programming models. The example includes four products selected from the introduction,

growth, mature and decline stages of the product life cycle and five suppliers. The

suppliers have varying performance levels for cost, lead-time, quality and delivery. Table

4.1 presents the supplier data by product life cycle phase. For the illustrative example, we

have excluded the fixed costs of using a supplier. The fixed tooling costs are included in

the example. Results for the various GP models will be discussed in the following sections

ending with an overall summary comparing the results of all the models. The first step is

to determine the ideal solutions, which will be discussed in the following section.

4.4.1 Ideal Solutions

The initial step in the GP model is to create and solve sixteen single objective linear integer

programs in order to determine the ideal values for the four objectives of cost, lead-time,

quality and delivery, for each of the four product life cycles.

Table 4.2 presents the optimal solutions for the sixteen single objective linear integer

programs. For example, the minimization of lead-time for the introduction product is

85

accomplished by first selecting supplier 5, who has the minimum lead-time of five weeks.

There is a capacity constraint on supplier 5 of 100 units, so it is necessary to choose a

second supplier in order to fulfill the demand requirement of 120 units. Supplier 1 is then

Table 4.1 Supplier Data for Illustrative Example

86

chosen to fulfill the remaining demand of 20 units since it has the next lowest lead-time at

6 weeks. The process continues for the introduction product, with suppliers being chosen

to maximize delivery and quality performance, and minimize cost. These are relatively

straightforward selections with the choice of the supplier with the best quality and delivery

performance and lowest overall cost.

The growth product requires a minimum selection of two suppliers to fulfill the demand of

230 units. Suppliers 2 and 4 have equivalent performance for lead-time and delivery at 6

weeks and 99% on-time delivery performance. The optimization software selects supplier

Table 4.2 Ideal Solutions for the Illustrative Example

87

2 for 220 units and supplier 4 for 10 units in order to fulfill the two supplier minimum as

well as the minimum order quantity of 10 units for supplier 4.

Similarly, the mature product’s constraints require the selection of two suppliers to fulfill

the demand of 390 units. The cost objective is met by ordering 380 units from supplier 3,

who has the overall minimum total cost, and 10 units from supplier 1, who has the next

lowest total overall cost. Supplier 2 is chosen for lead-time and delivery ordering 200 units

from supplier 2 and 190 units from supplier 3. Supplier 2 has a capacity constraint of 200

units. Even though it has the best performance for lead-time and delivery, an additional

190 units must be ordered from supplier 3, which has the second best performance for both

of these objectives.

The decline product selections are relatively straightforward with supplier assignments for

both delivery and quality being made from the best performing suppliers. Supplier 3 is

selected for the minimum lead-time at 10 weeks, but a capacity constraint of 80 units

requires the selection of supplier 2, which has the second lowest lead-time at 12 weeks.

Similarly Supplier 3 has the lowest cost but due to the capacity constraint, supplier 1 is

selected to fulfill the remaining 20 units.

The ideal solutions are now used to set target levels for the preemptive, non-preemptive

and Tchebycheff min-max models. The preemptive GP results are discussed in the next

section.

4.4.2 Preemptive Goal Programming Results

In preemptive goal programming, the decision maker (DM) identifies the order in which

goal constraints will be achieved after setting the target levels (T11 to T44) for the goals.

For illustrative purposes, the target levels are set at 90% of the ideal values for

maximization and 110% of the ideal values for minimization for the four objectives (cost,

lead-time, quality and delivery) and for each of the four products and their respective phase

in the product life cycle. This relaxing of target values provides some flexibility with

respect to attaining the conflicting goals. Next the DM rank orders the goal constraints

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(equations 4.13 to 4.28) from highest to lowest priority. Table 4.3 shows the goal priority

order, the product and product life cycle phase and the goal objective. Once again, these

priorities are for illustrative purposes only. A company can change these priorities based

on their needs.

Preemptive GP Priorities

The goal priority order, created for this illustrative example, will be discussed first. In the

introduction phase, quality was chosen as the highest priority followed by delivery, lead-

time and cost. Excellent quality performance is essential given the product is just being

introduced to the market and may be supplied by a single supplier. Single sourcing is

permitted for products in this phase given the potential for numerous product revisions and

enhancements. Single sourcing makes the supplier selection process even more critical

given the grave consequences of poor supplier delivery or quality performance during the

introductory phase of the product.

Table 4.3 Preemptive Goal Priority Order

Goal

Objective

Priority

Order (P i )

Product and Product

Life Cycle Stage Goal Objective

1 Introduction Maximize Quality Performance

2 Growth Minimize Lead-time

3 Mature Minimize Cost

4 Decline Minimize Cost

5 Introduction Maximize Delivery Performance

6 Growth Maximize Delivery Performance

7 Mature Maximize Quality Performance

8 Decline Maximize Delivery Performance

9 Introduction Minimize Lead-time

10 Growth Maximize Quality Performance

11 Mature Maximize Delivery Performance

12 Decline Maximize Quality Performance

13 Introduction Minimize Cost

14 Growth Minimize Cost

15 Mature Minimize Lead-time

16 Decline Minimize Lead-time

Preemptive GP Priorities

89

For the growth phase, lead-time is given the highest priority, followed by delivery, quality

and cost respectively. Minimizing lead-time is critical given an increasing product volume

in order to meet customer demand during this ramp-up period. The supply chain must be

responsive during the introduction and growth phases of the product life cycle as opposed

to being cost efficient (Fisher 1997). Since there are fewer competitors and the profit

margins of products in these phases of the product life cycle are generally higher, supply

chain responsiveness in the form of shortened lead-times and excellent delivery

performance is critical. Delivery performance is closely related to lead-time and is the

second highest priority. The real constraints require the selection of two or more suppliers

in order to provide alternative sources as a hedge against possible supplier quality or

delivery performance problems.

In the mature phase, cost is the highest priority for items in this phase of the product life

cycle (PLC) followed by quality, delivery and lead-time. Given the competitive nature of

products in the maturity phase of the PLC, cost is the key factor required to maintain a

competitive position. Quality performance must remain high and is ranked second.

Delivery and lead-time are assigned lower priorities, given that mature products should

have smaller variations in demand which in turn cause fewer supply chain disruptions. The

supply chain, like the products in this phase, should be well established, cost efficient and

capable of supplying products without delivery and lead-time problems. The real

constraints require the selection of two or more suppliers for this phase of the PLC, which

provides some backup in case one of the supplier’s performance falters, as well as ensures

an on-going price competition between several suppliers.

Like the mature phase, cost is the highest priority for items in the decline phase of the PLC

followed by delivery, quality and lead-time. Cost is the key factor for products in the

decline phase, given the availability of competing products and profit margins are low.

Products must be available to the customer and therefore delivery is ranked as the second

highest priority. Products in this phase of the PLC can be easily substituted. Therefore

delivery performance is a key factor in the supplier selection process insuring product

availability. Products in this phase should have well established manufacturing and quality

90

control processes. Since cost is a key factor, efficient supplier performance with respect

to productivity and overall product quality is a requirement to provide products in the

decline phase. Alternate supply sources should be available and can be utilized as needed

on short notice. Therefore the real constraint allows the selection of only one supplier for

a product in the decline phase of the PLC.

Preemptive GP Solution

The goal constraints, presented in equations 4.13 to 4.28, and the corresponding deviational

variables and real constraints, presented in equations 4.5 to 4.12, are solved sequentially

according to the priority sequence (Pi) shown in Table 4.3. This requires that the optimal

solution for priority 1, namely maximize quality performance for the introduction product,

be determined first. The achieved value for priority 1 is then inserted as a real constraint

into the priority 2 GP model in order to ensure that the achievement of priority 1 goal is

maintained during the solution of priority 2; hence the name preemptive goal programming.

Next the results for the first two goal programs are added to the third highest priority model,

which is to minimize the overall procurement cost for the mature product. These steps are

repeated until all 16 priorities are solved. This insures the achievement of the higher

priorities before the lower priority goals are even considered. Goal conflicts occur as goal

constraints are added for the lower priorities. The preemptive goal programming

methodology insures higher goals are maintained at the detriment of lower priority goals.

The preemptive GP method requires the decision maker to simply order the goal priorities

thereby greatly reducing the cognitive burden on the DM. Another advantage is that the

objectives/goals do not have to be scaled.

The illustrative example results for the preemptive goal program exceeded or achieved

fifteen of the sixteen targets. Only lead-time for the mature product missed the target value.

Table 4.4 presents the preemptive goal achievements and Table 4.5 presents the optimal

procurement plan. A quick examination of Table 4.4 shows targets being exceeded

between 4.90 and 11.11% with a median value of 8.48%. The relaxation of the target

values to 90% of the ideal values for maximization (quality and delivery performance) and

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110% of the ideal values for minimization (cost and lead-time) clearly had a significant

impact on the final results and procurement plan. Next the results for each of the product

life cycle phases will be examined in detail.

Introduction Phase Results (Tables 4.4 and 4.5)

Examining the results for the introduction PLC phase reveals that the suppliers with the

best values for achieving the objectives are not always chosen. For example, supplier 2 has

the highest quality yield at 97%, but suppliers 1 and 5 are selected since their combined

performance exceeds the target value of 104.76, for quality, while simultaneously

exceeding the delivery, lead-time and cost target levels. While the introduction goal

constraints for quality (4.21) and delivery (4.25) have higher priority rankings than lead-

time, it is the lead-time goal constraint (4.17) which drives the selections of supplier 1 and

5 in the final procurement plan. Suppliers 5 and 1 have the lowest lead-time, with 5 and 6

weeks respectively, which dictates their selection. Finally the cost target is achieved using

the same selections as lead-time, bypassing supplier 3, which has the overall lowest cost in

terms of variable and fixed costs. Unfortunately supplier 3’s quality and lead-time

performance prevent its selection given the quality and lead-times are higher priorities and

take precedence.

Growth Phase Results (Tables 4.4 and 4.5)

The growth PLC phase has the lead-time goal constraint (4.18) as Priority 2 in the

preemptive GP model. Suppliers 2 and 4 are selected since they provide the minimum

lead-time of 6 weeks. The minimum number of supplier constraint (4.11) requires the

selection of at least 2 suppliers for growth product; hence the selection of suppliers 2 and

4. The delivery goal constraint (4.26), which is Priority 6 in this model, will also select

supplier 2 and 4. They have the highest on-time delivery at 99%, so there is no compromise

required with respect to achieving this priority. Next the quality goal constraint (4.22) will

require the selection of supplier 4 prior to supplier 2, since supplier 4’s quality exceeds that

of supplier 2. To complete the supplier selection process for the growth product, the order

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allocation remains the same with suppliers 4 and 2 satisfying the cost goal constraint (4.14).

Examining the procurement plan for this product life cycle phase clearly demonstrates the

impact of the relaxation of the targets since all targets were met with no change in the

supplier selection following the delivery constraint solution. Supplier 4 was chosen for the

majority of the order quantity due to the superior quality performance as compared to

supplier 2.

Table 4.4 Preemptive GP Achievements with respect to Target Values

Product and

Product Life

Cycle Stage

Goal

Priority

Order Goal Constraints

Target Values

(90% or 110% of

Ideals)

Goal

Achievements Target Achievement

Introduction 1 Maximize Quality Performance 104.76 113.00 Exceeded by 7.87%

Introduction 5 Maximize Delivery Performance 105.84 112.00 Exceeded by 5.82%

Introduction 9 Minimize Lead-time 682.00 620.00 Exceeded by 9.09%

Introduction 13 Minimize Cost 25,300.00 25,300.00 Achieved Target

Growth 2 Minimize Lead-time 1,518.00 1,380.00 Exceeded by 9.09%

Growth 6 Maximize Delivery Performance 204.93 227.70 Exceeded by 11.11%

Growth 10 Maximize Quality Performance 202.77 222.60 Exceeded by 9.78%

Growth 14 Minimize Cost 89,375.00 85,000.00 Exceeded by 4.90%

Mature 3 Minimize Cost 106,095.00 96,450.00 Exceeded by 9.09%

Mature 7 Maximize Quality Performance 347.49 363.30 Exceeded by 4.55%

Mature 11 Maximize Delivery Performance 342.27 378.10 Exceeded by 10.47%

Mature 15 Minimize Lead-time 1,705.00 1,960.00 Missed by 14.96%

Decline 4 Minimize Cost 46,777.50 42,525.00 Exceeded by 9.09%

Decline 8 Maximize Delivery Performance 85.50 91.80 Exceeded by 7.37%

Decline 12 Maximize Quality Performance 89.10 94.80 Exceeded by 6.40%

Decline 16 Minimize Lead-time 1,144.00 1,100.00 Exceeded by 3.85%

Preemptive GP Model Results in Product Life Phase and Priority Sequence

93

Table 4.5 Preemptive GP Procurement Plan

Mature Phase Results (Tables 4.4 and 4.5)

In the mature PLC phase, the cost goal constraint (4.15), with priority 3 in the overall GP

model, actually achieves the ideal solution selecting the minimum cost suppliers.

Therefore this solution exceeds the relaxed targets, which in this case is set at 110% of the

ideal. The next priority (priority 7) is to maximize quality performance. Suppliers 1 and

3 are again selected matching the supplier selection for the cost constraint. While the other

supplier selections would improve the overall quality results, this selection still exceeds the

target. The lead-time goal constraint (4.19) target is missed by nearly 15%. This failure is

due to the priority ordering of the goals in the preemptive model with the higher priorities

taking precedence.

Decline Phase Results (Tables 4.4 and 4.5)

The decline phase of the PLC has cost as the top priority at priority 4 in the overall model.

Again the cost goal constraint (4.16) solution actually equals the ideal solution with the

selection of supplier 3, which has a capacity of 80 units, followed by the selection of

Product and Product Life Cycle Stage Supp

lier 1

Supp

lier 2

Supp

lier 3

Supp

lier 4

Supp

lier 5

Introduction1 20 0 0 0 100

Growth2 0 10 0 220 0

Mature3,4 10 0 380 0 -

Decline5 20 0 80 - -

2 Requires Minimum of two suppliers3 Requires Minimum of two suppliers and Maximum of three suppliers4 Supplier 1 capacity limit of 75, Supplier 3 capacity limit of 2005 Supplier 3 capacity limit of 80

Optimal Order Allocations to Suppliers

1 Supplier 5 capacity limit of 100

94

supplier 1 for the remaining 20 units. The relaxation of the cost goal target to 110% of the

ideal guarantees these selections will exceed the target. The delivery goal constraint (4.28)

exceeds the target value, which has been relaxed to 90% of the target. While supplier 1

has the best delivery performance at 95%, the selection of supplier 3 with a delivery

performance of 91% still allows the optimal solution to exceed the target value. Likewise,

the quality goal constraint (4.24) also exceeds the target value. Finally, the lead-time goal

constraint (4.20) is met primarily by the selection of supplier 3, which has the best lead-

time, for 80 of the 100 units required. Supplier 3’s capacity is limited to 80 units, therefore

the capacity constraint (4.5) requires the selection of an additional supplier to meet the

overall demand constraint (4.9). Even though supplier 1 has the highest lead-time, the

combined selection of suppliers 3 and 1 still exceed the lead-time target by 3.85%.

In summary, the overall results of the preemptive GP model benefit by the relaxation of

the ideal values to 90% for maximization and 110% for minimization objectives to set the

targets. This results in fifteen of the sixteen goal constraints meeting or exceeding the

target values. Six of the sixteen goal constraints actually achieve the ideal values, which

represent the best possible solutions. The preemptive GP methodology ensures the

satisfaction of higher priorities at the expense of lower priorities. In this example, while

six ideal values are achieved, the successive solutions of the model impact the initial

solutions as additional priorities are solved. While the program does not compromise the

higher priorities, the relaxation of the targets from the ideals does result in changes in the

final procurement plan. The next section will review the results of the non-preemptive

model.

4.4.3 Non-Preemptive Goal Programming Results

In non-preemptive goal programming, the DM determines the relative weights in achieving

the targets (T11 to T44) for the goal constraints. The goal constraints, deviational variables

and targets, given in equations 4.13 to 4.28, along with the real constraints, given in

equations 4.5 to 4.12, are incorporated into a single objective model. The DM sets the

relative weights (win) for each of the deviational variables (din) in the single objective

model. For illustrative purposes, the targets (T11 to T44), like the preemptive GP method,

95

are set at 90% for maximization goals and 110% for minimization goals, which is similar

to the preemptive GP model.

The Borda Count method or Analytic Hierarchy Process can be employed by the decision

maker to determine the relative weights of the goal constraints. This requirement, to

determine the weights for the goal constraints (4.13 to 4.28), increases the cognitive burden

on the DM given the 16 goal constraints. Goal constraints are scaled or normalized

facilitating comparison among the objectives.

Non-preemptive GP Weights

The product life cycle priority weights chosen for the illustrative example are shown in

Table 4.6. First, the overall weights are chosen for the products by product life cycle as

25%, 30%, 33% and 12% for the Introduction, Growth, Mature and Decline phases.

Mature products receive the highest overall weight at 33%, given the profit contribution of

this critical phase of the PLC. Many company’s product portfolio relies on mature products

to service established customers as well as to utilize assets dedicated to these products.

Growth products have the second highest overall weight at 30%. Growth products support

the company’s need to expand sales and profits through the organic development of

products. Without first-rate quality, delivery, lead-time and cost performance, a company

could lose the momentum gained from the successful launch of new product in the

transition from introduction to the growth phase of the PLC. Introduction products

performance must also be sustained, hence the 25% overall weight assigned to this phase

of the product life cycle. If introductory products experience problems in quality and

delivery, the new innovative products will suffer in the marketplace and hamper future

potential sales growth. Lastly, decline products are assigned a weight of 12%. Even though

96

these products are in the final phase of the PLC, they are still part of the company’s product

portfolio and can impact the firm’s reputation if performance is allowed to slip.

Table 4.6 Allocation of Weights by Objective for Non-Preemptive GP Model

Next the weights are fixed for cost, lead-time, quality and delivery objectives for the

product life cycle phases. Figure 4.1 shows an exploded view of the weights for each of

the sixteen goal constraints (4.13-4.28). For the introduction phase, quality is assigned the

highest weight at 45%, followed by delivery (40%), lead-time (10%) and cost (5%).

Excellent quality must be attained given the product is new to the market and may be

supplied by a single supplier. Likewise delivery performance, a close second at 40%

weight, is essential since these new products must meet promised delivery schedules in

order to reach customers. Lead-time and cost are rated lower, at 10 and 5% respectively,

since products in this stage of the PLC require a responsive supply chain.

For the growth phase, lead-time is given the highest weight (33%) followed closely by

delivery (30%) and quality (27%). The ramp-up period of the growth phase, requires lead-

time to take a higher priority as volume increases. Like the introduction phase of the PLC,

supply chain responsiveness is critical (Fisher 1997) requiring the minimization of lead-

time. Given reduced competition and higher profit margins, product must be available and

supply chains must have stable delivery and quality performance to meet growing customer

demand. Therefore, delivery and quality performance are critical and assigned much

higher weights than cost (10%). Cost is weighted less since competitors are slowly entering

the market making profit margins higher in this PLC phase.

25% 30% 33% 12%

Objectives Introduction Growth Maturity Decline

Cost 5% 10% 35% 50%

Lead-time 10% 33% 13% 10%

Quality 45% 27% 27% 18%

Delivery 40% 30% 25% 22%

Overall weights for Products by Product Life Cycle Phase

Non-Preemptive Goal Programming Models Priority

Weights

Objective Weights within Product Life Cycle Phase

97

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98

In the mature phase, cost is weighted highest at 35%, followed by quality (27%), delivery

(25%) and lead-time (13%). Cost is a key factor, given the competitive nature of products

in the mature phase of the product life cycle. Quality performance is also critical given the

availability of alternative products. Delivery and lead-time are assigned lower weights,

given a more stable demand pattern, which is an attribute of an efficient product (Fisher

1997). The stable demand pattern results in a more consistent supply chain delivery pattern

focused on improving efficiency and overall cost reduction. The real constraints require

the selection of two or more suppliers for this phase of the PLC, which provides some

backup in case one of the supplier’s performance falters, as well as ensures an on-going

price competition between several suppliers.

In the decline phase, cost is assigned the highest weight at 50%. Delivery is weighted at

22%, followed by quality and lead-time, weighted at 18 and 10% respectively. Given the

availability of competing products in the decline phase, cost is the key factor in maintaining

an efficient supply chain. Likewise, product availability is key, therefore delivery is

assigned the second highest weight given the widespread accessibility of substitute

products. Manufacturing and quality control processes should be well established for

products in this phase of the PLC, given the focus required to maintain an efficient supply

chain. Thus quality and lead-time are assigned lower weights than cost and delivery.

Alternate supply sources should be available and can be utilized as needed on short notice.

Therefore, the real constraint allows the selection of only one supplier for a product in the

decline phase of the PLC.

Non-preemptive GP Solution

The goal constraints, deviational variables and targets, given in equations 4.13 to 4.28,

along with the real constraints, given in equations 4.5 to 4.12, are solved in a single

objective model. Given this single objective, the goal constraints must be normalized or

scaled to facilitate an equal comparison among the goal objectives. This normalization is

accomplished by dividing all goal constraints (4.13 to 4.28) by corresponding target values

(T11 to T44). The relative weights (win), shown in Table 4.6 and Figure 4.1, for each of the

99

deviational variables (din) are included in this single objective model. The illustrative

example results exceeded all sixteen targets. Table 4.7 presents the non-preemptive goal

achievements and Table 4.8 presents the optimal procurement plan.


Evaluating the results for the introduction PLC phase shows suppliers with the best values

for achieving the goal constraints and targets are not always selected. The quality goal

constraint (4.21) has the highest overall weight at 11.25% in the introduction PLC phase.

Even with this high weight, the selection of suppliers 3 and 5 to supply 20 and 100 units

respectively, does not match the ideal solution in Table 4.2 and yet exceeds the target by

7.29%. Clearly the relaxation of the ideal values to the target values of 90% for

maximization and 110% for minimization objectives has a great impact on the optimal

order allocations. The delivery goal constraint (4.25) has the second highest goal weight

at 10.00% for this PLC phase, and delivery performance exceeds the target by 6.01%. The

lead-time goal constraint (4.17) has one of the lowest weights in the model and the goal

constraint target achievement exceeds 6.16%. The ideal solution (Table 4.2) assigns 100

units to supplier 5 and 20 units to supplier 3. The non-preemptive solution (Table 4.8)

matches the ideal with the 100 units being allocated to supplier 5 with 20 remaining units

being allocated to supplier 1. Finally, the cost goal (4.13) has one of the second lowest

weights in the model at 1.25% and the goal constraint target is still exceeded by 1.19%.

The ideal solution for cost for this PLC phase selects supplier 3 to supply all 120 units as

opposed to the non-preemptive GP solution, which selects supplier 3 to only supply 20

units with the remaining 100 units being supplied by supplier 5. It is interesting to examine

the solution for this PLC phase and note the solution matches part of the ideal solution for

one goal constraint and still exceeds all goal constraint targets. This is further evidence of

the impact of the relaxation of the ideal values to the target values of 90% for maximization

and 110% for minimization objectives on the non-preemptive GP model results.

100


Table 4.8 Non-preemptive GP Procurement Plan

Table 4.7 Non-preemptive GP Achievements with respect to Target Values

101

In the growth PLC phase, all targets were achieved for the goal constraints (4.14, 4.18, 4.22

and 4.26). Lead-time (4.18) was assigned the highest weight at 9.90%. Suppliers 2 and 4

had the lowest lead-time at 6 weeks, followed by supplier 3 at 7 weeks. The minimum

number of suppliers’ constraint (4.11) requires at least two suppliers be selected for the

growth phase. Suppliers 2 and 3 are selected with an order allocation of 220 and 10

respectively. Supplier 3 has a minimum order quantity requirement of 10 units. It is

interesting to note that, while suppliers 2 and 4 have the lowest lead-time, selecting supplier

3 instead of supplier 4 still achieves the target value, with the lead-time target being

exceeded by 8.43%. Delivery, with the second highest weight at 9.00%, follows a similar

scenario as the lead-time selection. Once again suppliers 2 and 4 have the highest delivery

performance at 99%, but supplier 3 is selected at 98% delivery performance, which still

exceeds the delivery performance target by 11.06%. Quality performance for suppliers 2

and 3 is lower than suppliers 1 or 4 but the selection of suppliers 2 and 3 once again exceeds

the target by 4.55%. Finally, the cost target is exceeded by 4.22%. It is interesting to note

that supplier 2 does not have the lowest overall cost and is therefore not included in the

ideal solution (see Table 4.2), but the selection of suppliers 2 and 3 does not exceed the

cost target. The result of the growth phase PLC supplier selections also demonstrate the

impact of the target value relaxation.


The mature PLC phase supplier selections allocate 123 units to supplier 2 and 267 units to

supplier 3. Cost, goal constraint 4.15, has the highest weight at 11.55%. Supplier 3 is

included in the ideal cost solution (see Table 4.2) for this PLC phase. Supplier 2’s cost are

slightly higher than supplier 1, which is included in the ideal cost solution, but the order

allocations to suppliers 2 and 3 still allow the cost target to be achieved and exceeded by

7.96%. The second highest weight, in the PLC phase, is assigned to quality at 8.91%. The

best quality rating is associated with suppliers 1 and 4 with both having a 99% on-time

performance. In spite of this best performance, the quality ratings of suppliers 2 and 3 at

96% and 93% respectively, the target achievement is only 5.44%. Delivery and lead-time

target achievement is 10.89% and 0.06% respectively. Both suppliers 2 and 3 are included

102

in the ideal solutions for delivery and lead-time. The noteworthy result is the 0.06%

achievement of the lead-time target. This is due to the quantity allocated to supplier 2; the

ideal solution has 200 units supplied by supplier 2 as compared to 123 in the non-

preemptive GP model. This difference accounts for the model’s modest target achievement

of 0.06%. These results again demonstrate the importance of the target value relaxation as

well as the need to balance conflicting weights in the final non-preemptive solution.


The decline phase of the PLC has the product cost goal constraint (4.16) as the highest

weight at 6.00%, followed by delivery (4.28) at 2.64%, quality (4.24) at 2.16% and last

lead-time (4.20) at 1.20%. The cost target for the goal constraint was exceed by 8.82% by

ordering 20 units from supplier 2 and 80 units from supplier 3. The supplier 3 order of 80

units matches the ideal solution but the ideal solution selects supplier 1 for the remaining

20 units (see Table 4.2). Relaxing the cost target to 110% of the ideal facilitates this goal

achievement and the selection of a supplier that does not match the ideal solution.

Similarly, the delivery goals constraint (4.28) ideal solution does not match the non-

preemptive GP model results. The ideal solution selects supplier 1 to supply all 100 units.

In spite of not selecting the ideal supplier, the goal constraint target is exceeded by 6.90%.

The quality goal constraint target is exceeded by 6.62%. Supplier 2 is allocated 100 units

in the quality goal constraint ideal solution as compared to only 20 units in this model’s

solution. Finally, the lead-time goal constraint exceeds the goal constraint target by 9.09%

and achieves the ideal solution despite lead-time having the lowest overall weight at 1.20%.

Like the other results from the PLC, the decline phase results benefit from the relaxation

of the goal constraint targets with respect to the goal weights.

In summary, the non-preemptive GP model has some very interesting results which should

be examined. All sixteen of the goal constraint targets are achieved in the model, with

results ranging from 0.06% to 11.06% and a median value of 6.54%. When all the goal

constraints are achieved, it generally implies that the solution is dominated. In other words,

the target values can be “tightened” some more namely from a 10% relaxation from the

103

ideal values to just 5% relaxation. The non-preemptive GP model relies on setting goal

weights and measuring the goal constraint target achievements. It is reasonable to expect

a direct positive relationship between the goal weights and target achievements. The

scatter plot, Pearson correlation coefficient and regression line shown in Figure 4.2

displays the relationship between the goal weights and the target achievements. The

Pearson correlation coefficient is weak (0.374) and is not statistically significant. This

contradicts the theory that higher goals weights are directly related to higher target

achievement. The detailed examination of the results from each of the PLC phases for this

model, consistently found that the ideal value solutions did not dominate. Unlike the

preemptive GP methodology which ensures higher priorities are met at the expense of

lower priorities, the non-preemptive model balances the goal constraint weights (win) and

generates a solution which satisfies the target levels for all of the goal constraints (4.13 to

4.28). The relaxation of the goal constraint targets with respect to the goal weights provides

flexibility and allowance for choosing a solution which is not related to the ideal solution

while still satisfying the goal constraint targets.

Figure 4.2 Non-Preemptive GP Model Goal Weights vs. Target Achievements

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4.4.3 Tchebycheff’s (Min-Max) Goal Programming

In Tchebycheff’s min-max GP, the objective is to minimize the maximum deviation, using

deviational variables (d11 to d44), from the target values (T11 to T44) set by the DM. For

illustrative purposes, the target values are set at 90% of the ideal values for maximization

and 110% of the ideal values for minimization. The ideal values are given in Table 4.2.

The goal constraints, deviational variables and targets, given in equations 4.13 to 4.28,

along with the real constraints, given in equations 4.5 to 4.12, are used in a single objective

model. Noting that the objective is to minimize the maximum deviation from the target

values, outliers could dominate the optimal solution. Tchebycheff’s min-max GP does not

require weights as inputs and therefore no cognitive burden on the DM, given the objective

of minimizing the maximum deviation from the target values. It should be noted that

scaling of the objective functions is necessary for this GP model also. Scaling is

accomplished by dividing all the goal constraints (4.13 to 4.28) by their corresponding

target values (T11 to T44).

Tchebycheff’s (Min-Max) GP Solution

Table 4.9 presents the goal achievements with respect to the target values. Table 4.10

presents the optimal procurement plan for the model. The optimal solution achieved all

sixteen goal constraint target values. The Table 4.9 results show the targets are exceeded

between 0.06% and 11.06%, with a median value of 6.28%. The relaxation of the target

values to 90% of the ideal values for maximization (quality and delivery performance) and

110% of the ideal values for minimization (cost and lead-time) clearly had a significant

impact on the final results and procurement plan.

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lier 1

Supp

lier 2

Supp

lier 3

Supp

lier 4

Supp

lier 5

Introduction1 0 0 20 0 100

Growth2,3 0 220 10 0 0

Mature4 0 123 267 0 -

Decline5 20 0 80 - -


3 Requires Minimum order quanity of 10


1 Supplier 5 capacity limit of 1002 Requires Minimum of two suppliers

4 Requires Minimum of two suppliers and Maximum of three suppliers

Table 4.9 Tchebycheff’s Min-Max GP Achievements with respect to Target Values

Table 4.10 Tchebycheff’s Min-Max GP Procurement Plan

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The results for the introduction PLC phase mirror the results of the non-preemptive GP

model. Given the objective of minimizing the maximum deviation from the target values,

the best values for achieving the goal constraints and targets are not always selected. The

solution allocates 100 units to supplier 5 and 20 units to supplier 3. This solution matches

only part of the ideal solution for lead-time with 100 units being allocated to supplier 5 and

20 units being allocated to supplier 1. The ideal solution for cost allocated 120 units to

supplier 3, while the solution for the Tchebycheff’s Min-Max model only allocates 20 units

to supplier 3. Suppliers 2 and 4 are excluded from the model solution, although they

incorporate the ideal solutions for quality and delivery respectively. Again, the relaxation

of the ideal values to the target values of 90% for maximization and 110% for minimization

objectives, combined with the objective of minimizing the maximum deviation from the

target values, have a great impact on the optimal order allocations.


Similar to the results for the introduction PLC phase, the growth phase results mirror the

results of the non-preemptive GP model. The solution for the growth PLC phase allocates

200 units to supplier 2 and 10 units to supplier 3. While supplier 2 is included in the ideal

solution for lead-time and delivery, supplier 3 is only included in part of the ideal solution

for cost. Interestingly, supplier 4 is excluded from the solution despite being included in

the ideal solution for both lead-time and quality. Likewise, suppliers 1 and 5 are also

excluded from the solution even though they represent the ideal quality solution. The result

further illustrates the impact of the target value relaxation on the optimal procurement plan.


The mature PLC phase supplier selections also duplicate the non-preemptive GP model

results allocating 123 units to supplier 2 and 267 units to supplier 3. Supplier 2 and 3 are

included in the ideal solutions for cost, lead-time and delivery. Only the quality ideal

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solution, which includes suppliers 1 and 4 are omitted from the mature phase optimal order

allocations. Ironically, this PLC phase solution includes the minimum and maximum target

achievements at 0.06% and 10.89% for lead-time and delivery respectively. The order

quantity of 267 units to supplier 3 had a significant negative result on the lead-time target

achievement of 0.06%. The results further illustrate the impact of the target value

relaxation as well as the requirement to generate an optimal procurement plan which

minimizes the maximum deviation from the target values.


Unlike the previous PLC phases, the decline phase of the PLC optimal procurement plan

differs from the non-preemptive GP model results. The Tchebycheff min-max optimal

procurement plan allocates 20 units to supplier 1 and 80 units to supplier 3 versus the non-

preemptive solution which allocates 20 units to supplier 2 and 80 units to supplier 3. The

selection of suppliers 1 and 3 improves the cost and delivery target achievements. The cost

target achievement is 9.09% for the Tchebycheff GP model as compared to 8.82% for the

non-preemptive GP model. Likewise, delivery target achievement is 7.37% compared to

6.90% for the non-preemptive GP model. While cost and delivery targets better the results

of the non-preemptive model, quality and lead-time performance does not meet the non-

preemptive GP model results. Quality target achievement for the Tchebycheff model is

6.40% as compared to 6.62% for the non-preemptive model. Lead-time target achievement

at 3.85% for the Tchebycheff model significantly lags the 9.90% lead-time target

achievement in the non-preemptive model. The results further emphasize the trade-offs

regarding target achievement as well as the influence of the target relaxation of 90% for

maximization and 110% for minimization objectives.

In summary, the Tchebycheff min-max GP model results mirror the non-preemptive GP

model results except for the decline PLC phase. All sixteen of the goal constraint targets

are achieved in the model, with results ranging from 0.06% to 11.06% and a median value

of 6.28%. Like the preemptive and the non-preemptive models, the Tchebycheff results

are impacted by the target relaxation.

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4.4.3 Fuzzy Goal Programming

Fuzzy GP minimizes the maximum deviation from the ideal values, which are used as

targets for the goal constraints. Thus, Fuzzy GP requires neither target levels nor

preferences among the targets creating no cognitive burden on the decision maker. Fuzzy

goal programming can be greatly influenced by outliers given the objective of minimizing

the maximum deviation from the goal constraints (4.13 to 4.28).

Fuzzy GP Solution

The results from the Fuzzy GP cannot be compared to the previous model results since

Fuzzy uses the ideal values for the goal constraint targets, while preemptive, non-

preemptive and Tchebycheff’s GP models relax the ideals to 90% for maximization and

110% for minimization goal constraints. Like the non-preemptive and Tchebycheff GP

models, scaling or normalization is required for comparison among the goal objectives.

This normalization is accomplished by dividing all goal constraints (4.13 to 4.28) by

corresponding target values (T11 to T44). Equal weights were used for all the goal

constraints. Table 4.11 presents the goal achievements with respect to the ideal values.

Table 4.12 presents the optimal procurement plan for the Fuzzy model. The optimal

solution missed fourteen ideal values while achieving two ideal values.

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Table 4.11 Fuzzy GP Achievements with respect to Ideal Values

Table 4.12 Fuzzy GP Procurement Plan

Product and Product

Life Cycle Stage Goal Constraints

Ideal

Values

Goal

Achievements

Ideal Value

Achievements

Introduction Minimize Lead-time 620.0 670.00 Missed by 8.06%

Introduction Maximize Delivery Performance 117.6 112.65 Missed by 4.21%

Introduction Maximize Quality Performance 116.4 112.10 Missed by 3.69%

Introduction Minimize Cost 23000.0 24865.00 Missed by 8.11%

Growth Minimize Lead-time 1380.0 1491.00 Missed by 8.04%

Growth Maximize Delivery Performance 227.7 226.59 Missed by 0.49%

Growth Maximize Quality Performance 225.3 213.50 Missed by 5.26%

Growth Minimize Cost 81250.0 85210.00 Missed by 4.87%

Mature Minimize Lead-time 1550.0 1550.00 Achieved Ideal

Mature Maximize Delivery Performance 380.3 380.30 Achieved Ideal

Mature Maximize Quality Performance 386.1 368.70 Missed by 4.51%

Mature Minimize Cost 96450.0 98425.00 Missed by 2.05%

Decline Minimize Lead-time 1040.0 1120.00 Missed by 7.69%

Decline Maximize Delivery Performance 95.0 91.96 Missed by 3.20%

Decline Maximize Quality Performance 99.0 94.96 Missed by 4.08%

Decline Minimize Cost 42525.0 42565.00 Missed by 0.10%

Fuzzy Model Results in Product Life Cycle Phase Order


lier 1

Supp

lier 2

Supp

lier 3

Supp

lier 4

Supp

lier 5

Introduction 0 0 35 0 85

Growth1 0 193 0 0 37

Mature2,3 0 200 190 0 -

Decline 24 0 76 - -



1 Requires Minimum of two suppliers2 Requires Minimum of two suppliers and Maximum of three suppliers

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Table 4.11 shows that missed targets ranged between 0.10% and 8.11% with a median

value of 4.36%. The cost goal constraint (4.13) for the introduction PLC phase has the

maximum deviation, missing the ideal solution by 8.11%. These results achieve the

Fuzzy goal program’s objective of minimizing the maximum deviation from the ideal

values. Only lead-time and delivery performance for the mature product were able to

achieve the ideal value targets. The goals for delivery in the growth product and cost in

the decline product were nearly achieved, missing only by 0.49% and 0.10% from their

ideals respectively.


The introduction phase results include the maximum deviation for the Fuzzy GP model.

Cost has the maximum deviation of 8.11% for this PLC phase. Suppliers 3 and 5 are

selected with order allocations of 35 and 85 respectively. It is interesting to note that the

ideal solution for the quality goal constraint (4.21) and the ideal solution for the delivery

goal constraint (4.25) does not include suppliers 3 and 5. The order allocations to

suppliers 3 and 5 include part of the ideal solution for the cost goal constraint (4.13) and

the ideal solution for the lead-time goal constraint (4.17). Since this phase of the PLC

contains the maximum deviation, any order allocation change will negatively impact the

overall model solution.


The solution for the growth PLC phase allocates 193 units to supplier 2 and 37 units to

supplier 5. The order allocations to suppliers 2 and 5 include portions of the ideal solution

for the cost (4.14), lead-time (4.18) and delivery (4.26) goal constraints. Only the ideal

solution, which utilizes supplier 1, for quality goal constraint (4.22) is left out of the growth

phase optimal order allocation. In spite of this exclusion, the quality goal constraint only

misses the ideal solution by 5.26%.

111


The mature PLC phase supplier selections achieve the ideal solutions for the lead-time

(4.19) and delivery (4.27) goal constraints. The solution to this PLC phase allocates 200

units to supplier 2 and 190 units to supplier 3. This solution excludes the ideal solution for

the quality goal constraint (4.23) but only misses by 4.51%. The cost goal constraint (4.15)

misses the ideal by only 2.05% since supplier 3 is also part of the ideal cost goal solution.


The decline PLC phase allocates 24 units to supplier 1 and 76 units to supplier 3. This

solution nearly matches the ideal solution for the cost goal constraint (4.16) and missed by

only 0.10%. The lead-time goal constraint (4.20) has the highest deviation from the ideal

at 7.69%. The exclusion of supplier 2 from the optimal order allocation is clearly the reason

for this miss. Likewise the quality goal constraint (4.24) ideal solution is excluded from

the decline phase optimal order allocation given the absence of supplier 2 from the order

allocations. In spite of this exclusion, the quality ideal is missed by only 4.08%.

The Fuzzy GP model results requires no input from the decision maker. Yet the results,

which provide the minimization of the maximum deviation from the ideal values, provide

an optimal order allocation which controls the worst case scenario. The Fuzzy model

assumes all goal constraints are equally important. One can use the relative weights

obtained earlier to solve a weighted Fuzzy GP model also. In the next section, the optimal

solutions from the preemptive, non-preemptive and Tchebycheff models will be compared

and contrasted.

4.4.4 Overall Model Results

This section reviews the overall model results using the Value Path approach discussed in

Ravindran and Warsing (2013). The Value Path approach allows the DM to view the

results and various tradeoffs in a complex model by displaying the results as a set of parallel

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scales. Summarized results for the Tchebycheff, non-preemptive and preemptive models

are presented in Table 4.13. These model results, representing the goal constraint (4.13

to 4.28) achievements for each phase of the PLC, are normalized. This normalization of

the goal achievements is accomplished by dividing each achieved goal constraint solution

by the best achieved goal constraint solution for minimizing objectives. For the

maximizing objectives, the normalization is done by dividing the maximum achieved value

by the value obtained by each GP model. Table 4.14 summarizes the normalized goal

achievements. For example, for the “minimize lead-time” goal for Introduction phase, the

best value is 620. Hence the normalized values for the three GP models are (640/620,

640/620, 620/620) or (1.032, 1.032, 1.0). Similarly, for the “maximize delivery

performance” goal for Introduction phase, the best value is 112.20. Hence the normalized

values are (112.20/112.20, 112.20/112.20, 112.20/112.00) = (1.0, 1.0, 1.002). The best

achievement for a specific goal constraint equals 1.0 with higher values being less

desirable. These models use the same target values (90% for maximization and 110% for

minimization objectives) making the Value Path approach a viable method to compare the

GP model results. The Fuzzy GP model results are excluded from the Value Path analysis

since the Fuzzy model uses the ideal values as targets for the goal constraints.

Table 4.13 Model Results and Target Values

Tchebycheffs Non-Preemptive Preemptive

Introduction Minimize Lead-time 682.00 640.00 640.00 620.00

Introduction Maximize Delivery Performance 105.84 112.20 112.20 112.00

Introduction Maximize Quality Performance 104.76 112.40 112.40 113.00

Introduction Minimize Cost 25,300.00 25,000.00 25,000.00 25,300.00

Growth Minimize Lead-time 1,518.00 1,390.00 1,390.00 1,380.00

Growth Maximize Delivery Performance 204.93 227.60 227.60 227.70

Growth Maximize Quality Performance 202.77 212.00 212.00 222.60

Growth Minimize Cost 89,375.00 85,600.00 85,600.00 85,000.00

Mature Minimize Lead-time 1,705.00 1,704.00 1,704.00 1,960.00

Mature Maximize Delivery Performance 342.27 379.53 379.53 378.10

Mature Maximize Quality Performance 347.49 366.39 366.39 363.30

Mature Minimize Cost 106,095.00 97,655.00 97,655.00 96,450.00

Decline Minimize Lead-time 1,144.00 1,100.00 1,040.00 1,100.00

Decline Maximize Delivery Performance 85.50 91.80 91.40 91.80

Decline Maximize Quality Performance 89.10 94.80 95.00 94.80

Decline Minimize Cost 46,777.50 42,525.00 42,650.00 42,525.00

Model Results and Target Values for Tchebycheff's, Non-Preemptive and Preemptive Models

Product and

Product Life

Cycle Phase Goals

GP ModelsTarget Values

(90% or 110% of

Ideals)

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Introduction Phase Results (Tables 4.13-14 and Figure 4.3)

The introduction PLC phase order allocations for the non-preemptive model (Table 4.8)

and Tchebycheff model (Table 4.10) are the same. Examining Tables 4.13-14 and Figure

4.3 reveals that lead-time goal results for the Tchebycheff and non-preemptive models are

worse compared to the results of the preemptive model by 3.2%. This result is consistent

with a lower weight allocation in the non-preemptive model and a goal constraint that does

not approach the maximum deviation from the target for the Tchebycheff GP model. The

second worst overall performance for this phase of the PLC is related to the cost goal. In

Table 4.14 Value Path Results

this case, the cost in the preemptive model exceeded the cost in the Tchebycheff and non-

preemptive models by 1.2%. The cost goal was given the lowest priority in the preemptive

GP model for this PLC phase and the results are consistent with this prioritization. The

results for the delivery goal and the quality goal have minimal differences in model results.

It is interesting to note that quality has the highest overall priority in the preemptive GP

Tchebycheffs Non-Preemptive Preemptive

Introduction Minimize Lead-time 1.032 1.032 1.000

Introduction Maximize Delivery Performance 1.000 1.000 1.002

Introduction Maximize Quality Performance 1.005 1.005 1.000

Introduction Minimize Cost 1.000 1.000 1.012

Growth Minimize Lead-time 1.007 1.007 1.000

Growth Maximize Delivery Performance 1.000 1.000 1.000

Growth Maximize Quality Performance 1.050 1.050 1.000

Growth Minimize Cost 1.007 1.007 1.000

Mature Minimize Lead-time 1.000 1.000 1.150

Mature Maximize Delivery Performance 1.000 1.000 1.004

Mature Maximize Quality Performance 1.000 1.000 1.009

Mature Minimize Cost 1.012 1.012 1.000

Decline Minimize Lead-time 1.058 1.000 1.058

Decline Maximize Delivery Performance 1.000 1.004 1.000

Decline Maximize Quality Performance 1.002 1.000 1.002

Decline Minimize Cost 1.000 1.003 1.000

Value Path Results for Tchebycheff's, Non-Preemptive and Preemptive Models

GP Models

Goals

Product and

Product Life Cycle

Phase

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Fig

ure

4.3

Valu

e P

ath

Mod

el R

esu

lts

Co

mp

ari

son

115

model and yet the results are only 0.5% better than the Tchebycheff and non-preemptive

models. The preemptive model exceeds the quality target by 7.87% (Table 4.4) so again

the relaxation of the target values to 90% for maximization and 110% for minimization

objectives has an influence on the model results and comparisons.

Growth Phase Results (Tables 4.13-14 and Figure 4.3)

The growth PLC phase order allocations for the non-preemptive model (Table 4.8) and

Tchebycheff model (Table 4.10) are also the same. Table 4.14 and Figure 4.3 show that

the quality goal achievement for the preemptive model is the best exceeding the

Tchebycheff and non-preemptive models by 5%, which is also the greatest difference in

this PLC phase. The lead-time goal, which has the 2nd highest overall priority in the

preemptive GP model, only achieves 0.7% better results than the non-preemptive and

Tchebycheff GP models. Once again, the relaxation of the target values has an impact on

the model results. The delivery goal results show that the non-preemptive and Tchebycheff

models’ performance are essentially equal to the preemptive model. Product cost has the

lowest priority in the growth phase of the PLC for the preemptive model, but it still

outperforms the other models by 0.7%. The results from this phase of the PLC demonstrate

that higher priorities drive supplier selection in the preemptive model. Quality

performance, which had the greatest difference at 5%, is more a result of good fortune,

since the best performing suppliers with respect to lead-time and delivery also had the best

quality performance.

Mature Phase Results (Tables 4.13-14 and Figure 4.3)

Like the introduction and growth phases, the order allocations for the non-preemptive

model (Table 4.8) and Tchebycheff model (Table 4.10) are the same. The mature phase

also includes the greatest variation among the different GP model results. Lead-time values

for the preemptive model exceed the non-preemptive and Tchebycheff GP values by 15%.

This is largely due to the priority structure in the preemptive model. The priority ordering

requires the achievement of the cost, quality and delivery goals prior to the lead-time goal.

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This precedence order does not allow a more balanced achievement of the target values,

such as the non-preemptive model, which uses goal constraint weights, or the Tchebycheff

model which focuses on minimizing the maximum deviation from the goal constraints. The

best performance in the preemptive model’s goal achievement is for cost, better than the

non-preemptive and Tchebycheff models by 1.2%. It should be noted that cost is the

highest priority goal in this phase of the PLC and yet it only improves the results by 1.2%.

Quality and delivery performance for the non-preemptive and Tchebycheff models only

exceed the preemptive model by 0.9% and 0.4%, respectively. While the preemptive

model has the best solution for cost, it also includes the worst performance in this PLC

phase with respect to lead-time. The results from the mature phase of the PLC provide the

DM with choices and tradeoffs regarding model selection.

Decline Phase Results (Tables 4.13-14 and Figure 4.3)

The decline PLC phase order allocations for the preemptive model and Tchebycheff model

are the same. Lead-time values for the preemptive and Tchebycheff models exceed the

non-preemptive GP values by 5.8%. Lead-time has the lowest overall priority in the

preemptive model and the third lowest overall goal achievement in the Tchebycheff model.

Given this low priority, the lead-time solution is superseded by the attainment of the cost,

delivery and quality goals, in that order, which helps explain the 5.8% difference in model

performance. The remaining goal constraints of cost, delivery and quality have small

differences in model performance. Delivery target performance is slightly better for the

preemptive and Tchebycheff models at 0.4%. Cost goal achievement is only slightly better

for the preemptive and Tchebycheff models by 0.3%, in spite of the cost goal constraint

having the highest preemptive model priority in this phase of the PLC. Quality goal

achievement for the non-preemptive model is 0.2% better than the preemptive and

Tchebycheff models. Higher goal priority in the preemptive model does not always

guarantee superior performance compared to the alternative GP models.

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4.4.7 Chapter Conclusion

In summary, the Value Path results combined with the Fuzzy GP model results (Tables

4.11 and 4.12) provide the DM with invaluable information regarding the supplier

selection. While the Fuzzy GP model requires no input from the decision maker, the use

of the ideal values as goal achievement targets combined with the overall objective of

minimizing the maximum deviation from the ideal, provides the DM with an optimal order

allocation which controls the worst case scenario. The Fuzzy goal programming solution

can be used as a baseline for comparison. In Fuzzy GP the ideal values are used as targets

and all goals have equal importance, instead of preferences set by the DM. Unlike the

Fuzzy GP model, target levels for the goal constraints (4.13 to 4.28) are utilized in the

preemptive, non-preemptive and Tchebycheff models. For illustrative purposes, the target

levels are set at 90% of the ideal values for maximization and 110% of the ideal values for

minimization to allow for some model flexibility. The Value Path results presented in

Figure 4.3 and Table 4.13 allow a comparison of the four objectives (cost, lead-time,

quality and delivery), for each of the four products in their respective phase of the product

life cycle. Clearly, there is no one single model which provides the overall best solution

for the supplier selection process. While the preemptive GP model may provide a supplier

solution for a number of goal constraints, it also has the largest deviations from the targets

(for example, introduction phase cost, mature phase lead-time and decline phase lead-time

performance). These conflicting results provide the DM with choices and alternative

solutions. It is also realistic to assume that the decision maker may ask for changes in the

target values, priority order or weights to evaluate a new set of model solutions. Multi-

criteria decision making is centered on providing alternative solutions and tradeoffs which

allow the DM to choose the best procurement plan, since an optimal procurement plan may

not exist given the conflicting objectives. In the next section, the models will be utilized

to solve a real world global industrial supplier selection problem.

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5. Case Study: Global Supplier Selection Problem across

Product Life Cycle – Supplier Ranking Results Chapter 5 is focused on applying and expanding the general supplier selection model from

Chapter 4 to a real-world case study. This case study is focused on a U.S. based consumer

products company which utilizes a diverse global supply chain to design, manufacture and

deliver products to traditional brick and mortar retailers, on-line retailers and distributors.

Three key executive decision makers were employed to identify and rank the key sourcing

criteria attributes for products representing the introduction, growth, mature and decline

phases of the product life cycle (PLC). Multiple criteria decision making models were

used to select suppliers based on the ranking of the key sourcing attributes, creating an

integrated supplier selection methodology. The MCDM results were then reviewed with

the DMs providing the tradeoffs associated with the varied supplier selection results. The

suppliers included in this case study have already been pre-screened or short-listed by the

company and hence, this study focused on the final supplier selection and order allocation.

5.1 Background of the Company and the Decision Makers

Annual company sales of the focus company are nearly $800 million, which are generated

from 10 major product families or categories. There are approximately 1,100 active SKU’s

with the top 100 products accounting for more than 80% of the total sales dollars and

volume. Products are manufactured or assembled at three facilities, two of which are

located in the United States. A global supply chain, with an estimated 410 suppliers,

provides raw materials, services, equipment and finished products. In addition, between

60 and 80 new products are launched each year with an equal number of product

retirements. New product launches account for a substantial percentage of annual revenue.

This important link between the successful introduction of new products to the overall

revenue stream and the retirement of products further emphasizes the importance of

managing the product life cycle.

Three executive decision makers, Chief Operating Officer (COO), Vice President of

Procurement and the Manager of Global Purchasing, identified and ranked key sourcing

119

criteria attributes for items representing the introduction, growth, mature and decline

phases of the product life cycle. These executives develop strategies to manage the global

supply chain which includes prescreening, selecting and allocating orders. As noted

previously, this case study is focused on the final supplier selection and order allocation,

since the suppliers have already been prescreened.

The Manager of Global Purchasing researches possible sources for new and existing

products. This research is presented to and reviewed with the COO and VP of

Procurement. These key DMs develop both a strategic and tactical procurement global

supply chain strategy and execution plan for the company. The strategy considers new

supply sources, locations, capabilities and the ability to develop a long-term business

relationship. Existing suppliers are also included in this discussion regarding expanding

business levels while considering capacity constraints and delivery requirements. These

three executives travel extensively reviewing supplier performance and new business

opportunities. This direct contact with the key suppliers provides the decision makers with

critical input about supplier performance, development and selection.

The ranking and weights presented in this chapter will be utilized in the preemptive and

non-preemptive goal programming MCDM models in Chapter 6. In addition to the

preemptive and non-preemptive GP models, Tchebycheff’s min-max and fuzzy models

were also employed to select suppliers and determine order allocations. The MCDM

results were then reviewed with the DMs providing the tradeoffs associated with the varied

supplier selection results. Chapter 6 will describe the model results, decision makers’

feedback on the results and the managerial implications.

120

5.2 Description of Products and Suppliers

Seven products, of the approximate 1,100 active products, were selected representing the

four phases of the product life cycle. Two products were at the introduction phase, one at

the growth phase, two in the mature phase and the last two products were in the decline

phase. Table 5.1 displays the seven products across the four phases of the PLC and the ten

suppliers considered in the selection process. The products are identified by three

categories, which represent their product family. As noted previously, the company

designs, manufactures and supplies 12 major product families. The products chosen for

this case study represent 3 of the 6 top selling product families. Purchased materials for

the company, including raw materials and finished goods, account for nearly 60% of the

cost of goods sold. The total purchase value of the products included in this study represent

over 20% of the total purchased value.

The suppliers included in this case study have already been pre-screened and short-listed.

Generally, suppliers for this company are specialized and tend not to produce items for all

product family categories. Table 5.1 represents all the suppliers, which were considered

following the initial supplier screening process. Suppliers three, four and five provide

similar product kit assembly and packaging services and are very competitive with each

other. Suppliers two, six and seven are also very competitive with one another and provide

products which requires significant capital, tooling and technological capability. Of the

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

1 Category 1 X X

2 Category 1 X X

Growth 3 Category 2 X X X

4 Category 1 X X X

5 Category 1 X X X X X

6 Category 3 X X

7 Category 1 X X

Products, Suppliers for Supplier Selection Case Study

Suppliers

Introduction

Mature

Product Life Cycle

StageCategory

Decline

Product

Table 5.1 Products and Suppliers for Case Study

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ten suppliers, only suppliers three and four are capable of producing items from two or

more categories included in this case study. Supplier four is the only vendor able to provide

products from all three categories. Several new suppliers, including 1, 8 and 10, were

included in the selection process.

Vendors for the introduction products are considered and added to the short list of potential

suppliers based on past history and performance. New supply sources are included on this

short list if they are able to provide new technology that is not available within the existing

supply chain capabilities. Additional revenue is the motivation for existing suppliers to

compete for introductory products, not to mention the competitive advantage gained by

providing products in the introduction phase of the PLC.

Yearly demand in number of units for the seven products is shown in Table 5.2. Demand

ranges from 15,000 to 340 million units, with the decline products having the lowest

quantities. DM3 provided the yearly demand information. Her job responsibilities

necessitate utilizing yearly demand forecasts in order to insure suppliers have the necessary

capacity required to meet the forecasted demand levels. As noted previously, the items

from the growth and mature products require significant capital, tooling and technological

capability and represent the highest yearly demands.

Table 5.2 Yearly Unit Product Demand

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5.3 Key Supplier Selection Criteria

Suppliers were evaluated based on 10 criteria for the four phases of the product life cycle.

The Manager of Global Purchasing provided the 10 criteria and definitions used in the case

study. They are included in the company’s evaluation and selection processes. The criteria

are briefly described below:

▪ Product Safety- all products are tested for hazardous materials and heavy metals

via a U.S. national safety testing laboratory. Suppliers are required to provide the

testing lab with formulations details (confidentiality of suppliers’ intellectual

property is strictly maintained by the testing laboratory).

▪ Quality- suppliers must adhere to International Council of Toy Industries (ICTI)

Code of Conduct (http://www.toy-icti.org/). ICTI and its member associations

are committed to the promotion of toy safety standards, to the reduction or

elimination of barriers to trade and to the advancement of social responsibility in

the industry with programs to address environmental concerns, fair and lawful

employment practices and workplace safety. New and existing suppliers are

subject to extensive on-site quality audits conducted by the company’s quality

assurance personnel. These factory process quality audits include evaluation of

the adherence to quality control procedures and policies. Compliance with

standard safety procedures, such as access to machine guarding, fire

extinguishers, electrical panels, emergency exits, etc. is included in the quality

evaluation. In addition to the audit of these quality control processes, the ethical

treatment of employees is a key attribute included in the assessment process.

▪ Product performance- relates to the supplier’s ability to meet the company’s

product specifications including safety. This criterion also includes the proper

handling of raw materials, which are frequently provided by the company. These

raw materials may include proprietary formulations, which are not shared with the

supplier in order to protect the company’s intellectual property.

▪ Tooling development time- is evaluated based on the ability to meet product

shipment requirements. Slow tooling development time is considered a negative

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attribute when selecting suppliers for introduction phase products. Suppliers with

in-house tooling development capability for dies have a competitive advantage.

▪ Pricing- includes the unit price and tooling cost, which is unitized based on

expected total purchase quantity.

▪ Advanced Technology- is evaluated based on the application with respect to

processes, product solutions and cost reductions throughout the product life cycle.

The ability of suppliers to quickly respond to problems using advanced

technology is critical.

▪ Delivery- is the actual supplier performance compared to the agreed upon

delivery dates.

▪ Lead-time- is assessed based on the supplier’s total expected time from order

placement to shipment.

▪ Service/Capacity Planning- is defined as the ability to deliver product according

to required delivery schedule. This criterion requires suppliers to provide a

detailed capacity plan based on supplier capacity, expected demand and delivery

requirements.

▪ Past Performance- is the supplier’s historical performance including delivery,

lead-time, problem solving, pricing performance, safety adherence, product

performance and quality. This represents an overall assessment of the supplier’s

performance.

Not all supplier selection criteria are utilized in all phases of the PLC. For example, past

performance is not included in the introduction phase of the product life cycle, because

there is no past performance history for a newly introduced product. Advanced

technology is excluded from the growth, mature and decline phases of the product life

cycle, but is a critical criterion for new products in the introduction phase. Past

performance is also excluded from the growth phase since the history would be limited to

the introduction phase. These decisions were made in consultation with the company

personnel.

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Suppliers’ performance on the selection criterion was rated on a scale of 1 to 10, with 10

being the highest and 1 being the lowest. Table 5.3 gives the supplier ratings, as well as

the maximum business levels for each of the vendors. This information was provided by

DM3, whose job responsibilities include having detailed knowledge and monitoring of

supplier performance. The maximum business level is determined as a percentage of

the supplier’s overall sales revenue in U.S. dollars. The business levels for each supplier

are researched and utilized in the overall sourcing decisions. This is a conscious effort to

make sure that suppliers do not become too reliant on the company for their revenues and

related profits. Landry (1998) termed this limiting of overall business as a power

balance, which needs to be maintained in the customer-supplier relationship. Landry’s

study recommended the power balance to be between 20% and 40% of the business

revenue of a specific supplier. While there was no reference to Landry by the company

in setting the maximum business levels, the intention of limiting business supports

Landry’s goals of maintaining a healthy customer-supplier relationship. Note that several

supplier’s maximum business levels are below the 20% lower limit referenced by Landry.

The decision to use a level lower than 20% was focused on new suppliers (e.g. supplier

S8). Since there was limited history with these suppliers, the company decided to further

limit overall purchasing levels in an effort to reduce risk with these potential new supply

sources.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

Product Safety 10.0 10.0 8.0 9.0 9.0 10.0 10.0 10.0 10.0 10.0

Quality 9.5 9.5 8.0 9.0 9.0 9.5 95.0 7.0 9.5 9.5

Product Performance 9.5 9.5 9.0 9.0 9.0 9.5 9.5 7.0 9.5 9.5

Tooling Development Time 6.0 9.0 8.0 8.0 8.0 7.0 9.0 5.0 9.0 9.0

Pricing

Advanced Technology 6.0 8.0 8.0 8.0 6.0 6.0 7.0 2.0 7.0 8.0

Delivery 9.5 9.0 9.5 9.5 9.5 9.0 9.0 6.0 9.0 8.0

Lead-Time 8.0 9.0 9.0 9.0 9.0 9.0 9.0 6.0 9.0 8.0

Service/Capacity Planning 8.0 9.0 8.0 8.0 8.0 9.0 9.0 6.0 8.0 8.0

Past Performance 8.0 9.0 8.0 8.0 8.0 9.0 9.0 6.0 9.0 8.0

Maximum Amount of Business* 20 40 40 30 20 40 40 10 30 20

* percentage of total sales $'s

Supplier Ratings vs. Key Criteria

SuppliersRating Criteria

Pricing varies by Product

Table 5.3 Supplier Performance Ratings and Maximum Business Levels

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The next section explains the key decision maker ranking methods used and the results

for the selection criteria across the product life cycle.

5.4 Ranking the Product Life Cycle Phases

The DMs for this case study will be identified by the following convention:

▪ Chief Operating Officer (COO) is designated DM1;

▪ Vice President of Procurement is designated DM2;

▪ Manager of Global Purchasing is designated DM3.

A number of multiple criteria ranking methods were employed in this case study to rank

the product life cycle phases and the supplier selection criteria. A brief explanation of each

of the method follows:

Rating method: A rating scale of 1 to 10 is employed. The rating for each of the criterion

is then normalized to get the criteria weights. The criterion weight is obtained by dividing

its rating by the sum of all criteria ratings. This method poses the least cognitive burden on

the decision maker.

Borda count utilizing pairwise comparison: Each DM completes a pairwise comparison

of the criteria. A pairwise comparison matrix identifies preferences of criterion (e.g.

introduction vs. growth PLC phase). Preferences are recorded in the pairwise comparison

matrix as a 1 (preferred) or 0 (not preferred) and the rows are summed for each criterion.

Finally, the weight for each criterion is computed by dividing its row sum by the sum of

all row sums. This method poses a moderate level of cognitive burden.

Analytic Hierarchy Process (AHP): DM’s provide the strength of preference in addition

to the pairwise comparison of criteria utilized in the Borda count method. A numerical

score representing a weight is then calculated for each criterion as discussed in Chapter 2.

AHP poses the highest cognitive burden on the decision maker.

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The first step in the ranking process is to evaluate the relative importance of the product

life cycle phases. All three DMs provided the necessary responses for the rating method,

Borda count with pairwise comparison and AHP. Figure 5.1 illustrates DM1’s actual

responses for the product life cycle phases. DM1 is the chief operating officer. The ranking

results for DM1 are shown in Table 5.4. The Borda and AHP methods yielded similar

results by ranking growth as the highest product life cycle phase, followed by mature,

introduction and decline. The results of the rating method ranked growth as the highest

followed by introduction, mature and decline. Following a review of the ranking results

with DM1, a number of questions were reviewed. Decision maker 1 did not agree with the

results of the rating method stating the growth phase was still the highest priority followed

by the mature phase. He also found the Borda count to be the easiest method to complete.

DM1 noted both the Borda count and AHP method focused thinking on the pairs, which in

his opinion was easier than ranking all the product life cycle phases at once. AHP was

deemed to be the hardest method to complete. When asked which method required more

thinking, the COO again responded that the rating method required more focused thought

Figure 5.1 Product Life Cycle Responses from DM1

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since other factors relating to product performance had to be simultaneously considered,

whereas the Borda and AHP provided a comparison of one PLC phase versus another via

the paired comparisons. Overall DM1 noted that the Borda and AHP rankings matched his

thinking most closely. Although DM1 ranked the growth phase highest followed by the

mature phase, he commented “there is no growth phase without a successful introduction

phase.”

Decision Maker 2’s ranking results are presented in Table 5.5. The results for the Borda

and AHP methods match but differ from the rating method with respect to the rankings of

the introduction and growth phases. DM2 chose introduction as the most important PLC

phase in the rating method. He noted introduction was the most important phase because

getting a product launched on time and to the customer is critical. Without a successful

introduction phase, there would be no growth phase for the new product. Getting it right

from the start is critical which includes product design, manufacturing and shipping. He

also noted that the focus of the introduction and growth phases was “chasing revenue

versus margin”. DM2 continued “typically a mature product has done well on the shelf,”

generating a solid sales volume. Given this good sales volume, the mature phase focus

was directed at generating revenue with the focus changing to “chasing margin versus

revenue.”

Product Life Cycle (PLC) Phase

Rating

Method

Borda

Method AHP

Introduction 2 3 3

Growth 1 1 1

Mature 3 2 2

Decline 4 4 4

Comparison of DM1 (Chief Operating Officer)

Rankings for Product Life Cycle Phase

Table 5.4 Ranking of PLC Phases by Different Methods for DM1

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DM2 found the rating method to be the easiest, since it allowed the PLC phases to be put

in a linear order which reduced the complexity of the rating process. AHP was deemed to

be the hardest ranking method and required the greatest cognitive effort, given there were

more things to consider such as the pair-wise comparisons and the strength of preference.

Overall, the rating method matched DM2’s thinking with respect to the product life cycle

phase rankings.

DM3’s ranking results are shown in Table 5.6. The ranking results are different for each

of the ranking methods. DM3 stated the growth phase was most important to make sure a

product is thriving as this starts to gain market share and add to the revenue stream of the

company. Increasing volume for a growth phase product also allows procurement

professionals to improve item pricing. This possible benefit of improved pricing can be

contrasted with the introduction phase. In the introduction phase the objective is to “get

the product on the shelf at the right profit margin as compared to the best possible profit

margin.” Like DM2, she noted in the introduction phase you are “chasing revenue not

margin.”

DM3 found the rating method to be the easiest method, while AHP was harder to complete

given the pair-wise comparisons and the evaluation of the strength of preference. The

rating methods results were most consistent with DM3’s preferences. She felt the growth

phase could be very difficult to manage given demand spikes of up to 25%, hence the


Rating

Method

Borda

Method AHP

Introduction 1 2 2

Growth 2 1 1

Mature 3 3 3

Decline 4 4 4

Comparison of DM2 (VP Procurement) Rankings for

Product Life Cycle Phase


129

growth phase is ranked highest. In addition, DM3 noted the introduction and growth

phases were the most difficult phases of the PLC to manage. While the introduction phase

puts emphasis and pressure on achieving the cost target, the growth phase requires constant

attention and rising demand may require additional capital equipment investment by the

supplier. Volume, history and the ability to increase competition for mature phase

products make this an easier PLC phase to manage. DM3 noted the decline phase is where

you are attempting to “save the program”, while not putting a great deal of time and effort

into a product which is near the end of its life cycle.

The DMs were asked to arrive at a consensus for the PLC phase rankings. Due to

scheduling issues, DM3 was not able to join decision makers 1 and 2 to address this

question. While both of these senior executives focus on strategic issues, such as

developing a worldwide supply chain, both commented that often the priority is greatly

influenced by the immediate needs of the market and customers. New products account

for 10% of overall revenue and are critical to the company’s organic growth further

emphasizing the importance of the introduction phase. Given the importance and

contribution to revenue, the introduction and growth phase products are high priority with

regard to supplier selection. Mature products can impact customer service levels, if they

are not available and overall company profitability, if gross profit margins are not meeting

target requirements. Improving gross profit margins on mature products is a major effort,

while not sacrificing product safety, quality and delivery. The consensus between these


Rating

Method

Borda

Method AHP

Introduction 3 2 1

Growth 1 1 3

Mature 2 3 2

Decline 4 4 4

Comparison of DM3 (Global Sourcing Manager)

Rankings for Product Life Cycle Phase


130

company officials is there must be a balance between how products from each phase of the

product life cycle are managed and prioritized. The introduction, growth and mature

phases all require a different focus and can impact the company’s overall performance if

neglected. Although the DMs were able to rate and rank the product life cycles

individually, they were unable to arrive at consensus regarding ranking of the PLC phases.

In Section 5.6, we discuss how we arrived at the final weights for the PLC phases for use

in the goal programming models. In the next section, the DMs will rank the selection

criteria by product life cycle phase.

5.5 Ranking the Supplier Selection Criteria by PLC Phase

This section reviews the supplier selection criteria rankings for each decision maker. Not

all criteria were utilized in all phases of the product life cycle. For example, past

performance is not included in the introduction phase, given there is no performance

history for the new product.

5.5.1 Ranking the Supplier Selection Criteria by DM1

Table 5.7 displays the Chief Operating Officer’s (DM1) rankings of the criterion for the

introduction and growth PLC phases. The growth phase does not include the advanced

Table 5.7 DM1’s Ranking of Criteria for Introduction and Growth Phases by

Different Methods

technology criterion, which is an important attribute for products in the introduction phase.

Given the similarity between the Borda and AHP methods, the rankings for both PLC

phases are nearly identical.

131

For the introduction phase, the rating method results substantially differ on product

performance, service and capacity planning, when compared to the Borda and AHP results.

Safety is a top priority for DM1 for all product life cycle phases and the ranking results

reflect this preference. Other top priorities in the introduction phase are quality and product

performance. Without high quality and product performance, sales would be negatively

impacted. Advanced technology would be an important criterion for supplier evaluation

in the introduction phase. Tooling development earned the lowest ranking in introduction

and DM1 noted that tooling is somewhat fixed and known, along with lead-time and

therefore leads to a lower ranking.

Product quality and safety are top ranked criteria in the growth phase for all methods. DM1

ranked price and product performance in the bottom 3 criteria for all approaches. He noted

that the Borda and AHP ranking results were most consistent with his criteria ranking. As

experienced in the PLC phase rating process, DM1 preferred the Borda and AHP methods

for ranking criteria.

Table 5.8 displays the ranking results by the different methods for DM1 for the mature and

decline phases. The results of the Borda and AHP methods for the mature and decline

phases rank safety quality and price in the top 3. These rankings contrast the introduction

and growth phase, which ranked price and quality in the bottom half or bottom 3 criteria.

The COO noted that price was very important for the mature and decline phases given the


Methods

132

impact on the company’s gross profit margin. He noted that the focus on price for these

final PLC phases was essential. Demand for mature product should be relatively stable,

which simplifies a price negotiation with suppliers. DM1 agreed with the ranking results

for the top and bottom three criteria for the Borda and AHP ranking methods and they gave

the best overall match to his preferences on the criteria.


Table 5.9 presents the VP of Procurement’s (DM2) rankings for the introduction and

growth phases of the PLC. DM2 noted the importance of getting the product on the shelf

and performing to required specifications. Given these requirements, good quality and

delivery performance should be given high priority with respect to supplier selection. In

both the introduction and growth phases the emphasis is on generating revenue versus gross

profit margin. DM2 preferred the Borda and AHP rankings, and they were most consistent

with the importance he placed on quality, safety, delivery and product performance.

As products migrate from introduction and growth to the mature product life cycle phase,

the emphasis on improving the gross profit margin significantly increases. Once again,

DM2 agreed with the Borda and AHP rankings for the mature and decline product life

cycle phases shown in Table 5.10. Given the emphasis on gross profit margin, DM2 ranked

price in the top 3 selection criteria, following safety and quality. A mature product for this


Different Methods

133

company has typically done well in terms of unit sales and should be part of a repeatable

buy with a number of alternative sources. Therefore, supplier selection practices should

include strategic sourcing with an emphasis on creating price competition within the supply

base in order to improve the overall gross margin of the purchased product. Unlike the

introduction and growth phases, the focus is on revenue; hence, the mature phase changes

the focus to increasing gross margin. DM2 has the same top 3 ranking for quality, safety

and price for decline products. Ideally DM2 would prefer for any of his procurement team

not to be directly involved or utilized in activity relating to a decline phase product, since

they are being phased out or being replaced by a product in the introduction phase.


Table 5.11 displays the Global Sourcing Manager’s (DM3) ranking results for the

introduction and growth phases. DM3 ranked safety, quality and delivery as the top criteria

in the introduction PLC phase. She noted that you are not able to successfully launch a

new product without solid performance in these key supplier attributes. In addition, she

also supported the VP of Procurement’s assertion that the focus of the introduction phase

is achieving the revenue targets for the new product as opposed to gross profit margin

contribution.


Methods

134


Different Methods

DM3 noted that the growth PLC phase was very similar to the introduction phase with its

emphasis on quality and safety. In addition to safety and quality, lead-time becomes very

important as sales volume increases or demand spikes in the growth phase. DM3

commented that the introduction and growth phases are the most difficult to manage.

Delivery schedules must be met, cost targets must be achieved and unexpected demands

can create havoc for the entire supply chain.

Table 5.12 presents the criteria rankings for DM3 for the mature and decline phases. As

the global purchasing manager, she commented that the mature phase allows you to “spice

it up” and focus on increasing price competition among the suppliers. While safety and

quality are the top 2 criteria, price is ranked third in both the Borda the AHP results. The


Methods

135

price competition between qualified suppliers, with excellent safety and quality

performance, provides the opportunity to improve the company’s gross profit margin.

While issues with the decline phase product has to be addressed, ideally the effort should

be minimized given the product has reached the end of its life cycle. The goal to minimize

the efforts on the decline products parallels DM2’s objective. DM3 preferred the Borda

and AHP results versus the rating method for all four phases of the product life cycle.

5.6 Final Ranking and Weights for the MCDM Supplier Selection

Models

The ranking results from all three decision makers will be incorporated into the supplier

selection MCDM models in Chapter 6. Since all three executives preferred the results of

the Borda and AHP methods, the results of the AHP ranking method will be used to

determine the weights utilized by the supplier selection models. The next section will

review these ranking results and illustrate the corresponding weights obtained from the

rankings.

5.6.1 Ranking and Weights of PLC Phases

It is interesting to review the ranking method that each of the decision makers preferred.

DM1 favored Borda and AHP methods since these methods focused thinking in pairs as

compared to ranking all the PLC phases or supplier selection attributes at once. Decision

makers 2 and 3 preferred the rating method based on cognitive burden. The rating method

also most closely matched their thinking regarding the PLC phases. It is interesting to

observe that all 3 DMs preferred the ranking results of the Borda and AHP methods for the

PLC supplier selection attributes. The average AHP weights for the product life cycle

phases and supplier selection criteria will be incorporated into the non-preemptive GP

model in Chapter 6. Table 5.13 displays the AHP weights for each decision maker for the


136

5.6.2 Final Criteria Weights by PLC Phase

Table 5.14 displays the average AHP weights for the supplier selection criteria by PLC

phase. The supplier selection results from decision makers 1 and 2 are nearly identical for

the top 3 criteria of growth, mature and decline phases. They only differ on the top 3

ranking results for the introduction phase criteria. In the introduction phase, DMs 1 and 2

agree on 2 of the 3 top criteria ranking product safety first and quality second. They only

differ on product performance and delivery. In spite of some differences in rankings

between the decision makers, it is clear all ranked quality and safety as top supplier

selection criteria for all phases of the PLC. The differences in the ranking results may be

attributed to the decision makers’ specific job responsibilities. DMs 1 and 2 may take a

more strategic view of the supplier selection criteria whereas DM3 may be focused on

tactical day to day requirements of the supplier selection process.

5.6.3 Overall Weights for the Goal Programming Models

The overall weights for the goal programming models, used in Chapter 6, are calculated by

multiplying the product life cycle phase weights by the respective weights of the supplier

selection criteria. Table 5.15 presents the final weights for the supplier selection models

by PLC phase and supplier selection criteria. The PLC supplier selection criteria AHP

weights were averaged for all the DMs prior to being multiplied by the PLC phase average

weights. The overall weights are listed in descending order representing the rank order of

goal priority for the preemptive GP. In the next section, the minimum and maximum

DM1 DM2 DM3

Average PLC

Weight

Introduction 16.2% 29.3% 47.7% 31.1%

Growth 49.7% 53.1% 14.4% 39.1%

Mature 29.4% 13.7% 33.4% 25.5%

Decline 4.7% 3.9% 4.5% 4.4%

Product Life

Cycle Phase

AHP Weights

Table 5.13 Average AHP weights by PLC Phase

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number of suppliers by product life cycle phase will be discussed.

Table 5.15 Overall AHP Weights for Goal Programming

Table 5.14 Average AHP Weights by PLC Phase and Supplier Selection Criteria

138

5.7 Minimum and Maximum Number of Suppliers by PLC Phase

The decision makers were also asked to record the minimum and maximum number of

suppliers which could be utilized by product life cycle phase. This information would be

utilized to create constraints on the number of suppliers in each of the goal programs. Table

5.16 displays the min-max number of suppliers by PLC phase obtained from the decision

makers. All three DM’s recommended one supplier as the minimum number of suppliers

for the introduction phase (products 1 and 2), while the maximum number ranged from one

to three. Based on discussion with these key executives and their emphasis on “getting the

product on the shelf” both the minimum and maximum number of suppliers was set at one.

This would reduce complexity in dealing with startup issues and also was a practical

decision since there were only two potential short-listed suppliers for each of the

introduction products.

For the growth phase (product 3), the minimum number of suppliers was set to one and

the maximum number to two. Only three suppliers made the short list of possible suppliers.

The growth phase had the highest overall weight in the PLC ranking and all DM’s noted

the importance of this critical PLC phase. Allowing up to two suppliers would encourage

competition while not overly complicating the supply chain with too many suppliers.

Product Life Cycle Phase

Minimum

Number of

Suppliers

Maximum

Number of

Suppliers

Introduction 1 1

Growth 1 2

Mature 2 3

Decline 1 2

Minimum and Maximum Number of

Supplier by Product Life Cycle Phase

Table 5.16 Number of Suppliers by PLC Phase

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For the mature phase (products 4 and 5), the number of short-listed suppliers for the two

products ranged from three to five. The minimum number of suppliers was set at two and

the maximum at three. This would still support competition among the suppliers by

guaranteeing that at least two suppliers would always be selected. This would allow the

DMs to “spice up” the competition with the objective of improving the overall gross margin

of the products. The executives were all consistently emphasizing the change from

generating revenue in the introduction and growth phases to generating improved margins

in the mature phase.

For the decline phase (products 6 and 7), the minimum and maximum number of suppliers

was set at one and two respectively. There were only two potential suppliers for both of

the decline products. Each of the decision makers commented that minimum procurement

effort should be applied to decline phase products. DM2 commented that as a product

transitions to the decline phase, there are fewer potential suppliers and decreased product

volumes. By minimizing the number of active decline phase suppliers, “it is easier to

manage scrap charges and the effects on a supplier’s health.”

The next chapter will describe the development and results of the goal programming

models for supplier selection and order allocation.

140

6. Case Study: Global Supplier Selection Problem across

Product Life Cycle – Supplier Selection and Order

Allocation Chapter 6 is focused on utilizing the supplier selection criteria weights and rankings,

detailed in Chapter 5. Goal programming methods will be employed in order to determine

the final supplier selections and order allocations for the case study. These goal

programming methods include:

▪ Preemptive;

▪ Non-preemptive;

▪ Tchebycheff’s min-max;

▪ Fuzzy.

Sections 4.3.3 to 4.3.6 respectively review the major characteristics of each of the GP

models. The next section will compare model statistics between the Chapter 4 illustrative

example and the case study for the preemptive GP model. This comparison will include

the number of variables and constraints, the time to solve and number of iterations.

Correlations among these model characteristics for the preemptive case study GP model

will also be analyzed.

6.1 Preemptive GP Model for the Case Study

Table 6.1 provides information

regarding major goal programming

attributes for the case study. The

case study is substantially larger

than the illustrative example,

presented in Chapter 4, with nearly

quadruple the number of real

variable and double the number of

real constraints. The number of real

variables in the case study are equal

to 62, integer variables are equal to

Case StudyNumber of Integer Variables 38

Number of Real Variables 62

Number of Deviational Variables 124

Total Number of Variables 224

Number of Goal Constraints 62

Number of Real Constraints 238

Total Number of Constraints 300

Time to Solve (seconds) 0.11

Table 6.1 Preemptive GP Model Characteristics

for the Case Study

141

38, deviational variables are equal to 124. The case study has 62 goal constraints and 238

real constraints. The model was solved using LINGO Version 17.0.74, which is an

optimization modeling software for linear, nonlinear, and integer programming. The

model took less than one second to solve running on a Dell Inspiron Model 7773 Laptop

Computer, using an Intel Core i7-8550U CPU at 1.80 GHz, and Windows 10 operating

system. Although the model took less than one second to solve, the number of integer

variable is highly correlated to time to solve. Ravindran, Phillips and Solberg (1987) note

the relationship between number of integer variable and time to solve and provide

recommendations for utilizing integer variables.

6.1.1 Preemptive GP Priorities

In preemptive goal programming, the DM identifies the order in which goal constraints

will be achieved after setting the target levels. The target levels were set at 95% of the

ideal values for maximization and 105% of ideal values for minimization objectives. This

relaxing of target values provides some flexibility with respect to attaining the conflicting

objectives.

In the case study, the DM’s ranked the goals from highest to lowest priority. This ranking

was generated by averaging the AHP weights for the PLC phase and the PLC phase

selection criteria, which was detailed in Sections 5.6.2 and 5.6.3. The average weights,

which were generated by multiplying the average PLC phase weight by the PLC supplier

selection criteria, were then enumerated in descending order. This order provided the goal

achievement priority order for the preemptive GP model. Table 6.2 displays the selection

criteria in priority order by product life cycle phase and criterion. The decision makers

ranked the growth phase highest followed by the introduction, mature and decline phases.

The quality and safety criteria were ranked highest and represent the top 5 criteria to be

achieved. Delivery performance was another highly ranked criterion for growth and

introduction phases. The decline phase was ranked the lowest and represents the bottom

third of the selection criteria priorities. It should be noted that price is the only criterion

that is minimized. All other selection criteria are maximized.

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Selection

Criteria Priority

Order

Product Life

Cycle Stage Selection Criteria

1 Growth Maximize Quality Performance

2 Growth Maximize Product Safety Performance

3 Introduction Maximize Product Safety Performance

4 Mature Maximize Product Safety Performance

5 Mature Maximize Quality Performance

6 Growth Maximize Delivery Performance

7 Introduction Maximize Quality Performance

8 Introduction Maximize Delivery Performance

9 Introduction Maximize Product Performance

10 Growth Maximize Lead-Time Performance

11 Growth Maximize Service/Capacity Planning Performance

12 Growth Maximize Product Performance

13 Growth Minimize Price

14 Mature Maximize Product Performance

15 Mature Minimize Price

16 Mature Maximize Maximize Past Performance

17 Introduction Minimize Price

18 Mature Maximize Delivery Performance

19 Introduction Maximize Advanced Technology Rating

20 Introduction Maximize Lead-Time Performance

21 Introduction Maximize Service/Capacity Planning Performance

22 Growth Maximize Tooling Development Time Performance

23 Mature Maximize Service/Capacity Planning Performance

24 Introduction Maximize Tooling Development Time Performance

25 Mature Maximize Lead-Time Performance

26 Decline Maximize Product Safety Performance

27 Decline Maximize Quality Performance

28 Mature Maximize Tooling Development Time Performance

29 Decline Minimize Price

30 Decline Maximize Maximize Past Performance

31 Decline Maximize Service/Capacity Planning Performance

32 Decline Maximize Product Performance

33 Decline Maximize Delivery Performance

34 Decline Maximize Lead-Time Performance

35 Decline Maximize Tooling Development Time Performance

Table 6.2 Preemptive GP Supplier Selection Priorities

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6.1.2 Preemptive GP Solution

The goal constraints are solved sequentially according to the priority order, shown in Table

6.2. This requires that the optimal solution for priority 1, which is the maximization of the

growth phase quality performance be determined first. The achieved value for priority 1

is then inserted as a real constraint in the priority 2 GP model. This ensures the

achievement of the priority 1 goal is maintained during the solution of priority 2, which is

the maximization of the growth phase safety performance. In other words, while

optimizing the priority 2 goal, only the alternative optimal solutions of priority 1 goal are

considered. This process continues as long as alternative optima exist at an iteration. This

insures the achievement of the higher priorities are maintained before the lower priority

goals are even considered. Hence, when the GP model reaches a unique optimal solution

from some priority, the algorithm stops since no improvements to lower priority goals are

possible. This ordering of priorities reduces the cognitive burden on the DM. Additionally,

objectives/goals do not have to be scaled. The next sections will review the supplier

selections and order allocations for each product and its corresponding product life cycle

phase.

6.1.3 Introduction Phase Results (Product 1)

Table 6.3 displays the

procurement plan for introduction

phase (product 1). Supplier 1 was

chosen to supply the entire yearly

demand. A constraint limiting the

maximum number of suppliers for

the introduction phase to one does

not allow splitting the demand

between the two suppliers. Table 6.4 presents the preemptive goal achievements for the

supplier selection criteria with respect to the target values. The highest priorities, which

include safety, quality, delivery and product performance, achieve and surpass the target

values by 5.3%. Supplier 1’s performance is equal to or better than supplier 2 for all these

S1 S2

10,000,000 0

1 Maximum number of suppliers set to one supplier

Optimal Order Allocations to SuppliersSuppliers

Order Allocations1

Table 6.3 Preemptive GP Procurement Plan for

Introduction Phase Product 1

144

top 4 selection criteria. Price misses the target by 5.8%, since supplier 2’s price is

approximately 10% less than supplier 1. This is an example of higher goal constraints

being achieved at the detriment of lower priority goals. Likewise, all the remaining goals

including advanced technology, lead-time, service/capacity planning and tooling

development time miss the target values. The tooling development time records the largest

target miss at 29.8%.



procurement plan for the

introduction phase (product 2).

Supplier 3 was chosen to supply

the entire yearly demand. Table

6.6 presents the preemptive goal

achievements for the supplier

selection criteria with respect to

the target values. Unlike product 1, the two highest priorities for maximizing safety and

quality performance miss the target achievement by 6.4%. This result may seem

counterintuitive given the high goal priority for these criteria. Detailed testing of the model

revealed the achievement of goal priorities 1 and 2 for growth phase product 3, have a

Goal

Priority

Order Selection Criteria

Target Values

(95% or 105%

of Ideals)

Goal


3 Maximize Product Safety Performance 95,000,000 100,000,000 Exceeded by 5.3%

7 Maximize Quality Performance 90,250,000 95,000,000 Exceeded by 5.3%

8 Maximize Delivery Performance 90,250,000 95,000,000 Exceeded by 5.3%

9 Maximize Product Performance 90,250,000 95,000,000 Exceeded by 5.3%

17 Minimize Price 472,500 500,000 Missed by 5.8%

19 Maximize Advanced Technology Rating 76,000,000 60,000,000 Missed by 21.1%

20 Maximize Lead-Time Performance 85,500,000 80,000,000 Missed by 6.4%

21 Maximize Service/Capacity Planning Performance 85,500,000 80,000,000 Missed by 6.4%

24 Maximize Tooling Development Time Performance 85,500,000 60,000,000 Missed by 29.8%

S3 S4

50,000 0



Order Allocations1

Table 6.4 Preemptive GP Achievements for Introduction Phase Product 1 with

respect to Target Values



145

direct impact on the supplier selection and order allocation of product 2. Supplier 4 clearly

had the best performance rating with respect to safety and quality, yet supplier 3 was

selected. Supplier 4 was chosen to supply 1.6 million units of product 3, which consumes

the entire capacity of supplier 4 with respect to business volume levels. This business

volume constraint combined with the best performance with respect to quality and safety

required the selection of supplier 4 for product 3. Achieving these higher priority goals

was again done at the detriment of the lower priority goals. This real example clearly

demonstrates the principles of preemptive goal programming. Ironically, all remaining

goals exceeded the target values by 4.8 to 5.3%. These goal achievements are possible

because of supplier 3’s better performance for delivery to tooling development time.

Table 6.6 Preemptive GP Achievements for Introduction Phase Product 2 with


6.1.5 Growth Phase Results (Product 3)

Table 6.7 displays the procurement plan for the growth phase (product 3). Suppliers 3 and

4 were chosen to supply the yearly demand. A constraint limiting the maximum number

of suppliers for the growth phase to two allows splitting the demand between the two

suppliers. Table 6.8 presents the preemptive goal achievements for the supplier selection

Goal

Priority


Target Values

(95% or 105%

of Ideals)

Goal


3 Maximize Product Safety Performance 427,500 400,000 Missed by 6.4%

7 Maximize Quality Performance 427,500 400,000 Missed by 6.4%

8 Maximize Delivery Performance 451,250 475,000 Exceeded by 5.3%

9 Maximize Product Performance 427,500 450,000 Exceeded by 5.3%

17 Minimize Price 91,350 87,000 Exceeded by 4.8%

19 Maximize Advanced Technology Rating 380,000 400,000 Exceeded by 5.3%

20 Maximize Lead-Time Performance 427,500 450,000 Exceeded by 5.3%

21 Maximize Service/Capacity Planning Performance 380,000 400,000 Exceeded by 5.3%

24 Maximize Tooling Development Time Performance 380,000 400,000 Exceeded by 5.3%

146

criteria with respect to the target values. Product 3 has the top 2 goal priorities in the

preemptive GP model maximizing quality and safety performance respectively. The

achievement of these goal priorities impacted the supplier selection process and order

allocations to the detriment of introduction phase (product 2). Even though product 3 has

the top goal priorities, both the quality and safety goal achievements did not achieve the

target values with both missing by 0.2%. Model testing revealed that while suppliers 4 and

5 had the best performance for both quality and safety, supplier 5 was not selected due the

business level constraint and the maximum limit of two suppliers for the growth phase

product. This business level constraint also resulted in the supplier 4 supplying the

maximum allowed volume of 1.6 million units. The remaining units would be supplied by

supplier 3. The supplier selection and order allocation for growth phase (product 3)

demonstrate the impact of both business constraints and goal achievements requirements.

The remaining goal constraints, except for price, exceed the target levels by 5.3%. Price

missed the target by 3%.

Goal

Priority


Target Values

(95% or 105%

of Ideals)

Goal


1 Maximize Quality Performance 25,650,000 25,600,000 Missed by 0.2%

2 Maximize Product Safety Performance 25,650,000 25,600,000 Missed by 0.2%


10 Maximize Lead-Time Performance 25,650,000 27,000,000 Exceeded by 5.3%

11 Maximize Service/Capacity Planning Performance 22,800,000 24,000,000 Exceeded by 5.3%

12 Maximize Product Performance 25,650,000 27,000,000 Exceeded by 5.3%

13 Minimize Price 27,342,000 28,152,000 Missed by 3%

22 Maximize Tooling Development Time Performance 22,800,000 24,000,000 Exceeded by 5.3%

S3 S4 S5

1,400,000 1,600,000 0


Order Allocations1

1 Maximum number of suppliers set to two suppliersTable 6.7 Preemptive GP Procurement Plan for Growth Phase Product 3

Table 6.8 Preemptive GP Achievements for Growth Phase Product 3 with respect to

Target Values

147

6.1.6 Mature Phase Results (Product 4)

Table 6.9 displays the procurement plan for the mature phase (product 4). Suppliers 6 and

7 were chosen to supply the yearly demand. Table 6.10 presents the preemptive goal

achievements for the supplier selection criteria with respect to the target values. Several

real constraints also impacted the selection of the mature PLC phase products. These

constraints set the minimum number of required suppliers to two and the maximum number

of suppliers to three, which supports the DM’s objective of creating price competition in

order to improve the gross profit margin for mature phase products.

Product 4 exceeds or achieves the target values for 8 out of 9 goal priorities. The target

achievements range from 0.8% to 5.3%, with 6 out of 8 goal priorities achieving the latter.

Product 4, which has a high expected yearly demand of 340MM units, achieves the price

target supporting the decision maker’s goal of improving product gross profit margin. The

only goal achievement which missed the target value was tooling development time, which

missed the target value by 4.3%. This miss was due to selecting supplier 6, which has a

tooling score of 7, to supply nearly 60% of the total expected demand as compared to

supplier 7, which has a tooling score of 9. This is another example of higher priority goals

being achieved at the detriment of lower priority goals.

S1 S6 S7

0 200,600,000 139,400,000 1 Minimum number of suppliers set to two suppliers


Order Allocations1,2

2 Maximum number of suppliers set to three suppliersTable 6.9 Preemptive GP Procurement Plan for Mature Phase Product 4

148

Table 6.10 Preemptive GP Achievements for Mature Phase Product 4 with respect

to Target Values


Table 6.11 displays the procurement plan for the mature phase (product 5). Suppliers 2

and 6 were chosen to supply the yearly demand of 110MM units. Table 6.12 presents the

goal achievements for the supplier selection criteria with respect to the target values.

Product 5 also requires a minimum of two suppliers and a maximum of three suppliers to

enhance competition among the suppliers, supporting improved gross profit margins for

mature phase products.

Product 5 exceeds the target values in 8 out of 9 goal priorities. The target achievements

range from 0.8% to 5.3%, with 6 out of 8 goal priorities achieving the latter. The price

goal priority exceeds the target by 4.8%, which like product 4, supports the DM’s goals of

improving gross profit margin for mature PLC phase products. Tooling development time

missed the target value by 13.5%. This miss was due to selecting supplier 6, which has a

tooling score of 7, to supply 80% of the total expected demand as compared to suppliers 2

and 9, which have tooling scores of 9.

Goal

Priority


Target Values

(95% or 105%

of Ideals)

Goal


4 Maximize Product Safety Performance 3,230,000,000 3,400,000,000 Exceeded by 5.3%

5 Maximize Quality Performance 3,068,500,000 3,230,000,000 Exceeded by 5.3%

14 Maximize Product Performance 3,068,500,000 3,230,000,000 Exceeded by 5.3%

15 Minimize Price 14,994,000 14,994,000 Achieved Target

16 Maximize Past Performance 2,907,000,000 3,060,000,000 Exceeded by 5.3%

18 Maximize Delivery Performance 3,036,200,000 3,060,000,000 Exceeded by 0.8%

23 Maximize Service/Capacity Planning Performance 2,907,000,000 3,060,000,000 Exceeded by 5.3%

25 Maximize Lead-Time Performance 2,907,000,000 3,060,000,000 Exceeded by 5.3%

28 Maximize Tooling Development Time Performance 2,777,800,000 2,658,800,000 MIssed by 4.3%

149

6.1.8 Decline Phase Results (Product 6)


procurement plan for decline

phase (product 6). Supplier 10

was chosen to supply the entire

yearly demand of 25,000 units.

Table 6.14 presents the

preemptive goal achievements for

the supplier selection criteria with

respect to the target values. The supplier selection for product 6 is impacted by the supplier

selection for growth phase (product 3). Product 3 had the top two goal priorities in the

preemptive model and supplier 4 was chosen to best achieve these goals. Selecting supplier

4 for product 3 consumed the entire capacity of supplier 4 based on business volume levels.

This previous supplier selection, which was determined via preceding goal constraints,

Goal

Priority


Target Values

(95% or 105%

of Ideals)

Goal


4 Maximize Product Safety Performance 1,045,000,000 1,100,000,000 Exceeded by 5.3%

5 Maximize Quality Performance 992,750,000 1,045,000,000 Exceeded by 5.3%

14 Maximize Product Performance 992,750,000 1,045,000,000 Exceeded by 5.3%

15 Minimize Price 4,435,200 4,224,000 Exceeded by 4.8%

16 Maximize Past Performance 940,500,000 990,000,000 Exceeded by 5.3%


23 Maximize Service/Capacity Planning Performance 940,500,000 990,000,000 Exceeded by 5.3%

25 Maximize Lead-Time Performance 940,500,000 990,000,000 Exceeded by 5.3%

28 Maximize Tooling Development Time Performance 940,500,000 814,000,000 MIssed by 13.5%

S1 S2 S6 S8 S9

0 22,000,000 88,000,000 0 0



1 Minimum number of suppliers set to two suppliers2 Maximum number of suppliers set to three suppliers

S4 S10

0 25,000


Order Allocations1

1 Maximum number of suppliers set to two suppliers

Table 6.12 Preemptive GP Achievements for Mature Phase Product 5 with respect

to Target Values

Table 6.11 Preemptive GP Procurement Plan for Mature Phase Product 5


Decline Phase Product 6

150

required the selection of supplier 10 for decline product 6. The only constraint impacting

supplier 10 would be maximum business volume levels which was easily achieved, so by

default supplier 10 was selected to supply all 25,000 units. Luckily, supplier 10’s

performance rankings are equal to or greater than those of supplier 4, expect for delivery

or lead-time performance.


range from 4.8% to 5.3%, with 6 out of 7 goal priorities achieving the latter. Delivery

performance missed the target value by 11.4% and lead-time performance missed by 6.4%.

As noted previously, these misses were due to selecting supplier 10, which had a score of

8, which was lower than supplier 4’s performance for both delivery and lead-time

performance.


Table 6.15 displays the procurement plan for the decline phase (product 7). Supplier 7 was

chosen to supply the entire yearly demand of 15,000 units. Table 6.16 presents the

preemptive goal achievements for the supplier selection criteria with respect to the target

values. Product 7 exceeds the target values in 8 out of 9 goal priorities. The target

Goal

Priority


Target Values

(95% or 105%

of Ideals)

Goal


26 Maximize Product Safety Performance 237,500 250,000 Exceeded by 5.3%

27 Maximize Quality Performance 225,625 237,500 Exceeded by 5.3%


30 Maximize Past Performance 190,000 200,000 Exceeded by 5.3%



33 Maximize Delivery Performance 225,625 200,000 Missed by 11.4%

34 Maximize Lead-Time Performance 213,750 200,000 Missed by 6.4%


Table 6.14 Preemptive GP Achievements for Decline Phase Product 6 with respect

to Target Values

151

achievements range from 4.8%

to 5.3%, with 7 out of 8 goal

priorities achieving the latter.

Maximizing service/capacity

planning performance missed the

target value by 0.3%. This

missed target was due to

selecting supplier 7, which had a

score of 9, which was slightly lower than supplier 1’s service/capacity planning rating of

9.5.

6.1.10 Optimal Order Allocations to Suppliers (All Products)

The previous sections have reviewed the detailed order allocations and goal achievements

versus the target values by PLC phase and product. Table 6.17 presents the over-all

procurement plan for all products by the preemptive GP model. The procurement plan

clearly demonstrates the key characteristic of the preemptive goal methodology and the

importance of the Product Life Cycle Model for supplier selection developed in this thesis.

Goal priority 1, which is to maximize quality performance for growth PLC product 1,

requires the selection of suppliers 4 or 5 given their high quality performance ratings.

Despite the high quality rating, supplier 5 is not selected due to the maximum business

level constraint combined with the constraint which limits the maximum number of

S1 S7

0 15,000


Order Allocations1


Goal

Priority


Target Values

(95% or 105%

of Ideals)

Goal


26 Maximize Product Safety Performance 142,500 150,000 Exceeded by 5.3%

27 Maximize Quality Performance 135,375 142,500 Exceeded by 5.3%


30 Maximize Past Performance 128,250 135,000 Exceeded by 5.3%



33 Maximize Delivery Performance 135,375 135,000 Missed by 0.3%

34 Maximize Lead-Time Performance 128,250 135,000 Exceeded by 5.3%




Table 6.16 Preemptive GP Achievements for Decline Phase Product 7 with respect

to Target Values

152

suppliers for a growth product to two. These combined constraints effectively prohibit the

selection on supplier 5 and by default necessitate the selection of supplier 3, which has the

lowest quality rating among the three potential suppliers. Ironically, the maximum

business level constraint limits the number of units supplier 4 can provide to 1.6MM. This

initial supplier selections for growth product 3 have a direct impact on the supplier

selection decision for both introduction product 2 and decline product 6. Supplier 4’s

maximum business level has been consumed by achieving the first goal priority at the

detriment to lower goal priorities clearly demonstrating one of the main characteristics of

the preemptive GP model in a real-world application. The next section will discuss the

results of the non-preemptive GP model.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

1 10,000,000 0 - - - - - - - -

2 - - 50,000 0 - - - - - -

Growth23 - - 1,400,000 1,600,000 0 - - - - -

4 0 - - - - 200,600,000 139,400,000 - - -

5 0 22,000,000 - - - 88,000,000 - 0 0 -

6 - - - 0 - - - - - 25,000

7 0 - - - - - 15,000 - -

4 Maximum number of suppliers set to three suppliers

Introduction1


Product Life

Cycle StageProducts

Suppliers

Mature3,4

Decline2

1 Maximum number of suppliers set at one supplier2

Maximum number of suppliers set to two suppliers3

Minimum number of suppliers set to two suppliers

Table 6.17 Preemptive GP Procurement Plan (All Products)

153

6.2 Non-Preemptive GP Model for the Case Study


regarding major non-preemptive

goal programming attributes for

the case study. The number of

variables in the non-preemptive

GP model are 38 integer

variables, and 124 deviational

variables. There are 62 goal

constraints and 115 real

constraints. The model was

solved using LINGO Version

17.0.74, which is an optimization

modeling software for linear, nonlinear, and integer programming. The model took 2.34

seconds to solve, running on a Dell Inspiron Model 7773 Laptop Computer, using an Intel

Core i7-8550U CPU at 1.80 GHz, and Windows 10 operating system.

6.2.1 Non-Preemptive GP Model Weights and Solution

Non-preemptive GP model requires the DM to estimate the relative weights in achieving

the conflicting goals. The weights can be determined using Rating, Borda count or AHP

methods. Like the preemptive GP model, the target levels were set at 95% of the ideal

values for maximization and 105% of ideal values for minimization of the criteria. The

non-preemptive GP model reduces to a single objective model. Because of the weights,

the goal constraints must be normalized or scaled to facilitate an equal comparison among

the goal objectives. The weights for the goal constraints were determined in Chapter 5

using the decision maker’s input. They are reproduced in Table 6.19. The optimal solution

to the non-preemptive GP model will be discussed next.








Time to Solve (seconds) 2.34Table 6.18 Non-preemptive GP Model

Characteristics for the Case Study

154



procurement plan for product 1,

in the introduction phase.

Supplier 2 is chosen to supply the

entire yearly demand. A

constraint limiting the maximum

number of suppliers for the

introduction phase to one does not

allow splitting the demand between two suppliers. Table 6.21 presents the non-preemptive

goal achievements for the supplier selection criteria with respect to the target values. The

top 2 priorities, product safety and quality, achieve and surpass the target values by 5.3%.

It should be noted both suppliers 1 and 2 have equal performance for safety and quality.

Delivery performance misses the target by 0.3% and is the only performance criterion in

which supplier 1 outperforms supplier 2. Supplier 1 and 2 also have equal performance for

product performance. Product performance exceeds the target values by 5.3%. The

selection of supplier 2 allows price, advanced technology, lead-time, service/capacity

S1 S2

0 10,000,000



Order Allocations1

Table 6.19 Non-preemptive GP Weights


for Introduction Phase Product 1

155

planning and tooling development time to exceed their target performance by either 5.3 or

4.8%. The non-preemptive GP solution for introduction phase product 1 provides a more

balanced solution with respect to goal achievement, with only delivery missing the target

by just 0.3%.




in the introduction phase.

Supplier 4 is chosen to supply the

entire yearly demand. The

constraint limiting the maximum

number of suppliers for the

introduction phase to one does not

allow splitting the demand

between two suppliers. Table 6.23 presents the non-preemptive goal achievements for the

supplier selection criteria with respect to the target values. Except for price, all the supplier

performance criteria exceed the target values. Price misses the target by 9.5%, while the

S3 S4

0 50,000



Order Allocations1

Goal

Weights Selection Criteria

Target Values

(95% or 105%

of Ideals)

Goal


8.7307% Maximize Product Safety Performance 95,000,000 100,000,000 Exceeded by 5.3%

5.0358% Maximize Quality Performance 90,250,000 95,000,000 Exceeded by 5.3%

4.5621% Maximize Delivery Performance 90,250,000 90,000,000 Missed by 0.3%

4.1544% Maximize Product Performance 90,250,000 95,000,000 Exceeded by 5.3%

2.0428% Minimize Price 472,500 450,000 Exceeded by 4.8%

1.7175% Maximize Advanced Technology Rating 76,000,000 80,000,000 Exceeded by 5.3%

1.7116% Maximize Lead-Time Performance 85,500,000 90,000,000 Exceeded by 5.3%

1.6986% Maximize Service/Capacity Planning Performance 85,500,000 90,000,000 Exceeded by 5.3%

1.4194% Maximize Tooling Development Time Performance 85,500,000 90,000,000 Exceeded by 5.3%

Table 6.21 Non-preemptive GP Achievements for Introduction Phase Product 1

with respect to Target Values



156

remaining criteria exceed the target by either 4.8 or 5.3%. This is an example of the non-

preemptive GP model generating an optimal solution based on relative goal weights.


Table 6.24 displays the procurement plan for product 3, in the growth phase. Suppliers 3

and 5 are chosen to supply the yearly demand. A constraint limiting the maximum number

of suppliers for the growth product to two allows for splitting the demand between two

suppliers. Table 6.25 presents the non-preemptive goal achievements for the supplier

selection criteria with respect to target values. The top two highest weight criteria for

product 3 are quality and safety, respectively. Even though product 3 has the highest

weighted criteria in quality and safety, both missed their target values by 3.6%. Several

factors necessitated the selection of suppliers 3 and 5. First, the maximum business volume

constraint of 20% of supplier 5’s total sales revenue limited the total units supplied by

supplier 5 to 723,996. The limit of 20% of supplier 5’s total sales revenue was

Goal


Target Values

(95% or 105%

of Ideals)

Goal


8.7307% Maximize Product Safety Performance 427,500 450,000 Exceeded by 5.3%

5.0358% Maximize Quality Performance 427,500 450,000 Exceeded by 5.3%

4.5621% Maximize Delivery Performance 451,250 475,000 Exceeded by 5.3%

4.1544% Maximize Product Performance 427,500 450,000 Exceeded by 5.3%

2.0428% Minimize Price 91,350 100,000 Missed by 9.5%

1.7175% Maximize Advanced Technology Rating 380,000 400,000 Exceeded by 5.3%

1.7116% Maximize Lead-Time Performance 427,500 450,000 Exceeded by 5.3%

1.6986% Maximize Service/Capacity Planning Performance 380,000 400,000 Exceeded by 4.8%

1.4194% Maximize Tooling Development Time Performance 380,000 400,000 Exceeded by 5.3%

S3 S4 S5

2,276,004 0 723,996


Order Allocations1


Table 6.23 Non-preemptive GP Achievements for Introduction Phase Product 2

with respect to Target Values

Table 6.24 Non-preemptive GP Procurement Plan for Growth Phase Product 3

157

implemented by the global sourcing manager in an attempt to mitigate potential risk in

dealing with a new supplier. Supplier 5’s performance was equal to or better than suppliers

3 and 4 in all the criteria, except price; therefore, the optimal solution maximized the

business volume of supplier 5. Price exceeded the target by 1.3%. Delivery, lead-time,

service and capacity planning, product performance and tooling development time

performance exceeded the target by 5.3%.


Table 6.26 displays the procurement plan for product 4, in the mature phase. Suppliers 6

and 7 are chosen to supply the yearly demand. Table 6.27 presents the non-preemptive


Several constraints impacted the selection of suppliers. The mature phase sets the

S1 S6 S7





Selection Criteria

Target Values

(95% or 105%

of Ideals)

Goal


Maximize Delivery Performance 27,075,000 28,500,000 Exceeded by 5.3%

Maximize Lead-Time Performance 25,650,000 27,000,000 Exceeded by 5.3%

Minimize Price 27,342,000 26,742,276 Exceeded by 1.3%

Maximize Tooling Development Time Performance 22,800,000 24,000,000 Exceeded by 5.3%

Maximize Service/Capacity Planning Performance 22,800,000 24,000,000 Exceeded by 5.3%

Maximize Product Performance 25,650,000 27,000,000 Exceeded by 5.3%

Maximize Product Safety Performance 25,650,000 24,723,996 Missed by 3.6%

Maximize Quality Performance 25,650,000 24,723,996 Missed by 3.6%

Table 6.25 Non-preemptive GP Achievements for Growth Phase Product 3 with


Table 6.26 Non-preemptive GP Procurement Plan for Mature Phase Product 4

158

minimum number of required suppliers to two and the maximum number of suppliers to

three. This constraint supports the DM’s objective of creating price competition in order

to improve the gross profit margin for mature phase products.

Product 4 exceeds or achieves the target values for 8 out of 9 goal priorities. The target

achievements range from 0.8% to 5.3%, with 6 out of 8 goal priorities achieving the latter.

Product 4, which has a high expected yearly demand of 340MM units, achieves the price

target supporting the decision maker’s goal of improving product gross profit margin. The

only goal achievement which missed the target value was tooling development time, which

missed the target value by 4.3%. This miss was due to the selection of supplier 6, which

has a tooling score of 7, to supply nearly 60% of the total expected demand as compared

to supplier 7, which has a tooling score of 9.



and 6 are chosen to supply the yearly demand of 110MM units. Table 6.29 presents the





Table 6.27 Non-preemptive GP Achievements for Mature Phase Product 4 with


159


range from 0.8% to 5.3%, with 6 out of 7 goal priorities achieving the latter. The price

goal achieves the target by selecting supplier 6, which has the overall lowest price, and

supplier 2 has the second lowest price. These order allocations support the DM’s goals of

improving gross profit margin for mature PLC phase products. Tooling development time

missed the target value by 10.8%. This is due to selecting supplier 6, which has a tooling

score of 7, to supply 68% of the total expected demand as compared to suppliers 2 and 9,

which have tooling scores of 9.


Table 6.30 displays the procurement plan for product 6, in the decline phase. Supplier 10

is chosen to supply the entire yearly demand of 25,000 units. Table 6.31 presents the non-

preemptive goal achievements for the supplier selection criteria with respect to the target

values.

S1 S2 S6 S8 S9

0 34,490,860 75,509,140 0 0




Goal


Target Values

(95% or 105%

of Ideals)

Goal


8.0555% Maximize Product Safety Performance 1,045,000,000 1,100,000,000 Exceeded by 5.3%

5.3315% Maximize Quality Performance 992,750,000 1,045,000,000 Exceeded by 5.3%

2.4386% Maximize Product Performance 992,750,000 1,045,000,000 Exceeded by 5.3%

2.4157% Minimize Price 4,435,200 4,436,345 Achieved Target

2.0883% Maximize Past Performance 940,500,000 990,000,000 Exceeded by 5.3%

1.7816% Maximize Delivery Performance 982,300,000 990,000,000 Exceeded by 0.8%

1.5485% Maximize Service/Capacity Planning Performance 940,500,000 990,000,000 Exceeded by 5.3%

1.3912% Maximize Lead-Time Performance 940,500,000 990,000,000 Exceeded by 5.3%

0.4551% Maximize Tooling Development Time Performance 940,500,000 838,981,720 Missed by 10.8%

Table 6.29 Non-preemptive GP Achievements for Mature Phase Product 5 with


Table 6.28 Non-preemptive GP Procurement Plan for Mature Phase Product 5

160

Product 6 exceeds the target

values for 7 out of 9 goal

priorities. The target

achievements range from 4.8%

to 5.3%, with 6 of the 7 targets

exceeded by 5.3%. The

selection of supplier 5 positively

impacted the product safety,

quality, product performance, tooling development time and price. Only delivery and lead-

time were negatively impacted given supplier 4’s better performance in these performance

criteria. This is an example the non-preemptive GP utilizing the goal weights to find an

optimal solution balancing goal achievements.




in the decline phase. Supplier 7

is chosen to supply the entire



preemptive goal achievements

for the supplier selection criteria

S1 S7

0 15,000


Order Allocations1


S4 S10

0 25,000


Order Allocations1


Goal


Target Values

(95% or 105%

of Ideals)

Goal


1.1292% Maximize Product Safety Performance 237,500 250,000 Exceeded by 5.3%

1.0915% Maximize Quality Performance 225,625 237,500 Exceeded by 5.3%

0.4306% Minimize Price 107,100 102,000 Exceeded by 4.8%

0.4037% Maximize Past Performance 190,000 200,000 Exceeded by 5.3%

0.3737% Maximize Service/Capacity Planning Performance 190,000 200,000 Exceeded by 5.3%

0.3127% Maximize Product Performance 225,625 237,500 Exceeded by 5.3%

0.3050% Maximize Delivery Performance 225,625 200,000 Missed by 11.4%

0.2256% Maximize Lead-Time Performance 213,750 200,000 Missed by 6.4%

0.0789% Maximize Tooling Development Time Performance 213,750 225,000 Exceeded by 5.3%

Table 6.31 Non-preemptive GP Achievements for Decline Phase Product 6 with



for Mature Phase Product 6


for Mature Phase Product 7

161

with respect to the target values. Product 7 exceeds the target values for 8 out of 9 goal

priorities. The target achievements range from 4.8% to 5.3%, with 7 out of 8 goal priorities

achieving the latter. Maximizing service/capacity planning performance missed the target

value by 0.3%. Missing this target was due to selecting supplier 7, which had a score of 9,

which was slightly lower than supplier 1’s service/capacity planning rating of 9.5.




procurement plan for all products by the non-preemptive GP model. The procurement

plan demonstrates the key characteristic of the non-preemptive goal methodology, which

is the utilization of goal weights to generate the overall optimal procurement plan. For

example, supplier 4 is selected for only one product, given lesser performance in a

number of performance criteria. This more balanced goal achievements, based on the

goal weights, leads to less drastic target misses. 50 of 62 selection criteria (81%) exceed

the target values and 2 selection criteria (3%) achieve the target values. Only 10 of the

62 goal constraints (16%) miss the target values, the median miss is 4.5% and the range is

0.3 to 11.4%.

Table 6.33 Non-preemptive GP Achievements for Decline Phase Product 7 with


162

Table 6.34 Non-preemptive GP Procurement Plan (All Products)

6.3 Tchebycheff’s Min-Max GP Model for the Case Study


regarding major Tchebycheff’s min-

max goal programming attributes for


variables in the Tchebycheff GP

model are 38 integer variables, 1 real

variable and 124 deviational variables.

There are 62 goal constraints and 177

real constraints. The model was

solved using LINGO Version 17.0.74,

which is an optimization modeling

software for linear, nonlinear and

integer programming. The model took 1.39 seconds to solve, running on a Dell Inspiron

Model 7773 Laptop Computer, using an Intel Core i7-8550U CPU at 1.80 GHz, and

Windows 10 operating system.

6.3.1 Tchebycheff’s Min-Max Goals/Targets and Solution

The Tchebycheff min-max GP model only requires the DM to specify the goals/targets for

each objective. The decision maker’s preferences on the goals are not required given that

the model minimizes the maximum deviation from the stated goals/targets. Like the non-

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

1 0 10,000,000 - - - - - - - -

2 - - 0 50,000 - - - - - -

Growth23 - - 2,276,004 0 723,996 - - - - -

4 0 - - - - 200,303,000 139,697,000 - - -

5 0 34,490,860 - - - 75,509,140 - 0 0 -

6 - - - 0 - - - - - 25,000

7 0 - - - - - 15,000 - -


Introduction1


Product Life

Cycle StageProducts

Suppliers

Mature3,4

Decline2











Time to Solve (seconds) 1.39

Table 6.35 Tchebycheff’s Min-Max GP

Model Characteristics for the Case Study

163

preemptive GP model, the target levels are set at 95% of the ideal values for maximization

and 105% of ideal values for minimization of the criteria. The Tchebycheff GP model

reduces to a single objective optimization model. The goal constraints must be normalized

or scaled to facilitate an equal comparison among the goal objectives. The optimal solution

to the Tchebycheff min-max GP model will be discussed next.



procurement plan for product

1, in the introduction phase.

Supplier 2 is chosen to supply

the entire yearly demand. A

constraint limiting the

maximum number of suppliers

for the introduction phase to

one does not allow splitting the

demand between two suppliers. Table 6.37 presents the Tchebycheff goal achievements

for the supplier selection with respect to the target values. Supplier 2’s performance is

equal to or better than supplier 1’s performance in 8 of the 9 selection criteria. Supplier 1

only exceeds supplier 2 in the delivery performance rating. Therefore, the goal

achievements for product 1 exceed the target values in 7 of the 8 performance criteria (4.8%

S1 S2

0 10,000,000



Order Allocations1

Selection Criteria

Target Values

(95% or 105%

of Ideals)

Goal


Maximize Delivery Performance 90,250,000 90,000,000 Missed by 0.3%


Minimize Price 472,500 450,000 Exceeded by 4.8%

Maximize Tooling Development Time Performance 85,500,000 90,000,000 Exceeded by 5.3%


Maximize Product Performance 90,250,000 95,000,000 Exceeded by 5.3%

Maximize Product Safety Performance 95,000,000 100,000,000 Exceeded by 5.3%

Maximize Advanced Technology Rating 76,000,000 80,000,000 Exceeded by 5.3%

Maximize Quality Performance 90,250,000 95,000,000 Exceeded by 5.3%

Table 6.37 Tchebycheff’s GP Achievements for Introduction Phase Product 1 with


Table 6.36 Tchebycheff’s Min-Max GP Procurement

Plan for Introduction Phase Product 1

164

- 5.3%). Only the delivery performance goal is missed by 0.3%. Given the restriction of

selecting only 1 supplier, this solution provides overall better performance than the

alternative of selecting supplier 1 to fulfill the yearly demand.



procurement plan for product

2, in the introduction phase.


the entire yearly demand. The

constraint limiting the number

of suppliers for the

introduction phase to one does

not allow splitting the demand between two suppliers. Table 6.39 presents the Tchebycheff


Supplier 3’s performance is equal to supplier 4’s performance in 6 of the 9 performance

criteria resulting in the target values being exceeded by 5.3%. Supplier 3’s price

performance is also better than that of supplier 4, causing the price performance to surpass

the target by 4.8%. Safety and quality performance miss the target values by 6.4%, since

supplier 3’s performance is below supplier 4 in both of these criteria.

S3 S4

50,000 0



Order Allocations1

Selection Criteria

Target Values

(95% or 105%

of Ideals)

Goal


Maximize Delivery Performance 451,250 475,000 Exceeded by 5.3%

Maximize Lead-Time Performance 427,500 450,000 Exceeded by 5.3%

Minimize Price 91,350 87,000 Exceeded by 4.8%

Maximize Tooling Development Time Performance 380,000 400,000 Exceeded by 5.3%

Maximize Service/Capacity Planning Performance 380,000 400,000 Exceeded by 5.3%

Maximize Product Performance 427,500 450,000 Exceeded by 5.3%

Maximize Product Safety Performance 427,500 400,000 Missed by 6.4%

Maximize Advanced Technology Rating 380,000 400,000 Exceeded by 5.3%

Maximize Quality Performance 427,500 400,000 Missed by 6.4%

Table 6.39 Tchebycheff’s GP Achievements for Introduction Phase Product 2 with


Table 6.38 Tchebycheff’s Min-Max GP Procurement

Plan for Introduction Phase Product 2

165




of suppliers for the growth phase product to two allows for splitting the demand between

two suppliers. Table 6.41 presents the Tchebycheff goal achievements for the supplier

selection with respect to target values. Suppliers 3, 4 and 5 have equal performance ratings

in 5 of the 8 selection criteria. This equal performance rating results in the target values

being exceeded by 5.3% for these criteria. Overall price performance surpasses the target

by 4.0%. The optimal solution allocates 92% of the yearly demand to supplier 3. This

results in both product safety and quality performance missing the target values by 5.6%,

given supplier 3 has the lowest performance rating in both of these criteria.

Table 6.41 Tchebycheff’s GP Achievements for Growth Phase Product 3 with


S3 S4 S5

2,777,995 0 222,005


Order Allocations1


Table 6.40 Tchebycheff’s Min-Max GP Procurement Plan for Growth Phase

Product 3

166



and 7 are chosen to supply the yearly demand. Table 6.43 presents the Tchebycheff goal

achievements for the supplier selection criteria with respect to the target values. Several

constraints impacted the selection of suppliers. The mature phase sets the minimum

number of required suppliers to two and the maximum number of suppliers to three. This

constraint supports the DM’s objective of creating price competition in order to improve

the gross margin for mature phase products.

The procurement plan for product 4 exceeds the target values for 8 of the 9 goal priorities

(Table 6.43). The excess target achievements range from 0.8% to 5.3%, with 6 of the 8

goal priorities exceeding by 5.3%. Supplier 6 is selected to supply 69% of the yearly

demand and has the highest criteria performance rankings in 8 of the 9 criteria, supporting

the target achievements. Product 4, which has a higher yearly demand of 340MM units,

achieves the price target supporting the decision maker’s goal of improving product gross

profit margin. It is important to note that these Tchebycheff min-max GP goal

achievements are accomplished without the use of a DM’s priority ranking or goal weights.

The only goal achievement, which misses the target value is tooling development time,

which misses the target by 6.9%. This miss is due to the selection of supplier 6, which has

a tooling score of 7, as compared to supplier 7, which has a tooling performance ranking

of 9.

S1 S6 S7





Table 6.42 Tchebycheff’s Min-Max GP Procurement Plan for Mature Phase

Product 4

167

Table 6.43 Tchebycheff’s GP Achievements for Mature Phase Product 4 with




and 6 are chosen to supply the yearly demand of 110MM units. Table 6.45 presents the





The procurement plan for product 5 exceeds the target values in 7 out of 9 goal priorities

(Table 6.45). The excess target achievements range from 0.8% to 5.3%, with 6 out of 7

goal priorities exceeding by 5.3%. Supplier 2 is selected to supply 47% of the yearly

demand. The selection of supplier 2 has a negative impact on price performance, which

recorded a target miss of 7.0%. This 7.0% target miss is the maximum miss for all product

Table 6.44 Tchebycheff’s Min-Max GP Procurement Plan for Mature Phase

Product 5

168

life cycle phases and products. Tooling development time performance also misses the

target by 6.9%, due supplier 6’s performance rating. While supplier 6 has the best price

performance rating, the poor tooling development time contributed to the 6.9% target miss.

It should be noted that the price performance does not meet the DM’s objectives of

improving price performance for mature products. This result is not unexpected given

Tchebycheff GP’s min-max criterion, which does not use any decision maker’s input and

simply minimizes the maximum deviation from the target values.




in the decline phase. Both

supplier 4 and 10 are selected to

supply 45% and 55% of the

yearly demand respectively.


Tchebycheff min-max goal

achievements for the supplier selection criteria with respect to the target values.

S4 S10

11,198 13,802


Order Allocations1


Selection Criteria

Target Values

(95% or 105%

of Ideals)

Goal


Maximize Delivery Performance 982,300,000 990,000,000 Exceeded by 0.8%


Minimize Price 4,435,200 4,744,782 Missed by 7.0%

Maximize Tooling Development Time Performance 940,500,000 875,268,460 Missed by 6.9%


Maximize Product Performance 992,750,000 1,045,000,000 Exceeded by 5.3%

Maximize Product Safety Performance 1,045,000,000 1,100,000,000 Exceeded by 5.3%

Maximize Past Performance 940,500,000 990,000,000 Exceeded by 5.3%

Maximize Quality Performance 992,750,000 1,045,000,000 Exceeded by 5.3%

Table 6.45 Tchebycheff’s GP Achievements for Mature Phase Product 5 with



Procurement Plan for Decline Phase Product 6

169

The procurement plan for product 6 exceeds or achieves the target values in 6 of 9 goal

priorities (Table 6.47). The excess target achievements range from 0% to 5.3%. Splitting

the demand between two suppliers negatively impacts all of the performance criteria,

excluding the service and capacity planning and past performance criteria. Supplier

performance is equal for both of these criteria. The performance target misses are lead-

time by 1.2%, delivery by 3.9% and price by 6.9%. The price target miss of 6.9% nearly

matches the maximum overall miss of 7.0%, which is related to mature phase product 5’s

price performance (see Table 6.45). The optimal solution divides the yearly demand

between the two suppliers, while achieving the overall minimization of the maximum

deviation from the target values.

Selection Criteria

Target Values

(95% or 105%

of Ideals)

Goal


Maximize Delivery Performance 225,625 216,797 Missed by 3.9%

Maximize Lead-Time Performance 213,750 211,198 Missed by 1.2%

Minimize Price 107,100 114,542 Missed by 6.9%

Maximize Tooling Development Time Performance 213,750 213,802 Achieved Target

Maximize Service/Capacity Planning Performance 190,000 200,000 Exceeded by 5.3%

Maximize Product Performance 225,625 231,901 Exceeded by 2.8%

Maximize Product Safety Performance 237,500 238,802 Exceeded by 0.5%

Maximize Past Performance 190,000 200,000 Exceeded by 5.3%

Maximize Quality Performance 225,625 231,901 Exceeded by 2.8%

Table 6.47 Tchebycheff’s GP Achievements for Decline Phase Product 6 with


170








Tchebycheff min-max goal

achievements for the supplier

selection criteria with respect to the target values. The procurement plan for product 7

exceeds the target values for 8 out of 9 goal priorities. The excess target achievements

range from 4.8% to 5.3%, with 7 out of 8 goal priorities exceeding by 5.3%. Maximizing

service/capacity planning performance misses the target value by 0.3%. This is due to

selecting supplier 7, which has a score of 9, slightly lower than supplier 1’s service/capacity

planning rating of 9.5.

S1 S7

0 15,000


Order Allocations1



Procurement Plan for Decline Phase Product 7

Table 6.49 Tchebycheff’s GP Achievements for Decline Phase Product 7 with


171




procurement plan for all products by the Tchebycheff’s min-max GP model. The optimal

procurement plan demonstrates the key characteristic of the Tchebycheff min-max goal

methodology, which is the minimization of the maximum deviation from the target

values.

The maximum deviation from the targets for the Tchebycheff min-max GP model is a

7.0%, which is related to mature product 5’s price performance. This 7.0% target miss is

less than the maximum target misses from both the preemptive and non-preemptive

models, which are 29.8% and 11.4% respectively. While the target miss of 7.0% does

not meet the DM’s objectives by of improving gross margin for mature products, this

result clearly illustrates the objective of minimizing the maximum deviation from the

target values without any DM’s input.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

1 0 10,000,000 - - - - - - - -

2 - - 50,000 0 - - - - - -

Growth23 - - 2,777,995 0 222,005 - - - - -

4 0 - - - - 237,623,700 102,376,300 - - -

5 0 52,634,230 - - - 57,365,770 - 0 0 -

6 - - - 11,198 - - - - - 13,802

7 0 - - - - - 15,000 - -


Introduction1


Product Life

Cycle StageProducts

Suppliers

Mature3,4

Decline2




Table 6.50 Tchebycheff’s Min-Max GP Procurement Plan (All Products)

172

6.4 Fuzzy Min-Max GP Model for the Case Study


regarding major fuzzy min-max

goal programming attributes for


variables in the fuzzy GP model

are 38 integer variables, 1 real

variable and 124 deviational

variables. There are 62 goal

constraints and 177 real

constraints. The model was

solved using LINGO Version

17.0.74, which is an optimization

modeling software for linear, nonlinear and integer programming. The model took 1.82

seconds to solve, running on a Dell Inspiron Model 7773 Laptop Computer, using an Intel

Core i7-8550U CPU at 1.80 GHz, and Windows 10 operating system.

6.4.1 Fuzzy Min-Max Ideals and Solution

The fuzzy min-max GP model does not require any inputs from the DM, since the ideal

values are used as the targets for each objective. The decision maker’s preferences on the

objectives are not required given that the model minimizes the maximum deviation from

the ideal values. Recall that the ideal values are the maximum/minimum values for each

objective, depending on whether the objective is to maximize/minimize. The fuzzy GP

model reduces to a single objective optimization model. The goal constraints must be

normalized or scaled to facilitate an equal comparison among the goal objectives. Results

from the fuzzy GP model provide a benchmark to compare the results based on the ideal

solution versus the DM’s target values, which are used in preemptive, non-preemptive and

Tchebycheff’s min-max models. The optimal solution to the fuzzy min-max GP model will

be discussed next.








Time to Solve (seconds) 1.82Table 6.51 Fuzzy Min-Max GP Model

Characteristics for the Case Study

173



procurement plan for product 1, in

the introduction phase. Supplier 2


yearly demand. A constraint

limiting the maximum number of

suppliers for the introduction

phase to one does not allow

splitting the demand between two suppliers. Table 6.53 presents the fuzzy GP criteria

achievements with respect to the ideal values. Supplier 2’s performance is equal to or

better than supplier 1’s performance in 8 of the 9 selection criteria. Supplier 1 only exceeds

supplier 2 in the delivery performance rating. Therefore, the goal achievements for product

1 achieve the ideal values in 7 of the 8 performance criteria. Only the ideal delivery target

is missed by 5.3%. Given the restriction of selecting only 1 supplier, this solution provides

overall better performance than the alternative of selecting supplier 1 to fulfill the yearly

demand.

S1 S2

0 10,000,000



Order Allocations1

Table 6.52 Fuzzy Min-Max GP Procurement Plan


Table 6.53 Fuzzy GP Achievements for Introduction Phase Product 1 with respect

to Ideal Values

174



procurement plan in the

introduction phase for product

2. Supplier 3 was chosen to

supply the entire yearly

demand. Table 6.55 presents

the fuzzy GP criteria

achievements with respect to

the ideal values. Supplier 3 is chosen to supply the entire yearly demand. The constraint

limiting the maximum number of suppliers for the introduction phase to one does not allow

splitting the demand between two suppliers. Except for safety and quality performance,

all the supplier performance criteria achieve the ideal values. Both safety and quality miss

the ideal values by 11.1%. This illustrates the logic of the fuzzy GP model, which

determines an optimal solution based on minimizing the maximum deviation from the ideal

values.

S3 S4

50,000 0



Order Allocations1

Selection Criteria Ideal Values Criteria Values

Criteria

Achievements

Maximize Delivery Performance 475,000 475,000 Achieved Ideal

Maximize Lead-Time Performance 450,000 450,000 Achieved Ideal

Minimize Price 87,000 87,000 Achieved Ideal

Maximize Tooling Development Time Performance 400,000 400,000 Achieved Ideal

Maximize Service/Capacity Planning Performance 400,000 400,000 Achieved Ideal

Maximize Product Performance 450,000 450,000 Achieved Ideal


Maximize Advanced Technology Rating 400,000 400,000 Achieved Ideal


Table 6.54 Fuzzy GP Procurement Plan for


Table 6.55 Fuzzy GP Achievements for Introduction Phase Product 2 with respect

to Ideal Values

175




of suppliers for the growth product to two allows for the splitting of the demand between

two suppliers. Table 6.57 presents the fuzzy GP criteria achievements with respect to the

ideal values. The procurement plan for product 3 achieves the ideal values for 5 out of 8

supplier criteria. Price, safety and quality miss the ideal values by 2.7%, 8.4% and 8.4%

respectively. The safety and quality performance of supplier 3 is below those of both

supplier 4 and 5, which explains the failure to achieve the ideal values. While supplier 3

has the best price, the choice of supplier 5 to supply nearly 32% of the yearly demand

degrades the price performance. It should be noted that the DM ranked growth phase

quality and safety (see Table 6.2) as the highest priorities for the preemptive GP model.

The fuzzy GP does not consider DM’s inputs and generates the optimal solution by simply

minimizing the maximum deviation from the ideal values!

S3 S4 S5

2,276,004 0 723,996


Order Allocations1



Criteria

Achievements

Maximize Delivery Performance 28,500,000 28,500,000 Achieved Ideal

Maximize Lead-Time Performance 27,000,000 27,000,000 Achieved Ideal


Maximize Tooling Development Time Performance 24,000,000 24,000,000 Achieved Ideal

Maximize Service/Capacity Planning Performance 24,000,000 24,000,000 Achieved Ideal

Maximize Product Performance 27,000,000 27,000,000 Achieved Ideal

Maximize Product Safety Performance 27,000,000 24,723,996 Missed by 8.4%

Maximize Quality Performance 27,000,000 24,723,996 Missed by 8.4%

Table 6.56 Fuzzy GP Procurement Plan for Growth Phase Product 3

Table 6.57 Fuzzy GP Achievements for Growth Phase Product 3 with respect to

Ideal Values

176



and 7 are chosen to supply the yearly demand. Table 6.59 presents the fuzzy GP criteria

achievements with respect to the ideal values. Several constraints impacted the selection

of suppliers. The mature phase sets the minimum number of required suppliers to two and

the maximum number of suppliers to three.

The procurement plan for product 4 achieves the target values for 6 of the 9 supplier criteria

(Table 6.59). Delivery, price and tooling development time miss the ideal values by 4.3%,

2.2% and 11.8% respectively. The delivery performance of both suppliers 6 and 7 are

below that of supplier 1 leading to the 4.3% miss. The price miss of 2.2% is due to the

allocation of 29% of the yearly demand to supplier 7. The ideal solution for minimizing

the price objective allocates 20% to either suppliers 1 or 7, since they both have the same

price performance.

S1 S6 S7






Criteria

Achievements

Maximize Delivery Performance 3,196,000,000 3,060,000,090 Missed by 4.3%

Maximize Lead-Time Performance 3,060,000,000 3,060,000,090 Achieved Ideal


Maximize Tooling Development Time Performance 2,924,000,000 2,579,737,090 Missed by 11.8%

Maximize Service/Capacity Planning Performance 3,060,000,000 3,060,000,090 Achieved Ideal

Maximize Product Performance 3,230,000,000 3,230,000,095 Achieved Ideal

Maximize Product Safety Performance 3,400,000,000 3,400,000,100 Achieved Ideal

Maximize Past Performance 3,060,000,000 3,060,000,090 Achieved Ideal

Maximize Quality Performance 3,230,000,000 3,230,000,095 Achieved Ideal

Table 6.58 Fuzzy GP Procurement Plan for Mature Phase Product 4

Table 6.59 Fuzzy GP Achievements for Mature Phase Product 4 with respect to

Ideal Values

177

The highest miss of 11.8% for tooling development time is due to the allocation of nearly

70.5% of the yearly demand to supplier 6, which has a second best tooling development

time performance with supplier 7 having the best performance. This 11.8% miss is tied for

the maximum deviation from the ideal solutions in the fuzzy GP model.



and 6 were chosen to supply the expected yearly demand of 110MM units. Table 6.61

presents the fuzzy GP criteria achievements with respect to the ideal values. Product 5 also

requires a minimum of two suppliers and a maximum of three suppliers to enhance

competition among the suppliers, supporting improved gross profit margins for mature

phase products.

The procurement plan for product 5 achieves the ideal values for 6 of the 9 supplier

objectives (Table 6.61). Delivery, price and tooling development time miss the ideal values

by 4.3%, 11.8% and 11.8% respectively. The delivery performance of both suppliers 2 and

6 are below that of supplier 1, leading to the 4.3% miss. The price miss of 11.8%, which

matches the maximum deviation from the ideal solution, is due to the allocation of only

58.7% of the yearly demand to supplier 6. The ideal solution that minimizes prices

allocates 80% to supplier 6.

S1 S2 S6 S8 S9

0 51,390,730 58,609,270 0 0




Table 6.60 Fuzzy GP Procurement Plan for Mature Phase Product 5

178

Supplier 2 has the second best price performance, otherwise the price performance would

be even worse. The 11.8% tooling development time miss is due to the supplier 6’s

performance for this selection criterion. Suppliers 2 and 9 have the best performance for

tooling development time and the 11.8% miss again ties for the overall maximum deviation

from the ideal values for the fuzzy GP model.



procurement plan for product 6, in

the decline phase. Both supplier 4

and 10 are selected to supply

43.2% and 56.8% of the yearly

demand respectively. Table 6.63

presents the fuzzy GP criteria

achievements with respect to the

ideal values.

The procurement plan for product 6 achieves the ideal values for only 2 of the 9 supplier

criteria. The criteria misses range from a minimum of 2.3% to a maximum of 11.8%.

Splitting the demand between two suppliers negatively impacts all of the performance

criteria, excluding the service and capacity planning and past performance. Supplier

S4 S10

10,791 14,209


Order Allocations1



Criteria

Achievements

Maximize Delivery Performance 1,034,000,000 990,000,000 Missed by 4.3%

Maximize Lead-Time Performance 990,000,000 990,000,000 Achieved Ideal


Maximize Tooling Development Time Performance 990,000,000 872,781,460 Missed by 11.8%

Maximize Service/Capacity Planning Performance 990,000,000 990,000,000 Achieved Ideal

Maximize Product Performance 1,045,000,000 1,045,000,000 Achieved Ideal

Maximize Product Safety Performance 1,100,000,000 1,100,000,000 Achieved Ideal

Maximize Past Performance 990,000,000 990,000,000 Achieved Ideal

Maximize Quality Performance 1,045,000,000 1,045,000,000 Achieved Ideal


Ideal Values

Table 6.62 Fuzzy GP Procurement Plan for Decline

Phase Product 6

179

performance is equal for both of these criteria. The criteria misses are delivery by 9.0%,

lead-time by 6.3%, price by 11.8%, tooling development time by 4.8%, product

performance by 2.3%, product safety by 4.3% and quality by 2.3%. The price miss matches

the maximum overall fuzzy GP miss of 11.8%. The optimal solution divides the yearly

demand between the two suppliers, while achieving the overall minimization of the

maximum deviation from the ideal values.







Table 6.65 presents the fuzzy GP

criteria achievements with

respect to the ideal values.

S1 S7

0 15,000


Order Allocations1



Criteria

Achievements


Maximize Lead-Time Performance 225,000 210,791 Missed by 6.3%

Minimize Price 102,000 114,086 Missed by 11.8%

Maximize Tooling Development Time Performance 225,000 214,209 Missed by 4.8%


Maximize Product Performance 237,500 232,105 Missed by 2.3%


Maximize Past Performance 200,000 200,000 Achieved Ideal



Ideal Values

Table 6.64 Fuzzy GP Procurement Plan for Decline

Phase Product 7

180

The procurement plan for product 7 achieves the ideal values for 8 out of 9 supplier criteria.

Only the delivery performance misses the ideal value by 5.3%. Missing this target is due

to selecting supplier 7, which has a score of 9, slightly lower than supplier 1’s delivery

performance rating of 9.5.


The previous sections have reviewed the detailed order allocations and criteria

achievements with respect to the ideal values by PLC phase and product. Table 6.66

presents the over-all procurement plan for all products by the fuzzy min-max GP model.

The optimal procurement plan demonstrates the key characteristic of the fuzzy GP, which

is the minimization of the maximum deviation from the ideal values without any input from

the decision makers.

The optimal solution for the fuzzy model achieves 67.7% of the ideal values (see Figure

6.1). The ideal value misses range from 2.2% to 11.8%, with a median value of 5.8%. The

maximum deviations of 11.8% are related to mature product 4 tooling development time,

mature product 5 price, mature product 5 tooling development time and decline product 6

price performance. While these price ideal misses do not meet the DM’s objectives of

improving gross margin for mature products by improving price performance, they


Criteria

Achievements


Maximize Lead-Time Performance 135,000 135,000 Achieved Ideal

Minimize Price 16,800 16,800 Achieved Ideal

Maximize Tooling Development Time Performance 135,000 135,000 Achieved Ideal


Maximize Product Performance 142,500 142,500 Achieved Ideal

Maximize Product Safety Performance 150,000 150,000 Achieved Ideal

Maximize Past Performance 135,000 135,000 Achieved Ideal

Maximize Quality Performance 142,500 142,500 Achieved Ideal

Table 6.65 Fuzzy GP Achievements for Decline Phase Product 7 with respect to

Ideal Values

181

represent the optimal fuzzy GP logic. These results should not be a surprise given the fuzzy

GP’s objective of minimizing the maximum deviation from the ideal values without any

DM’s input. A direct comparison of the fuzzy GP model results to the preemptive, non-

preemptive and Tchebycheff min-max model results is not appropriate, since no targets

specified by the DMs are used in the fuzzy GP. However, the fuzzy GP results do provide

an insight into the ideal criteria achievements without any DM input.

Figure 6.1 Fuzzy GP Results

Table 6.66 Fuzzy Min-Max GP Procurement Plan (All Products)

182

6.5 Value Path Results

This section reviews the overall model results by product life cycle phase and product using

the Value Path approach discussed in Ravindran and Warsing (2013). The Value Path

approach allows the DM to view the results and various tradeoffs in a complex model by

displaying the results as a set of parallel scales. This graphical display allows the decision

makers to choose the best-compromise solution. Value Path results by product life cycle

phase and product for the Tchebycheff, non-preemptive and preemptive models will be

discussed. The fuzzy GP model results will not be included in this section, since no targets

specified by the DMs are used in the fuzzy GP. Fuzzy GP results are based on achievement

of the ideal values versus the Tchebycheff non-preemptive and preemptive models which

use decision maker specified target values.

6.5.1 Introduction Phase (Product 1)


procurement plan by GP model

for product 1, in the introduction

phase. A constraint limiting the


for the introduction phase to one

does not allow splitting the

demand between the two

suppliers. Supplier 2 is chosen to

supply the entire yearly demand

of 10 MM units for both the

Tchebycheff min-max and non-

preemptive GP models. Selecting supplier 1 to provide the total yearly demand is the

optimal solution for the preemptive GP model. Table 6.68 presents the target values and

goal achievements for each of the goal programming models. The goals are listed in the

preemptive GP goal priority order (see Table 6.2). These goal priorities are directly related

to the criteria weights utilized by the non-preemptive GP model (see Table 6.19). The goal

S1 S2

Tchebycheff's Min/Max 0 10,000,000

Non-preemptive 0 10,000,000

Preemptive 10,000,000 0


Optimal Order Allocations1 to Suppliers

by GP ModelSuppliers

GP Model

Table 6.67 Procurement Plan by GP Model for


183

priority order or goal weights are not relevant to Tchebycheff’s min-max model, which

minimizes the maximum deviation from the target values without any DM input.

These model results, representing the goal achievements for product 1, are normalized.

This normalization of the goal achievements is accomplished by dividing each achieved

goal constraint solution by the best achieved goal constraint solution for minimizing

objectives. For the maximizing objectives, the normalization is done by dividing the

maximum achieved value by the value obtained by each GP model. For example, in Table

6.68, for the Priority 8 goal, “Maximize Delivery Performance”, for product 1, the highest

value is 95,000,000. Hence the normalized values for the three GP models are

(95,000,000/90,000,000; 95,000,000/90,000,000; 95,000,000/95,000,000) or (1.05556,

1.0556, 1.00000). Similarly, in Table 6.68, for Priority 17 goal, “Minimize Price” for

introduction phase, the highest value is 450,000. Hence the normalized values are

(450,000/450,000; 450,000/450,000; 500,000/450,000) = (1.00000, 1.00000, 1.11111).

Thus, the best achievement for a specific goal constraint equals 1.0 with higher values

being less desirable. These models use the same target values (95% for maximization and

105% for minimization objectives) making the Value Path approach a viable method to

compare the GP model results. The fuzzy GP model results are excluded from the Value

Path analysis since the fuzzy model uses the ideal values as targets for the goal constraints.

Table 6.69 displays the normalized goal achievements for product 1.

Table 6.68 Model Results and Target Values for Introduction Phase Product 1

184

Figure 6.2 displays the Value Path graph for product 1 using the results of Table 6.68. The

results for product safety and quality are equal for all three GP models. However, the

delivery results for the Tchebycheff and non-preemptive models are worse compared to

the results of the preemptive model by 5.6%. Product performance is equal for all three

models. The goal attainments for preemptive GP model for all for the remaining priorities

are worse than those of the Tchebycheff and non-preemptive models. These poor goal

performances are for price, advanced technology, lead-time, service and capacity planning

and tooling development time with misses of 11.1%, 13.3%, 12.5%, 12.5% and 50%

respectively. The Value Path graph provides a clear picture of the disparities in goal

achievements by the three models and the results of the actual order allocations to the DMs.

During the review of the supplier selection results, the decision makers commented that

the tradeoff of 5.6% in delivery performance maybe considered in order to improve the

remaining goals performance. These comments present the power of the Value Path

method in comparing alternative solutions and performance tradeoffs.

Preemptive GP Supplier Goal Priority Order (Pi )

Tchebycheff's

Min/Max Results

Non-Preemptive

Results

Preemptive

Results

3.0 Maximize Product Safety 1.00000 1.00000 1.00000

7.0 Maximize Quality 1.00000 1.00000 1.00000

8.0 Maximize Delivery Performance 1.05556 1.05556 1.00000

9.0 Maximize Product Performance 1.00000 1.00000 1.00000

17.0 Minimize Price 1.00000 1.00000 1.11111

19.0 Maximize Advanced Technology 1.00000 1.00000 1.33333

20.0 Maximize Lead-time Performance 1.00000 1.00000 1.12500

21.0 Maximize Service Cap. Planning 1.00000 1.00000 1.12500

24.0 Maximize Tooling Dev. Time Performance 1.00000 1.00000 1.50000

Table 6.69 Value Path Results for Introduction Phase Product 1

185




for product 2, in the introduction






suppliers. Supplier 3 is chosen to

supply the entire yearly demand of

50,000 units for both the

Tchebycheff min-max and preemptive GP models. The optimal solution for the non-

preemptive model selects supplier 4 to provide the total yearly demand. Table 6.71

presents the target values and goal achievements for each of the goal programming models.

S3 S4

Tchebycheff's Min/Max 50,000 0

Non-preemptive 0 50,000

Preemptive 50,000 0




GP Model

Figure 6.2 Value Path Model Results Comparison for Introduction Phase Product 1



186

The goals are listed in the preemptive GP goal priority order (see Table 6.2). These goal

priorities are directly related to the criteria weights utilized by the non-preemptive GP

model (see Table 6.19). The goal priority order or goal weights are not relevant to

Tchebycheff’s min-max model, which minimizes the maximum deviation from the target

values without any DM input.

Table 6.72 summarizes the normalized goal achievements for product 2 using the same

procedure described for product 1. Figure 6.3 displays the Value Path graph for product 2.

Examining the Value Path graph and Table 6.72, reveals the preemptive and Tchebycheff

GP models have worse performance than the non-preemptive GP model for product safety

and quality. These results may seem surprising given the high goal priorities for product


Tchebycheff's

Min/Max Results

Non-

Preemptive

Results

Preemptive

Results





17.0 Minimize Price 1.00000 1.14943 1.00000

19.0 Maximize Advanced Technology 1.00000 1.00000 1.00000

20.0 Maximize Lead-time Performance 1.00000 1.00000 1.00000



Table 6.71 Model Results and Target Values for Introduction Phase Product 2

Table 6.72 Value Path Results for Introduction Phase Product 2

187

safety and quality of 3 and 7 respectively. Detailed testing of the model discovered the

achievement of goal priorities 1 and 2 for growth phase product 3, have a direct impact on

the supplier selection and order allocation of product 2. Despite supplier 4 having the best

performance rating with respect to safety and quality, supplier 3 was selected. Supplier 4

was chosen to supply 1.6 million units of product 3, which consumes the entire capacity of

supplier 4 with respect to business volume levels. This business volume constraint

combined with the best performance with respect to quality and safety required the

selection of supplier 4 for product 3. Achieving these higher priority goals was again done

at the detriment of the lower priority goals. This real-world example clearly demonstrates

the principles of preemptive goal programming. The non-preemptive GP model results are

equal to or better than the preemptive and Tchebycheff GP model results in all goal

priorities except price, which misses by 14.9%.

The decision makers were presented with a tradeoff between safety and quality

performance versus price. Another consideration discussed by the DMs was to relax the

business volume constraint on supplier 4, allowing a higher overall volume to be purchased

Figure 6.3 Value Path Model Results Comparison for Introduction Phase Product 2

188

from supplier 4. This relaxation of the business volume constraint could substantially

change the models results and require retesting in order to generate the new optimal

solutions for all the GP models. This discussion demonstrates the positive attributes of the

Value Path graph, which provides a clear picture of the tradeoffs among the conflicting

criteria.

6.5.3 Growth Phase (Product 3)

Table 6.73 displays the procurement plan by GP model for product 3, in the growth phase.

Suppliers 3 and 5 were chosen to supply the yearly demand for both the Tchebycheff and

non-preemptive GP models. Suppliers 3 and 4 where selected to supply the yearly demand

for the preemptive model. A constraint limiting the maximum number of suppliers for the

growth phase to two allows splitting the demand between the two suppliers. Table 6.74

presents the target values and goal achievements for each of the goal programming models.

The goals are listed in the preemptive GP goal priority order (see Table 6.2). These goal

priorities are directly related to the criteria weights utilized by the non-preemptive GP

model (see Table 6.19). The goal priority order or goal weights are not relevant to

Tchebycheff’s min-max model, which minimizes the maximum deviation from the target

values without any DM input.

S3 S4 S5

Tchebycheff's Min/Max 2,777,995 0 222,005

Non-preemptive 2,276,004 0 723,996

Preemptive 1,400,000 1,600,000 0

Optimal Order Allocations1 to Suppliers by GP

ModelSuppliers


GP Model

Table 6.73 Procurement Plan by GP Model for Growth Phase Product 3

189

Table 6.75 summarizes the normalized goal achievements for the product 3. Figure 6.4

displays the Value Path graph for product 3. Examining the Value Path graph and Table

6.75 reveals the Tchebycheff and non-preemptive GP models have worse performance than

the preemptive model for both quality and safety missing by 5.7% and 3.5% respectively

for both quality and safety. These goal performance differences should not be a surprise

give n that quality and safe ty are the highest ranked goals in the preemptive goal

programming model. All the models perform equally well for the delivery, lead-time,

service and capacity planning, product performance and tooling development time goals.

The preemptive GP misses price by 7.2%, while the non-preemptive model misses price

by 1.8%.


Tchebycheff's

Min/Max Results

Non-

Preemptive

Results

Preemptive

Results




10.0 Maximzie Leadtime Performance 1.00000 1.00000 1.00000



13.0 Minimize Price 1.00000 1.01855 1.07224


Table 6.74 Model Results and Target Values for Growth Phase Product 3

Table 6.75 Value Path Results for Growth Phase Product 3

190

The results of the growth phase models present the DMs with a number of alternative

solutions and results. Quality and safety performance can be maximized by choosing the

preemptive GP solution understanding that price will miss the best solution by 7.2%.

Selecting the Tchebycheff model solution will yield the best price performance, but also

the worst performance with respect to quality and safety. Once again, the Value Path graph

clearly presents the solutions results and tradeoffs visually for the goal priorities.

6.5.4 Mature Phase (Product 4)

Table 6.76 displays the procurement plan by GP model for product 4, in the mature phase.

Suppliers 6 and 7 were chosen to supply the yearly demand for all of the GP models with

order allocations varying by model. Several constraints impacted the selection of

suppliers. The mature phase sets the minimum number of required suppliers to two and

the maximum number of suppliers to three. This constraint supports the DM’s objective

of creating price competition in order to improve the gross profit margin for mature phase

products. Table 6.77 presents the target values and goal achievements for each of the goal

Figure 6.4 Value Path Model Results Comparison for Growth Phase Product 3

191

programming models. The goals are listed in the preemptive GP goal priority order (see

Table 6.2). These goal priorities are directly related to the criteria weights utilized by the

non-preemptive GP model (see Table 6.19). The goal priority order or goal weights are

not relevant to Tchebycheff’s min-max model, which minimizes the maximum deviation

from the target values without any DM input.



6.78 reveals the non-preemptive and preemptive GP models have worse performance than

the Tchebycheff model for price missing by 2.6% and 2.5% respectively. All the models

perform equally well for the safety, quality, product performance, past performance,

delivery, service and capacity planning and lead-time goals. The Tchebycheff min-max

model misses the best tooling development time performance by 2.9%.

Table 6.76 Procurement Plan by GP Model for Mature Phase Product 4

Table 6.77 Model Results and Target Values for Mature Phase Product 4

192

The results of the product 4 mature phase models present the DMs with basically two

choices. Choosing the non-preemptive and preemptive models’ solutions would mean a

miss of approximately 2.5% in price. Selecting the Tchebycheff min-max model results

would cause a 2.9% tooling development time miss. Given the DM’s stated goals of


Tchebycheff's

Min/Max Results

Non-

Preemptive

Results

Preemptive

Results




15.0 Minimize Price 1.00000 1.02552 1.02532

16.0 Maximize Past Performance 1.00000 1.00000 1.00000



25.0 Maximize Leadtime Performance 1.00000 1.00000 1.00000


Figure 6.5 Value Path Model Results Comparison for Mature Phase Product 4

Table 6.78 Value Path Results for Mature Phase Product 4

193

improving gross margins for mature products, it is reasonable to assume improved price

performance would be selected over higher tooling development time performance.


Table 6.79 displays the procurement plan by GP model for product 5, in the mature phase.

Suppliers 2 and 6 were chosen to supply the yearly demand for all of the GP models with

order allocations varying by model. Several constraints impacted the selection of

suppliers. The mature phase sets the minimum number of required suppliers to two and

the maximum number of suppliers to three. This constraint supports the DM’s objective

of creating price competition in order to improve the gross profit margin for mature phase

products. Table 6.80 presents the target values and goal achievements for each of the goal

programming models. The goals are listed in the preemptive GP goal priority order (see

Table 6.2). These goal priorities are directly related to the criteria weights utilized by the

non-preemptive GP model (see Table 6.19). The goal priority order or goal weights are

not relevant to Tchebycheff’s min-max model, which minimizes the maximum deviation

from the target values without any DM input.



6.81 table reveal that all three models perform equally well on the top three priority goals

of safety, quality and product performance. The Tchebycheff GP model misses price by

12.3%, while the non-preemptive model misses price by 5.0%. Past performance, delivery,

service and capacity planning and lead-time performance are equal for all models. Tooling

Table 6.79 Procurement Plan by GP Model for Mature Phase Product 5

194

development time missed by 4.3% and 7.5% for the non-preemptive and preemptive

models respectively.

Once again, the DMs are faced with choosing between improved price or tooling

development time. The results of the product 4 mature phase models present the DMs with

basically two choices. A price miss of 12.3% is in clear contrast to the decision makers’

stated goals of improving gross margins for mature products. Even a price miss of 5.0%

maybe an unacceptable choice when compared to the preemptive GP model’s 7.5% tooling

miss. The Value Path graph clearly presents these tradeoffs to the DMs making it a

powerful managerial tool for MCDM problems.

Table 6.80 Model Results and Target Values for Mature Phase Product 5

Table 6.81 Value Path Results for Mature Phase Product 5

195

6.5.6 Decline Phase (Product 6)


procurement plan by GP model for

product 6, in the decline phase.

Tchebycheff’s min- max GP

model selects suppliers 4 and 10 to

provide are selected to supply 45%

and 55% of the yearly demand

respectively. The non-preemptive

and preemptive models allocate all

of the demand to supplier 10.

Table 6.83 presents the target

values and goal achievements for each of the goal programming models. The goals are

listed in the preemptive GP goal priority order (see Table 6.2). These goal priorities are

S4 S10

Tchebycheff's Min/Max 11,198 13,802


Preemptive 0 25,000




GP Model

Figure 6.6 Value Path Model Results Comparison for Mature Phase Product 5



196

directly related to the criteria weights utilized by the non-preemptive GP model (see Table

6.19). The goal priority order or goal weights are not relevant to Tchebycheff’s min-max

model, which minimizes the minimizes the maximum deviation from the target values

without any DM input.



6.84 show that the non-preemptive and preemptive GP models display the best

performances for the top three decline phase priorities of safety, quality and price. All

three models have equal performance for past performance and service and capacity

planning. The Tchebycheff model misses product performance by 2.4%. Delivery and

lead-time are missed by 8.4% and 5.6% respectively for the non-preemptive and

preemptive GP models. Finally, the Tchebycheff model records a 5.2% miss for tooling

development time.

The decision makers are again faced with choosing better performance for higher ranked

and weighted goals associated with the non-preemptive and preemptive models versus

better performance for delivery and lead-time associated with Tchebycheff’s min-max GP.

Table 6.83 Model Results and Target Values for Decline Phase Product 6

Table 6.84 Value Path Results for Decline Phase Product 6

197




for product 7, in the decline

phase. The order allocation is

exactly the same for all the

models making Value Path

analysis a trivial exercise. Table

6.86 presents the target values

and goal achievements for each

of the goal programming

models. The goal achievements exceed the target values for all goals except delivery

performance, which misses the target by 0.3%.

S1 S7

Tchebycheff's Min/Max 0 15,000


Preemptive 0 15,0001 Maximum number of suppliers set to two suppliers



GP Model

Figure 6.7 Value Path Model Results Comparison for Decline Phase Product 6



198

Table 6.87 summarizes the normalized goal achievements for the product 7. Since the

order allocations are the same for all the GP models, all the normalized values are equal to

1. Figure 6.8 displays the Value Path graph for product 7. Likewise, all values in the Value

Path graph equal 1.

Since the order allocations are equal for the GP models, there are no tradeoffs or decisions

required by the DMs.


Tchebycheff's

Min/Max Results

Non-

Preemptive

Results

Preemptive

Results



29.0 Minimize Price 1.00000 1.00000 1.00000

30.0 Maximize Past Performance 1.00000 1.00000 1.00000




34.0 Maximize Leadtime Performance 1.00000 1.00000 1.00000


Table 6.86 Model Results and Target Values for Decline Phase Product 7

Table 6.87 Value Path Results for Decline Phase Product 7

199

6.6 Managerial Implications

This section discusses the presentation of the model results for supplier selection with the

decision makers involved in the case study and their reactions and comments. Following

the review of the models results with the key DMs, it was asked how do the model results

compare to the actual order allocations used by the company? The answer to this important

question would truly assess the effectiveness of the GP model results versus the “real-

world” order allocations.

This section expands the model comparisons completed in Section 6.5. Comparisons were

made on the supplier selection criteria values achieved by the GP models and those by the

actual order allocations used earlier by the company. These results were shared with the

Chief Operating Officer (COO), who was one of the three key executives who provided

the initial rankings used in the GP models. Using the feedback of this senior level DM, the

managerial implications of the use of the GP models for supplier selection are discussed.

Figure 6.8 Value Path Model Results Comparison for Decline Phase Product 7

200

6.6.1 Actual Order Allocations

In order to demonstrate the effectiveness of the GP model order allocations, the actual order

allocations used by the company had to be determined. The supplier orders for the case

study items for the same time period were obtained for this managerial evaluation. Table

6.88 displays the actual order allocations by product life cycle phase.

Several key differences exist with regards to the constraints utilized to create the GP

models and the actual order allocations. The company relaxed some of the constraints

during the actual order allocations. First, the limit on the number of suppliers selected to

provide products in the introduction phase was relaxed from 1 to 2 suppliers (see Table

5.15 for rules on number of suppliers by PLC phase) for product 1. Both suppliers 1 and

2 were selected to provide introduction phase product 1. Next, the constraint on the

maximum number of suppliers allowed to provide mature products was relaxed from 3 to

4 for product 4. In addition, supplier 2 was selected to supply mature phase product 4,

although they were not included in the initial supplier listing (see Table 5.1).

Table 6.88 Actual Procurement Plan All Products

201

Managerial Feedback

Reviewing these differences with the DM, it was established that the decision to include a

second source for introduction phase product 1 was based on a supply chain design issue.

Some of the product 1 was being used in combination with other products and by utilizing

supplier 1, duty payments, transportation cost, etc. would be reduced and the supply chain

design would be simplified. The addition of a fourth supplier for mature phase for product

4, was due to significant unanticipated customer demand for similar products. This demand

spike created world-wide capacity issues for this product family. In an effort to meet this

unexpected demand, supplier 2 was added as an additional source for mature phase product

4. This addition of supplier 2 also required the creation of duplicate tooling, increasing the

product cost. In the following sections, the comparisons of actual order results to GP model

results and the corresponding DM feedback will be discussed. It should be pointed out that

the relaxation of some of the constraints by the company puts the GP model results at a

disadvantage!



procurement plan by the GP

model and the actual orders for

product 1, in the introduction





demand between two suppliers.


the entire yearly demand of 10

MM units for both the

Tchebycheff min-max and non-

preemptive GP models. Selecting supplier 1 to provide the total yearly demand is the

S1 S2

Tchebycheff's Min/Max 0 10,000,000

Non-preemptive 0 10,000,000

Preemptive 10,000,000 0

Actual Orders 2,000,000 8,000,000

Optimal Order Allocations1,2 to Suppliers


GP Model

2 Maximum supplier rule of one supplier relaxed in actual

ordering


Table 6.89 Procurement Plan by GP Model and

Actual Orders for Introduction Phase Product 1

202

optimal solution for the preemptive GP model. Suppliers 1 and 2 were chosen to supply

the yearly demand for product 1. This ignores the constraint limiting the maximum number

of suppliers for an introduction phase to one (see Table 5.15).

Managerial Feedback

The DM noted this constraint was relaxed in the creation of the actual orders due to supply

chain design issues impacting duty paid, transportation cost and proximity to a facility

providing value added services, which included adding product 1 as part of a larger

package. Table 6.90 compares the key supplier criteria values between the actual orders

and the GP model allocations. The criteria are listed in priority order.

These actual orders and model results, representing the criteria achievements for product

1, are normalized. This normalization of the criteria values is accomplished by dividing

each criteria value by the target values for minimization criterion. For example, in Table

6.90, for the criterion “Minimize Price”, for product 1, the target value is 472,500. Hence,

the normalized values for the actual order and three GP models are (460,000/472,500;

450,000/472,500; 450,000/472,500; 500,000/472,500) or (0.97354, 0.95238, 0,95238,

1.05820). For maximization criteria, normalization is done by dividing the target values

by the criteria values achieved by the GP models and the actual orders. Thus, normalized

values less than 1.0 indicate criteria achievements “better” than the targets, while

normalized values greater than 1.0 indicate criteria achievements “worse” than the

Actual Order

Allocation

Tchebycheff's

Min-Max GP

Non-preemptive

GP

Preemptive

GP

Maximize Product Safety Performance 95,000,000 100,000,000 100,000,000 100,000,000 100,000,000

Maximize Quality Performance 90,250,000 95,000,000 95,000,000 95,000,000 95,000,000

Maximize Delivery Performance 90,250,000 91,000,000 90,000,000 90,000,000 95,000,000

Maximize Product Performance 90,250,000 95,000,000 95,000,000 95,000,000 95,000,000

Minimize Price 472,500 460,000 450,000 450,000 500,000

Maximize Advanced Technology Rating 76,000,000 76,000,000 80,000,000 80,000,000 60,000,000

Maximize Lead-Time Performance 85,500,000 88,000,000 90,000,000 90,000,000 80,000,000

Maximize Service/Capacity Planning Performance 85,500,000 88,000,000 90,000,000 90,000,000 80,000,000

Maximize Tooling Development Time Performance 85,500,000 84,000,000 90,000,000 90,000,000 60,000,000

DM Target

Values (95%

or 105% of

Ideals)Prioritized Supplier Criteria

Criteria Values



203

targets. The actual order and GP models use the same target values (95% for maximization

and 105% for minimization objectives) making the Value Path approach a viable method

to compare the GP model and actual order results. The fuzzy GP model results are excluded

from the Value Path analysis since the fuzzy model uses the ideal values as targets for the

goal constraints. Table 6.91 displays the normalized criteria achievements for product 1.

Figure 6.9 presents a copy of the PowerPoint slide, included in the review with the DM,

illustrating the normalized value calculations for the supplier criteria for product 1.

Figure 6.10 displays the Value Path graph for product 1 using the results of Table 6.90.

The results for product safety, quality and product performance are equal for the actual and

all three GP models. The actual orders minimally outperform two of the GP models in

only the delivery performance criterion. The actual order performance is equal to or better

than the target value in all but the tooling development time criterion. The Tchebycheff

and non-preemptive models outperform the actual orders in price, advanced technology,

lead-time, service and capacity planning and tooling development time.

Figure 6.11 presents a copy of the PowerPoint slide, used in the DM review, comparing

the criteria values achieved by the GP model and the actual orders using the Value Path

graph. The decision maker commented how the Value Path graph made it easy to view the

tradeoffs between the actual and GP models. He noted that while the supply chain design

drove the company to add a second supplier for product 1, the results of that decision were

clearly illustrated across the selection criteria using the Value Path model and graph.

Table 6.91 Normalized Criteria Values for Value Path Graph for Introduction

Phase Product 1

204

Figure 6.9 DM Review of the Value Path Calculations for Introduction Phase

Product 1



205





product 2, in the introduction






suppliers. Supplier 3 is chosen

to supply the entire yearly

demand of 50,000 units for the

actual orders, Tchebycheff min-max and preemptive GP models. However, the optimal

S3 S4

Tchebycheff's Min/Max 50,000 0


Preemptive 50,000 0

Actual Orders 50,000 0




GP Model

Figure 6.11 DM Review Actual Orders and GP Model Allocations and Value Path

Graph for Introduction Phase Product 1


Actual Orders for Introduction Phase Product 2

206

solution for the non-preemptive model selects supplier 4 to provide the total yearly

demand. Table 6.93 presents the target values and criteria achievements for the GP models

and the actual orders. The criteria are listed in priority order.

Table 6.94 summarizes the normalized criteria achievements for product 2 using the

procedure described for product 1. Figure 6.12 displays the Value Path graph for product

2. Examining the Value Path graph and Table 6.94, reveals the actual orders and the

preemptive and Tchebycheff GP models have worse performance than that of the non-

preemptive GP model for product safety and quality. These results may seem surprising

given the high priority assigned to both product safety and quality. Detailed testing of the

model discovered that the achievement of higher priority criteria for the growth phase

product 3, had a direct impact on the supplier selection and order allocation of product 2.

Despite supplier 4 having the best performance rating with respect to safety and quality,

Actual Order

Allocation

Tchebycheff's

Min-Max GP

Non-preemptive

GP

Preemptive

GP

Maximize Product Safety Performance 427,500 400,000 400,000 450,000 400,000

Maximize Quality Performance 427,500 400,000 400,000 450,000 400,000

Maximize Delivery Performance 451,250 475,000 475,000 475,000 475,000

Maximize Product Performance 427,500 450,000 450,000 450,000 450,000

Minimize Price 91,350 87,000 87,000 100,000 87,000

Maximize Advanced Technology Rating 380,000 400,000 400,000 400,000 400,000

Maximize Lead-Time Performance 427,500 450,000 450,000 450,000 450,000

Maximize Service/Capacity Planning Performance 380,000 400,000 400,000 400,000 400,000

Maximize Tooling Development Time Performance 380,000 400,000 400,000 400,000 400,000

Prioritized Supplier Criteria

DM Target

Values (95%

or 105% of

Ideals)

Criteria Values



Table 6.94 Normalized Criteria Values for Value Path Graph for Introduction

Phase Product 2

207

supplier 3 was selected. Supplier 4 was chosen to supply 1.6 million units of product 3,

which consumed the entire capacity of supplier 4 with respect to its business volume level.

This business volume constraint combined with the best performance with respect to

quality and safety required the selection of supplier 4 for product 3. Achieving these higher

criteria was again done at the detriment of the lower priority criteria. This real-world

example clearly demonstrates the principles of preemptive goal programming. The non-

preemptive GP model results are equal to or better than the actual orders and those of the

preemptive and Tchebycheff GP models in all criteria except price, which misses by 9.5%.

Figure 6.13 presents a copy of the PowerPoint slide, used in the DM review, which

compares the criteria values achieved by the GP models and the actual orders using the

Value Path graph.



208

Managerial Feedback

The discussion related to Figure 6.13 focused on the ability to identify opportunities for

supplier performance improvement in order to improve overall performance using the

Value Path graph. Supplier 3’s performance with respect to product safety and quality was

rated at 8 versus supplier 4’s rating of 9 in both of these important criteria. The DM

commented that this situation presented an opportunity for supplier development related to

safety and quality performance. This opportunity could include visits, supplier coaching

and compliance audits by the company’s quality assurance team in order to mitigate any

possible risk from choosing supplier 3, with the intent of improving supplier 3’s safety and

quality performance. The company would then benefit from improved performance from

supplier 3 and be able to take advantage of the price advantage provided by supplier 3 for

product 2.


Graph for Introduction Phase Product 2

209

6.6.4 Growth Phase (Product 3)

Table 6.95 displays the procurement plan by the GP models and the actual orders for

product 3, in the growth phase. Suppliers 3 and 5 were chosen to supply the yearly demand

for both the Tchebycheff and non-preemptive GP models. Suppliers 3 and 4 where selected

to supply the yearly demand for the preemptive model. Supplier 5 was chosen to supply

all of the yearly demand for the actual orders. A constraint limiting the maximum number

of suppliers for the growth phase to two allows splitting the demand between the two

suppliers. Table 6.96 presents the target values and criteria achievements for the actual

orders and the goal programming models. The criteria are listed in priority order.

Actual Order

Allocation

Tchebycheff's

Min-Max GP

Non-preemptive

GP

Preemptive

GP

Maximize Quality Performance 25,650,000 27,000,000 24,222,005 24,723,996 25,600,000

Maximize Product Safety Performance 25,650,000 27,000,000 24,222,005 24,723,996 25,600,000

Maximize Delivery Performance 27,075,000 28,500,000 28,500,000 28,500,000 28,500,000



Maximize Product Performance 25,650,000 27,000,000 27,000,000 27,000,000 27,000,000

Minimize Price 27,342,000 28,950,000 26,255,345 26,742,276 28,152,000



DM Target

Values (95%

or 105% of

Ideals)

Criteria Values

Table 6.95 Procurement Plan by GP Model and Actual Orders for Growth Phase

Product 3


Growth Phase Product 3

210

Table 6.97 summarizes the normalized criteria achievements for the product 3. Figure 6.14


6.97 reveals that the Tchebycheff, non-preemptive and preemptive GP models have worse

performance than those of the actual orders for both quality and safety missing, by 5.9%,

3.7% and 0.2% respectively for both quality and safety. These performance differences

should not be a surprise given that supplier 4 has the highest performance among the


Growth Phase Product 3

Table 6.97 Normalized Criteria Values for Value Path Graph for Growth Phase

Product 3

211

potential suppliers for both quality and safety. All the models and the actual orders perform

equally well for the delivery, lead-time, service and capacity planning, product

performance and tooling development time goals. Tchebycheff’s min-max model beats

the price target by 5% followed by the non-preemptive model, which beats the price by

2.2%. The preemptive and actual orders miss the price target by 3.0% and 5.9%

respectively.

Managerial Feedback

The results of the growth phase presented the DM with 4 different results with respect to

quality, safety and price performance. While the actual orders displayed the best

performance with respect to quality and safety, it also had the worst performance with

respect to price. The price difference was substantial and the Value Path graph clearly

presents these tradeoffs. Prior to these results being shared with the DM, the company

chose to stop production of product 3 at supplier 5, moving production to the other

suppliers for reasons including better price, reduced transportation costs, reduced duty

costs and proximity to the company’s warehouse.


Table 6.98 displays the procurement plan by the GP model and the actual orders for product

4, in the mature phase. Suppliers 6 and 7 were chosen to supply the yearly demand for all

of the GP models with order allocations varying by model. Suppliers 1, 2, 6 and 7 were

chosen for the actual orders. Supplier 2 was not included in the initial supplier listing for

the GP models. The company added this supplier later. This selection of a fourth supplier

to provide a mature phase product relaxed the constraint of selecting a maximum of 3

suppliers. The GP models imposed the constraints setting the minimum number of required

suppliers to two and the maximum number of suppliers to three. These constraints

supported the DM’s objective of creating price competition in order to improve the gross

profit margin for mature phase products.

212

Managerial Feedback

The review with the DM pointed out the addition of a fourth supplier and the addition of

supplier 2 as a supply source for product 4 (see Figure 6.15). The DM noted the addition

of a fourth supplier for mature phase product 4, was due to significant unanticipated

customer demand for similar products. This demand spike created world-wide capacity

issues for this product family. In an effort to meet this unexpected demand, supplier 2 was

added as an additional source for mature phase product 4. This addition of supplier 2 also

required the creation of duplicate tooling, increasing the product cost.

Table 6.98 Procurement Plan by GP Model and Actual Orders for Mature Phase

Product 4

213

Table 6.99 presents the target values and criteria achievements for the actual orders and the

goal programming models. The criteria are listed in the priority order.

Table 6.100 summarizes the normalized criteria achievements for the product 4. Figure

6.16 displays the Value Path graph for product 4. The actual orders and the GP models

perform equally well for the safety, quality and product performance. Examining the Value

Actual Order

Allocation

Tchebycheff's

Min-Max GP

Non-preemptive

GP

Preemptive

GP

Maximize Product Safety Performance 3,230,000,000 3,400,000,000 3,400,000,000 3,400,000,000 3,400,000,000

Maximize Quality Performance 3,068,500,000 3,230,000,000 3,230,000,000 3,230,000,000 3,230,000,000

Maximize Product Performance 3,068,500,000 3,230,000,000 3,230,000,000 3,230,000,000 3,230,000,000

Minimize Price 14,994,000 16,490,000 14,623,763 14,996,970 14,994,000

Maximize Past Performance 2,907,000,000 3,026,000,000 3,060,000,000 3,060,000,000 3,060,000,000

Maximize Delivery Performance 3,036,200,000 3,077,000,000 3,060,000,000 3,060,000,000 3,060,000,000

Maximize Service/Capacity Planning Performance 2,907,000,000 3,026,000,000 3,060,000,000 3,060,000,000 3,060,000,000

Maximize Lead-Time Performance 2,907,000,000 3,026,000,000 3,060,000,000 3,060,000,000 3,060,000,000

Maximize Tooling Development Time Performance 2,777,800,000 2,856,000,000 2,584,752,600 2,659,394,000 2,658,800,000


DM Target

Values (95% or

105% of Ideals)

Criteria Values


Graph for Mature Phase Product 4


Mature Phase Product 4

214

Path graph and Table 6.100 reveals that the non-preemptive and preemptive GP models

have worse performance than the Tchebycheff model but still achieve the price target. The

actual orders miss the price target by nearly 10%. Delivery performance is slightly better

than the target value for the actual order and the GP models. Likewise, the performance

for both service and capacity planning and lead-time are better than the target values.

While. the actual model betters the tooling development time target by 2.7%, the non-

preemptive, preemptive and Tchebycheff min-max model miss by 4.5%, 4.5% and 7.5%

respectively.



Actual Order

Results

Tchebycheff's

Min/Max GP

Results

Non-Preemptive

GP Results

Preemptive GP

Results

Maximize Product Safety 0.95000 0.95000 0.95000 0.95000

Maximize Quality 0.95000 0.95000 0.95000 0.95000

Maximize Product Performance 0.95000 0.95000 0.95000 0.95000

Minimize Price 1.09977 0.97531 1.00020 1.00000

Maximize Past Performance 0.96067 0.95000 0.95000 0.95000

Maximize Delivery Performance 0.98674 0.99222 0.99222 0.99222

Maximize Service Cap. Planning 0.96067 0.99222 0.95000 0.95000

Maximize Lead-time Performance 0.96067 0.95000 0.95000 0.95000

Maximize Tooling Dev. Time Performance 0.97262 1.07469 1.04452 1.04476


Normalized Criteria Values

Table 6.100 Normalized Criteria Values for Value Path Graph for Mature Phase

Product 4

215

Managerial Feedback

While the Value Path graph shows a substantial opportunity to reduce price by choosing

the supplier order allocations using the GP models, it does not consider overall capacity in

the industry and the need to react to unexpected market opportunities. The company made

the choice to service their customers under stressful market and capacity conditions while

sacrificing some product gross margins via increased price.


Table 6.101 displays the procurement plan by the actual orders and the GP models for

product 5, in the mature phase. Suppliers 2 and 6 were chosen to supply the yearly demand

for all of the GP models with order allocations varying by model. Suppliers 1 and 2 were

chosen for the actual orders. Several constraints impacted the selection of suppliers. The

mature phase sets the minimum number of required suppliers to two and the maximum

number of suppliers to three. This constraint supports the DM’s objective of creating price

competition in order to improve the gross profit margin for mature phase products. Unlike

product 4, these constraints controlling the number of suppliers was adhered to. Table

6.102 presents the target values and criteria achievements for each of the goal programming

models and the actual orders. The criteria are listed in priority order.

Table 6.101 Procurement Plan by GP Model and Actual Orders for Mature Phase

Product 5

216


6.17 displays the Value Path graph for product 5. Examining the Value Path graph and

Table 6.103 table reveal that all three GP models and the actual orders perform equally

well on the top three priority goals of safety, quality and product performance. The actual

orders miss the price target by 32.7%, while the Tchebycheff GP model misses the price

target by 7.0%, the non-preemptive model achieves the price target and the preemptive GP

solution is better than the price target by 4.8%. Past performance, delivery, service and

capacity planning and lead-time performance perform equal well for all GP models,

bettering the target values by 5.0%, 0.8%, 5.0% and 5.0% respectively. The actual orders

are slightly worse than the target values for past performance, service and capacity

planning and lead-time missing the target values by 0.6%. Delivery performance for the

actual orders betters the target values by 3.5%. All of the models and the actual orders

miss the tooling development time with the misses ranging from 7.5% to 15.5%.

Actual Order

Results

Tchebycheff's

Min/Max GP

Results

Non-Preemptive

GP Results

Preemptive GP

Results




Minimize Price 1.32688 1.06980 1.00026 0.95238








Actual Order

Allocation

Tchebycheff's

Min-Max GP

Non-preemptive

GP

Preemptive

GP

Maximize Product Safety Performance 1,045,000,000 1,100,000,000 1,100,000,000 1,100,000,000 1,100,000,000

Maximize Quality Performance 992,750,000 1,045,000,000 1,045,000,000 1,045,000,000 1,045,000,000

Maximize Product Performance 992,750,000 1,045,000,000 1,045,000,000 1,045,000,000 1,045,000,000

Minimize Price 4,435,200 5,885,000 4,744,782 4,436,345 4,224,000

Maximize Past Performance 940,500,000 935,000,000 990,000,000 990,000,000 990,000,000

Maximize Delivery Performance 982,300,000 1,017,500,000 990,000,000 990,000,000 990,000,000





DM Target

Values (95% or

105% of Ideals)

Criteria Values



Table 6.103 Normalized Criteria Values for Value Path Graph for Mature Phase

Product 5

217

Managerial Feedback

The discussion with the DM, regarding the choice of suppliers 1 and 2, discerned the

supplier selections were heavily influenced by supply chain design issues impacting duty

paid, transportation cost and proximity to a facility providing value added services, which

included adding product 5 as part of a larger end consumer package. Supplier 6’s

geographically location negatively impacted duty and transportation costs. Despite these

negative factors regarding the selection of supplier 6, the DM recognized the significant

opportunity to improve price performance presented by the GP model supplier selections.

The DM remains committed to improving the gross profit margin of mature products and

this is a prime example of that opportunity.

Figure 6.17 Value Path Actual Orders and Model Results Comparison for Mature

Phase Product 5

218


Table 6.104 displays the procurement plan by the GP models and the actual orders for

product 6, in the decline phase. Tchebycheff’s min- max GP model selects suppliers 4 and

10 to provide 45% and 55% of the yearly demand respectively. The non-preemptive and

preemptive models allocate all of the demand to supplier 10. Supplier 4 was chosen to

supply all the yearly demand for the actual orders. Table 6.105 presents the target values

and criteria achievements for each of the goal programming models and the actual orders.

The goals are listed in priority order.

S4 S10

Tchebycheff's Min/Max 11,198 13,802


Preemptive 0 25,000

Actual Orders 25,000 0




GP Model

Actual Order

Allocation

Tchebycheff's

Min-Max GP

Non-preemptive

GP

Preemptive

GP



Minimize Price 107,100 130,000 114,542 102,000 102,000

Maximize Past Performance 190,000 200,000 200,000 200,000 200,000







DM Target

Values (95% or

105% of Ideals)

Criteria Values

Table 6.104 Procurement Plan by GP Model and Actual Orders for Decline

Phase Product 6



219


6.18 displays the Value Path graph for product 6. Examining the Value Path graph and

Table 6.106 show that the non-preemptive and preemptive GP models display the best

performances for the top three decline phase priorities of safety, quality and price with the

actual orders performing the worst. The actual orders miss the price target by 21.4%. The

three GP models and the actual orders have equal performance for past performance and

service and capacity planning. While all three GP models perform better than the target

value for product performance, the actual orders are slightly worse than the target value

missing by 0.3%. The actual orders outperform the GP models in both the delivery and

lead-time criteria bettering the target values by 5%. The misses by the GP models for these

criteria range from 1.2% to 12.8%. Finally, the actual orders miss the tooling development

time by 6.9%, while the GP model results are better than the target values.

Table 6.106 Normalized Criteria Values for Value Path Graph for Decline Phase

Product 6

220

Managerial Feedback

Like product 4, the DM noted that the selection of supplier 4, to provide all the yearly

demand of product 6, was also impacted by the unanticipated customer demand for similar

products. This demand spike created world-wide capacity issues for this product family.

Supplier 4 was chosen at a higher unit price due to capacity availability, negatively

impacting the gross profit margin in an effort to fulfill customer demand.

Figure 6.18 Value Path Actual Orders and Model Results Comparison for Decline

Phase Product 6

221





product 7, in the decline phase.

The order allocation is exactly

the same for all the models and

the actual orders making Value

Path analysis a trivial exercise.

Table 6.108 presents the target

values and criteria achievements

for each of the goal programming

models. The goal achievements

exceed the target values for all goals except delivery performance, which misses the target

by 0.3%.

Table 6.109 summarizes the normalized criteria achievements for the product 7. Since the

order allocations are the same for all the GP models and the actual, all the normalized

values are the same. Figure 6.19 displays the Value Path graph for product 7. Likewise,

all values in the Value Path graph are equal.

Actual Order

Allocation

Tchebycheff's

Min-Max GP

Non-preemptive

GP

Preemptive

GP



Minimize Price 17,640 16,800 16,800 16,800 16,800

Maximize Past Performance 128,250 135,000 135,000 135,000 135,000







DM Target

Values (95% or

105% of Ideals)

Criteria Values

S1 S7

Tchebycheff's Min/Max 0 15,000


Preemptive 0 15,000

Actual Orders 0 15,0001 Maximum number of suppliers set to two suppliers



GP Model


Actual Orders for Decline Phase Product 7



222

Actual Order

Results

Tchebycheff's

Min/Max GP

Results

Non-Preemptive

GP Results

Preemptive GP

Results



Minimize Price 0.95238 0.95238 0.95238 0.95238









Figure 6.19 Value Path Actual Orders and Model Results Comparison for Decline

Phase Product 7

Table 6.109 Normalized Criteria Values for Value Path Graph for Decline Phase

Product 7

223

6.6.9 Impact on Procurement Cost

This section compares the price criteria results between the actual order and the GP models.

It should be remembered that the preemptive and non-preemptive GP models are based on

the DMs ranking of the criteria. Therefore, the models are simply determining the optimal

order allocations and supplier selections using the decision makers inputs and constraints.

Tchebycheff’s min-max model minimizes the maximum deviation from the target values,

which were set by the DM.

Figure 6.20 displays the procurement cost comparisons for the introduction phase products

1 and 2. The highest total procurement cost for the introduction phase product 1 is the

preemptive GP model at $500M, followed by the actual orders at $460M and the non-

preemptive and Tchebycheff’s min-max model at $450M. This result for the preemptive

model is not surprising given the relatively low priority assigned to the price criterion

during the introduction phase of the product. The price criterion is preceded by product

safety, product quality, delivery performance and product performance supplier selection

criteria and would be subjected to the supplier selections which maximize these criteria.


Introduction Phase Products 1 and 2

224

The best price performance for product 1 belongs to the non-preemptive and Tchebycheff

min-max GP models. The actual orders price performance misses these models by $10,000

or 2.2%.

The introduction phase product 2 has a tie for the best price performance at $87M, which

was achieved by the actual orders, preemptive and Tchebycheff’s min-max GP models.

The non-preemptive model has the worst price performance, which misses the best results

by $13,000 or 14.9%.

Figure 6.21 displays the procurement cost comparisons for the growth phase product 3 and

the mature phase products 4 and 5. The actual procurement cost for growth phase product

3 is the highest at $28.95MM, followed by the preemptive model at $28.152MM, the non-

preemptive model at $26.742MM and finally Tchebycheff’s min-max model at

$26.255MM. The preemptive model has the second highest overall procurement cost

which is not unexpected, given the relatively low priority assigned to the price criterion

during the growth phase of the product. Price is prioritized seventh out of 8 criteria for


Growth Phase Product 3 and Mature Phase Products 4 and 5

225

product 3. The maximum savings opportunity, comparing the actual orders to the

Tchebycheff min-max model results is $2.695MM or a 9.3%.

It should be pointed out that prior to these results being shared with the DM, the company

chose to stop production of product 3 at supplier 5, moving production to other suppliers.

The reasons for this move included better price, reduced transportation costs, reduced duty

costs and proximity to the company’s warehouse. This GP model results clearly support

the company’s decision to move product 3 from supplier 5 to supplier 3.

The actual procurement cost for mature phase product 4 is the highest at $16.49MM,

followed by the non-preemptive model at $14.997MM, the preemptive model at

$14.994MM and finally Tchebycheff’s min-max model at $14.624MM. The non-

preemptive and preemptive model costs are virtually tied. The preemptive model for the

mature phase performs far better than the growth phase. This is not surprising given that

price is now the fourth highest criteria out of 9 selection criteria and compared to the growth

phase price criteria which is ranked seventh out of 8 criteria. The actual procurement cost

misses the best cost performance of the Tchebycheff min-max model by $1.866MM or

11.3%.

The actual order allocations and supplier selections for product 4, was impacted by

significant unanticipated customer demand for similar products. This demand spike

created world-wide capacity issues for this product family. In an effort to meet this

unexpected demand, supplier 2 was added as an additional source for mature phase product

4. This addition of supplier 2 also required the creation of duplicate tooling, increasing the

product cost. All these efforts were done with the intention of providing the best possible

service levels to company’s customers with the possible sacrifice of product 4’s gross profit

margin.

The actual procurement cost for mature phase product 5 is again the highest at $5.885MM,

followed by Tchebycheff’s min-max model at $4.725MM, the non-preemptive model at

$4.436MM and the preemptive model at $4.224MM. It may seem surprising that the

226

preemptive model had the lowest procurement cost given the cost criterion results for the

previous products. The price criterion is prioritized below safety, quality and product

performance for mature phase products. Suppliers 2 and 6 perform equal to or better than

the other potential suppliers for safety, quality and product performance. They also have

the lowest overall unit pricing and therefore have the best procurement cost performance

of the GP models. The actual procurement cost misses this best cost performance by

$1.661MM or 28.2%.

The selection of suppliers 1 and 2 for product 5’s actual orders were heavily influenced by

the supply chain design issues impacting duty paid, transportation cost and proximity to a

facility providing value added services, which included adding product 5 as part of a larger

end consumer package. Supplier 6’s geographic location increased duty and transportation

costs. Despite these increased costs regarding the selection of supplier 6, the DM

recognized the significant opportunity to improve price performance presented by the GP

model supplier selections. The DM remains committed to improving the gross profit

margin of mature products and the GP models provide that opportunity.

Figure 6.22 displays the procurement cost comparisons for the decline phase products 6

and 7. The actual cost for decline phase product 6 is the highest at $130M, followed by

Tchebycheff’s min-max model at $114M, the non-preemptive and the preemptive models

at $102M. Once again, the preemptive model has the lowest procurement price criteria

performance along with the non-preemptive model. Supplier 10 has the best performance

for safety, quality and price, necessitating the choice of supplier 10 to supply all of the

demand for product 6. The actual orders were allocated to supplier 4 at a substantial price

increase as compared to supplier 10. This example further emphasizes the effectiveness of

the GP models to provide optimal solutions using the DM’s criteria ranking and weights.

227

Decline phase product 7’s procurement cost results are the same for the actual orders and

the GP models. Supplier 7 is selected to supply all the demand for the actual orders and

all the GP models and they all have the same procurement cost of $17M.

Figure 6.23 presents a copy of the PowerPoint slide, included in the review with the DM

illustrating the cost comparisons between the actual order allocations and the goal

programming models. The best total price improvement, compared to the actual order

allocations, is the Tchebycheff min-max model with a $5.727MM savings and a 11.0%

price improvement. The next highest price improvement versus the actual orders is the

non-preemptive model, with a $5.174MM savings and a 9.9% price improvement. Finally,

the preemptive model saves $3.943MM with a corresponding 7.6% price performance

improvement. While there were a number of “real world” circumstances which impacted

the actual order allocations, such as unanticipated demand, transportation and supply chain

design issues, these potential savings projections reinforce the effectiveness of the GP

models in generating significant savings in supplier procurement costs.


Decline Phase Products 6 and 7

228

6.7 Chapter Summary

The case study results illustrate the effectiveness of the Goal Programming (GP) models in

selecting suppliers and allocating orders for products across the product life cycle. The

focal company is a U.S. based consumer products company which utilizes a diverse global

supply chain to design, manufacture and deliver products to traditional brick and mortar

retailers, on-line retailers and distributors. Three key executive decision makers were

employed to identify and rank the key sourcing criteria attributes for products representing

the introduction, growth, mature and decline phases of the product life cycle. Seven

products were selected representing three of four top selling product families. The

suppliers included in this case study have already been pre-screened and short-listed by the

company.

Figure 6.23 DM Review of the Price Achievements for Actual Order and GP Models

229

The case study also provides a unique opportunity to view the supplier attributes, such as

product safety, product performance, service and capacity planning, which are driving the

global supplier selection process throughout the product life cycle. Many of the

performance criteria are beyond the traditional metrics of delivery, quality, cost and lead-

time. In addition to these selection criteria, there were business volume constraints

included in the GP models. These business volume constraints were based on several

factors including whether the suppliers were new or existing suppliers. Total purchases

from new suppliers were limited to 10% or 20% of their total sales revenue, while existing

suppliers were limited to 30% or 40% of their total sales revenue. These limits were set

by the company in an effort to minimize supplier performance risk. Additionally, there

were constraints on the minimum and maximum number of suppliers by product life cycle

phase. New products, during their introduction phase, were limited to just one supplier.

Mature products were required to have a minimum of two suppliers and a maximum of

three suppliers in order to insure there was price competition among the suppliers, with the

objective of improving the gross profit margin of mature products.

The preemptive and non-preemptive GP models utilized the DM’s ranking results to

determine the optimal supplier selections and order allocations that come as close as

possible to the targets set by the DMs for the different supplier criteria. The Tchebycheff

min-max model, which does not use any decision maker’s inputs simply minimized the

maximum deviation from the target values. The results were shared with the DMs using

the Value Path approach, which graphically displays the results and allows decision makers

to choose the best solution. During the review of the product 1 results (introduction phase),

using the Value Path graphs, the decision makers commented that the tradeoff of 5.6% in

delivery performance improvement may be considered in order to improve the other

supplier criteria. This is an example of the power of the Value Path method in comparing

alternative solutions and performance tradeoffs. The DMs also considered relaxing some

of the business constraints, such as total business volume for supplier 4. Relaxing this

business volume constraint could positively impact the safety and quality performance for

both the introduction phase (product 2) and growth phase (product 3). Additional model

testing could be easily accomplished providing a new set of optimal solutions to the DMs.

230

This is another benefit of utilizing an integrated supplier selection model to support

sourcing decisions. While the results from no single GP model generated optimal results

for all products and across the product life cycle, the Value Path results presented the DMs

with performance tradeoffs that could be used in selecting the optimal sourcing strategies.

In order to test the effectiveness of the model, the actual orders were compared to the GP

model results. The actual supplier orders for the case study items, for the same time period,

were obtained for this managerial evaluation. Several key differences existed with regards

to the constraints utilized to create the GP models and the actual order allocations. The

company relaxed some constraints during the actual order allocations. Limits on the

maximum number of suppliers by product life cycle phase were increased. Suppliers not

included in the initial supplier listing were utilized in the actual order allocations. These

business rules or constraints were relaxed for a number of reasons, including supplier chain

design issues impacting duty paid, transportation cost and geographic location. In addition,

unanticipated demands created world-wide capacity issues for several product families

included in the case study. Suppliers were added to meet this increased demand at higher

procurement costs.

Even though the GP models used more restrictive constraints, the actual orders in general

performed poorly, when compared to the GP model results! One of the significant

conclusions was that the model results provided a substantial savings in procurement costs,

while maintaining quality, delivery and safety criteria performance. The potential total

savings in procurement costs, from utilizing the GP models, was $5.727MM for the

Tchebycheff min-max model (11% reduction) and $5.174MM savings for the non-

preemptive and preemptive models (9.9% reduction). Imagine the potential savings, if this

integrated supplier selection model is widely implemented to support procurement

strategies and supplier selection decisions across all the company products!

In addition to identifying the substantial cost advantage, another benefit of comparing the

actual results to the GP model results is the ability to identify opportunities for supplier

development. During the review of the Value Path results, the decision maker identified

231

several opportunities for supplier development to improve the supplier performance in

product safety and quality, in order to mitigate potential risk, and gain better price

performance. Visualizing these tradeoffs using the Value Path graphs, identified potential

tradeoffs between the GP models and the actual results. The DM commented that the actual

orders could be blended with the model results to generate better sourcing strategies. This

statement reveals another significant benefit of utilizing this integrated decision-making

model, which allows “what if” scenarios to be easily tried, including changing goal priority

weights, priorities and relaxing business constraints providing the DMs with alternative

solutions and better results. While there may not be a single solution which meets all the

DM’s objectives, a blended solution generated through a number of problem-solving

iterations may provide a best answer to the product life cycle sourcing problem.

232

7. Conclusion and Future Research This final chapter will summarize the dissertation’s contributions to modeling the supplier

selection problem, its practical significance and potential future research.

7.1 Summary of Model Contributions to Supplier Selection

This dissertation develops an integrated multiple criteria supplier selection model with

product life cycle considerations. A summary of modeling contributions of this dissertation

are:

▪ An approach to analyze supplier performance using ordinal logistic regression to

test relationships between key supplier attributes and quality and delivery

performance. An empirical model was created using supplier attributes and

performance data from an industrial equipment manufacturer. A number of

hypotheses were tested on the relationship between attributes such as quality

certifications, such as ISO 9001:2015, and quality and delivery performance. The

results and methodology provide a framework which can be used to develop

constraints to be included in an integrated supplier selection model, with the intent

of improving supplier performance. This methodology could also be used to short-

list suppliers in a multi-step supplier selection process (Chapter 3).

▪ Supplier selection is a multiple criteria optimization problem with conflicting

criteria, such as quality, delivery, service, product safety and others. Several

multiple criteria sourcing models exist in the literature. Very rarely they have

considered the fact that the relative importance of the supplier attributes depends

on the product life cycle phase. For example, during the Introduction phase,

companies may work with a single supplier emphasizing product safety, quality

and delivery. Revenue targets are more important than gross profit margins.

However, during the Growth phase, multiple suppliers may be used to meet surging

demand and to introduce price competition among the suppliers. In the Mature

phase, controlling procurement cost becomes important in order to boost the

233

product gross profit margin. In addition, many suppliers can deliver materials

needed for multiple products under various stages of the product life cycle phase.

Companies may also limit the business volume to new and existing suppliers. All

these factors are integrated into a general model in this thesis. A multiple criteria,

multiple products, supplier selection model that explicitly considers the product life

cycle phases of the products is developed. The goal programming approach is used

to solve the multiple criteria problem. An example was used to illustrate the

different programming solution approaches, including preemptive GP, non-

preemptive GP, Tchebycheff’s min-max GP and fuzzy GP. This general model

included products from the introduction, growth, maturity and decline phases of the

product life cycle. Also included in the model were parameters, based on the

results of the empirical study, including maximum business level, minimum

financial condition and minimum required quality technical capability. Multiple

objectives for the model included the minimization of cost, lead-time, quality

defects and late deliveries. Constraints specifying the maximum and minimum

number of suppliers by product life cycle phase were included in the model. The

Value Path method was utilized to provide visual tradeoffs of the multiple criteria

for product across the product life cycle (Chapter 4).

▪ The general supplier selection model from Chapter 4 was applied to a real-world

problem. This case study is focused on a U.S. based consumer products company

which utilizes a diverse global supply chain to design, manufacture and deliver

products to traditional brick and mortar retailers, on-line retailers and distributors.

Three key executive decision makers (Chief Operating Officer,

VP for Procurement and Global Sourcing Manager) were employed to identify and

rank the key sourcing criteria attributes for products representing the introduction,

growth, mature and decline phases of the product life cycle. Ranking methods

included rating method, Borda count utilizing pairwise comparisons and the

Analytic Hierarchy Process. The DMs shared their feedback on the cognitive

burden for each of the ranking methods. Ranking results indicate the DM’s

priorities change based on the product life cycle phase. The ranking results were

234

reviewed with the DMs and the feedback from this review was noted. The

contributions of this first part of the case study include providing insights relating

supplier attributes, to the DMs’ preferences which change throughout the product

life cycle and feedback regarding the cognitive burden required by the ranking

methods (Chapter 5).

▪ The supplier selection criteria weights and rankings, determined in Chapter 5, were

utilized in the preemptive and non-preemptive goal programming models to

determine final supplier selections and optimal order allocations for the case study.

In addition, Tchebycheff’s min-max and fuzzy goal programming methods were

also employed to determine final supplier selections and optimal order allocations.

The Value Path approach was utilized to compare the results from the preemptive,

non-preemptive and Tchebycheff GP models. The results were reviewed with the

DMs and the feedback from this review was discussed. In order to assess the

effectiveness of the model results, a comparison of the GP model results to actual

order allocations that were used by the company was completed. The GP model

results exceeded the actual order allocation criteria performance for many of the

products throughout the product life cycle phases. The results were presented to

the Chief Operating Officer (COO) using the Value Path approach. The COO

provided extensive feedback and had observations on how the model results could

be blended to examine alternate solutions. Also, he identified opportunities for the

company to improve supplier performance in an effort to minimize supplier

performance risk (Chapter 6).

7.2 Summary of Practical Significance

The methodology in Chapter 3 provides a template which can be customized for a company

linking key supplier attributes and performance measures. The ordinal logistic regression

is a general model which can be utilized to determine significant factors or supplier

attributes impacting performance. While the empirical model tests supplier quality and

delivery performance to key supplier attributes, a company can choose performance

metrics critical to their organization. For example, product safety would be of utmost

235

importance in the children’s toy industry and therefore be included in the modeling process.

Once the relationships are statistically confirmed and the model results are verified, the

predictive model results can be included in the creation of supplier selection models via

the addition of specific constraints. These results could also be utilized in a multi-step

supplier selection process providing guidance to create a short-list of suppliers. Companies

using this approach could gain a competitive edge through the use of this predictive model

with the intent of reducing the risk from poor supplier performance.

The general model presented in Chapter 4 offers a methodology to integrate the results of

a predictive supplier performance model from Chapter 3 into an integrated supplier

selection model with product life cycle considerations. The results of the preemptive, non-

preemptive and Tchebycheff min-max GP models provide alternative optimal solutions,

which can be used by DM to evaluate the results and tradeoffs for conflicting multiple

criteria across the product life cycle. The fuzzy GP results provide the DM with a result

which minimizes the maximum deviation from the ideal values, without any input from the

DM. The fuzzy model results inform the DM of the minimization of the “worst case”

optimal scenario. The Value Path approach provides a visual representation of the four

objectives (cost, lead-time, quality and delivery), for each of the products in their respective

phase of the product life cycle. While there is no single GP model that provides the best

overall solution for all product life cycle phases, the Value Path method provides the DM

with alternative solutions to choose from. Another significant benefit of using the GP

models and Value Path method is the flexibility to respond to the decision maker requests,

such as changing the target values, priority order or weights, by quickly providing

alternative solutions. Once the models have been developed and tested, it is relatively easy

to respond to these requests, providing a powerful decision support tool to the DM.

The real-world case study presented in Chapters 5 and 6, provides a unique opportunity to

view the supplier attributes, such as product safety, product performance, service and

capacity planning, which are driving the global supplier selection process. Many of the

performance criteria are beyond the traditional metrics of delivery, quality, cost and lead-

time. In addition to these selection criteria, there were business volume constraints applied

236

to the suppliers. This business volume was based on several factors including whether the

suppliers were new or existing suppliers. Total purchases from new suppliers were limited

to 10% or 20% of their total sales revenue, while existing suppliers were limited to 30% or

40% of their total sales revenue. These limits were created in an effort to minimize supplier

performance risk to the company. Additionally, there were constraints on the minimum

and maximum number of suppliers by product life cycle phase. Mature products were

required to have at minimum of two suppliers and a maximum of three suppliers in order

to insure there was price competition among the suppliers, with the objective of improving

the gross profit margins of high volume mature products. This was a stated goal of the

senior decision makers. The ranking results also varied by decision maker, the Chief

Operating Officer and the VP of Procurement were very consistent in their rankings results

and took a more strategic view of the selection criteria and product life cycle phases, while

the Global Sourcing Manager’s ranking results reflected a more tactical view. The results

provided an unfiltered view of the executive decision makers’ criteria selection and other

methods used to achieve improved supplier and supply chain results, while minimizing

supplier risk.

The case study results illustrate the effectiveness of the goal programming models in

selecting suppliers and allocating orders for products across the product life cycle. The

decision makers quickly understood the significance of the Value Path results and were

focused on choosing the best overall sourcing strategy based on their supplier criteria

rankings and weights. The DMs also considered relaxing some of their earlier business

constraints, such as total business volume, given the impact of this constraint on the optimal

solution. Additional model testing with modified constraints could be easily accomplished

to provide a new set of solutions to the DMs. This is another practical benefit of utilizing

an integrated supplier selection model to support sourcing decisions.

In order to test the effectiveness of the model, the actual orders were compared to the GP

model results. The actual orders in general performed poorly, when compared to the GP

model results. One of the significant conclusions was that the model results provided

substantial savings in procurement costs, while maintaining quality, delivery and safety

237

criteria performance. A number of factors, such as unanticipated demand, supplier capacity

constraints and supply chain designs, impacted the actual order allocations. One of the

benefits of comparing the actual results to the GP model results includes the ability to

identify opportunities for long term supplier development. During the review of the Value

Path results, comparing the GP models to the actual order allocations, the decision maker

identified opportunities for supplier development to improve supplier performance in

product safety and quality, in order to mitigate potential risk, while gaining better price

performance. The DM clearly understood the tradeoffs in supplier criteria between the GP

models and the actual results using the Value Path results. He observed that the actual

orders could be blended with the model results with the intent of generating better sourcing

strategies. This statement truly reveals the strength of this integrated decision-making

model, which allows “what if” scenarios to be easily tried, including changing goal

priorities and relaxing business constraints and provides the DMs with alternative sourcing

strategies to consider.

7.3 Future Research Opportunities

While this research provides an integrated global supplier selection model with product life

cycle considerations, a number of alternative methods and extensions of the model would

further extend the theoretical and practical applications. Other multiple criteria approaches

that do not use goal programming could be tested and results can be compared.

Another extension of this research would be to consider interactive methods. The case

study (Chapter 5) provides complete information from the DMs with regards to preferences

which were generated using the Analytic Hierarchy Process. Additionally, specific

constraints such as the percentage of total revenue allowed by supplier, minimum and

maximum number of suppliers by product life cycle phase, etc. making this case study a

seemingly good fit for interactive methods.

The integrated model can be extended to a multiple period supplier selection problem in

order to determine the actual supplier orders for each time period over a planning horizon.

Dynamic programming could be used to solve this problem. The stages of the problem

238

could represent transitions to the next stage as products move through the product life cycle

phases, with demand, supplier selection criteria and constraints changing. Initially, a

deterministic dynamic program could be formulated and solved. Next, demand variation

could be included as random variables transforming the problem to a stochastic dynamic

program.

The addition of multiple periods to the preemptive, non-preemptive, Tchebycheff’s min-

max and fuzzy goal programs would be a beneficial extension of the general model.

Products could transition through the product life cycle phases with supply sources,

constraints, demand and goal objectives via the addition of multiple periods. As noted

previously, this addition of multiple periods might also be a great fit for the dynamic

programming methodology.

239

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VITA

Richard J. Titus, Jr.

Richard J. Titus, Jr. earned a bachelor of science degree in Industrial Engineering from

Lehigh University, Bethlehem, PA in 1981. After graduation, he began his career at

Ingersoll-Rand, Phillipsburg, NJ. During his nearly 20 year career at Ingersoll-Rand, he

worked in a variety of roles and departments including industrial engineering,

information systems, inventory control, procurement, product engineering, quality and

shop operations. In 1984, he was awarded a company sponsored graduate sabbatical and

was enrolled in Lehigh University’s Manufacturing Systems Engineering (MSE)

Program. He earned his Masters Degree in MSE in 1986 completing a thesis titled

“Group Technology Based Flow Line Scheduling to Minimize Maximum Tardiness”.

Richard earned his APICS CPIM (Certified in Production and Inventory Management)

certification and his Lean Application Specialist and Six Sigma Black Belt certifications

in 1999. In 2000, he completed his Six Sigma Master Black Belt certification by Six

Sigma Qualtec.

In August, 2000, he joined the Management Department at Lehigh University working as

a Lecturer. He had previously taught as an adjunct instructor in Industrial and Systems

Engineering at Lehigh University. Courses taught included Operations Management,

Simulation, Database Design, Transportation and Logistics, Applied Supply Chain

Models and Applied Statistics. In 2004, he was admitted to the Harold and Inge Marcus

Department of Industrial and Manufacturing Engineering at the Pennsylvania State

University at University Park.

In addition to his graduate work, he continued to work as an adjunct at Lehigh University

and as an independent consultant in Lean Six Sigma. He has worked with over 50

companies over the past 11 years focusing on process improvement in a variety of

industries. He is a member of the Institute of Industrial Engineers (IIE) and the Institute

for Operations Research and Management Science (INFORMS).

an integrated supplier selection model with product life

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