Thạc Sĩ Three Extensions to the Inventory Theoretic Approach: A Transportation Selection Model, A Discrete E

ii





Abstract


The objective of this research is to provide three extensions to the inventory
theoretic approach which was developed to explain carrier/mode selection. One of the
strengths of the approach is that it accounts for both demand and lead time uncertainty
when calculating total logistics costs. As the world’s economies become more and more
interconnected, supply chains are growing in length and complexity resulting in increased
lead time uncertainty. To manage costs effectively, supply chain managers need to
account for lead time uncertainty. This research attempts to extend the inventory
theoretic approach in three stand-alone papers that examine issues such as product value,
variation in demand of lead time, equipment shortages, overbooking, and currency
fluctuations across multiple methodologies. The first chapter introduces the inventory
theoretic approach and gives a brief overview of the remaining chapters.
Chapter two develops an optimization model based on the inventory theoretic
approach in an effort to aide managers in selecting the best carrier/mode for their product.
Findings suggest that total logistics costs are minimized by selecting a faster mode of
transportation as the value of the product and the coefficient of variation in demand
increase. The model extends the existing state of the art in the inventory theoretic
transportation selection literature by precluding the need for conducting multiple
experiments among all available transportation options. Converting the inventory iii
theoretic approach into an optimization problem provides a first step towards extending
the inventory theoretic approach into the facility location literature stream.
Chapter three uses the inventory theoretic approach in a discrete event simulation
in an effort to investigate the accuracy of the numerical approach in estimating total
logistics costs and rank-ordering the best to worst carriers. The inventory theoretic
literature stream is replete with numerical examples and individual case studies, but has
few examples of research that uses simulation. Empirical data for this study are gathered
from a company. The case company uses a portfolio of carriers to ship product world-
wide. Findings suggest that the numerical approach used in the inventory theoretic
approach is robust for selecting the best carriers. In addition, carrier schedules were
found to have an impact on which carrier provides the lowest total logistics cost. Finally,
delays such as equipment shortages, ordering errors, and carrier overbooking were
quantified. The results suggest that delays should be tracked by shippers, because an
excessive number of delays by a carrier can impact the rank-ordering of carriers.
Chapter four, the final chapter, extends the inventory theoretic approach to the
postponement literature stream. A review of the postponement literature found that
transportation uncertainty is largely ignored, lacks examples of an (s, Q) inventory
model, and generally ignores the cost of in-transit stock, which is considered here. The
fourth chapter also explores the concept of postponement as it relates to product life
cycles. The literature supports the notion that postponement is more applicable to
products with short product life cycles due to the risk of obsolescence. The second
concept supported by the literature is the idea that products in the introduction/growth
stage of a product life cycle should use a speculation strategy, while products in the iv
mature/decline stages should use postponement. Empirical data for chapter four were
gathered from a Global 500 Company. Results from this essay suggest that ignoring
transportation uncertainty can underestimate the cost of using postponement and lead to
the selection of a supply chain strategy that is more expensive. Other findings suggest
that postponement strategies can be used for products with long product life cycles to
reduce the total cost of a product. This is occurs for both products in the
introduction/growth stage of the product life cycle as well as products at the
mature/decline stage. Finally, this research suggests that fluctuations in currency
exchange rates can be mitigated by use of an assemble-to-order strategy which is a form
of manufacturing postponement. v





Dedication


Soli Deo Gloria

and

in loving memory of my Dad, Doral R. Sandlin





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Acknowledgments

A special thanks to my five committee members for their patience, wisdom and
encouraging support. Completing this project was definitely a team effort that could not
have been accomplished without their help. My dissertation chair, Dr. Martha Cooper,
for guiding me through the dissertation process. Not only is Dr. Cooper a gifted scholar,
she spends countless hours mentoring and guiding students in their individual endeavors.
She is truly dedicated to helping people out. I have truly enjoyed working with Dr.
Cooper throughout this process.
The impetus for this research began in early 2007 during my search for
dissertation research topics. When I first met with Dr. John Saldanha, we discovered that
we had a mutual interest in transportation-related research due to our common
backgrounds; he as a former First Officer on ocean carriers and myself as an Air Force
pilot. This commonality in backgrounds eventually led to the selection of a
transportation related research topic and also a friendship between our families. His
insight and guidance on my research has been invaluable and I thank him for the time he
has invested in my studies.
The three other members of my committee also played pivotal roles to any degree
of success that I have achieved in this program. Dr. Keely Croxton was my academic
advisor during my course work at The Ohio State University and her knowledge of piece-
wise linear optimization was a key part of the research done during the first article. Dr.
Alan Johnson was my research advisor and my simulation professor at the Air Force vii
Institute of Technology. I appreciate his insightful feedback during my research. Finally,
Dr. Walter Zinn was instrumental in ensuring that my knowledge of inventory theory was
sound and offering excellent advice on establishing validity for my simulations. The
contributions made by all of my committee members were greatly appreciated and I
thank them for all of the time and effort that they invested in guiding my research efforts.
Two unnamed individuals from the case companies in Chapter three and Chapter
four deserve special recognition. Without their assistance, which was a significant
investment in time, I would not have been able to collect the data nor would I have had a
proper understanding of their company’s supply chains.
A special thanks to my fellow PhD students, past and present, to include Ping,
Francois, Matias, Rudi, Tim and Chris. I appreciate your friendship and advice. You
seven were a pleasure to work with and will make outstanding scholars.
However, the most important contribution to this work came from my family for
their unconditional support during my long, long hours researching, studying, and
writing. Georgi, Maddie, and Chase you three are my pride and my joy. I look forward
to seeing what life has in store for you. Thank you for your prayers, hugs, words of
encouragement, and constant entertainment. Any tough day at the office was overcome
by spending time with you three.
Finally, to the person I owe the biggest debt of gratitude in supporting all of my
academic endeavors is my beautiful wife. Shannon, thank you for patiently putting up
with my schedule and carrying more than your fair share during my doctoral studies. I
never would have made it through this program without you, nor would I have wanted to
do so. I cherish your love, support, and friendship. You are a special gift from God. viii




Vita

1992 .Bachelor of Science, Civil Engineering
The United States Air Force Academy, Colorado
Springs, Colorado

2004 .Masters of Business Administration
Rutgers University, Camden, New Jersey

2006 .Masters of Logistics Management
Air Force Institute of Technology, Dayton, Ohio

2009 .Masters of Arts in Logistics
The Ohio State University, Columbus, Ohio


Publications

Bird, Donald M., Gregory E. Seely, Carolyn L. Miller, Doral E. Sandlin, Matthew R.
Yakely, and Anthony C. Gomillion, and Peter J. Holland (1993), ―Harnessing the
Resources of Space in the Recovery of Potable Water from Wastewater by Lyophilization
(Freeze-Drying),‖ Proceedings of the 23 rd
International Conference on Environmental
Systems, July 12-15 1993, Colorado Springs, CO.

Holland, Peter J., Carolyn L. Miller, Donald. M. Bird, Jenny E. Yung, and Doral E.
Sandlin (1992), ―Recovering Potable Water from Wastewater in Space Platforms by
Lyophilization,‖ Proceedings of the 22 nd
International Conference on Environmental
Systems, July 13-16 1992, Seattle, WA.


Fields of Study

Major Field: Business Administration

Area of Specialization: Logistics

Minor Field: Operations Management
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Table of Contents

page

Abstract .ii
Dedication . v
Acknowledgments . vi
Vita viii
Table of Contents ix
List of Tables xii
List of Figures xiv
Chapter 1: Introduction 1
References . 6
Chapter 2: Optimizing Transportation Using a Total Logistics Cost Approach 8
Introduction 8
Literature Review . 10
Research Setting . 16
Model Framework 18
The Model . 18
Experimental levels 23
Results . 25
Sensitivity Analyses: Selecting a Sub-Optimal Transportation Option . 26
Sensitivity Analyses: Freight Rates 30 x
Managerial Implications 32
Limitations and Future Research 34
Conclusions 35
References . 37
Chapter 3: A Discrete Event Simulation of the Inventory Theoretic Approach 41
Introduction 41
Literature review 44
Demand During Lead Time . 44
Inventory Theoretic Simulations . 48
Scheduling Effects 50
Quantifying the Cost of Delays . 51
Research Design . 53
Research Setting . 54
Calculating Estimated Total Logistics Costs Using the Analytical Approach . 56
Calculating Estimated Total Logistics Costs Using a Discrete Event Simulation . 59
Model Parameters . 63
Assumptions . 68
Findings 69
Scenario #1: Low Value/High Volume 69
Scenario #1: High Value/Low Volume 73
Scenario #2: Scheduling Impact . 74
Scenario #3: Order Delays 75
Limitations 77 xi
Implications and Conclusions . 78
References 79
Chapter 4: The Impact of Product Life Cycle and Transportation Uncertainty upon
Speculation and Postponement . 83
Introduction 83
Literature Review . 86
Cost Models 87
Postponement and Product Life Cycle 93
Research Design . 100
Supply Chain Strategies . 102
Total Cost Model 107
Research Setting . 110
Findings 119
Total Cost Results . 119
Lead Time Uncertainty . 122
Sensitivity Analysis 125
Limitations 131
Implications and Conclusions . 131
References 133
Bibliography . 139
Appendix A . 150


xii





List of Tables

Table 2.1: A Survey of the Inventory Theoretic Approach 13

Table 2.2: Experimental Levels for Product Attributes 23

Table 2.3: Optimal Speed & Reliability for Different Product Profiles and Coefficient
Variations of Demand 26

Table 2.4: The Relative Change in Optimal Logistics Costs for a One Day Difference in
Speed . 27

Table 2.5: The Relative Change in Optimal Logistics Costs for a Half Day Difference in
Reliability . 28

Table 2.6: The Relative Change in Optimal Logistics Costs for a One-Day Difference in
Speed and a Half-Day Difference in Reliability 29

Table 2.7: The Affect of Changing Freight Rates on Optimal Speed & Reliability . 30

Table 3.1: Transportation Selection Mode and Carrier Research Methodologies 50

Table 3.2: Transit-time Country A & Country B Destination is Country C (Days) . 64

Table 3.3: Simulation Parameters 65

Table 3.4: Scenario #1 Experimental Levels – Door-to-Port Transit Times . 66

Table 3.5: Rail and Ocean Carrier Weekly Cutoff Dates . 66

Table 3.6: Scenario #2 Experimental Levels–Scheduling plus Transit Times 67

Table 3.7: Carrier Delays 68

Table 3.8: Estimated Total Logistics Costs of Product Family #1 Using Analytical
Approach by Value, Volume, and Customer Service Level . 69

Table 3.9: Estimated Total Logistics Costs of Product Family #1 Using Discrete Event
Simulation Approach by Value, Volume, and Customer Service Level 71

Table 3.10: Percent Difference between the Estimated Total Logistics Costs Using the
Numerical Approach and the Simulation Approach 72 xiii
Table 3.11: Actual Level of Customer Service Provided Product Family #1 . 72

Table 3.12: Estimated Total Logistics Costs of Product Family #2 Using Numerical
Analysis by Value, Volume, and Customer Service Level . 73

Table 3.13: Estimated Total Logistics Costs of Product Family #2 Using Simulation by
Value, Volume, and Customer Service Level 74

Table 3.14: Estimated Total Logistics Costs of Product Family #1 w/ Carrier Schedules
Using Simulation by Value, Volume, and Customer Service Level 75

Table 3.15: Estimated Total Logistics Costs of Product Family #1 w/ Carrier Schedules
& Delays Using Discrete Event Simulation by Value, Volume, and Customer
Service Level 76

Table 3.16: Summary Chart of Carrier Selection 77

Table 4.1: Survey of Postponement Literature 93

Table 4.2: Product Part Commonality Matrix . 112

Table 4.3: Traditional System Category ―A‖ Component Lead Times . 113

Table 4.4: New System Category ―A‖ Component Lead Times 115

Table 4.5: Customer, Customs, Inventory and Distribution Parameters . 117

Table 4.6: Experimental Levels 118

Table 4.7: Traditional System Total Cost per Unit . 120

Table 4.8: New System Total Cost per Unit 121

Table 4.9: The Cost of Uncertainty 123

Table A.1: 95% Confidence Intervals for Table 3.9 . 151

Table A.2: 95% Confidence Intervals for Table 3.14 . 153

Table A.3: 95% Confidence Intervals for Table 3.15 . 154 xiv





List of Figures

Figure 2.1: Speed and Reliability Profiles of International Door-to-Door Transportation
Options . 16

Figure 2.2: Mean Lead Time Function 20

Figure 2.3: Piecewise Linear Notation . 21

Figure 2.4: Door-to-Door Freight Rates as a Function of Mean and Standard Deviation
of Lead Time 24

Figure 3.1: Determining Safety Stock . 47

Figure 3.2: Research Steps . 54

Figure 3.3: The Case Company’s Distribution Channel Studied . 56

Figure 3.4: Steps in a Simulation Study 60

Figure 4.1: The Postponement and Speculation Matrix . 95

Figure 4.2: Product Life Cycle During Limited Time Offers 97

Figure 4.3: Research Steps . 101

Figure 4.4: Full Speculation – Make-to-stock . 103

Figure 4.5: Manufacturing Postponement – Assemble-to-Order . 104

Figure 4.6: Logistics Postponement – Ship-to-Order . 105

Figure 4.7: Full Postponement – Make-to-Order 106

Figure 4.8: Total Landed cost vs Changes in Transportation Costs 126

Figure 4.9: Total Landed Cost vs Changes in Holding Cost 127

Figure 4.10: Total Landed Cost vs Change in MachineCo’s Host Nation Currency
Relative to the Dollar . 129 Figure 4.11: Total Landed Cost vs Change in MachineCo’s Level of Customer Service
. 130