THE IMPACT OF CLOUD BASED SUPPLY CHAIN MANAGEMENT ON SUPPLY CHAIN

RESILIENCE

Cigdem Gonul Kochan, B.S, M.S.

Dissertation Prepared for the Degree of

DOCTOR OF PHILOSOPHY

UNIVERSITY OF NORTH TEXAS

August 2015

APPROVED: David R. Nowicki, Committee Chair Wesley S. Randall, Committee Member Brian Sauser, Committee Member Shailesh Kulkarni, Committee Member Jeff Sager, Chair of the Department of Marketing & Logistics Mark Wardell, Dean of the Graduate School

Kochan, Cigdem Gonul. The Impact of Cloud Based Supply Chain Management on

Supply Chain Resilience. Doctor of Philosophy (Logistics), August 2015, 161 pp., 23 tables, 28

figures, references, 280 titles.

On March 2011 a destructive 9.0-magnitude earthquake and tsunami along with nuclear

explosions struck northeastern Japan; killing thousands of people, halting industry and crippling

infrastructure. A large manufacturing company operating outside of Japan received the news in

the middle of the night. Within a few hours of the tsunami hitting Japan, this manufacturer’s

logistics team ran global materials management reports to communicate the precise status of the

products originating from Japan to their entire global network of facilities. With this quick and

far reaching communication the manufacturer was able to launch a successful contingency plan.

Alternative suppliers, already existing as part of their global network, were evaluated and used to

mitigate Japan’s disruptive impact. The resiliency of this manufacturer’s trusted network of

supply chain trading partners allowed for minimum disruptions, saving countless money and

maintaining continuity for its end-to-end supply chain. This manufacturer was part of a cloud-

based supply chain that provided the catalyst to quickly shift its resources to allay the impact of

no longer being able to receive product from Japan.

Today's supply chains are global and complex networks of enterprises that aim to deliver

products in the right quantity, in the right place, and at the right time in an increasingly volatile

and unpredictable environment. To cope with internal and external supply chain instability and

disruptions, supply chains need to be resilient to survive. A supply chain's ability to

collaboratively share information with its supply chain partners is one of the most important

factors that enhance a supply chain’s resilience. Cloud based supply chain management (SCM)

creates a platform that enables collaborative information sharing that helps to identify, monitor

and reduce supply chain risks, vulnerabilities and disruptions. However, supply chain academics

and practitioners are at its infancy in understanding the capabilities of cloud based supply chains

and its impact on resiliency. The goal of this dissertation is to explore how cloud based SCM

make supply chains more resilient to disruptions. To achieve this goal the present research

addresses the following fundamental research question: What is the impact of cloud based supply

chain management (SCM) on supply chain resilience? To address this research question, this

dissertation is comprised of three separate but interrelated essays. The first essay uses the

systematically literature review (SLR) method to provide clear definitions of supporting

constructs of supply chain resiliency (SCRES), classify the capabilities of SCRES, and identify

existing research gaps and future SCRES research ideas. The second essay applies resource-

based view (RBV) and dynamic capabilities as the theoretical lens to investigate the role of cloud

based SCM in establishing SCRES. The second essay develops a theory-driven, conceptual

model to illustrate and explain the relationships among cloud based SCM, SCRES, and the

supply chain capabilities identified in the first essay. The third essay uses systems dynamics

theory to develop two novel casual loop diagrams (CLD) and its equivalent systems dynamics

(SD) models to quantitatively analyze the impact of cloud based information sharing on supply

chain performance. A hospital supply chain is used as an illustrative example to show the

positive impact on performance. Lead-time, inventory spend, and customer service levels are the

comparative performance metrics used in this essay and are consistent with the findings of essays

1 and 2. One CLD and its equivalent SD model represent a traditional on-premise hospital

supply chain information sharing platform and the other represent a cloud based hospital

information sharing platform. The SD models simulate and compare the performance of the

traditional and cloud based hospital supply chain platforms.

ii

Copyright 2015

by

Cigdem Gonul Kochan

iii

ACKNOWLEDGMENTS

I would like to express my deepest gratitude to my advisor, Dr. David Nowicki, for his

guidance, encouragement, patience and continuous support throughout the course of this

research. I truly value what I have learned during the process. I would like to thank my

dissertation committee, Dr. Wesley Randall, Dr. Brian Sauser, and Dr. Shailesh Kulkarni for

their expertise and valuable insights. I am grateful to have had the opportunity to work with

them. I would also like to thank Dr. Victor Prybutok for all the advice and support over the

years. I further want to thank all may Ph.D. colleagues, especially Saba Pourreza, for her

invaluable friendship.

On the personal side, I would like to express my special thanks to my husband Mucahit

Kochan, my uncle Ahmet Guvengez, my aunts Tuba Yavuzkurt and Gulten Yazici, and my mom

for their unconditional love, motivation and unyielding support throughout this challenging

process. Finally, I would like to dedicate this dissertation to my amazing grandparents Drs.

Fatma and Zihni Guvengez for making me the person I am today. Without all of you, this

dissertation would not have been possible.

iv

TABLE OF CONTENTS

THE IMPACT OF CLOUD BASED SUPPLY CHAIN MANAGEMENT ON SUPPLY CHAIN

RESILIENCE ................................................................................................................................... i

Copyright 2015 ............................................................................................................................... ii

ACKNOWLEDGMENTS ............................................................................................................. iii

TABLE OF CONTENTS ............................................................................................................... iv

LIST OF TABLES ........................................................................................................................ vii

LIST OF FIGURES ....................................................................................................................... ix

CHAPTER 1 INTRODUCTION .................................................................................................... 1

Background ................................................................................................................................. 1

Supply Chain Disruptions ........................................................................................................ 1

Supply Chain Vulnerabilities, Risks, and Risk Management .................................................. 2

Supply Chain Resilience (SCRES) .......................................................................................... 4

The Role of Cloud Based Supply Chain Management (cloud based SCM) in Achieving SCRES ..................................................................................................................................... 4

Statement of the Purpose and Overarching Research Question .................................................. 6

Research Design and Contributions ............................................................................................ 7

Dissertation Organization ............................................................................................................ 8

CHAPTER 2 ESSAY 1 ................................................................................................................. 10

Abstract ..................................................................................................................................... 10

Introduction ............................................................................................................................... 10

Research Methodology .............................................................................................................. 14

Systematic Literature Review (SLR) Approach .................................................................... 14

Findings ..................................................................................................................................... 21

Definitions of SCRES ............................................................................................................ 21

The goal of SCRES................................................................................................................ 23

A Typology of SCRES .............................................................................................................. 24

SCRES Contexts (C) ............................................................................................................. 26

SCRES Interventions (I) ........................................................................................................ 32

v

SCRES Mechanisms (M)....................................................................................................... 44

SCRES Outcomes (O) ........................................................................................................... 51

Nature of the Selected Studies .................................................................................................. 52

Empirical Research ................................................................................................................ 53

Literature Reviews ................................................................................................................. 53

Analytical Research ............................................................................................................... 55

Conclusions ............................................................................................................................... 68

Limitations and future research ................................................................................................. 69

CHAPTER 3 ESSAY 2 ................................................................................................................. 70

Abstract ..................................................................................................................................... 70

Introduction ............................................................................................................................... 70

Theoretical Background ............................................................................................................ 73

Resource Based View (RBV) ................................................................................................ 73

Relational View (RV) ............................................................................................................ 74

Literature Review and Hypothesis Development...................................................................... 75

Cloud Computing .................................................................................................................. 75

Cloud based SCM .................................................................................................................. 77

Supply Chain Agility ............................................................................................................. 81

Impact of Cloud based SCM-enabled Collaboration on Supply Chain Agility .................... 82

Impact of Cloud based SCM-enabled Communication on Supply Chain Agility ................. 84

Impact of Cloud based SCM-enabled Integration on Supply Chain Agility ......................... 86

Impact of Supply Chain Agility on SCRES .......................................................................... 87

Methodology ............................................................................................................................. 88

Sample and Data Collection .................................................................................................. 88

Measures ................................................................................................................................ 89

Conclusions and Future Research ............................................................................................. 90

CHAPTER 4 ESSAY 3 ................................................................................................................. 91

Abstract ..................................................................................................................................... 91

Introduction ............................................................................................................................... 92

Literature Review ...................................................................................................................... 96

Information Sharing and Information Technology (IT) ........................................................ 96

Vendor Managed Inventory (VMI) ....................................................................................... 97

vi

eSCMS, Collaboration and Trust........................................................................................... 98

Cloud Computing ................................................................................................................ 100

Systems Theory and System Dynamics............................................................................... 101

The General Model.................................................................................................................. 103

Problem Description ............................................................................................................ 105

The Casual Loop Diagrams (CLDs) .................................................................................... 108

Numerical Study ...................................................................................................................... 116

Comparing the Traditional and Cloud Based Hospital Supply Chains: Model Run Results and Analysis ........................................................................................................................ 119

Conclusions and Managerial Implications .............................................................................. 125

Limitations and Future Research............................................................................................. 127

APPENDIX A. ............................................................................................................................ 129

Impact of Cloud based SCM on Supply Chain Resilience Survey...................................... 129

APPENDIX B ............................................................................................................................. 133

Stock and Flow Diagrams ....................................................................................................... 133

Hospital ................................................................................................................................ 133

Distributor ............................................................................................................................ 133

Manufacturer ....................................................................................................................... 134

APPENDIX C. ............................................................................................................................ 135

System dynamics equations .................................................................................................... 135

Order fulfillment .................................................................................................................. 135

Inventory Control ................................................................................................................ 136

Production ............................................................................................................................ 138

REFERENCES ........................................................................................................................... 140

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LIST OF TABLES

Table 1. Keywords and codes utilized in the search ..................................................................... 17

Table 2. Summary of selection criteria ......................................................................................... 18

Table 3. Top journals contributing to the field of SCRES ............................................................ 19

Table 4.Categories used in extracting and analyzing information in SLR ................................... 20

Table 5. Definitions of SRES........................................................................................................ 22

Table 6. Examples of disruptions and effects on global supply chains ........................................ 27

Table 7. Supply chain vulnerability definitions ............................................................................ 33

Table 8. A taxonomy of supply chain vulnerabilities ................................................................... 36

Table 9. A taxonomy of supply chain capabilities ........................................................................ 43

Table 10. Summary of theories applied in SCRES research ........................................................ 51

Table 11. Empirical studies ........................................................................................................... 53

Table 12. Literature review research ............................................................................................ 55

Table 13. Analytical Research ...................................................................................................... 67

Table 14. Cloud based SCM related definitions ........................................................................... 78

Table 15. Cloud based SCM applications ..................................................................................... 80

Table 16. Summary of the constructs and measures ..................................................................... 90

Table 17. Notation for the general health care supply chain models .......................................... 104

Table 18. Illustrative Parameters for SD models ........................................................................ 117

Table 19. Order fulfillment ratio table ........................................................................................ 118

Table 20. Demand and desired orders in a traditional and cloud based hospital supply chain .. 120

Table 21. Results for inventory levels in traditional and cloud based hospital supply chain SD 122

Table 22. Delivery Delays in traditional and cloud based hospital supply chain SD ................. 123

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Table 23. Order Backlogs in traditional and cloud based hospital supply chain SD .................. 124

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LIST OF FIGURES

Figure 1. SLR Methodology ......................................................................................................... 15

Figure 2. CIMO Framework ......................................................................................................... 16

Figure 3. Number of publications by year .................................................................................... 19

Figure 4. Keyword analysis in the existing definitions of SCRES ............................................... 23

Figure 5. Typology of SCRES based on the CIMO logic............................................................. 25

Figure 6. Supply chain disruptions categories .............................................................................. 28

Figure 7. The disruptions profile adopted from Sheffi and Rice (2005b) .................................... 28

Figure 8. Supply chain risks.......................................................................................................... 31

Figure 9. Supply chain vulnerabilities .......................................................................................... 35

Figure 10. Supply chain capabilities ............................................................................................. 41

Figure 11. SCRES outcomes......................................................................................................... 52

Figure 12. Number of articles by research methodologies ........................................................... 52

Figure 13. Proposed relationships ................................................................................................. 88

Figure 14. Internal and External Hospital Supply Chains ............................................................ 94

Figure 15. Information and product flow in an N-echelon, traditional hospital supply chain .... 105

Figure 16. Information and product flow in an N-echelon, cloud based hospital supply chain . 106

Figure 17. The Hospitals’ CLD .................................................................................................. 110

Figure 18.The Distributor’s CLD ............................................................................................... 110

Figure 19. The Manufacturer’s CLD .......................................................................................... 111

Figure 20. CLDs of cloud based hospital supply chain .............................................................. 112

Figure 21.Information and medicine flow in a three echelon traditional hospital supply chain 116

x

Figure 22. Information and medicine flow in a three echelon cloud hospital supply chain ....... 116

Figure 23. Order fulfillment as function of inventory ................................................................ 118

Figure 24. Inventory levels in a traditional hospital supply chain .............................................. 121

Figure 25. Inventory levels in a cloud based hospital supply chain ........................................... 121

Figure 26. Manufacturing lead time in cloud based hospital supply chain ................................ 123

Figure 27. Manufacturing lead time in traditional hospital supply chain ................................... 123

Figure 28. Order Backlogs in cloud based hospital supply chain ............................................... 125

Figure 29. Order Backlogs in traditional hospital supply chain ................................................. 125

1

CHAPTER 1

INTRODUCTION

Background

Supply Chain Disruptions

Today's supply chains constitute a global and complex network of enterprises that aim to

deliver products in the right quantity, in the right place, and at the right time in increasingly

volatile and unpredictable markets. Persistent instability in global markets exposes supply chains

to potential disruptions (Pettit, Fiksel, & Croxton, 2010). Disruptions can arise from external

sources such as natural disasters (e.g., earthquakes, hurricanes, and tsunami’s) or manmade

disasters (e.g., accidents, wars, terrorist attacks, strikes, financial crises, and sabotage).

Disruptions can also arise from internal supply chain sources such as a failure to coordinate

multiple functions, an occurrence of a fire at a manufacturing plant, or a loss of a critical supplier

(Christopher & Peck, 2004; Ponomorov & Holcomb, 2009; Soni & Jain, 2011; Wagner &

Neshat, 2010). The following are examples of disruptions and their effect on global supply

chains:

• A destructive earthquake and tsunami along with the nuclear explosions that struck

northeastern Japan in 2011 led to plant shutdowns, reducing the supplies of semiconductors

and car parts to manufacturers across the globe (Soni & Jain, 2011).

• An eight-minute fire at a Philips semiconductor plant led Ericsson to lose its market share

and 400 million Euros (Rice & Caniato, 2003; Sheffi, 2005b; Snyder, Scaparra, Daskin, &

Church, 2006; Tang & Tomlin, 2008).

2

• As a result of September 11 world trade center terrorist attacks Ford Motor Co. had to idle

several assembly lines intermittently, and Toyota Motors Corp., came within hours of halting

production at its plant in Indiana (Sheffi & Rice, 2005).

While a supply chain disruption is the trigger that leads to the occurrence of risk, supply

chain vulnerability is the factor that causes loss to supply chains (Wagner & Bode, 2006).

Supply Chain Vulnerabilities, Risks, and Risk Management

Christopher and Peck (2004) define vulnerability as "an exposure to serious disturbance,

arising from risks within the supply chain as well as risks external to the supply chain". Every

supply chain is vulnerable to various supply chain risks (Knemeyer, Zinn, & Eroglu, 2009).

Supply chain risks comprise of any random disturbance that lead to deviations from normal flow

of the expected or planned activities and experiences negative consequences (Svensson, 2000).

Fiksel (2003), Christopher and Peck (2004) and Bogataj and Bogataj (2007) classify supply

chain risks as: 1) internal to the firm – e.g., process and control risks; 2) external to the firm,

internal to the supply chain network – e.g., demand and supply risks; and 3) external to the

supply chain – e.g., environmental risks As supply chain risks increase, supply chains become

more vulnerable to unforeseen disruptions (Wagner & Bode, 2006).

Supply chain’s ability to cope with supply chain disruptions is limited to a practitioner’s

understanding of potential supply chain vulnerabilities and risks. (Jüttner, Peck, & Christopher,

2003) suggest risk management enhances the understanding and improves the classification of

supply chain risks and vulnerabilities. Even though supply chain risk management is lacking a

grounded definition in the literature, a widely accepted definition of supply chain risk

management is “the identification and management of risks for the supply chain, through a

3

coordinated approach among supply chain members, to reduce supply chain vulnerability as a

whole” (Jüttner et al., 2003).

Efficient supply chain risk management may reduce supply chain vulnerabilities by

decreasing the likelihood further disruptions (Manuj & Mentzer, 2008; Sheffi & Rice, 2005).

However, traditional supply chain risk management often fails when supply chain disruptions

and the performance effect of these disruptions are unknown (Pettit et al., 2010; Wagner & Bode,

2008).

To address the insufficiency of traditional risk management strategies on identifying risks

and subsequent vulnerabilities, researchers suggest various risk mitigation strategies such as

postponement, avoidance, hedging, control, sharing and security (Manuj & Mentzer, 2008;

Svensson, 2000). However, the effectiveness of these risk mitigation strategies are not

adequately justified in the literature (Ponomarov, 2012). The problem lies in the difficulty of

applying traditional risk management and mitigation strategies to each link in complex, global

supply chains for every possible disruption (Pettit et al., 2010).

Studies suggest developing a set of supply chain capabilities such as flexibility, agility,

redundancy, visibility, and collaboration as a way to address the problem of risk management

and mitigation strategies (Carvalho, Azevedo, & Cruz-Machado, 2012; Carvalho, Duarte, &

Machado, 2011; Charles, Lauras, & Wassenhove, 2010; Craighead, Blackhurst,

Rungtusanatham, & Handfield, 2007; Jüttner & Maklan, 2011; Ponomorov & Holcomb, 2009;

Tang & Tomlin, 2008). These set of capabilities lead to a relatively young supply chain

phenomena called supply chain resilience (SCRES).

4

Supply Chain Resilience (SCRES)

SCRES is a multidisciplinary and underexplored research discipline; therefore, there is a

lack of consensus in the literature on the definition of SCRES (Hohenstein, Feisel, Hartmann,

Giunipero, & Saenz, 2015). The present research adopts a widely cited definition of SCRES first

proposed by Ponomorov and Holcomb (2009, p.131):

The adaptive capability of the supply chain to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function.

The Role of Cloud Based Supply Chain Management (cloud based SCM) in Achieving SCRES

Supply chain partners' limited and untimely information about demand and inventory

reduces the visibility of upstream and downstream supply chain partners. According to the World

Economic Forum (WEF)'s supply chain risk survey, 64 percent of the supply chain executives

stated that the lack of shared information due to the poor visibility is the second largest reason

for supply chain vulnerability. The survey implies that 64 percent of major companies do not

have visibility across their supply chain network (WEF, 2012). Christopher and Peck (2004)

also identify the lack of visibility as a major supply chain vulnerability that causes information

distortions, most prominently demand information distortion.

Demand information distortion leads to various problems such as inaccurate demand

forecasting, non-zero lead time, order batching, supply shortages and price fluctuations (Lee,

Padmanabhan, & Whang, 1997). For example, in 2003, due to the product shortages in Western

Europe, Nokia customers ordered more than what they needed because they believed that Nokia

might start restricting allocations. Unfortunately, the exaggerated figures distorted Nokia's

reading of the market, causing the company to inaccurately forecast sales (Chopra & Sodhi,

2004). In the context of supply chain, the information distortion among different stages of the

5

supply chain is called Bullwhip effect (Lee et al., 1997). Bullwhip effect causes the demand

amplification from customer to the manufacturer, as demand information passes back through

the supply chain (Chatfield, Kim, Harrison, & Hayya, 2004).

One way to address the bullwhip effect is the use of information technology (IT) to

facilitate agility (visibility and velocity) through information sharing (Christopher & Lee, 2004).

However, embedding complex IT systems in a firm's supply chain processes does not guarantee

better performance or competitive advantage (Wu, Yeniyurt, Kim, & Cavusgil, 2006).

Organizations may still struggle with the cost and lack the necessary information visibility even

with the adoption of complex IT systems (Casey, Jones-Farmer, Yun, & Benjamin, 2012). For

instance, when a disruption occurs in a company's sophisticated on-premise Enterprise Resource

Planning (ERP) system that company loses its visibility. Real time data for in-transit items is lost

before the items reach their buyers. The items may arrive lost or damaged, and the count of

inventory may not include in-transit items (GTNexus, 2012). Additionally, competing firms can

easily replicate a resource provided by an IT system. Therefore, how an IT system as a resource

can create competitive advantage remains unresolved (Barney, 2001; Wu et al., 2006).

To address the poor visibility, velocity issues, and cost of complex on-premise IT system

implementations firms are now adopting internet-enabled electronic supply chain management

systems (eSCMs) (Cegielski, Jones-Farmer, Wu, & Hazen, 2012; Cristina Giménez & Helena R.

Lourenço, 2008; Lin, 2014). eSCM offers a platform that enhances communication, coordination

and collaboration (Lin, 2014). Emerging from eSCM are cloud based supply chain management

systems (cloud based SCM) that continue to gain the attention among competing firms. Cloud

based SCM is an internet enabled, inter-organizational system (IIO) that has its roots in cloud

computing technology used for intra-organizations. When compared to traditional on-premise IT

6

systems, cloud based SCM systems provide on-demand self-service, broad network access,

resource pooling, measured service, and rapid elasticity; all at a lower cost of ownership via pay-

per-use. Pay-per-use offers a cost advantage for companies that cannot afford the initial

investment required for purchasing and implementing complex on-premise IT systems

(Durowoju, Chan, & Wang, 2011). Therefore, firms adopt cloud based SCM systems as an

efficient, cost effective solution to gain visibility, velocity and flexibility to improve the

resilience of their supply chains. For the present research, we define cloud based SCM as “an

emergent eSCM that senses the changes in real time and executes the optimal response by

providing a platform for collaboration, communication and integration across the supply chain”.

This definition of cloud based SCM is a synthesis of other mostly complimentary definitions in

the literature (Cegielski et al., 2012; Giménez & Lourenço, 2008; Lin, 2014; Liu, Ke, Wei, &

Hua, 2013; Wong, Lai, Cheng, & Lun, 2015).

Statement of the Purpose and Overarching Research Question

Cloud computing applications in supply chain management is a relatively new concept.

In the supply chain management literature, cloud computing studies exists but have been limited

to the benefits and complications of adopting cloud computing technology (Buyya, Yeo,

Venugopal, Broberg, & Brandic, 2009; Casey et al., 2012; Cegielski et al., 2012). Many aspects

of cloud implementation in supply chain management have not been thoroughly studied and its

full potential has not yet been explored (Toka, Aivazidou, Antoniou, & Arvanitopoulos-Darginis,

2013). Additionally, there is a limited explanation for application of cloud computing in supply

chains from theoretical perspective (Wu, Cegielski, Hazen, & Hall, 2013). Furthermore, industry

adoptions of cloud based SCM as a way to cope with disasters continue to increase. However,

organizations are still unaware of the long-term cost benefits, reliability, and resilience

7

capabilities of cloud solutions. This implies that supply chain academics and practitioners are at

its infancy in understanding the capabilities of cloud based SCM and its impact on resilience.

The goal of this dissertation is to explore how cloud based SCM make supply chains

more resilient to disruptions. This dissertation is an attempt to bridge gaps in the extant literature

at the intersection of SCRES and cloud based SCM. To achieve this goal, the present research

addresses the following fundamental research question: What is the impact of cloud based supply

chain management (SCM) on supply chain resilience?

Research Design and Contributions

We develop three separate but highly interrelated essays to address the fundamental

research question of the present research, what is the impact of cloud based supply chain

management (SCM) on supply chain resilience? The first essay uses the systematically

literature review (SLR) and context-interventions-mechanism-outcomes (CIMO) logic approach

to provide clear definitions of supporting constructs of supply chain resiliency (SCRES), classify

the capabilities of SCRES, and identify existing research gaps and future SCRES research ideas.

The essay develops a typological framework from a classification of the context, supply chain

capabilities, and SCRES outcomes. This essay makes three key contributions to the SCRES

body of literature, the essay: 1) adopts all four of the CIMO attributes in the SLR approach, 2)

develops an overarching typological framework that draws upon the SLR findings, and 3)

provides an extensive review and classification of the theories and analytical methods applied in

the SCRES literature.

The second essay apply resource-based view (RBV) (Barney, 1991; Wernerfelt, 1984)

and relational view (RV) (Dyer & Singh, 1998) as the theoretical lens to investigate the role of

cloud based SCM in establishing SCRES. The second essay develops a theory-driven, conceptual

8

model to illustrate and explain the relationships among cloud based SCM, SCRES, and the

supply chain capabilities identified in the first essay. This essay establishes a theoretical

background for cloud based SCM and develops a valid instrument to measure cloud based

SCM’s impact on SCRES. This essay intends to enhance a manager’s understanding of utilizing

cloud Based SCM to build resilience in their supply chains.

The third essay uses systems theory (Forrester, 1961; Von Bertalanffy, 1950) to develop

two casual loop diagrams (CLD) and its equivalent systems dynamics (SD) models to simulate

the impact of cloud based information sharing on supply chain performance. A hospital supply

chain is used in the comparative analysis to illustrate the positive impact cloud based technology

has on supply chain performance. Lead-time, inventory spend, and customer service levels are

the comparative performance metrics used in this essay and are consistent with the findings of

essays 1 and 2. One CLD and its equivalent SD model represent a traditional on-premise

hospital supply chain information sharing platform and the other represent a cloud based hospital

information sharing platform. The SD models simulate and compare the performance of the

traditional and cloud based hospital supply chain platforms. We believe we are the first to use

systems theory to develop generalizable CLDs and SD models to compare the performance of an

end-to-end traditional, on-premise hospital supply chain and an information-sharing, cloud-based

hospital supply chain. The findings of this essay will help health care decision makers to

understand the structure and the benefits of utilizing cloud based information systems in their

hospital supply chain.

Dissertation Organization

This dissertation is organized in five chapters. Chapter 1 provides the foundation for

studying the role of cloud based SCM in establishing SCRES. This chapter outlines the concepts,

9

existing problems and gaps, provides the statement of the purpose, discusses the research design

and contributions, and puts forth the outline of the dissertation. Essays 1, 2 and 3 are in Chapters

2, 3 and 4. Chapter 2 presents the first essay, a systematic literature review of SCRES. Chapter 3

presents the second essay, a theory-driven conceptual model development for cloud based

SCRES. Chapter 4 presents the third essay, a supply chain dynamics framework of a cloud based

hospital information sharing platform.

10

CHAPTER 2

ESSAY 1

Abstract

The study of supply chain resilience (SCRES) continues to gain interest in the academic

and practitioner communities, starting with the 2000 fuel protest disrupting transportation in the

United Kingdom and especially after the terrorist attacks in the United States on September 11,

2001. The purpose of this paper is to present a focused review of the SCRES literature by

investigating supply chain capabilities, their relationship to SCRES outcomes, and the

underpinning theoretical mechanisms of this relationship. This study uses the systematic

literature review (SLR) approach to examine 255 articles and to down select to the most relevant

134 peer-reviewed studies, published during 2000 to 2015. The studies are organized and

synthesized using the context-interventions-mechanisms-outcomes (CIMO) logic. From a

classification of the SCRES literature based on CIMO, a typological framework is developed.

The typology of SCRES literature can help practitioners to better understand SCRES and to

measure and assess the resilience of their supply chains. The findings of this study also outline

the gaps in the SCRES literature and present an agenda for future research.

Introduction

Supply chains exposure to continual instability in global markets creates vulnerability for

unpredictable disruptions.(Pettit et al., 2010). While supply chain disruption is the trigger that

leads to the occurrence of risk, vulnerability is the factor that causes loss to supply chain

(Wagner & Bode, 2006). Thus, supply chain “vulnerability” to a disruptive event can be viewed

as a combination of the probability of a disruption and its potential severity (Sheffi, 2005b).

Although firms can develop effective supply chain risk management strategies to cope with

11

disruptions and reduce vulnerability (Manuj & Mentzer, 2008), applying traditional risk

management strategies to each link in the global supply chain for every possible disruption is

difficult (Pettit et al., 2010). Therefore, supply chains need a set of strategies that not only

identify, monitor, and reduce supply chain risks and disruptions; but also reacts quickly and cost-

effectively (Melnyk, Davis, Spekman, & Sandor, 2010). These set of strategies lead to Supply

chain resilience (SCRES).

The first wide-spread study on SCRES occurred in the United Kingdom following the

transportation disruptions from the fuel protests in 2000 and the outbreak of foot-and-mouth

disease in early 2001. The SCRES study about UK’s industrial knowledge base revealed the gaps

in understanding supply chain vulnerability (Peck, Abley, Christopher, Haywood, Saw,

Rutherford & Strathern, 2003). Recently, interest in understanding supply chain vulnarabilites to

disruptions has increased as the negative consequences of disruptions to a firm’s operations and

performance has increased (Blackhurst, Dunn, & Craighead, 2011). For instance, the earthquake

in central Japan in 2007 damaged the facilities and utilities of Riken corp., a supplier of

automobile components, which forced Toyota, one of Riken’s many customers, to shut down all

12 of its domestic assembly plants, and caused a production delay affecting approximately

55,000 vehicles (Pettit et al., 2010). Having a single supplier made Toyota vulnerable to the

inevitable earthquake. Although it is hard to eliminate the probability of disruptions,

vulnerabilities to disruptions can be minimized to reduce the impact of the disruptions. In the

supply chain management literature, studies suggest improving SCRES reduces the

vulnerabilities to disruptions (Bhamra, Dani, & Burnard, 2011; Blackhurst et al., 2011; Pettit et

al., 2010; Ponomorov & Holcomb, 2009). Scholars explore various design characteristics/

capabilities and mitigation strategies to enhance SCRES (Carvalho, Azevedo, et al., 2012;

12

Carvalho et al., 2011; Charles et al., 2010; Craighead et al., 2007; Jüttner & Maklan, 2011;

Ponomorov & Holcomb, 2009; Tang & Tomlin, 2008). However, in todays global business

environment being resilient to cope with disruptions is not enough. It is important to turn the

resiliency’s positive impact on a firm’s performance into a competitive advantage (Carvalho,

Azevedo, et al., 2012; Sheffi, 2005b). The purpose of this study is to review the existing research

in an effort to understand the concept of SCRES, its factors, and outcomes that can be ultimately

be used to improve a firm’s competitive position.

In the literature, the concept of SCRES is still in its infancy (Blackhurst et al., 2011;

Hohenstein et al., 2015; Ponomorov & Holcomb, 2009). Since it is recognized that significant

contributions can be made by an effective review of the extant SCRES literature, a substantial

amount of conceptual work of SCRES has been published. The SCRES conceptual publications

include previous literature reviews that define SCRES (Ponis & Koronis, 2012; Ponomorov &

Holcomb, 2009), present antecedents and consequences of SCRES (Briano, Caballini, Giribone,

& Revetria, 2010; Christopher & Peck, 2004; Pettit et al., 2010), and offer guidelines based on

practices (Sheffi, 2005b; Y. Sheffi & J. B. Rice, 2005). However, the use of the SCRES’

supporting constructs and construct definitions remain inconsistent due to the divergent concepts

from theory building (Hohenstein et al., 2015; Ponomorov & Holcomb, 2009), and there is a lack

of consensus over a well-grounded definition of SCRES (Mensah & Merkuryev, 2014;

Tukamuhabwa, Stevenson, Busby, & Zorzini, 2015). With the aim of addressing these gaps,

Hohenstein et al. (2015) and Tukamuhabwa et al. (2015) have conducted compherensive reviews

of the SCRES literature by applying the systematic literature review (SLR) approach (Denyer &

Tranfield, 2009; Rousseau, Manning, & Denyer, 2008; Tranfield, Denyer, & Smart, 2003).

Tukamuhabwa et al. (2015) review the existing literature to provide a comprehensive definition

13

of SCRES, asess the contributions to the literature and suggest Complex Adaptive Systems

(CAS) as a theoretical lens for studying SCRES. Likewise, Hohenstein et al. (2015) review the

SCRES literature with the emphasis on the assestment and measurement of SCRES by the level

of readiness, response, and recovery time. Hohenstein et al. (2015) develop a conceptual

framework with propositions. In this study, we apply the SLR approach and CIMO (context,

intervention, mechanisms and outcomes) framework (Denyer & Tranfield, 2009) to classify,

synthesize, and report the findings from across the SCRES literature. Although Tukamuhabwa et

al. (2015) and Hohenstein et al. (2015) categorize and synthesize SCRES studies and SCRES

factors, indentify the gaps and future research, there still appears to be no overarching typology

to outline exactly what constitutes SCRES. In addressing this gap, we develop a new typological

framework drawing upon the SLR finding using the CIMO framework to address our

fundamental research question, “How do supply chain capabilities and vulnerabilities create

specific outcomes in the contexts of SCRES?” The objectives of this paper are to: 1) identify and

define SCRES factors; 2) examine the antecedents and consequences of SCRES applying the

CIMO-logic; 3) develop a typological framework; and 4) identify gaps and future research

directions.

This study makes three key contributions to the body of the SCRES literature. We believe we

are the first to completely adopt the CIMO framework as part of the SLR approach to examine

the SCRES literature. There are only a few studies that exist in the supply chain literature that

adopt all atrribute of CIMO framework (Pilbeam, Alvarez, & Wilson, 2012) and none of them

are in the area of SCRES. We develop an overarching typological framework based on the

CIMO logic drawing upon the findings from the SLR. Although Hohenstein et al. (2015) and

Tukamuhabwa et al. (2015) briefly identify various methodological approaches such as case

14

studies, conceptual/theoretical, emprical, and analytical research in the SCRES literature, there is

no compherensive examination of the relevalant, gradually increasing analytical studies.

Therefore, we provide an extensive review of the analytical methods such as mathematical

modeling and simulation that are applied in the SCRES literature to gain a better understanding

of SCRES and its measures and to provide a basis for future research.

The organization of this paper is as follows: The first section describes the research

methodology, defining the SLR approach. Section 2 presents the findings of the study organized

based on the CIMO logic. Section 3 categorizes the research methodologies used in the SCRES

literaure. Section 4 presents a typological framework developed based on the CIMO logic of

SCRES. Next, Section 5 presents the conclusions and managerial implications. Finally, Section

6 discusses research limitations and future research directions.

Research Methodology

The goal of the present research is to produce a reliable knowledge base and highlight

opportunities for future research by identifying what is known and what is not known about

SCRES. We use the systematic literature review (SLR) approach and evidence syntheses (Briner

& Denyer, 2012; Rousseau et al., 2008) to achieve this goal.

Systematic Literature Review (SLR) Approach

Originated from the medical sciences, SLR has been widely recognized within the

management and organization sciences as an evidence-based approach for identifying, selecting,

analyzing, synthesizing and reporting “best evidence” secondary data (Briner & Denyer, 2012;

Denyer & Tranfield, 2009; Tranfield et al., 2003). Unlike the traditional narrative literature

reviews, SLR aims to eliminate bias and improve quality of the review process by ensuring rigor,

replicability, and consequently relevant results (Tranfield et al., 2003). According to Denyer and

15

Tranfield (2009) the SLR approach has four methodological core principles. The SLR approach

is transparent, inclusive, explanatory, and heuristic based. These set of principles are

incorporated into the five review stages shown in Figure 1: 1) question formulation; 2) locating

studies; 3) article selection and evaluation; 4) analysis and synthesis; and 5) reporting and using

the results. The following sections will discuss each of the five review stages to improve the

validity and quality of the SLR findings.

Figure 1. SLR Methodology

Stage 1: Question Formulation

A good systematic review question needs to be formulated as an answerable question that

could be broken down into number of questions (Briner & Denyer, 2012). Denyer and Tranfield

(2009) propose to form review questions by utilizing a CIMO framework that consists of context

(C), interventions (I), mechanisms (M), and outcomes (O). Context questions identify which

individuals, institutional settings, or wider systems are the subject of the study. Intervention

questions detect the effects of what event, action, or activity. Mechanisms questions pinpoint the

relationships between interventions and outcomes. Outcomes questions ascertain the effects of

the intervention, measures of outcomes and intended and unintended effects. We adapt CIMO-

logic to form our initial review question and establish a foundation for synthesizing and reporting

the findings from the SLR approach. As shown in the Figure 2, we suggest that the supply chain

Question Formulation

Stage 1

Locating Studies

Stage 2 Article

Selection and Evaluation

Stage 3

Analysis and Synthesis

Stage 4 Reporting and Using the Results

Stage 5

16

capabilities and vulnerabilities (I) may produce different SCRES outcomes (O) based on

different mechanisms (M) depending on the specific SCRES contexts (C). Therefore, we use the

CIMO framework to addresses the following research question: How do supply chain

capabilities and vulnerabilities create specific outcomes in given SCRES contexts?

Figure 2. CIMO Framework

Stage 2: Locating Studies

A total of 18 keywords related to SCRES and the concepts of SCRES were identified.

The keywords include the phrases “supply chain resilience”, “supply chain resiliency”, “supply

chain vulnerability”, “supply chain risk”, “supply chain disruptions”, “supply chain disruptions”,

and “supply chain” and “resilience” in the title, keywords, or in the abstract of the articles. Table

1 depicts the keywords, constructs, and codes utilized in the search. In the process of refining

the keywords, a group of three academics with expertise in the area of supply chain risk and

supply chain resilience were consulted to minimize bias during the keyword search. A large

variety of search databases were used including: ABI/Inform Complete, EBSCOhost, Science

Direct, Wiley, Emerald, Taylor & Francis, Web of Science, and Google scholar.

Constructs Keywords Codes

SCRES Supply chain resilience Supply chain resiliency Resilient supply chain

supply chain* AND resilien*

Supply chain vulnerability Supply chain vulnerability Vulnerability in supply chain

supply chain* AND resilien* AND vulnerab* supply chain* AND vulnerab*

Con

text

Supply Chain Disruptions Supply Chain Risk

Inte

rven

tions

Supply Chain Capabilities Supply Chain Vulnerabilities

Mec

hani

sms SCRES Theories

Out

com

es

Improved Performance Excessive Risks Eroded Profitability Sustainable Competitive Advantage

17

Supply chain risk

Supply chain risk Supply chain risk management

supply chain* AND resilien* AND risk* supply chain* AND risk management

Supply chain capabilities

Supply chain capabilities supply chain agility supply chain flexibility supply chain velocity supply chain visibility

supply chain* AND resilien* AND capabili* OR agility OR flexibility OR velocity OR visibility

Supply chain disruptions

Supply chain disruptions Disruptions in supply chains Supply chain disasters Disasters in supply chain Supply chain disturbances Disturbance in supply chain

supply chain* AND resilien* AND disrupt* OR disaster* OR disturban* supply chain* AND disrupt* OR disaster OR disturban*

Note: Table 1 is constructed based on (Pereira, Christopher, & Andrea Lago Da, 2014)

Table 1. Keywords and codes utilized in the search

Stage 3: Study Selection and Evaluation

We chose a period of 15 years (2000-2015) and noticed that the first SCRES research

article has was published in 2003 (Jorge Verissimo, 2009; Tukamuhabwa et al., 2015). We

excluded book chapters, conference proceedings, and PhD dissertations and limited our study

selection to only peer-reviewed journal articles. Based on David and Han (2004) study, Newbert

(2007) develops criteria that restrict the article search in order to enhance the quality and

relevance of the articles. We adopt Newbert (2007) and Colicchia and Strozzi (2012) selection

criteria to determine what articles to include and exclude for the present study. We start with 255

and finish with 134 articles. Table 2 shows each selection criterion and the number of articles

returned from the selection methodology.

Description Number of Articles

1. Search for peer-reviewed articles published in the last 15 years in variety of databases including ABI/Inform Complete, EBSCOhost, Science Direct, Wiley, Emerald, Taylor & Francis, Web of Science, and Google scholar

255

18

2. Ensure substantive relevance by requiring that selected articles contain at least keywords “resilien*” and “supply chain” in their title or abstract.

183

3. Eliminate substantively irrelevant articles by excluding papers related to very narrow aspects or contexts

147

4. Ensure substantive and empirical relevance by reading all remaining abstracts for substantive context and empirical content.

141

5. Further ensure substantive and empirical relevance by reading all remaining articles in their entirety.

134

Table 2. Summary of selection criteria

Table 3 presents the top peer-reviewed journals where the SCRES articles have been

published, the number of articles published, and the percentage of the journal’s contribution to

the SCRES field. Out of 134 articles, 12 of the articles were published in the Supply Chain

Management: An International Journal representing 9 percent of the all publications.

Journal Number of Articles Percentage

Supply Chain Management: An International Journal 12 9.0% International Journal of Production Economics 6 4.5% International Journal of Logistics Research and Applications: A Leading Journal of Supply Chain Management 5 3.7%

International Journal of Physical Distribution & Logistics Management 5 3.7% Journal of Business Logistics 5 3.7% International Journal of Production Research 4 3.0% MIT Sloan Management Review 4 3.0% European Journal of Operational Research 3 2.2% Journal of Cleaner Production 3 2.2% Omega 3 2.2% Production and Operations Management 3 2.2% Transportation Research Part E: Logistics and Transportation Review 3 2.2% Environmental science & technology 2 1.5% IEEE Systems Journal 2 1.5% International Journal of Logistics Management 2 1.5% International Journal of Logistics Systems and Management 2 1.5% Journal of Operations Management 2 1.5% Journal of Transportation Security 2 1.5% The International Journal of Logistics Management 2 1.5% Other peer-reviewed journals where a single SCRES article was published 64 47.8% Total 134 100.0%

19

Table 3. Top journals contributing to the field of SCRES

Figure 3 shows the publishing trend in the SCRES field from the years 2003 to June of

2015. An increasing trend in number of the publications can be observed especially after the

year 2010. Yet, the number of publications are considerably low compared to other supply chain

management areas such as supply chain risk (Pereira et al., 2014). Therefore, scholars consider

SCRES as an underexplored research area (Blackhurst et al., 2011; Hohenstein et al., 2015;

Ponomorov & Holcomb, 2009).

Figure 3. Number of publications by year

Stage 4: Analyses and Syntheses

According to Tranfield et al. (2003), systematic review should synthesize the findings of

individual studies into a new or different arrangement. There are four different approaches to

synthesize the literature: aggregation, integration, interpretation, and explanation (Rousseau et

al., 2008). The synthesizing approach should be made explicit and a justification given for all

decisions (Briner & Denyer, 2012). In this study, we apply the integration and explanation

2 3 4

8

3 46 6

13

19

2725

14

20

approaches. The integration approach is a summary of qualitative and quantitative methods used

in the selected articles. The explanation approach is the summary and interpretation of the

selected articles that provide a deeper understanding of the constructs. In this stage, we extracted

and documented the information from each of the 134 sources. Table 1.4 illustrates the

categories used in extracting and analyzing data in the SLR. Area, categories and further article

categorization method were adapted from (Pilbeam et al., 2012). We organized the categories

and documented the information using a MS-Excel spreadsheet.

Area Category Information Descriptive Year Publication year Journal Name of the peer-reviewed journal in which the article was published in. Title Full title of the article Methodology Article type Categorize articles as analytical, empirical, and literature review Analytical articles are further categorized as modeling and simulation Empirical articles are further categorized as case studies and surveys Literature review articles are further categorized as conceptual articles and

systematic literature reviews Theoretical lens Identification of theories applied in SCRES articles Thematic Context Categorization of context within which the SCRES is created such as

uncertainty/disruptions and risk Intervention Categorization of supply chain capabilities and vulnerabilities that creates

SCRES outcomes Mechanisms Theoretical mechanisms underpinning the relationship between

interventions and outcomes Outcome Identification of SCRES dimension and its possible outcomes Other Other information presented in this study such as comprehensive review

of the analytical models

Table 4.Categories used in extracting and analyzing information in SLR

Stage 5: Reporting and Using the Results

We report the findings as an outline of what is known and what is not known about the

review question. The aim of the SLR approach is to summarize the literature in a way to make

the findings more understandable for practitioners and researchers. Consistent with Tranfield et

21

al. (2003), we offer descriptions, examples, and an audit trail justifying conclusions in our SLR

findings.

Findings

Definitions of SCRES

In the extant literature, the concept of “resilience” is extensively defined in physical,

ecological, and socio-ecological systems, psychology, economy, disaster management,

engineering, and organizational research (Ponomorov & Holcomb, 2009). Even though the

context of the term resilience differs in each discipline, across all disciplines the concept of

resilience does not change (Bhamra et al., 2011). Rice and Caniato (2003) are the first authors

who define resilience at an organizational level. Later, Christopher and Peck (2004) adopt the

ecosystem definition of resilience, “the ability of the system to return to its original state or move

to a new more desirable state after being disturbed”, and use this definition in the supply chain

context. To date, authors propose various definitions of SCRES captured in Table 5.

Author SCRES definitions

Rice and Caniato (2003) Maintain operations following a disruption and a competitive advantage if the company responds more favorably to disruption than the competition.

Fiksel (2003) The capacities for an enterprise to survive, adapt, and grow in the face of turbulent change.

Stoltz (2004) A key to developing a strategic plan that is sustainable and capable of producing results that are better than those of less resilient competitor

Christopher and Peck (2004) The supply chain’s ability to cope with the consequences of unavoidable risk events in order to return its original operations or move to a new more desirable state after being disturb

Sheffi(2005) The ability of a supply chain to bounce back from a large disruption

Sheffi and Rice (2005) A function of its competitive position and the responsiveness of its supply chain that can be achieved by either creating redundancy or increasing flexibility

Datta et al. (2007) Not only the ability to maintain control over performance variability in the face of disturbance, but also a property of being adaptive and capable of sustained response to sudden and significant shifts in the environment in the form of uncertain demands.

Briano et al. (2009) Not only the ability to manage risks, but also to be better placed with respect to the competitors in the damage management, but also to take advantage from it

22

Ponomorov and Holcomb (2009) The adaptive capability of the supply chain to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function

Klibi et al. (2010) A strategic posture of deployed resources (facilities, systems capacity and inventories), suppliers and product-markets, as a physical insurance against SC risk exposure, providing the means to avoid disruptions as much as possible, as well as the means to bounce back quickly when hit

Barroso et al.(2011) The supply chain’s ability to react to the negative effects caused by disturbances that occur at a given moment in order to maintain the supply chain’s objectives

Carvalho et al.(2012) The supply chain’s ability to cope with unexpected disturbances

Schmitt and Singh (2012) The ability of a system or component to bounce back from a setback whereas reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time, or even resist failure

Colicchia and Strozzi (2012) Not only the ability to recover from mishaps, but also a proactive, structured and integrated exploration of capabilities within the supply chain to cope with unforeseen events

Ponis and Koronis (2012)

The ability to proactively plan and design the supply chain network for anticipating unexpected disruptive events, respond adaptively to disruptions while maintaining control over structure and function an transcending to a post-event robust state of operations, if possible, more favorable than the one prior to the event, thus gaining competitive advantage.

Pettit, Croxton, and Fiksel (2013) The ability to survive, adapt, and grow the face of turbulent change.

Sawik (2013) A firm’s capacity to survive, adapt and grow in the face of change and uncertainty

Tukamuhabwa et al. (2015) The adaptive capability of a supply chain to prepare for and/or respond to disruptions, to make a timely and cost effective recovery, and therefore progress to a post-disruption state of operations-ideally, a better state than prior to disruption

Table 5. Definitions of SRES

The wide variety of SCRES definitions in the existent literature suggests that there is a

lack of consensus in its definition. We identified the keywords/concepts that are commonly used

in the SCRES definition using Wordle, the result is shown in Figure 4. Wordle is an online

visualization tool that creates word clouds based on the frequency of the word appearance in the

text.

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Figure 4. Keyword analysis in the existing definitions of SCRES

According to the Wordle keyword analysis ability appears 11 times and adapt, change,

control, cope, grow, and react appear 4 times each in the various definitions of SCRES. The

keyword analysis results indicate that the authors mainly define SCRES as an ability. We further

find that ability and capability are used interchangeably. Based on our findings, we believe

Ponomorov and Holcomb (2009, p.131) provide the most comprehensive, multidisciplinary

definition of SCRES:

The adaptive capability of the supply chain to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function. Ponomorov and Holcomb (2009) essentially define SCRES as an adaptive capability. In

alignment with our Wordle findings, we adapt Ponomorov and Holcomb (2009)’s SCRES

definition in this paper.

The goal of SCRES

According to Haimes (2006), the goal of the SCRES is to recover to the desired state of

the system within an acceptable time and cost, and to reduce the impact of a disturbance by

24

changing the effectiveness level of a potential threat (Carvalho, Barroso, Machado, Azevedo, &

Cruz-Machado, 2012). This goal can be achieved by enabling the shift towards desirable states in

which failure modes would not occur (Carvalho, Azevedo, et al., 2012). For instance, when faced

with a disruption, a resilient supply chain would shift flow to other customers and markets so

that its supply source continues at full operating rates (Bradley, 2005). However, “resilience

depends on choices made before the disruption than the actions taken on the midst of the

disruption” (Sheffi, 2005b). Therefore, to enable such a shift towards to desirable state, resilience

needs to be designed into supply chain (Christopher & Peck, 2004). Resilience can be designed

into supply chains by integrating supply chain capabilities such as redundancy and flexibility

(Sheffi, 2005b; Tang & Tomlin, 2008; Williams, Lueg, & LeMay, 2008), and by creating risk

management culture (Christopher & Peck, 2004). Once resilience is achieved, supply chains can

overcome vulnerabilities, unforeseen disruptions, threats, and catastrophic events by reducing the

risks (Sheffi, 2006).

A Typology of SCRES

In this section, we present our typological framework of SCRES shown in Figure 5. The

purpose of the framework is to organize the identified elements and links of SCRES based on the

CIMO logic. We develop the typological framework in the proceeding sections corresponding to

each CIMO: (C) context, (I) intervention, (M) mechanism, and (O) outcome.

25

InterventionsInterventionsContext Context

Supply Chain Risks

Internal Supply Chain Risks

• Demand risk• Supply risk

External Supply Chain Risks

• Environmental risk

Internal Firm Risks

• Process risk• Control risk

Supply Chain Disruptions

External Disruptions

• Natural disasters• Man-made disasters

Internal Disruptions

• Uncertain demand• Uncertain supply yields• Uncertain lead times• Uncertain supply

capacity • Uncertain supply cost

Supply Chain Capabilities

Responsiveness

• Agility • Velocity • Visibility• Flexibility

• Redundancy

Recovery

• Adaptability• Crises management• Resource mobilization• Communication

strategies• Consequence

mitigation

Readiness

• Efficiency• Dispersion• Market position• Security• Collaboration• Financial Strength• Revenue management• Market strength• Organizational culture• Anticipation

Supply Chain Vulnerabilities

Internal Vulnerabilities

• Resource limits• Supplier• Customer• Infrastructure • Deliberate threats

Structural Vulnerabilities

• Supply chain structure• Supply chain design

characteristics• Supply chain

complexity

External Vulnerabilities

• Turbulence• Regulatory, legal and

bureaucratic• Financial

Theories

• Resource based view (RBV)• Systems theory• Control theory• Complex adaptive systems theory

(CAS)• Dynamic capabilities • Relational view (RV)• Contingency theory• Social capital theory• Rational choice theory

MechanismsMechanisms Outcomes Outcomes

• Improved Performance (Balanced SCRES)

• Excessive Risk (Unbalanced SCRES-high vulnerability& low capabilities)

• Eroded Profitability (Unbalanced SCRES-low vulnerabilities& high capabilities)

• Sustained Competitive Advantage

Figure 5. Typology of SCRES based on the CIMO logic

26

SCRES Contexts (C)

In the extant literature authors examine SCRES in wide variety of industry sectors such

as agri-food (Leat & Revoredo-Giha, 2013; Yang & Xu, 2015), automotive (Azevedo,

Govindan, Carvalho, & Cruz-Machado, 2013; Carvalho & Azevedo, 2014; Carvalho, Azevedo,

& Cruz–Machado, 2013; K. Govindan, Azevedo, Carvalho, & Cruz-Machado, 2015), chemical

and petrochemical (Ehlen, Sun, Pepple, Eidson, & Jones, 2014; Vugrin, Warren, & Ehlen, 2011),

coal (Guo, 2013), counterfeit (Stevenson & Busby, 2015), humanitarian and disaster relief (Day,

2014; Dubey, Ali, Aital, & Venkatesh, 2014), electronic (Rajesh & Ravi, 2015), energy

(Urciuoli, Mohanty, Hintsa, & Boekesteijn, 2014), healthcare (Jerry, 2011; Johnstone & Geelen-

Baass, 2008; Xiao & Wang, 2014), maritime (Berle, Norstad, & Asbjørnslett, 2013), military

(Zhao, Kumar, Harrison, & Yen, 2011), and retail (Salehi Sadghiani, Torabi, & Sahebjamnia,

2015). We analyzed the SCRES articles that cover different industries and identified two

common contextual environments: disruptions and risk.

Supply Chain Disruption

Increased number of natural and manmade disasters, supply chain complexity, firms’

attempt to improve their financial performance, and global competition have made supply chains

more prone to disruptions (Tang & Tomlin, 2008; Wagner & Neshat, 2010). Kleindorfer and

Saad (2005) define supply chain disruptions as unplanned and unanticipated events that disrupt

the normal flow of goods and materials within a supply chain. The disruptions can arise from: 1)

external sources such as natural disasters (e.g., earthquakes, hurricanes, tsunami’s) or man-made

disasters (e.g., accidents, wars, terrorist attacks, strikes, financial crises or sabotage), and 2)

internal sources such as a failure to integrate all functions, a fire at a manufacturing plant, or a

loss of a critical supplier in a supply chain (Christopher & Peck, 2004; Ponomorov & Holcomb,

27

2009; Wagner & Neshat, 2010; Soni & Jain, 2011). These unavoidable disruptions such as

natural disasters, accidents and national attacks may cause operational and financial risks within

the supply chain (Sheffi, 2005b). When the conditions for a disruption are present and not

addressed the likelihood of a disruption – even a low probability disruption is not low anymore

(Sheffi, 2005b). Therefore, supply chains must be examined in order to understand supply level

of risk exposure (Schmitt & Singh, 2012). Table 6 provides a list of disruptions and their effect

on global supply chains.

Year Disruption

1998 A strike at two General Motors parts plants led to the shutdowns of 26 assembly plants resulted in a production loss (L.V. Snyder et al., 2006).

2001 An eight-minute fire at a Philips semiconductor plant led Ericsson to lose its market share and 400 million Euros (Rice & Caniato, 2003; Sheffi, 2005b; L.V. Snyder et al., 2006; C. Tang & Tomlin, 2008).

2001

After terrorist attacks of September 11, the U.S. government closed the country’s borders and shut down all incoming and outgoing flights. As a result, Ford Motor Co.’s had to idle several assembly lines intermittently, and Toyota Motors Corp., came within hours of halting production at its plant in Indiana (Y. Sheffi & J. B. J. Rice, 2005).

2003 Blackouts in the northeast region of the United States are resulted equipment malfunctions and systemic failures that caused discontinuity in supply (Kleindorfer & Saad, 2005).

2005 Hurricanes Katrina and Rita destroyed the nation’s oil refining capacity and large inventories of coffee and lumber (L.V. Snyder et al., 2006).

2006 A fire hazard in 2006 caused Dell to recall 4 million laptop computer batteries made by Sony(C. Tang & Tomlin, 2008).

2007 An earthquake in central Japan damaged the facilities and utilities of Riken corp., a supplier of automobile components, forced Toyota, one of Riken’s many customers, to shut down all 12 of its domestic assembly plants, and delayed production of approximately 55,000 vehicles (Pettit et al., 2010).

2011 A destructive earthquake and tsunami along with the nuclear explosions that struck northeastern Japan has led to plant shut downs and threaten supplies of everything including semiconductors and car parts to the manufacturers across the globe (U. Soni & Jain, 2011).

Table 6. Examples of disruptions and effects on global supply chains

28

Consistent with the extant literature we categorize disruptions as internal and external to the

supply chain. Figure 6 presents our categorization of the disruptions.

Supply Chain Disruptions

External Disruptions

• Natural disasters• Man-made disasters

Internal Disruptions

• Uncertain demand• Uncertain supply yields• Uncertain lead times• Uncertain supply

capacity • Uncertain supply cost

Figure 6. Supply chain disruptions categories

According to Sheffi and Rice (2005), stages of a disruption and the response can be

characterized in eight phases: 1) preparation, 2) the disruptive event, 3) first response, 4) initial

impact depends on the magnitude of the disruption, 5) full impact, 6) recovery preparations, 7)

recovery, and 8) long-term impact. As seen in Figure 7, it takes time to recover from disruptions,

and the supply chain may or may not return to it is original state, especially if customer

relationships are damaged, One example of the impact of damaged customer relationships is

Ericsson’s loss of its market share after the Philips semiconductor disruption in 2001.

Figure 7. The disruptions profile adopted from Sheffi and Rice (2005b)

29

In the SCRES literature, supply chain disturbance, supply chain disruptions, supply chain

vulnerability, supply chain risk, and supply chain risk source terms are highly interrelated

(Carvalho, Barroso, et al., 2012). Especially, supply chain disruption and supply chain

disturbance concepts are used interchangeably. In this study, we define supply chain disturbance

as low magnitude unplanned and unanticipated events that may cause disruptions in a supply

chain.

Wagner and Bode (2006) are the first authors to distinguish between supply chain disruption,

risk, risk source and vulnerability. They describe supply chain disruption as “an unintended,

untoward situation, which leads to supply chain risk”; supply chain risk source as “a source

where risk emerges from when a negative deviation value of a performance measure is the result

of a supply chain disruption”; supply chain risk as “detriment of a supply chain disruption, i.e.

the realized harm or loss”. Then, they use Svensson’s (2000) atomistic approach to postulate,

“supply chain vulnerability is a function of certain supply chain characteristics and that the loss

of a firm incurs is a result of its supply chain vulnerability to a given supply chain disruption”

(Wagner & Bode, 2006). This paper adapts Wagner and Bode’s (2006) definitions of supply

chain risk, risk source, and disruption. A supply chain disruption can be considered as an event

that leads to the occurrence of risk. Even though disruption leads to the occurrence of risk, it is

not the only determinant of the result (Stephan M. Wagner & Bode, 2006). Susceptibility of the

supply chain to the disruptions, vulnerability, also needs to be examined.

Supply Chain Risk

Supply chain risk is closely related to the supply chain vulnerability (Colicchia & Strozzi,

2012; Wagner & Bode, 2008). Supply chain risk can be described in the context of supply chain

vulnerability as “ the existence of random disturbances that lead to deviations in the supply chain

30

from normal, expected or planned activities, all of which cause negative effects or

consequences” (Svensson, 2000). Supply chain risk is “detriment of a supply chain disruption,

i.e. the realized harm or loss” (Wagner & Bode, 2006, p.303). As supply chain risks increase,

companies become more vulnerable to unforeseen disruptions.

In the literature, there are many articles identify and categorize the sources of supply

chain risks (Bogataj & Bogataj, 2007; Christopher & Peck, 2004; Fiksel, 2003; Kleindorfer &

Saad, 2005). According to Kleindorfer and Saad (2005), there are two broad categories of risks

affecting supply chain design and management include risks arising from the problems of

coordinating supply and demand and risks arising from disruptions to normal activities such as

natural disasters, from strikes and economic disruptions, and from acts of purposeful agents,

including terrorists. Sheffi and Rice (2005) identify the primary risk as the risk in demand. They

suggest customer expectations, global competition, complex supply chains, and short life cycles

increase the uncertainty in demand. Risks are often categorized as: internal to the firm -

including process and control risks; external to the firm and internal to the supply chain network

- including demand and supply risks; and external to the supply chain - including environmental

risks (Bogataj & Bogataj, 2007; Christopher & Peck, 2004; Fiksel, 2003). Consistent with the

extant literature, we categorize the supply chain risks as internal and external supply chain risks

as shown in the Figure 8.

31

Supply Chain Risks

Internal Supply Chain Risks

• Demand risk• Supply risk

External Supply Chain Risks

• Environmental risks

Internal Firm Risks

• Process risk• Control risk

Figure 8. Supply chain risks

There is a strong consensus that risk management is a critical capability that increases

SCRES (Colicchia & Strozzi, 2012). Jüttner et al. (2003) define supply chain risk management

as, ‘‘the identification and management of risks for the supply chain, through a coordinated

approach among supply chain members, to reduce supply chain vulnerability as a whole’’.

Efficient supply chain risk management reduces vulnerability by reducing the likelihood of a

further disruption (Sheffi & Rice, 2005); therefore, increasing SCRES (Bogataj & Bogataj,

2007).

In practice, Wagner and Bode (2008) state that supply chain risk management is only

possible when the probabilities and the effects of the supply chain disruptions on supply chain

performance are known. The traditional risk assessment strategies cannot adequately deal with

the unexpected events (Pettit et al., 2010). In order to address the insufficiency of risk

management strategies on identifying risks and subsequent vulnerabilities, researchers suggest

various risk mitigation strategies such as postponement, avoidance, hedging, control, sharing and

security (Manuj & Mentzer, 2008; Svensson, 2000). However, the effectiveness of risk

mitigation strategies are not adequately justified in the literature (Ponomarov, 2012). Logistics

32

processes and capabilities are also needed to cope with disruptions (Ponomorov & Holcomb,

2009; Sheffi & Rice, 2005).

SCRES Interventions (I)

Supply Chain Vulnerability

In the extant literature, there are a number of conceptual approaches and definitions of

supply chain vulnerability. Similar to SCRES concept, supply chain vulnerability lacks a

grounded definition. Svensson (2000) is known as the first author to develop a conceptual

framework for the vulnerability concept that can be operationalized in a supply chain context. He

introduces atomistic vulnerability approach that considers a minor and limited part of the supply

chain, and holistic vulnerability approach that considers the full extent of the supply chain

(Svensson, 2000). Later, he conceptualizes the vulnerability in terms of time and relationship

dependencies in companies’ inbound and outbound supply chains. Thus, he defines vulnerability

as “a condition that is caused by time and relationship dependencies in a company’s activities in

a supply chain” (Svensson, 2002). According to Christopher and Peck (2004), the increasing

interdependencies of organizations and their supply chains generate risks. From the risk

management perspective, Christopher and Peck (2004, p. 3) define vulnerability as "an exposure

to serious disturbance, arising from risks within the supply chain as well as risks external to the

supply chain". Later, Jüttner et al. (2003, p. 200) define supply chain vulnerability as “the

propensity of risk sources and risk drivers to outweigh risk mitigating strategies, thus causing

adverse supply chain consequences”. Sheffi (2005b) conceptualizes “vulnerability” to a

disruptive event as “a combination of the likelihood of a disruption and its potential severity”.

Table 7 depicts commonly used definitions of supply chain vulnerability.

33

Author Definition Svensson (2002) A condition that is caused by time and relationship dependencies in a company’s

activities in a supply chain Jüttner et al.(2003) The propensity of risk sources and risk drivers to outweigh risk mitigating

strategies, thus causing adverse supply chain consequences Christopher and Peck (2004) An exposure to serious disturbance, arising from risks within the supply chain as

well as risks external to the supply chain Sheffi (2005a) A combination of the likelihood of a disruption and its potential severity

Wagner and Bode (2006) A function of certain supply chain characteristics and that the loss a firm incurs is a result of its supply chain vulnerability to a given supply chain disruption

Gould et al.(2010)

Not only the possibility of an undesired event taking place, or of an attack bypassing the security measures, but also the extent of the impact the event would have

Pettit et al. (2010) A fundamental factor that makes an enterprise susceptible to disruptions

Jüttner and Maklan (2011) A latent condition which only becomes manifest if a disruptive event occurs as well as an exposure to a risk

Table 7. Supply chain vulnerability definitions

Sheffi (2005b, p.25-26) categorizes supply chain vulnerabilities as:

1) operations vulnerabilities - includes everything from supplier business disruptions to theft by employees; 2) hazard vulnerabilities - includes both random disruptions (resulting from severe weather, earthquake, or accidents) and malicious disruptions such as international terrorism and product tampering; 3) financial vulnerabilities - includes a wide range of macro-economic and internal financial troubles, from currency exchange fluctuations to credit rating downgrades to irregularities in the financial statement; and 4) strategic vulnerabilities - includes everything from new foreign competitors to external public boycotts to internal ethics violations.

Practitioner’s failure to understand the potential supply chain vulnerabilities and supply

chain risks negatively affects the supply chain’s ability to handle supply chain disruptions.

Therefore, various studies examine supply chain vulnerability and supply chain risk management

from a practitioner’s perspective. Jüttner et al. (2003) suggest that risk management reduces

supply chain vulnerability as whole. In order to identify risk drivers of a supply chain strategy,

managers need new approaches to track the vulnerabilities. If supply chain managers can track

and manage supply chain vulnerability, they could reduce the impact of supply chain disruptions.

34

Wagner and Neshat (2010) employ a novel approach using graph theory to support a

manager’s ability to track and assess their supply chain vulnerability and compare different risk

mitigation strategies. According to Sheffi (2001), supply chain risk mitigation strategies lead to

trade-off decisions: 1) repeatability versus unpredictability; 2) the lowest bidder versus the

known supplier; 3) centralization versus dispersion decisions in production and distribution; 4)

collaboration versus secrecy, and 5) redundancy versus efficiency help to cope with vulnerability

(Jüttner et al., 2003; Sheffi, 2001). Applying risk mitigation strategies to a supply chain is

possible by understanding how risk/performance trade-offs are managed. Jüttner (2005) explores

the risk/ performance trade-offs and other strategies to provide direction to managers for

choosing right risk management strategies in a global supply chain. Kleindorfer and Saad (2005)

categorize the focus of risk mitigation strategies as: 1) the design of the product; 2) the supply

chain itself, including location of inventories, transportation modes, and sourcing arrangements;

and 3) the operational control of the supply chain, including emergency (or crisis) response.

They create a framework to address the impact of supply chain design characteristics on

efficiency and robustness of the supply chain.

In the literature, vulnerabilities (Sheffi, 2005b), vulnerability drivers/ factors (Pettit et al.,

2010; Stephan M. Wagner & Bode, 2006), risks (Jüttner & Maklan, 2011), resiliency reducers

(Blackhurst et al., 2011), uncertainties (Sheffi & Rice, 2005), risk sources/factors, risk drivers

and disruption terms are often used interchangeably. For the purpose of this study, we synthesize

all the terms as vulnerabilities. We adapt Pettit et al. (2010)’s vulnerability taxonomy to identify

vulnerabilities and vulnerability drivers (sub-factor) discussed in the supply chain management

(SCM) literature. In the SCM literature, there are many articles that focus on supply chain

vulnerabilities. However, we limit our analysis to 13 articles that explicitly discuss

35

vulnerabilities. Table 8 is our taxonomy of supply chain vulnerabilities that categorizes

vulnerabilities as: 1) external vulnerabilities that include turbulence, regulatory, legal, and

bureaucratic and financial pressures; 2) internal vulnerabilities that include resource limits,

supplier, customer, infrastructure, and deliberate threats; and 3) structural vulnerabilities that

include supply chain structure, design characteristics and complexity. Figure 9 shows the

categories of the vulnerabilities.

Figure 9. Supply chain vulnerabilities

Supply Chain (SC) Vulnerabilities

Internal Vulnerabilities

• Resource limits• Supplier• Customer• Infrastructure • Deliberate threats

Structural Vulnerabilities

• SC structure• SC design

characteristics• SC complexity

External Vulnerabilities

• Turbulence• Regulatory, legal and

bureaucratic• Financial

36

Vulnerabilities Vulnerability drivers Svennson (2002)

Hamel and Velikangas

(2003)

Christopher and

Rutherford (2004)

Peck (2005)

Sheffi (2005)

Kleidorfer and Saad

(2005)

Wagner and Bode

(2006)

Craighead et al.(2007)

Tang and

Tomlin (2008)

Pettit et al.

(2010)

Wagner and

Neshat (2010)

Blackhurst et. al(2011)

Adenso -Diaz et.

al (2012)

Ext

erna

l

Turbulence:

Natural disasters X X X X X X X Pandemic Exposure to geopolitical disruptions X X X X Terrorism and sabotage X X X X X Unpredictability of demand X X X X X Civil unrest X X X Socio political instability X X Social/ Cultural changes X X Industrial espionage X Product Tampering X X

Regulator, Legal and Bureaucratic:

Administrative barriers X X Legal changes X X Environment legislation X X Custom regulations X Quality requirements X

Financial: Fluctuations in currencies and prices X Price pressures X X Credit rating downgrades X

Inte

rnal

Resource limits: Capacities: Supplier, production, and distribution capacity X Availabilities: raw material, utilities, and labor X X Lack of redundancies X

Sensitivity:

Fragility X Reliability of equipment X Potential safety hazards X Visibility of disruptions to stake holders X Supplier trust, loyalty , relations and liability X X X

Supplier:

Supplier's form of financial distress and bankruptcy X X Supplier dependency X X Supply base reduction/Small supply base X X X X Loss of a key supplier X Volatility of supplier location X

Customer: Customer dependency X X Customer's financial distress and bankruptcy X Customer's expectations X X

Infrastructure:

IT failures X X X Technological Innovations X Equipment malfunctions X X X Disruptions in the supply of electricity and water X X Human centered issues X X X Increased outsourcing X Sourcing from supply clusters X

Deliberate threats: Privacy and theft X X X Labor disputes X X X

Stru

ctur

al

Supply chain structure:

Centralized distribution X Low inventory X Lean inventory X X X Specialized factories X

Design Characteristics: Density X X Complexity X X Node Criticality- #of nodes X X X

Complexity:

More product variety X More -frequent new product introductions X More sales channels/markets X Global Competition/ Innovation X X Scale/ Extent of supply network/ Global Sourcing Network X X X X X X X Just in time inventory systems X Shorter product life cycle X X X Reliance upon information flow X X X X Degree of outsourcing X X Vendor Managed Inventory X Outsource manufacturing X X

Table 8. A taxonomy of supply chain vulnerabilities

37

Supply Chain Capabilities

From our review, we recognize that the terminology for the supply chain capabilities is

inconsistent. The authors use supply chain capabilities (Blackhurst et al., 2011; Rice & Caniato,

2003), capability factors (Pettit et al., 2010; Zhang, Dadkhah, & Ekwall, 2011), logistics

capabilities (Ponomorov & Holcomb, 2009), resilience capabilities (Jüttner & Maklan, 2011),

supply chain characteristics (Carvalho et al., 2012), and resilience strategies (Cox, Prager, &

Rose, 2011; Sheffi, 2005a) interchangeably. In the present study, we use the term supply chain

capabilities.

(Sheffi, 2005b; Wagner & Bode, 2006) suggest supply chain capabilities are the

antecedents of supply chain vulnerability that impact both the probability of occurrence and the

severity of supply chain disruptions. Accordingly, Petit et al. (2010) define capabilities as

“attributes that enable an enterprise to anticipate and overcome disruptions”. Petit et al. (2010)

create a framework to show that developing capabilities reduce vulnerabilities and create a

balance between investment and risk. They define this state as “resilience” and postulate that

SCRES increases as capabilities increase and vulnerabilities decrease.

Researchers explored various supply chain capabilities that reduce vulnerabilities, detect,

prevent or reduce the occurrences of supply chain disruptions. Craighead et al. (2007) investigate

the impact of supply chain design characteristics (density, complexity, and node critically) and

mitigation capabilities (recovery and warning) on supply chain disruption severity. Their

findings suggest that when density, complexity, and node critically of supply chain increase,

severity of supply chain disruptions increases. Conversely, when the recovery and warning

capabilities increase, SCRES increase and severity of supply chain disruptions decreases. Jüttner

& Maklan (2011) empirically investigate the relationship among SCRES, supply chain risk

38

management, and supply chain vulnerability in a global context. One of their findings highlights

that supply chain capabilities such as flexibility, velocity, visibility, and collaboration enhance

SCRES. Also the degree of these supply chain capabilities influence the speed at which supply

chain participants are able to response to changes in demand and recover from a disturbance;

therefore, influencing SCRES (Carvalho et al., 2012). An effective implementation of a supply

chain’s capabilities lead to improved performance when matched with its vulnerabilities

(Carvalho et al., 2012). Ponomorov and Holcomb (2009) suggest that integrating supply chain

capabilities will lead to a firm’s sustainable competitive advantage.

In the literature, there is a problem of inconsistent terminology for the concepts of

capabilities as well. The terms resilience, robustness, reliability, agility and flexibility terms are

used interchangeably (Christopher & Peck, 2004; Christopher & Rutherford, 2004; Klibi et al.,

2010; Schmitt & Singh, 2012). In an attempt to provide clarity we identify and compare these

interrelated concepts from the SCRES literature.

Robustness and Resilience

The robustness concept is defined as the ability of supply chains to carry out its functions

for a varity of plausible functions (Colicchia & Strozzi, 2012; Klibi et al., 2010). A robust supply

chain has the ability to resist disruptions, remain effective in the event of a disruption, whereas a

resilient supply chain has the ability to adapt a new desirable state after being disturbed (Vlajic,

van der Vorst, & Haijema, 2012). Therefore, a robust supply chain is not necessarily a resilient

supply chain, but a resilient supply chain is a robust supply chain that can respond to an

unexpected shifts in the level and variability of output (Christopher & Rutherford, 2004).

Therefore, robustness can be identified as one of the dimensions of SCRES ( Wieland &

Wallenburg, 2013).

39

Reliability, Robustness and Resilience

From a systems perspective, supply chain reliability is the ability of a supply chain to

perform its required functions under stated conditions for a specified period of time, or even

resist failure, whereas resilience refers to the ability of supply chains to bounce back from a

disruption (Schmitt & Singh, 2012). Robustness and reliability concepts often overlap in the

supply chain literature but their definitions slightly differ. Robust supply chains can perform well

with respect to uncertainties in the demand data wheareas reliable supply chains perform well

when parts of the system fail (Snyder, 2003). A resilient supply chain can be both robust and

reliable at the same time.

Agility and Flexibility

Some authors see supply chain agility and flexibility concepts as capabilities of supply

chain resilience (Christopher & Peck, 2004; Christopher & Rutherford, 2004; Pettit et al., 2010;

Ponomorov & Holcomb, 2009; Sheffi, 2005b), others view these concepts and SCRES as

different approaches (Carvalho et al., 2012; Charles et al., 2010). Some authors consider

flexibility as a dimension of agility (Carvalho, Azevedo, et al., 2012; Tang & Tomlin, 2008) and

others view flexibility and agility as different concepts (Christopher & Rutherford, 2004). In

alignment with Tang and Tomlin (2008), we use Christopher and Peck (2004) definition of

agility: “the ability to respond rapidly to unpredictable changes”. Thus, we suggest flexibility,

visibility, and velocity are all dimensions of agility.

Flexibility and Redundancy

SCRES is related to the development of responsiveness capabilities through redundancy

and agility (Carvalho et al., 2012; Rice & Caniato, 2003). Some studies define redundancy as a

dimension of flexibility (Carvalho et al., 2012; Jüttner & Maklan, 2011), others define

40

redundancy and flexibility as different resilience capabilities (Rice & Caniato, 2003; Sheffi,

2005a). Carvalho et al. (2011) and Tang and Tomlin (2008) suggest that flexibility is related to

investments in infrastructure and resources before they actually are needed, whereas redundancy

is concerned with maintaining the capacity to respond to disruptions in the supply network,

largely through investments in capital and capacity prior to the point of need. Although

redundancy capacity may or may not be used, flexibility entails restructure to the previously

existing capacity (Rice & Caniato, 2003). Consistent with the above arguments, we consider

flexibility and redundancy as different resilience capabilities.

Relational Capabilities

According to our review of the literature, the role of relational capabilities/relational

competencies in achieving SCRES is underexplored. We identify four articles that investigate the

relationship among relational capabilities and SCRES. Wieland and Wallenburg (2013)

investigate the communication, cooperation, and integration’s impact on SCRES dimensions

robustness and agility. Johnson, Elliott, and Drake (2013) examine the influence of trust, norms,

and obligations on supply chain network resilience. Similarly, Mandal (2013) develops a

framework to examine the impact of relational resources such as trust, commitment,

communication, cooperation, adaptation and interdependence on the effectiveness of supply

chain resilience as a dynamic capability. Recently, Scholten and Schilder (2015) investigate the

role of collaboration in establishing SCRES.

From the SCRES literature, we identify 16 studies that extensively focus on supply chain

capabilities. Table 9 represents the taxonomy of supply chain capabilities adapted from Pettit et

al. (2010)’s study. Based on our findings, applying the phases of SCRES (readiness, response

and recovery) (Ponomorov & Holcomb, 2009), we categorize the supply chain capabilities as: 1)

41

capabilities for readiness include efficiency, dispersion, market position, security, collaboration,

financial strength, revenue management, organizational culture and anticipation; 2) capabilities

for responsiveness include agility (flexibility, velocity, visibility) and redundancy; and 3)

capabilities for recovery include adaptability. Figure 10 represents the categories of supply chain

capabilities.

Supply Chain Capabilities

Responsiveness

• Agility • Velocity • Visibility• Flexibility

• Redundancy

Recovery

• Adaptability• Crises management• Resource mobilization• Communication

strategies• Consequence

mitigation

Readiness

• Efficiency• Dispersion• Market position• Security• Collaboration• Financial Strength• Revenue management• Market strength• Organizational culture• Anticipation

Figure 10. Supply chain capabilities

42

Main Factors Descriptors /Sub Factors Sheffi (2001)

Rice and

Caniato (2003)

Cranfield et al.

(2002-2003)

Hamel and Valikangas

(2003)

Fiksel (2003)

Peck (2005)

Sheffi (2005-2006)

Tang (2006)

Craighead et al.

(2007)

Tang and

Tomlin (2008)

Ponomarov and

Holcomb (2009)

Pettit et al.

(2010)

Juttner and

Maklan (2010)

Zhang et

al.(2011)

Blackhurst et al.

(2011)

Carvalho et al

(2012)

Res

pons

iven

ess

Agility-Flexibility- sourcing Input /part commonality X X X Modularity and interchangeability X X X X Multiple uses for supplies X X X X Supplier contract flexibility X X X X X X X X X X Cross train employees X X Use single source/Key supplier X X X Agility-Flexibility- fulfillment Alternate distribution channels X X X X X X X Manufacturing process flexibility X X Risk pooling/sharing X X X X Standard processes X X Multi-sourcing (peak vs. base) X

Delayed commitment, Production postponement X X X X X

Inventory management X X X Fast re-routing of requirements X Multiple sites, multiple plants X X Agility-Velocity Lead time, Pace of flexible adaptations X X X Agility-Visibility Business intelligence gathering X X X Information technology X X X X Products, Assets, People visibility X X X X X Node monitoring exception tools X X Shipment visibility X X Visibility of demand information X Collaborative information exchange X X

Redundancy Reserve capacity (materials, assets, labor, inventory) X X X X X X X X

Backup energy sources/communications X X X

Multiple sources/local sources/suppliers X X X X X X X X X X

Redundancy ( safety stock) X X X X Backup data X

Rea

dine

ss

Efficiency Waste elimination X X X X Labor productivity X X Asset utilization X X Product variability reduction X Failure prevention X X Dispersion Distributed decision-making X X X X Distributed capacity & assets X X X X X X X Decentralization of key sources X X X X Location specific empowerment X X Geographic dispersion of markets X Market Position Product differentiation X X Customer loyalty/ retention X Market share X Brand Equity X Customer relationships X Customer communications X Security Layered defenses X X X X X Access restriction X X X Employee involvement in security X X X Collaboration with governments X X X X Cyber-security X X X Personnel security X Collaboration Collaborative forecasting, CRM X X X X X X X X X Communications -Internal, external X X X X X X X X Postponement of orders X Product life cycle management X Public Private Partnership X X

43

Main Factors Descriptors /Sub Factors Sheffi (2001)

Rice and

Caniato (2003)

Cranfield et al.

(2002-2003)

Hamel and Valikangas

(2003)

Fiksel (2003)

Peck (2005)

Sheffi (2005-2006)

Tang (2006)

Craighead et al.

(2007)

Tang and

Tomlin (2008)

Ponomarov and

Holcomb (2009)

Pettit et al.

(2010)

Juttner and

Maklan (2010)

Zhang et

al.(2011)

Blackhurst et al.

(2011)

Carvalho et al

(2012) Risk sharing with partners X X Financial Strength Insurance X X Portfolio diversification X X X X Financial reserves & liquidity X X X X Price margin X Revenue Management Dynamic X Promotion X Reponsive Pricing X Organization Culture Learning, benchmarking, feedback X X

Responsibility, accountability and empowerment X X

Teamwork, creative problem solving X X X X X Training, cross train workers X X X X X Substitude leadership capacity X X

Continuous communication among employees X X X

Conditioning for disruptions X X Passion of work X X

Modify production process for unsikilled labor X X

Backup knowledge X X X X Backup relationships X X Cost benefit knowledge X Post disruption feedback X X Culture of caring for employees X Anticipation Monitoring early warning signals X X X X X Forecasting X X X X Deviation, Near-miss analysis X X X

Contingency planning, Preparedness (Training/Drill/Exercise plans) X X X X

Recognition of opportunities X X

Risk management, Business continuity planning X X X X X X X

Communication protocols X X Supplier relationship management X X

Customs programs/ Post diversification plans X X

Rec

over

y

Adaptability Fast re-routing of requirements X X X X

Process Improvement, Lead time reduction X X X X X X X

Strategic gaming & simulation X X X X Seizing advantage from disruptions X X X Alternative technology development X X X Ability to quickly redesign X X

Learning from experience, Reengineering X X X X X

Recovery Crisis management X X X X X X Resource mobilization X X Communications strategy X X Consequence mitigation X

Table 9. A taxonomy of supply chain capabilities

44

SCRES Mechanisms (M)

In this section, we introduce 13 different theories applied as mechanisms to investigate

SCRES and its relationships with various concepts. In the next subsections, we present an

overview for each of the theories that were utilized in at least two peer-reviewed articles.

Resource-Based View (RBV)

In SCRES research, RBV theory has been the one of the most commonly used theoretical

lens to explain SCRES phenomena. Out of 24 different articles reviewed, 4 of them incorporate

RBV. The RBV perspective explains how competitive advantage within firms is created by

building bundles of resources and/or capabilities and how such advantage is sustained over time

(Barney, 1991; Wernerfelt, 1984). The RBV has been primarily used as a basis to explore the

relationships among specific resources, capabilities, and performance in SCRES research. Since

there are criticisms of RBV as being static in nature, the SCRES authors mainly use theories that

extend RBV or complements RBV such as the dynamic capabilities view or dynamic capabilities

theory (DCT) (Golgeci & Ponomarov, 2013; Ponomorov & Holcomb, 2009); RBV, dynamic

capabilities and relational view (RV) Mandal (2013); contingent RBV perspective (Brandon‐

Jones, Squire, Autry, & Petersen, 2014); and systems theory with RBV (Blackhurst et al., 2011).

Dynamic Capabilities

Dynamic capabilities refers to the “the firm’s ability to integrate, build and reconfigure

internal and external competences to address rapidly changing environments” (Teece, Pisano, &

Shuen, 1997, p.516). Dynamic capabilities grow from an individual firm’s capabilities as an

adaptive response to the conditions of environmental uncertainty. Even though dynamic

capabilities may lead to competitive advantage, they cannot ensure the sustainability of the

competitive advantage, especially under the conditions of uncertainty (Ponomorov & Holcomb,

45

2009). Therefore, Ponomorov and Holcomb (2009) propose that the only way to achieve

sustainable competitive advantage under uncertainty is enhancing the dynamic integration of

logistics capabilities by SCRES. The authors use a dynamic capabilities perspective (Teece et

al., 1997) as an extension of RBV to explain the relationships linking logistics capabilities,

SCRES and sustainable competitive advantage. Later, Golgeci and Ponomarov (2013) apply

dynamic capabilities perspective to explain relationships among the firm innovativeness,

innovation magnitude, disruption severity, and supply chain resilience.

Contingency Theory

According to Ling-Yee (2007)’s argument, RBV theory lacks in identifying

contingencies that capture the capabilities and resources as valuable in certain conditions

(Brandon‐Jones et al., 2014). Therefore, Brandon‐Jones et al. (2014) extend RBV to contingency

theory (Donaldson, 2001; Lawrence & Lorsch, 1967) that states internal and external conditions

affect decisions on the resources or capabilities needed in order to enhance the performance of

organizations or supply chains (Brandon‐Jones et al., 2014). Brandon‐Jones et al. (2014) apply a

contingent resource-based perspective to understand the relationship between specific resources

(information sharing and connectivity), capabilities (visibility) and performance in terms of

SCRES and robustness.

Contingency theory (Donaldson, 2001; Lawrence & Lorsch, 1967) explains how

organizations’ processes and structures need to match with their environment in order to improve

the organizations’ performance. Contingency theory argues that a single set of choices, a single

theory, or a single method cannot be applied effectively in all situations (Flynn, Huo, & Zhao,

2010). In other words, organizational strategies differ from organization to organization since the

environmental exposure of each organization is different (Trkman & McCormack, 2009).

46

Organization-specific strategies cause a lack of generalizability that makes it difficult for

organizations to adopt a common strategy (Fredericks, 2005; Trkman & McCormack, 2009).

Today’s supply chains or organizations constantly face uncertainties. To cope with uncertainties

and gain a sustainable competitive advantage, supply chains or organizations need to develop or

select their own strategies (Trkman & McCormack, 2009).

Boone, Craighead, Hanna, and Nair (2013) state that when used strategically, inventory

management can become an essential method for mitigating both internal and external

disruptions and enhancing resiliency. In order to use inventory management strategically, a

firm’s inventory management approach and its overall supply chain strategy needs to be aligned.

According to contingency theory (Donaldson, 2001; Lawrence & Lorsch, 1967), the alignment

between strategy and performance, the “fit” (Venkatraman & Prescott, 1990), is the factor that

improves an organization’s performance (Flynn et al., 2010). Boone et al. (2013) describe the

“fit” as the alignment between an inventory management approach and a supply chain strategy.

They utilize contingency theory to explain how improving the “fit” enhances the firm

performance as well as continuity and resiliency.

Systems and Control Theories

Systems theory is commonly used in SCRES research. Systems theory suggests that the

organizations are continually evolving systems; therefore, the performance of organizations

depends on the collaborative performance of individual organizations linked through business

processes and relationships (Von Bertalanffy, 1950, 1968). From this view, supply chains are

considered as systems since they are composed of constantly evolving and environment

dependent subsystems such as organizations, people, infrastructure, information and material

flows, services and relationships (Caddy & Helou, 2007).

47

When unanticipated events disrupt the normal flow of goods and information, the

ultimate state of the system is transformed (Blackhurst et al., 2011). From this perspective,

Blackhurst et al. (2011) utilize RBV theory to frame mitigation capabilities and extend RBV to

systems theory to establish supply chain characteristics that reduce resiliency. The authors

suggest that the effect of disruptions on a system varies depending on the level of supply chain

resilience. Zhang et al. (2011) use a system and security theory perspective to explain how

designing a robust, resilient, and flexible security system can strengthen a supply chain and

improve its ability to cope with antagonistic threats.

System and control theory (CT) constitute a base for studying multi-stage, multi-period

dynamic systems and can be applied in domain of supply chain dynamics (Dmitry Ivanov &

Sokolov, 2013). Ivanov, Dolgui, and Sokolov (2012) apply control and systems theory to

investigate supply chain robustness, supply chain adaptability, and SCRES’s reliability with

operations planning and execution control. Ivanov and Sokolov (2013) review the supply chain

dynamics literature using CT and systems theory as a theoretical lens to identify the supply chain

dynamics for planning, analysis and adaptation of performance under uncertainty. In terms of

SCRES, they investigate the supply chain’s ability to maintain, execute, and successfully recover

from disruptions to the expected performance. They further suggest that the area of SCRES and

adaptation is currently underdeveloped in the domain of supply chain dynamics.

Ehlen et al. (2014) use CT to capture how distributed control, controlling a system

through the distributed actions of individual components, effects dynamic and decentralized

supply chain systems. They develop large-scale simulations of chemical-plant activities and

supply chain interactions to estimate the impacts of duration of disruptive-events on overall

supply chain system resilience.

48

Systems theory addresses many of the shortcomings of theories such as RBV, dynamic

capabilities, and contingency theory when studying SCRES. Since today’s supply chains are

complex networks of enterprises that consist of adaptive and coevolving processes, systems

theory becomes inadequate to explain SCRES (Tukamuhabwa et al., 2015). To overcome the

limitations of systems theory, Day (2014) uses a complex adaptive supply network (CASN) lens

to develop a framework to explain how relief organizations, their interactions and environment

determine the level of relief supply network resilience after disasters. Day (2014) suggest

propositions to increase the level of resilience in a relief supply network. Later on,

Tukamuhabwa et al. (2015) extends the CASN framework to make it applicable to other types of

supply chains. They propose a complex adaptive systems (CAS) theoretical framework to

examine SCRES and enhance the understanding of SCRES. They show that there is a strategic fit

between the SCRES and CAS characteristics such as adaptation, coevolution, non-linearity, self-

organization and emergence.

Relational View (RV)

The RV builds on the RBV by stating that the competitive advantage of a firm is

dependent on the network of relationships (Dyer & Singh, 1998). RV theory emphasizes the

relational-specific assets, knowledge-sharing routines, and complementary resources including

trust, collaboration, benevolence, and communication. From an RV perspective, the more the

firms invest in key relationships to improve partner capabilities through inter-firm knowledge-

sharing routines, the more firms gain relational advantage (Day, Fawcett, Fawcett, & Magnan,

2013). In the SCRES literature, Wieland and Wallenburg (2013) use anticipation, the knowledge

about potential changes, and visibility, the knowledge about actual changes that are now

occurring, as relational competencies that establish the two dimensions of SCRES: robustness

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and agility. The authors apply RV as the theoretical basis to explain the relationships between

relational competencies: communication, cooperation, and integration and the dimensions of

resilience: robustness and agility.

Similarly, Mandal (2013) utilizes RV in addition to RBV and dynamic capabilities and

develop a theory-driven conceptual model that explains SCRES as a dynamic capability. Mandal

(2013) explores the relationships among relational resources/competencies such as trust,

commitment, communication, cooperation, adaptation and interdependence and SCRES. In

addition, Mandal (2013) argues for a moderating role of environmental uncertainty on the

relationships among relational resources and SCRES.

Other Theories

There are many other theories have been used to explain SCRES and its relationships

with various factors and outcomes. The following theories appeared only once in SCRES

literature. Social Capital Theory: Building on the aforementioned relational competencies

explored using RV in the SCRES literature, Johnson et al. (2013) utilize cognitive and structural

competencies that constitutes social capital theory to explain SCRES phenomena. The authors

emphasize the role of social capital dimensions (cognitive, structural and relational) in

facilitating SCRES’ four formative capabilities: flexibility, velocity, visibility, and collaboration.

Rational Choice Theory: Urciuoli et al. (2014) uses rational choice theory as a base theory to

understand how energy supply chains can build resiliency against security threats. Game theory:

Bakshi and Kleindorfer (2009) utilize Harsanyi–Selten–Nash’s bargaining framework to model

the supply chain participants’ choice of risk mitigation investments to enhance resilience. Graph

theory: Kim, Chen, and Linderman (2015) apply graph theory to conceptualize supply network

(SN) disruptions and SN resilience by investigating four different structural relationships among

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collection of nodes (facilities) and the connecting arcs (transportation). Grey theory/Grey

systems theory: Rajesh and Ravi (2015) employ grey theory to investigate the decisions related

to selecting a supplier in an electronic supply chain by considering resilient attributes. Grey

systems methodology can handle many of the ambiguities generated from imprecise human

decisions. Signaling theory and the resource-based view (RBV): Stevenson and Busby (2015)

utilize signaling theory and the resource-based view (RBV) to explain impact of counterfeiting

on competitive resources (quality, trademark, and reputation) and to propose strategies to

enhance SCRES to cope with counterfeiting threat. Table 10 indicates the list of theories, number

of articles and the authors. Our findings suggest that out of 134 articles reviewed only 24 articles

apply a theory to explain SCRES and its relationship with other concepts. Consistent with

Tukamuhabwa et al. (2015) findings, our findings suggest that there is a limited explanation for

SCRES from the theoretical perspective, either from theory development or theory validation.

Theory Number of articles

References

Resource based view (RBV) 4 Ponomorov & Holcomb, 2009, Blackhurst et al., 2011, Mandal, 2013, Brandon‐Jones et al., 2014

Systems theory 3 Blackhurst et al., 2011, Zhang et al., 2011, Ivanov et al., 2012 Control theory 3 Ivanov et al., 2012, Ivanov & Sokolov, 2013, Ehlen et al.,

2014 Complex adaptive systems theory (CAS)

2 Day, 2014, Tukamuhabwa et al., 2015

Dynamic capabilities 2 Ponomorov & Holcomb, 2009, Golgeci & Ponomarov, 2013, Mandal, 2013

Relational view (RV) 2 Mandal, 2013, Wieland & Wallenburg, 2013 Contingency theory 2 Boone et al., 2013,Brandon‐Jones et al., 2014 Social capital theory 1 Johnson et al., 2013 Rational choice theory 1 Urciuoli et al., 2014 Normal accident theory 1 Marley, Ward, & Hill, 2014 Game theory 1 Bakshi & Kleindorfer, 2009 Grey theory 1 Rajesh & Ravi, 2015 Signaling theory 1 Stevenson & Busby, 2015

51

Table 10. Summary of theories applied in SCRES research

SCRES Outcomes (O)

Firms can achieve SCRES through a balance between capabilities and vulnerabilities (Pettit et

al., 2010). In this study, capabilities and vulnerabilities are synthesized as interventions that lead

to different SCRES outcomes. Effective application of a set of capabilities leads to improved

performance. However, in order to apply the set of capabilities effectively, these capabilities

need to match with the vulnerabilities of the supply chain (Pettit et al., 2010). In alignment with

Pettit et al. (2010) study, we define the outcome of SCRES as improved performance when there

is a balance between capabilities and vulnerabilities; excessive risk when a supply chain has high

vulnerabilities and low capabilities; eroded profitability a supply chain has low vulnerabilities

and high capabilities.

Previous studies have demonstrated that there is a significant relationship between

competitive advantage and performance. SCRES sustains the current level of supply chain

performance even when disruptions occur (Carvalho et al., 2012). Under uncertain conditions,

SCRES plays a key role in sustaining capabilities and creating a sustainable competitive

advantage (Ponomarov & Holcomb, 2009). Figure 11 summarizes our SCRES outcomes

findings.

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SCRES Outcomes

• Improved Performance (Balanced SCRES)

• Excessive Risk (Unbalanced SCRES-high vulnerability& low capabilities)

• Eroded Profitability (Unbalanced SCRES-low vulnerabilities& high capabilities)

• Sustained Competitive Advantage

Figure 11. SCRES outcomes

Nature of the Selected Studies

We classify the 134 selected articles as empirical (28), systematic literature reviews and

conceptual studies (35), and analytical (39) articles based on Hohenstein et al. (2015) and

Pilbeam et al. (2012) research methodology categorization. Figure 12 shows the number of

articles by nature of studies.

Figure 12. Number of articles by research methodologies

0 10 20 30 40 50

Emprical Resarch

Literature Review

Analytical Research

Number of articles

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Empirical Research

Empirical research papers include case studies, surveys, field studies, and longitudinal

studies. Of the 28 empirical research articles, we identified 18 case studies, 7 surveys, 1 field

study, 1 longitudinal field study, and 1 longitudinal case study. Table 11 shows the type of

empirical research method, number of articles, and authors. Our findings suggest that there is a

lack of focus on survey, longitudinal and field studies.

Research Methodology Number of articles

Authors

Case study 18

Pettit et al., 2010, Blackhurst et al., 2011,Jüttner & Maklan, 2011, Cabral, Grilo, & Cruz-Machado, 2012, Omera Khan, 2012, Azevedo et al., 2013, Johnson et al., 2013, Leat & Revoredo-Giha, 2013, Pettit et al., 2013, Carvalho & Azevedo, 2014, Govindan, Azevedo, Carvalho, & Cruz-Machado, 2014, Nikookar, Takala, Sahebi, & Kantola, 2014 , Scholten, Sharkey, & Fynes, 2014,Urciuoli et al., 2014, Utami, Holt, & McKay, 2014, Haraguchi & Lall, 2015, Sprecher et al., 2015, Stevenson & Busby, 2015

Survey 7 Zsidisin & Wagner, 2010, Mandal, 2012, Golgeci & Ponomarov, 2013, Wieland & Wallenburg, 2013, Brandon‐Jones et al., 2014, Dubey et al., 2014, Ambulkar, Blackhurst, & Grawe, 2015

Field study 1 Marley et al., 2014 Longitudinal case study Jüttner & Maklan, 2011

Longitudinal field study 1 Boone et al., 2013

Table 11. Empirical studies

Literature Reviews

This study, further categorizes the literature reviews as conceptual articles and systemic

literature reviews. Out of 134 peer-reviewed articles, we classify 28 as conceptual and 7 as

systematic literature reviews. We briefly overview the focus and the findings of the recent

systematic literature review articles to point out the gaps in the SCRES literature.

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Ponomorov and Holcomb (2009) conduct an integrative, multidicplinerary literature

review of SCRES. They present a comprehensive definition of SCRES and a SCRES framework

based on a logistics perspective. Their findings point out the need for a unified theory of SCRES.

Pereira (2009) sytematically reviews the literature with the focus on system dynamics-discrete

event simulation (SD-DES) to model the behavioral characteristics of SC-IT problems that can

improve the supply chain robustness and SCRES. Their findings highlight the significance of

appying SD-DES for solving and analysing complex problems of strategic importance, especially

in both continuous and discrete events in SCRES studies. Klibi et al. (2010) present a literature

review of optimization models that are used to solve Supply Chain Network (SCN) design

problems under uncertainty. They conclude that deterministic SCN design models do not include

resilience and responsiveness of supply chain and many stochastic models barely address these

concepts. They suggest that the explicit incorporation of risk mitigation constructs, such as back-

up suppliers or insurance capacity, in optimization models might lead to more resilient and

robust SCN designs. Ponis and Koronis (2012) present an extensive literature review to provide a

comprehensive definition of SCRES. They find that the most grounded antecedents of SCRES

are agility, flexibility, velocity, visibility, availability, redundancy, mobilization of resources,

collaboration and supply chain structure knowledge. Pereira et al. (2014) conduct a systematic

literature review to understand the role of procurement in identifying and managing the intra-

and inter-organizational issues which impact supply chain resilience. Their findings suggest that

the procurement activities significantly contributes building SCRES. Hohenstein et al. (2015)

present a systematic literature review on SCRES. Based on their literature review, the authors

develop a comprehensive SCRES framework based on the sand cone model. Their findings

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highlight the need for clear terminology, a well-grounded definition, and measures for SCRES.

Table 12. presents the literature review articles that appear in the extant SCRES research.

Number of articles Authors

Conceptual articles Aigbogun, Ghazali, & Razali, 2014; Alan, 2014; Boin, Kelle, & Clay Whybark, 2010; Bradley, 2005; Briano et al., 2010; Briano et al., 2009; Carvalho, Azevedo, et al., 2012; Carvalho, Azevedo, & Cruz-Machado, 2014; Carvalho & Cruz-Machado, 2009; Carvalho, Cruz-Machado, & Tavares, 2012; Christopher & Peck, 2004; Christopher & Rutherford, 2004; J. M. Day, 2014; Fiksel, Goodman, & Hecht, 2014; Fiksel, Polyviou, Croxton, & Pettit, 2015; Glickman & White, 2006; Gould et al., 2010; Guo, 2013; A. R. Johnson, 2013; Jorge Verissimo, 2009; Levesque, 2012; Martin & Lee, 2004; Mascaritolo & Holcomb, 2009; McKinnon, 2014; Melnyk et al., 2010; Mensah & Merkuryev, 2013, 2014; Meyer-Larsen, Drupsteen, Gräf, Maier, & Müller, 2013; Miao & Banister, 2012; Palin, 2013; Park, Seager, & Rao, 2011; Pettit et al., 2010; Ponomorov & Holcomb, 2009; Popa, 2013; Sáenz & Revilla, 2014; Samii, Umit, & Meyers, 2014; Savage & Gibson, 2013; Schenk & Stich, 2014; Sheffi, 2005a, 2006; Y. Sheffi & J. B. J. Rice, 2005; U. Soni & Jain, 2011; Peck et al., 2003

Systematic literature reviews Hohenstein et al., 2015; Jorge Verissimo, 2009; Klibi et al., 2010; Pereira et al., 2014; Ponis & Koronis, 2012; Ponomorov & Holcomb, 2009; Tukamuhabwa et al., 2015

Table 12. Literature review research

Analytical Research

In this section, we focus on the 39 analytical research articles that use mathematical

modeling and simulation methods to measure and examine SCRES. In the next subsections, we

discuss the mathematical modeling techniques that appear in two or more SCRES articles.

Mixed Integer Programming

From the review of the extant SCRES literature we identify the use of mixed integer

programming (MP) models including mixed integer linear programming (MILP) to develop tools

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to assess SCRES. Some studies develop MILP to optimize facility location selection in face of

node failures and uncertainties. For instance, Aviral, Vishal Agarwal, and Venkat (2011) develop

a MILP to address the facility and link failure problems. The authors utilize robustness (expected

disruption cost (EDC)) and efficiency (operational cost) measures for evaluating the supply chain

network resilience (SCNRES). Their results show that the proposed MILP model build a

significant amount of robustness into the system without compromising a lot on efficiency.

Other studies utilize MILP for supply portfolio selection and SCN design. For instance,

Sawik (2013) utilizes MILP for selection of resilient supply portfolio with the objective of

minimizing cost of suppliers’ protection, emergency inventory pre-positioning, parts ordering,

purchasing, transportation and shortage and mitigating the impact of disruption risks by

minimizing the potential worst-case cost. The study provides decision makers a tool for selecting

a resilient supply portfolio with protected suppliers capable of supplying parts in the face of

disruption events. Cardoso, Paula Barbosa-Póvoa, Relvas, and Novais (2015) develop a multi-

product, multi-period MILP model to determine the design and planning decisions of a SCN

subject to different disruptions. Salehi Sadghiani et al. (2015) develop a retail SCN optimization

model that consists of deterministic multiple set covering (MSCM) and a MILP probabilistic

scenario-based robust model to design a robust and resilient retail SCN. The results of the

proposed model show that designing retail SCN without considering operational and disruption

risks is misleading, and multiple covering of retail stores as the measure of redundancy increases

SCNRES significantly. The authors develop five different SCN structures. The resilience of

these five SCN structures are evaluated by the following indicators: 1) network design indicators

such as node complexity, flow complexity, density, and node criticality; 2) network

centralization indicators such as outdegree nodes, indegree nodes, outdegree flows, and Indegree

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flows; and 3) operational indicators such as the expected net present value (ENPV), expected

customer service level and investment. Isfaq (2012) develops a MILP transportation model to

find shipment routes that balance the efficiency and flexibility considerations in the form of two-

connected paths in a multi-mode logistics network. They apply this model to actual inter-state

highway and multi-mode logistics network in the northeastern, midwestern and southern regions

of the US. The findings of the study show that companies can improve the resilience of their

supply chains by maintaining a multi-mode transport capability.

Location Allocation Models

Based on the review of SCRES literature, we classify location allocation models as

resource allocation, facility location allocation, set cover location, and location transportation

models. Ratick, Meacham, and Aoyama (2008) propose a set cover location model (SCL) to

support supply chain and logistics managers. They suggest possible ways to utilize backup

facilities to reduce the effects of the supply chain threats. Dingwei and Ip (2009) develop a

resource allocation model to optimize the allocation of resources with connections, distribution

centers or warehouses in SCN. They develop a resilience index that refers to the ratio of the

available supply over the demand in failure or attack cases. Based on the results of their study,

the authors design a service logistic network called Resilience Information Management System

for Aircraft Service (RIMAS) that monitors the operation of the logistic networks and enhances

the resilience. Rabbani, Bahadornia, and Torabi (2015) develop a SCN optimization model for

capacitated facility location–allocation problem (CFLP) to optimize supplier selection in order to

cope with oil-supply disruption. In the proposed model, the authors utilize conditional value at

risk (CVar), oil dependency, and co-vary diversity reliability index (HHI3) to assess the SCN

resilience. The results indicate that the proposed model improves the efficiency of the oil SCN

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by minimizing the distances among the entry, storage, and refining ports and enhances security

by maximizing oil-storage dispersion. Klibi and Martel (2012) model the impact of multihazards

on SCN capacity and demand using a capacitated single product two-echelon location-

transportation solution under uncertainty. They formulate stochastic design models that

elaborates risk avoidance and resilience strategies using a scenario based approach. They

evaluate the designs provided by these models using Monte-Carlo Methods. Their results

demonstrate that significant gains can be made by using more precise respresentations of

delivery decisions and more robust designs can be obtained by explicitly modeling disruptions.

Kristianto, Gunasekaran, Helo, and Hao (2014) develop a two stage non-linear SCN optimization

model to solve an inventory allocation and transportation routing problem under disruptions with

the objective of enhancing SCNRES. The proposed model allocates the inventory in advance and

anticipates the changes in transportation routings. In addition, the authors develop a fuzzy

shortest path algorithm to solve the complexity in the lead-time and capacity. They suggest that

the proposed two-stage programming model with fuzzy shortest path provides a competitive

advantage due to shorter computational time.

Fuzzy-based Decision Making Models

The authors utilize fuzzy mathematical approaches and fuzzy based decision-making

models to suggest ways to improve SCRES under uncertainties. Mitra, Gudi, Patwardhan, and

Sardar (2009) use a fuzzy mathematical approach to formulate a multi-site, multi-product, multi-

period supply chain planning problem under uncertainty to improve supply chain resiliency.

They analyze supply chain performance metrics such as total planning cost, extent of permissible

uncertainty, and demand satisfaction in different uncertain scenarios to analyze the effect of

variation in uncertainty on the planning model. Their results show that their fuzzy mathematical

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approach provides a quick,easy to model, quality solution; therefore showing that a fuzzy

approach can handle large-scale supply chain problems under uncertainty. Fakoor, Olfat, Feizi,

and Amiri (2013) use a fuzzy-based decision model and Delphi technique to measure SCRES of

an automotive supply chain. The results show that external factors and sourcing limitations are

the most serious vulnerabilities while security and flexibility in order fulfillment are the two

important capabilities for the selected automotive supply chain. Adtiya, Kumar, Datta, and

Mahapatra (2014) propose a fuzzy-based decision model for Order Preference by Similarity to

Ideal solution (TOPSIS) to select a resilient supplier. The authors define a closeness coefficient

as a measure to rank the order of alternative suppliers by calculating the distances to both the

fuzzy positive ideal solution and fuzzy negative ideal solution. Their results show that the

proposed model is appropriate for multi-criteria decision-making optimization in supplier

selection under uncertainty.

Economic Models

Dynes, Johnson, Andrijcic, and Horowitz (2007) investigate information system

disruptions (cyber disruptions) within a supply chain and the economic vulnerability to these

disruptions. The authors conduct a field study that consists of a set of interviews with top supply

chain executives in each participating firm; then, they estimate macro-economic costs of

information disruption using a Leontief-based input-output model. The results show that supply

chain resiliency to information disruptions depends on how the technology is used to cope with

the type of attack experienced, not the types of technology employed. Wieland (2013) proposes a

mathematical cost model for selecting an optimal supply chain strategy (agility, robustness,

resilience and rigidity) based on the risk probability and risk impact. The author suggests that

resilience is optimal when supply chain risk probability and impact are both high, rigidity is

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optimal when risk probability and impact are both low, agility is optimal when only risk

probability is low, and robustness is optimal when only risk impact is low. Yang and Xu (2015)

develop a mathematical optimization model to maximize the profit and recovery rate of a grain

processor and retailer in order to ensure an agriculture grain SCRES. The authors consider two

supply chain recovery methods such as contingent sourcing and government aid for grain

processor recovery. The results demonstrate that government intervention is a necessary

mechanism for grain processor recovery but cannot fully replace the backup supplier.

Quantitative Decision Making Models

Bhattacharya, Geraghty, Young, and Byrne (2013) develop a framework that captures the

trade-offs among multiple and conflicting-in-nature criteria to provide a design of a resilient

shock absorber (RSA) for disrupted SCNs. The authors suggest that the proposed RSA

framework facilitates the assessment of resiliency strategies for SCNs susceptible to excursion

events such with low probability of occurrence and high impact. In addition, they incorporate

dynamic planning and decision tools such as artificial neural network (ANN) or fuzzy sets,

multi-criteria decision making (MCDM), multiple attribute decision making (MADM), and

advanced quality function deployment (AQFD) in RSA framework to achieve the desired level

of mitigation strategies through shock dampening. Their result show that proposed shock-

dampening fortification framework also enables practitioners to identify and quantitatively assess

the islands of the excursion events in SCN. Harrison, Houm, Thomas, and Craighead (2013)

propose a SCN optimization model called READI- Resiliency Enhancement Analysis via

Deletion (of key supply chain node, flow, or activity) and Insertion (of mitigation strategy) to

enhance SCRES. The authors show that READI can measure the criticality of supply chain

nodes and current levels of SCNR to provide insights to enhance SCNR. Gong, Mitchell,

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Krishnamurthy, and Wallace (2013) develop an interdependent layered network (ILN)

optimization model that represents logical dependencies between the supply chain and

infrastructure systems. The model aims to minimize the total cost and unmet demands subject to

the limited resources available for restoration. They measure SCRES using total cost, delivery

time, quality, flexibility, and revenue. The results provide efficient restoration strategies that

support the supply chain in recovering from a disruption and improving SCRES and identify the

best restoration strategy to help the supply chain managers and infrastructure managers cooperate

to mitigate the impact of a disruption.

There are other types of decision-making models developed by SCRES authors such as

analytic network process (ANP) models (Cabral et al., 2012), interpretive structural modeling

(ISM) (Govindan et al., 2015; Soni, Jain, & Kumar, 2014), and Grey relational analysis (Rajesh

& Ravi, 2015).

Simulation

Simulation is a common analytical research method in SCRES studies since simulation

allows for detailed results and nearly optimal solutions for complex problems. The main

drawback of simulation methods is the lack of generalizability since the results of the simulation

are highly dependent on the conditions specified by a case study or a numerical example. The

articles we reviewed encompass different types of simulation approaches such as agent-based

simulation, discrete event simulation, and Monte Carlo simulation. Some authors use simulation

methods as a sole modeling approach and others to complement the primary analytical technique.

We categorize the articles that use simulation model to supplement other mathematical models as

hybrid models.

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The authors mainly use simulation to evaluate mitigation strategies and their impact on

SCN resilience. For instance, Allen, Datta, and Christopher (2006) and Datta et al. (2007)

present an agent-based simulation (ABS) to investigate the strategies that improve the resilience

of a complex multi-product, multi-country SCN facing an uncertain demand and limited

production and distribution capacities. They measure SCN resilience using four supply chain

performance measures: customer service level, production change over time, average inventory

level and total average network inventory level. The authors suggest that a decentralized

information structure, flexible decision rules, monitoring key performance indicators at regular

intervals, full information sharing across the members of SCN enhance SCRES. Carvalho et

al.(2012) develop a simulation model to support decision making processes in supply chain

design strategies to improve SCRES when there is a disturbance. They develop two performance

measures (total cost and lead time ratio) to explain how mitigation strategies affect each supply

chain performance. The authors use the simulation results to compare the supply chain behavior

after the disturbance under two different supply chain resilience strategies, flexibility and

redundancy. The simulation results show that both resilience strategies reduce the impact of a

disturbance on supply chain performance, but while the flexibility strategy based has a higher

impact on total cost, the redundancy strategy has a higher impact on lead time ratio. Wu, Huang,

Blackhurst, Zhang, and Wang (2013) develop an ABS to study the impact of retailer product

stockouts on SCRES. They measure SCRES using variability in market share, product type, and

stockout duration. The authors suggest that stockout mitigation strategies may differ for each

type of product and the impact of a chosen mitigation strategy may differ for each supply chain

partner.

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Azadeh, Atrchin, Salehi, and Shojaei (2013) develop a simulation model using Visual

Simulation Language for Analogue Modelling (SLAM) to analyze the resilience of a three-

echelon supply chain in the face of different levels of transportation delays. The authors use four

SCRES factors (visibility, velocity, redundancy and flexibility). They measure SCRES based on

average time in system, utility of resources, number of breakdowns, and total cost. Then, they

use fuzzy data envelopment analysis (F-DEA) to choose the preferred scenario. The results of the

study depict that visibility and redundancy are essential factors enhancing the SCRES. Berle et

al. (2013) use the formal vulnerability assessment (FVA) approach to develop a simulation

model to measure disruption risk and effect of mitigation measures in a maritime liquefied

natural gas (LNG) supply chain. The authors assess the simulation results based on comparing

mitigating measures (additional storage at the export port, storage at the import port and vessels)

as well as cost/efficiency estimates. The results suggest that LNG storage facilities, in particular

on the export side, provide increase delivery volumes for the system. In addition, they show that,

having the flexibility to move more cargo cannot outweigh the cost of hiring an additional vessel

due to the high cost of LNG carriers

Several other studies use simulation models to evaluate the resilience of a SCN. Huang,

Li, Tsai, Chung, and Shih (2014) study the impact of pull and push SCM system practices on

SCRES using a simulation method. The authors compare the results of the models using

inventory level as a SCRES measure. The results of the simulation demonstrate: (1) if the initial

system inventory of both systems is enough to cover the demand during the disruption period,

the actual damage is far less than expected for the period; (2) the pull system outperforms the

push system for its ability to restore the system to stable operation after the disruption. Schmitt

and Singh (2012) present a large discrete event simulation (DES) model constructed using

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ARIMA to capture an actual network for a consumer packaged goods company. They use

inventory level and order fill rate to analyze the simulation results. Their findings show that

network utilization and proactive planning reduce supply chain disruption impact and improve

the SCN resilience.

Hybrid models

We consider the use of simulation tools to complement mathematical/optimization

models as hybrid models. For instance, Fanga, Lib, and Xiaoa (2012) develop a flexible network

SCN design and optimization model based on brand differentiation. They analyze SCN using

integration of multi-sourcing/pre-warning cost/manufacturing cost with brand

differentiation/losses cost of shortage/ordering cost of transportation/resilient member selection.

In addition, they simulate the SCN model based on different system reliabilities. Their results

show that the proposed model is effective in establishing a resilient SCN. Xu, Wang, and Zhao

(2014) develop a SCN mathematical model to assess predicted SCRES based on the structural

evolution against random supply disruptions. The authors introduce redundancy and customer

satisfaction as a SCRES measure. They simulate the proposed model and the simulation results

suggest that supply chain reliability and SCRES improve by adding redundant components to

back up suppliers in each tier. Geng, Xiao, and Xie (2013) propose a resilient cluster SCN

optimization model to analyze the dynamic evolution process when failures occur in clustered

SCN. The model focuses on resilient recovery through local self-organization reconstruction

behavior. They simulate the proposed model and find that the self-organization characteristic

provides guidance for local control and helps to achieve resilient optimization. Zhao et al. (2011)

present a hybrid and tunable SCN optimization growth model called Degree and Locality-based

Attachment (DLA) to simulate resilience of different SCN topologies. The authors develop

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SCRES metrics such as supply availability rate, connectivity (size of the largest functional sub-

network (LFSN)), accessibility (average supply path length in the LFSN, and maximum supply

path length in the LSFN). The results indicate that the DLA based supply network topology

provides balanced resilience against both random and targeted disruptions. Vugrin et al. (2011)

develop a resilience assessment framework that consists of a quantitative resilience cost

measurement, qualitative system characteristics assestment, and ABS to analyze petrochemical

SCRES. The authors develop a resilience cost measure that refers to the ratio of systemic

impact/productivity (SI) and market and transportation recovery effort (TRE) over the target

market value of production. The results show that proposed framework provides a

comprehensive evaluation of a system’s resilience. Xiao, Yu, and Gong (2012) utilize swarm

theory and optimization to develop an ant colony’s labor division model in order to solve task

allocation during emergency incidents. The authors simulate the proposed optimization model to

analyze task allocation status, supply chain coordination, and the impact of different emergency

incidents on task allocation. The simulation results indicate that the proposed model offers an

effective way to allocate tasks to enhance supply chain resilience during emergencies. Wang, Ip,

Muddada, Huang, and Zhang (2013) develop two models (disruption of a single firm and

propagation of disruption) for holistic supply chain network (H-SCN) systems. They analyze

both models by using a petri net technique and discrete event dynamic system simulation. The

results of the study suggest that the proposed petri net technique may help firms to build

resiliency in their supply chain by estimating the possible effects of any disruption and the

duration for each disruption effect. Table 13 summarizes the analytical research in the SCRES

literature, research methodologies, measures used, and novelty of each paper. Our findings show

that inventory level, lead-time and cost are commonly used measures to assess SCRES.

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Authors Simulation Modeling SCRES Measures Novelty Snyder (2006) SCN Expected cost, Worst-case cost Categorization of SCN models by the status of the existing

network

Datta et al.(2007) X Customer Service Level, Production Change Over Time, The Average Inventory Level, The total average network inventory

ABS framework to enhance resilience of complex production distribution systems

Dynes et al.(2007) EM Inoperability A new approach for estimating economic vulnerability of supply chainsdue to supply chain information failures.

Ratick et al.(2008) SCP Euclidean/ network path distance, Cost of opening a new facility

Development of a cover location modeling as a decision to determine the number of backup facilities to locate under varying cover, anticover, and complementary anticover distances.

Mitra et al. (2009) FP Total planning cost, Extent of permissible uncertainty and Demand satisfaction

A fuzzy mathematical programming approach that additionally consider the more realistic scenarios of uncertainty in operational parameters such as product demands, machine uptimes, safety targets.

Dingwei et al. (2009)

SCN, RA Resilience index Design of a service logistic network called RIMAS to enhance SCRES.

Aviral et al. (2011) SCN, MILP Expected disruption cost , Operational cost Use of a proactive scenario planning approach which incorporates robustness into the supply chain network design

Vugrin et al. (2011)

X EM Resilience cost measure A resilience assessment framework consist of a novel quantitative resilience cost measurement, qualitative system characteristics assessment, and ABS.

Zhao et al. (2011) X SCN Supply availability rate, Connectivity, Accessibility

SCRES metrics such as supply availability rate, connectivity, and accessibility. Use of DLA to simulate resilience of several supply network topologies.

Carvalho et al. (2012)

X Total cost, Lead time ratio Utilization of ABS as a tool to support the decision making process in SC design to enhance SCRES.

Xiao et al. (2012) X TA, LP Task allocation rate Application of ant colony’s labor division model based on swarm intelligence theory to solve supply chain task allocation problem during emergencies.

Schmitt and Singh (2012)

X Customer fill rate Development of a DES model to generalize the impact of disruptions on multi-echelon supply chains based on the down time and recovery of the system.

Fang et al. (2012) X SCN Pre-warning cost, Brand differentiation cost, Manufacturing cost, Cost of shortage, Ordering cost

Design of a competitive multi-sourcing resilient network with brand differentiation.

Cabral (2012) ANP Service level, Lead time, Cost, Quality of product

An ANP approach that offers the ability to prioritize enablers, KPIs, practices, and paradigms in complex, situations, helping to overcome AHP limitations derived from ignoring feedbacks and inner dependencies.

Azadeh et al. (2013)

X F-DEA Average time in system, Utility of resources, Number of breakdowns, Total cost

Introduction of the best policy in bringing the resilient factors into an SC transportation system with fuzzy parameters.

Berle et al.(2013) X Additional storage (export and import) Additional vessels

The combination of risk assessment methods and inventory routing simulation of maritime supply chain problems.

Wu et al. (2013) X Market share, Product type, Stockout duration An application of ABS model to investigate stockouts in terms of consumers, retailers, and manufacturers.

Wang et al. (2103) X SCN Material supply, Capacity of production, Inventory level, Throughput time, Production costs, Product revenues

Application of Petri net technique for Holistic SCN in the aspect of reduction of complexity arising from vast amounts of construct instances.

Harrison et al.(2013)

SCN Product flexibility A versatile optimization approach, READI--Resiliency Enhancement Analysis via Deletion and Insertion

Xiao and Wang (2013)

SCN,MILP Node failure probability, Node failure costs A distribution network resilience model based on the node failure probability, node failure costs and other factors.

Bhattacharya (2013)

SCN, FP N/A A shock-dampening fortification framework called RSA

Fakoor et al.(2013)

DT Adaptability, Effectiveness, Recovery,…etc. A method for measuring resilience based on fuzzy logic

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Authors Simulation Modeling SCRES Measures Novelty Geng et al.(2013) X SCN Dynamic evolution, Self-organization A cluster supply chain network structure generation model based

on cascading effect model to analyze dynamic evolution process when cluster supply chain failure happens.

Gong et al.(2013) ILN Total cost, Delivery time, Quality, Flexibility, Revenue

An interdependent layered network (ILN) optimization model that represents logical dependencies between the supply chain and infrastructure systems.

Sawik (2013) MILP Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), Expected cost

A mixed integer programming approach to determine risk-neutral, risk-averse or mean-risk supply portfolios, with conditional value-at-risk applied to control the risk of worst-case cost.

Wieland (2013) EM Risk probability p, Risk impact i Development of a model that links risk impact and risk probability to the four supply chain strategies.

Huang et al. (2014)

X Inventory level Application of Theory of Constraints (TOC) distribution and supply chain simulation to compare the impact of pull and push SCM system practices on SCRES.

Kristianto et al. (2014)

NP Lead time, Capacity Design of an integrated multi-level resilient SC network for reducing the impact of a disruption by allocating additional capacity and/or inventory at potential pinch points efficiently, with or without reliable delivery and supply.

Adtiya et al. (2014)

FP Closenes coefficient Development and implementation of an efficient decision-making tool to support resilient supplier evaluation.

Xu et al.(2014) X SCN Redundancy Development of a SCN model of predicted supply chain resilience based on the disaster resilience triangle with the extension of redundancy measure.

Soni et al. (2014) ISM Supply chain resilience index (SCRI)r A novel approach to measure and manage SCRES that quantifies resilience by a single numerical index (SCRI)r.

Garcia-Herreros et al.(2014)

SP Investment cost, Expected distribution cost Design of a SCN that includes DCs capacity as a design decision.

Kim et al. (2015) X SCN Walks, Average walk length, Maximum and Minimum walk length, Connectivity, Betweenness centrality, Centralization

Development of an analytical approach to assess various SCN structures for resilience.

Cardoso et al.(2015)

SCN, MILP Network design indicators, Network centralization indicators, Operational indicators

Development of a SCN design and planning model that considers product demand uncertainty is applied to a forward supply chain plus four closed-loop supply chains with different structures that are subjected to several disruptions.

Rabbani et al. (2015)

SCN, CFLP Co-vary diversity reliability index (HHI3), Oil dependency, Conditional value at risk(CVaR)

A SCN model consist of capacitated facility location–allocation problem (CFLP) to optimize supplier selection in order to cope with oil-supply disruption.

Sadghiani et al. (2015)

SCN, SCP,SP,

MILP

Number of supply facilities, Number of vehicles

A SCN design model that considers robustness and resilience measures concurrently.

Yang and Xu (2015)

EM Recovery rate, Recovery cost Extension of the research boundary from a single organization to a two-stage SC, and consideration of the effect of different recovery levels of upstream member on the profit of downstream member, which makes improvement for the resilience framework.

Govindan et al. (2015)

ISM Operational costs, Business wastage, Environmental costs, Customer satisfaction

Development of linkages among various lean, green and resilient practices and performance measures through a single systemic framework called ISM.

Rajesh and Ravi (2015)

GRA Quality, Cost, Flexibility etc. Application of GRA to select suppliers by considering attributes of resilience typically seen in an electronic supply chain.

Note: SCN: Supply chain network optimization, EM: Economic model , SCP: Set cover problem, FP: Fuzzy programming, RA: Resource allocation, MILP: Mixed integer linear programming, TA: Task allocation, LP: Linear Programming, ANP: Analytic network process, F-DEA: Fuzzy data envelopment analysis, DT: Delphi technique, ILN: Interdependent layered network, NP: Nonlinear programming, ISM: Interpretive structural Modeling, SP: Stochastic programming, CFLP: Capacity facility location-allocation problem, GRA: Grey rational analysis

Table 13. Analytical Research

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Conclusions

Unforeseen disruptions tend to increase along with the magnitude of their negative

impact on supply chains. Supply chain networks are more prone to disruptions than ever due to

their increasing complexity in an increasingly competitive and expanding global market. When a

firm fails to identify and understand its supply chain’s potential vulnerabilities, risks, and

mitigation strategies to disruptions its competiveness and ultimately its survival are in jeopardy.

Risk management techniques currently exist to identify the risks and track supply chain

vulnerabilities (Jüttner et al., 2003). However, these traditional risk management techniques are

not enough to deal with the unexpected events (Pettit et al., 2010). Thus, companies need to

enhance their SCRES in order to cope with disruptions to remain competitive. To enhance the

supply chain resilience, managers need to understand the concept of SCRES. This study

contributes to the SCRES literature by offering clear definitions of the supporting constructs of

SCRES, by developing a typological framework based on CIMO logic to establish a consistent

means to SCRES, by identifying measures and assessment techniques of SCRES through

extensive review of the existing analytical approaches. In addition, building on Pettit et al.

(2010) work, we develop taxonomies that include the vulnerabilities and capabilities that are

examined in the literature.

Our findings show: 1) there is a lack of consensus on the SCRES definition and the

terminology of its concepts; 2) the role of relational capabilities/relational competencies in

achieving SCRES is underexplored; 3) the amount of empirical studies are insufficient compared

to analytical research and literature reviews; especially survey, field and longitudinal studies; 4)

out of 134 articles reviewed, only 24 articles utilize theory to explain SCRES ; 5) supply chain

dynamics-discrete event simulation methods to model capabilities that forms SCRES is absent

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from the SCRES analytical research literature; 6) most widely used mathematical modeling

approach is mixed integer linear programming; and 7) common measures of SCRES are lead

time, inventory level, and cost.

In conclusion, the proposed typological framework and taxonomies will assist managers

to identify the vulnerabilities and capabilities of their supply chains. In addition, the clear

definitions and distinctions of the interrelated terms will help practitioners better understand the

factors influencing supply chain resilience.

Limitations and future research

This is exploratory research; therefore, the typological model presented in this paper is

just one of the possible perspectives. Our review is limited to 134 peer-reviewed articles. Based

on the findings of this study, we call on researchers to conduct longitudinal and survey studies to

develop and validate theoretical and conceptual SCRES research. We recommend further

conceptualization of SCRES using different research perspectives. Accordingly, we encourage

researchers to incorporate behavioral and relational aspects of the SCRES. There is a limited use

of theoretical frameworks to explain SCRES phenomena. RBV, dynamic capabilities and

systems theory are the common theories used to investigate SCRES. Further research should

exploit different theories in order to gain a better understanding of SCRES. Furthermore, in line

with Hohenstein et al. (2015) and Tukamuhabwa et al. (2015), we suggest authors to investigate

SCRES in different cultures.

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CHAPTER 3

ESSAY 2

Abstract

In today’s complex, global, and uncertain business environment, supply chains are more than

ever susceptible to the disruptions. To cope with internal and external supply chain instability

and disruptions, supply chains need to be resilient to survive. One of the most important factors

that enhance a supply chain’s resilience is a firm’s ability to collaboratively share information

with its supply chain partners. Cloud based supply chain management systems (cloud based

SCM) provide a collaborative information sharing platform that helps to identify, monitor, and

reduce supply chain risks, vulnerabilities, and disruptions. However, understanding the

capabilities of cloud based SCM and its impact on supply chain resilience (SCRES) is in its

infancy among supply chain academics and practitioners. This study aims to address this gap by

applying resource-based view (RBV) and relational view (RV) to explain the role of cloud based

SCM in facilitating SCRES. The proposed research contributes to the literature by developing a

new theoretical model that explains the relationships among cloud based SCM enabled relational

resources, supply chain agility, and SCRES. The results of this study may help to enhance supply

chain managers’ understanding on how cloud computing can improve their firm’s competitive

position by building resiliency into their supply chains.

Introduction

In today’s instable and uncertain business environment, every supply chain is vulnerable

to potential disruptions (Ambulkar et al., 2015; Knemeyer et al., 2009). Supply chain disruptions

can take many forms such as natural disasters (e.g., earthquakes, hurricanes, and tsunamis),

manmade disasters (e.g., accidents, wars, terrorist attacks, strikes, financial crises, and sabotage)

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or internal supply chain disruptions (e.g., occurrence of a fire at a plant, loss of a critical supplier,

missed shipments, and unexpected downtime) (Christopher & Peck, 2004; Harrison et al., 2013;

Ponomorov & Holcomb, 2009; Soni & Jain, 2011; Stephan M. Wagner & Neshat, 2010).

Disruptions have the potential to cause significant financial losses for companies and damage

relationships between customers and suppliers (Bode & Wagner; Fiksel et al., 2015). For

instance, after an eight-minute fire at a Philips semiconductor plant in year 2000, Ericsson lost

market share and more than 400 million Euros. According to the business continuity institute

(BCI)’s 2014 report, 81% of the respondent organizations reported at least one disruption in the

last 12 months out of which approximately 40% of them reported over one million Euros in costs

(Institude, 2014). To cope with the negative consequences of supply chain disruptions firms need

to construct resilient supply chains (Golgeci & Ponomarov, 2013; Peck, 2005). Ponomorov and

Holcomb (2009, p.131) define supply chain resilience (SCRES) as:

The adaptive capability of the supply chain to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function. One of the three principles of resilience, connectedness, refers to a supply chain partners'

level of effective and efficient coordination through an integrated network (Ponomorov &

Holcomb, 2009). Connectedness is possible through information sharing (Brandon‐Jones et al.,

2014). The sharing of appropriate and timely demand and inventory information among supply

chain partners improves supply chain visibility (Brandon‐Jones et al., 2014; Martin & Lee, 2004;

C. S. Tang, 2006).

Information technology (IT) solutions often provide for more effective supply chain risk

management with the ultimate goal of a stockless, risk free supply chain where supply and

demand are perfectly balanced (Peck, 2006). However, organizations struggle with the

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implementation and support cost of IT solutions and the poor quality, visibility, and timeliness of

its information (Casey et al., 2012). This struggle is highlighted in the results of the World

Economic Forum’s (WEF, 2012) supply chain risk survey. According to 64% of the supply chain

executives, the second largest area of supply chain vulnerability is the availability of shared

information. That implies 64 % of major companies do not have adequate visibility across their

supply chain network. Firms use information and telecommunication technologies (ICT) such as

internet enabled, inter-organizational systems (IIOS) to gain visibility into their supply chain

(Wu & Chang, 2012). A form of IIOS, electronic supply chain management system (eSCM), is a

platform that enhances communication, coordination, and collaboration thereby improving

visibility among supply chain partners (Lin, 2014). In recent years, firms continue to gain

interest in adopting a new kind of eSCM technology, cloud based SCM, to cost-effectively

enhance the resilience of their supply chains.

The use of cloud computing in supply chains is a relatively new, even as the practical use

of cloud computing to support supply chain operations continue to increase. In the extant

literature cloud computing studies mainly focus on the benefits and complications of adopting

cloud computing in supply chains (Buyya et al., 2009; Casey et al., 2012; Cegielski et al., 2012).

There is a limited explanation on cloud computing's impact on supply chains from a theoretical

perspective (Wu et al., 2013). To address this gap in the literature we apply resource-based view

(RBV) (Barney, 1991; Wernerfelt, 1984) and relational view (RV) (Dyer & Singh, 1998) to

determine relational resources facilitated by cloud based SCM such as collaboration,

communication, and integration. We then develop a theoretical model, based on Wieland and

Wallenburg’s (2013) model, to study the impact cloud based SCM enabled relational resources

have on supply chain agility that forms SCRES.

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This study contributes to the literature in two ways; we develop a theoretical model to

explain the role of cloud based SCM in establishing SCRES, and we provide a definition for

cloud based SCM that addresses the definitional inconsistencies in the literature.

In the following sections, this study presents a theoretical background. We then present a

cloud computing and cloud based SCM literature followed by the development of our

hypothesis. Next, we describe our methodology and measures. Finally, we present conclusions

and suggestions for future research.

Theoretical Background

Resource Based View (RBV)

RBV explains how an organization can achieve a competitive advantage by developing

resources and capabilities (Barney, 1991; Wernerfelt, 1984). Barney (1991) asserts that a firm

can achieve a competitive advantage by creating bundles of valuable, rare, inimitable, and non-

substitutable (VRIN) resources. A firm's resources refer to the inputs for the firm’s processes

(Grant, 1991). In the RBV literature, major resources are identified as heterogeneous resources

such as financial, physical, human, technological, reputational, and organizational resources

(Barney, 1991; Grant, 1991; Penrose, 1959). These heterogonous resources are assumed to be

immobile (Barney, 1991). Barney’s (1991) explanation of RBV has been criticized due to its

static nature and its failure to explain the missing link between the resource possession and

exploitation (Newbert, 2007). To address this missing link, Mahoney and Pandian (1992) point

out Penrose’s (1959) argument that suggests a firm’s achievement not only depends on the

valuable resources it possesses but also depends on how well the firm allocates its resources.

Therefore, a firm needs to leverage, manage or exploit its resources in order to obtain a

competitive advantage. A firm can exploit the full potential of its resources’ underlying value by

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integrating two or more heterogeneous resources to create higher order capabilities. Grant (1991)

defines capabilities as an outcome of groups of resources working together. The capabilities

create core competencies (Prahalad & Hamel, 1990). Creating distinctive core competencies that

lead to a competitive advantage depends on how well the capabilities of the firm are established

relative to its competitors and how hard it is for the firm’s competitors to imitate the firm’s

competencies. In the SCRES literature, there are studies that use the RBV perspective to

understand the relationships among resources, supply chain capabilities, and SCRES (Blackhurst

et al., 2011; Brandon‐Jones et al., 2014).

One major drawback of RBV is that it focuses mainly on a firm’s internal resources and

capabilities; thus, RBV ignores the fact that an individual firm’s performance highly depends on

its relationships with other firms.

Relational View (RV)

The RV builds on the RBV by stating that the competitive advantage of a firm is

dependent on the network of relationships (Dyer & Singh, 1998). RV theory emphasizes the

relational outcomes such as trust, commitment, communication, co-operation, adaptation,

interdependence, benevolence, credibility and effectiveness of the relationships. From an RV

perspective, the more firms invest in key relationships to improve partner capabilities through

inter-firm knowledge-sharing routines, the more firms gain relational advantage (Day et al.,

2013). In the SCRES literature, Wieland and Wallenburg (2013) use anticipation, the knowledge

about potential changes, and visibility, the knowledge about actual changes that are now

occurring, as relational competencies that establish two dimensions of SCRES; robustness and

agility. The authors apply RV as the theoretical basis to explain the relationships between

relational competencies: communication, cooperation, and integration and the dimensions of

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resilience: robustness and agility. Similarly, Mandal (2013) combines RV in with RBV and

dynamic capabilities to develop a theory-driven conceptual model that explains SCRES as a

dynamic capability. Mandal (2013) explores the relationships among relational

resources/competencies such as trust, commitment, communication, cooperation, adaptation, and

interdependence and SCRES. In addition, Mandal (2013) argues for the moderating role of

environmental uncertainty on the relationships among relational resources and SCRES.

In this research, we use Wieland and Wallenburg’s (2013) relational view framework, to

propose that cloud based SCM acts as a control tower that enables rapid high quality information

sharing through relational resources such as collaboration, communication, and integration with

external partners in a supply chain.

Literature Review and Hypothesis Development

Cloud Computing

X. Zhang, Donk, and Vaart (2011, p.1217) define information and telecommunication

technologies (ICT) as a “family of technologies used to process, store, and disseminate

information, facilitating the performance of information-related human activities”. From the

RBV perspective, cloud computing is considered as an ICT resource.

The term cloud computing first emerged in 2007 (Leukel, S. Kirn, & Schlegel, 2011).

Casey et al. (2012, p.185) define cloud computing as "a connectivity-facilitated virtualized

resource (e.g. software, infrastructure, or platforms) that is dynamically reconfigurable to support

various degrees of organizational need, which allows for optimized systems utilization”. Cloud

computing offers pools of virtualized resources in the form of software as a service (SaaS),

infrastructure as a service (IaaS), and platform as a service (PaaS). SaaS deploys software and

integrated business applications such as ERP, CRM, and POS and delivers the capabilities of

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these integrated application to a firm as a service. These services are provided on demand and

are accessible from anywhere in the world. IaaS delivers a computer infrastructure, a virtualized

computer or data center as a service that allows for storage and processing capacity. IaaS allows

customers to rent computing resources rather than install them in their own data centers (Cao,

Schniederjans, Triche, & Schniederjans, 2013). PaaS delivers a computing platform for

developers as a service. PaaS manages the underlying hardware, provides the facilities required

for building and deploying web applications and services entirely from the internet.

Organizations engage in Service Level Agreements (SLA) with cloud service providers to use

the hosted SaaS, IaaS and PaaS resources via internet (Joerg Leukel, Stefan Kirn, & Thomas

Schlegel, 2011).

The fundamental difference between traditional IT systems (e.g., on-premise Enterprise

Resource Planning (ERP) systems) and cloud computing offerings is the shift from a push of

resources to a pull of resources. Cloud computing lends itself to on-demand self-service, broad

network access, resource pooling, measured service, and reduced total cost of ownership via pay-

per-use. Pay-per-use offers cost advantage for companies that cannot afford the investment

commitment required for deploying complex IT systems (Durowoju et al., 2011).

Apart from all the advantages, cloud computing as an ICT resource by itself does not

meet Barney’s (1991) RBV VRIN criteria. IT and IT services are valuable but not rare resources

anymore since they are available to any firm and can easily be replicated by a firm’s competitors

(Fawcett, Wallin, Allred, Fawcett, & Magnan, 2011). However, when embedded into firm's

supply chain process, IT can facilitate capabilities which are hard to imitate by other firms such

as supply chain coordination and collaboration (Wu et al., 2006). That is where cloud based

SCM and cloud based enabled relational resources come into play.

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Cloud based SCM

In the context of supply chains, Lindner et al. (2010) is the first to develop a framework

to explain the application of cloud computing technology in supply chains. The authors introduce

the concept of cloud supply chain and define it as "two or more parties linked by the provision of

cloud services, related information and funds". In recent years, service providers have started

offering a form of eSCM, called cloud based SCM system, for managing the supply chain and

enhancing its agility (Wu et al., 2013). Toka et al. (2013) conduct a cloud based SCM

conceptual study. Even though the term cloud based SCM is extensively used in industry and

appears a few times in the literature, cloud based SCM has not been formally defined. We make

a distinction between cloud computing and cloud based SCM. We view cloud computing as a

pool of virtualized IT resources and cloud based SCM as the use of cloud computing resources

for managing supply chains. For instance, service providers such as Amazon, Google, and

Microsoft offer various cloud-computing applications – e.g., cloud storage, cloud SQL, and

cloud data storage for mainly intra-organization use. On the other hand, service providers such as

GTNexus and One Network Enterprises provide enterprise resource planning (ERP), customer

relationship management (CRM), supplier relationship management (SRM), and transportation

management system (TMS) services available for inter-organizational use (the firm and firm’s

supply chain partners). Some studies view cloud computing as a component of ICT (Mensah &

Merkuryev, 2013; Urciuoli, 2015), eSCM (Cegielski et al., 2012), and IT innovation (Lin, 2014).

Others do not offer a specific term for the application of cloud computing in supply chains

(Damodaram & Ravindranath, 2010; Dubey & Jain, 2014; Subramanian, Abdulrahman, & Zhou,

2015; Wu et al., 2013). In this paper, we use the term cloud based SCM for the application of

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cloud computing for managing supply chain operations. Table 14 presents various definitions of

cloud based SCM related concepts.

Author Related Concept Definition

Cegielski et al. (2012) Cloud computing as a component of eSCM

A generalized systems tool may serve as a technical infrastructure support component of many of the specific applications by providing an infrastructure to enable the applications that facilitate communication, coordination, and collaboration across organizational boundaries that are the desired outcomes of eSCMs.

Liu, Ke, Wei, Gu, and Chen (2010) and Lin (2014)

eSCM A form of internet-based inter organizational system (IIOS), offers firms a platform to enhance communication, coordination and collaboration.

Cristina Giménez and Helena R. Lourenço (2008)

eSCM Impact that the internet has on the integration of key business process from end user through original suppliers that provides product, service and information that add value for customers and other stake holders

Wong et al. (2015) Inter organizational information integration

The extent to which firms electronically link and deploy information technology for information sharing across partner firms

Table 14. Cloud based SCM related definitions

For the present research, we define cloud based SCM as “an emergent eSCM that senses

the changes in real time and executes the optimal response by providing a platform for

collaboration, communication, and integration across the supply chain” by synthesizing the

definitions of eSCM and cloud computing in the literature listed in Table 2.1.

In the literature, the existing studies mainly focus on investigating a firm’s cloud

computing adoption in support of supply chain operations. For instance, Casey et al. (2012)

empirically study the decision makers' intention to adopt cloud computing in supply chains using

organizational information processing theory. Their results suggest that a firm's information

requirements and capabilities affect the intention to adopt cloud computing. Consequently, Wu et

al. (2013) empirically study a firm’s intention to adopt cloud computing technologies in support

of its supply chain operations by employing combining the theories of innovation diffusion and

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information processing view. Similarly, Oliveira, Thomas, and Espadanal (2014) develop a

theoretical model based on diffusion of innovation (DOI) and technology-organization-

environment and environment (TOE) framework to study the factors that influence the adoption

of cloud computing in the manufacturing and service industry. Other papers investigate the

benefits and challenges of cloud computing (Zhou, Zhu, Lin, & Bentley, 2012), partner selection

using grey theory (Duan, Huang, Yang, & Wan, 2013), cost and green benefits of cloud

computing supply chain applications (Singh, Mishra, Ali, Shukla, & Shankar; Subramanian et

al., 2015), cloud enabled real time supply chain trading (Kong, Fang, Luo, & Huang),

collaborative and interoperable manufacturing models via cloud computing ( cloud

manufacturing) (Helo, Suorsa, Hao, & Anussornnitisarn, 2014; Wang & Wang, 2014; D. Wu,

Rosen, Wang, & Schaefer, 2015; Xu, 2012), and the capabilities of decision support systems of

cloud computing (Demirkan & Delen, 2013). Lately, there is a growing interest in the role of

ICT, especially cloud computing, in establishing cyber-resilience (Davis, 2015; Urciuoli, 2015)

and ICT’s impact for building SCRES (Mensah & Merkuryev, 2013, 2014). This study

contributes to the body of knowledge by developing a theoretical framework to explain the role

of cloud based SCM on SCRES.

Benefits and challenges of cloud based SCM

Cloud computing technology executes rapid and accurate statistical demand forecast for

all supply chain partners by reducing the time demand data and resulting forecasts are

communicated throughout the supply chain (Schramn et al., 2011). Such processes can lead to a

significant decrease of the bullwhip effect, the information distortion among different stages of

the supply chain (Lee et al. 1997). For instance, with a traditional on-premise ERP system, when

a disaster hits, decision makers would have hard time to find data on the current status of

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inventory levels, production, or availability of alternative transportation modes. Using cloud

based SCM supply chain partners can see the risks, rapidly develop a plan to mitigate the impact

of a disruption and as a result, gain competitive advantage.

The integrity of supply chain visibility is jeopardized when an on-premise ERP system’s

inventory position is compromised. An example is when the inventory counts at a location do

not include the correct in-transit items due to a latency problem. Cloud based SCM bridges this

information gap by providing real-time data on in-transit items and updating it instantly across

the supply chain network (GTNexus, 2012). Obtaining real-time data and instant updates help

supply chain partners to make rapid decisions in response to supply chain disruptions. For

instance, a major specialty chemicals player operating in 25 countries used its cloud based SCM

to proactively plan for Suez Canal closure due to civil unrests and protests in 2011. Using data

from carriers around the world on their cloud based supply chain platform, the company was able

to rapidly develop alternate transport routes to for its critical items (GTNexus, 2012). Table 15

summarizes cloud based SCM applications.

Supply Chain Activity How it works in Cloud based SCM system

Forecasting and Planning • Obtains sales data via internet • Performs analytics • Executes accurate statistical demand forecast in a single platform

Sourcing and Procurement • Operates as a database which contains data about different suppliers that makes it easy to switch suppliers or source

• Develops contracts • Allows buyers to use scenario based decision making in sourcing

Logistics • Offers tracking by inventory visibility of in-transit inventory • Acts as a virtual warehouse for products in pipeline, • Allows dynamic re-routing based on the actual product location information • Creates exception-based alerts based on chosen performance indicators to warn about

potential delays and disasters Note: Adapted from Toka et al. (2013) and GTNexus (2013).

Table 15. Cloud based SCM applications

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Apart from the benefits of cloud based SCM, challenges remain. Subramanian et al.

(2015) discuss the cloud based SCM challenges of security, trust, privacy, and the lack of control

over physical infrastructure. To address the security and privacy concerns, cloud service

providers provide high-level expertise in IT security and data protection. The trust between a

firm and firm’s supply chain partners remains an issue since without trust supply chain partners

maybe reluctant to share information and resources (Johnson et al., 2013). In this study, we

assume there is a high level of trust between supply chain partners.

Supply Chain Agility

Christopher (2004, p.18) states that “One of the most powerful ways of achieving

resilience is to create networks, which are capable of more rapid response to changed conditions.

This is the idea of agility”. Supply chain agility refers to a "supply chains' ability to respond

rapidly to unpredictable changes" (Christopher & Peck, 2004, p.10). Supply chain agility consist

of two key elements: visibility and velocity (Christopher & Peck, 2004; Peck et al., 2003).

Supply chain visibility refers to a “clear view of upstream and downstream information such as

inventories, demand and supply conditions, and production and purchasing schedules”

(Christopher & Peck, 2004, p. 10). Supply chain velocity (speed) refers to the supply chain's

reaction time to changes in demand, upstream or downstream (Christopher & Peck, 2004).

Integration, communication, and collaboration improve both visibility and velocity (

Johnson et al., 2013; Wieland & Wallenburg, 2013). Cloud based SCM allows synchronization

by ensuring information integration, timely communication, and collaborative efforts that

improve both visibility and velocity. Improved velocity and visibility (agility) during disruptions

enhances SCRES (Johnson et al., 2013; Jüttner & Maklan, 2011; Scholten et al., 2014).

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The following example shows how cloud based SCM enhance agility and resilience

during disasters: On March 2011 a destructive 9.0-magnitude earthquake and tsunami along with

nuclear explosions struck northeastern Japan; killing thousands of people, halting industry and

crippling infrastructure. A large manufacturing company operating outside of Japan received the

news in the middle of the night. Within a few hours of the tsunami hitting Japan, this

manufacturer’s logistics team ran global materials management reports to communicate the

precise status of the products originating from Japan to their entire global network of facilities.

With this quick and far-reaching communication, the manufacturer was able to launch a

successful contingency plan. Alternative suppliers, already existing as part of their global

network, were evaluated and used to mitigate Japan’s disruptive impact. The agility of this

manufacturer’s trusted network of supply chain trading partners allowed for minimum

disruptions, saving countless money and maintaining continuity for its end-to-end supply chain.

This manufacturer was part of a cloud-based supply chain that provided the catalyst to quickly

shift its resources to allay the impact of no longer being able to receive product from Japan

(GTNexus, 2012).

Impact of Cloud based SCM-enabled Collaboration on Supply Chain Agility

In the literature, authors consider collaboration as either a formative element of SCRES

(Jüttner & Maklan, 2011) or an antecedent to SCRES constructs (Scholten & Schilder, 2015). In

this study, we consider collaboration as an antecedent to supply chain agility which is a SCRES

construct. We operationalize the collaboration construct as the extent to which cloud based SCM

enables a firm's ability to work effectively with other firms for mutual benefit.

Jüttner and Maklan (2011, p.251) define collaboration as "the level of joint decision

making and working together at a tactical, operational or strategic level between two or more

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supply chain members”. The aim of the collaboration is to cope with inter-organizational issues

that may not be tackled by any organization acting alone (Johnson et al., 2013; Vangen &

Huxham, 2003). When there is a supply chain disruption, high level of collaboration through

information sharing across supply chains can help significantly mitigate the risk (Peck et al.,

2003). The problem is to create conditions in which collaborative working becomes possible.

The decision synchronization and incentive alignment are two architectural elements of

supply chain collaboration that are essential for effective system-level responses to disruption

(Jüttner & Maklan, 2011). These conditions can be satisfied by creating a platform for

information sharing (Brandon‐Jones et al., 2014). However, information sharing among supply

chain partners with traditional on-premise IT systems might cause information inconsistencies

since the information comes from multiple sources. Cao et al. (2013) suggest that cloud based

SCM prevents information inconsistencies, reduces uncertainty, and eliminates conflicts in

collaboration by coordinating and integrating interactive processes. Timely collaboration through

sharing reliable, consistent and good quality information enables quick response to supply chain

disruptions; therefore increasing velocity (Scholten & Schilder, 2015).

Collaborative information sharing and the application of shared knowledge reduces the

uncertainty across the supply chain (Christopher & Peck, 2004) and increases visibility by

providing a clear view of the upstream and downstream information needed to detect

vulnerabilities to disruptions (Scholten et al., 2014). IT can facilitate the visibility of supply

chain. However, with a traditional on-premise IT system, when a disruption hits, decision makers

have hard time to find data on the current status of inventory, production, or availability of

alternative transportation modes (GTNexus, 2012). Cloud based SCM provides a high level

visibility by executing rapid and accurate pipeline inventory data and statistical demand forecasts

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for all the supply chain partners (Toka et al., 2013). When a disruption strikes, Cloud based SCM

enables agility by creating a reliable real-time data platform that allows rapid collaborative

decision-making. Therefore, collaboration achieved through cloud Based SCM improves

visibility and velocity. Thus, we propose:

P1. Cloud based SCM-enabled collaboration will be positively associated with supply

chain agility.

Impact of Cloud based SCM-enabled Communication on Supply Chain Agility

Inter-organizational communication (IOC) refers to the flow of explicit, meaningful and

timely information (Modi & Mabert, 2007; A. Wieland & Wallenburg, 2013). IOC can be

categorized by frequency, degree of formality, level of willingness to share proprietary

information, and timeliness (Krause, Handfield, & Tyler, 2007). Communication methods

include traditional communication methods (e.g., e-mail, written, face to face, and telephone)

and advanced communication methods (e.g., computer-to-computer links, electronic data

interchange (EDI), and ERP) (Prahinski & Benton, 2004). Advanced communication methods

improve external communications by enabling accelerated information flows (Sriram & Stump,

2004).

Effective communication between firms are characterized as genuine, frequent and

involving personal contacts (Chen & Paulraj, 2004). Frequent exchange of information through

IOC help to reduce conflicts between supply chain partners, product, and performance related

errors and enhances inter-organizational relationships such as cooperation and trust between

supply chain partners (Paulraj, Lado, & Chen, 2008). IT facilitates more frequent communication

and higher quality inter-organizational relationships (Prahinski & Benton, 2004; Sriram &

Stump, 2004). Mohr and Nevin (1990) state that communication is “the glue that holds together a

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channel of distribution” and Sriram and Stump (2004) assert that IT is a “glue gun” that directs

and controls collaborative communication. In the present study, we consider cloud based SCM as

an advanced communication method that facilitates effective communication. We operationalize

the IOC as a firm's relevant, timely, accurate, complete and confidential information

exchange with its supply chain partners.

Wieland and Wallenburg (2013) suggest that communication of disruption data enables

collaborative communication between supply chain partners and facilitates join decision making

that minimizes the effects of disruptions. Their results show that communication between supply

chain partners improves both visibility and speed. The following example demonstrates how a

company can quickly change the routing and mode of transportation for outbound shipments

using cloud based SCM-enabled communication. A major specialty chemicals firm operating in

25 countries used its cloud based SCM platform to proactively plan for a disaster. In 2011,

protests near Egypt’s heavily-used Suez Canal threatened to close a trade route that carried 1.4

million tons of cargo the previous year. The closure of the Suez Canal would have blocked ocean

shipments from passing from Europe to Asia through the Mediterranean and Red Seas. Using

data from carriers around the world on their Cloud Based SCM, a major specialty chemicals

company quickly developed an alternate key raw material transport route that rerouted key

materials from the Indian subcontinent east to California. The company monitored their supply

flows in real time and kept response plans up to date in the event that the political situation

threatened their supply chains (GTNexus, 2012). Since cloud based SCM provides supply chain

partners to foresee the supply chain risks and quickly plan to mitigate the impact of a disruption

through effective communication, we propose that:

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P2. Cloud based SCM-enabled communication will be positively associated with supply

chain agility.

Impact of Cloud based SCM-enabled Integration on Supply Chain Agility

In the literature, integration, collaboration or cooperation and other relational constructs

are often used interchangeably (Adams, Richey, Autry, Morgan, & Gabler, 2014; Chen,

Daugherty, & Roath, 2009). Unlike collaboration or cooperation, integration refers to

reconfiguration of firm processes through process and informational connectivity in order to

align internal and external resources (Adams et al., 2014; Chen et al., 2009). Adams et al. (2014)

observe that integration and collaboration are distinct concepts. In this study, we operationalize

the integration construct as inter-organizational information integration achieved by cloud based

SCM; the extent to which cloud based SCM is used to coordinate and integrate information with

partners within their supply chain (Swafford, Ghosh, & Murthy, 2008).

Inter-organizational information integration provides compatible information systems and

databases that increase visibility among supply chain partners (Wong et al., 2015). Therefore,

information integration is considered as the key for timely and accurate information sharing that

helps supply chain partners in joint decision making, planning and executing supply chain

activities (Overby, Bharadwaj, & Sambamurthy, 2006). In addition, information integration

ensures faster data exchange that accelerates the processes of information sharing, joint decision

making, and planning (Wieland & Wallenburg, 2013). Consequently, information integration

creates a platform for sensing disruptions and joint problem solving to respond to disruptions

faster through enhanced visibility (Kleindorfer & Saad, 2005). There are contradictions about the

relationship between integration and supply chain agility. Although, numerous studies suggest

that information integration plays a critical role in achieving supply chain agility that leads to

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enhanced performance (Gligor & Holcomb, 2014; Swafford et al., 2008), in the SCRES context

(Wieland & Wallenburg, 2013) show that integration does not yield a significant increase in

agility and SCRES. To test the information integration’s effect on supply chain agility, we

propose:

P3. Cloud based SCM-enabled integration will be associated with supply chain agility.

Impact of Supply Chain Agility on SCRES

According to Haimes (2006), the goal of the SCRES is to recover to the desired state of

the system within an acceptable time and cost, and to reduce the impact of a disturbance by

changing the effectiveness level of a potential threat (Carvalho, Barroso, et al., 2012). This goal

can be achieved by enabling the shift towards desirable states in which failure modes would not

occur (Carvalho et al., 2012). For instance, when faced with a disruption, a resilient supply chain

would shift flow to other customers and markets so that its supply source continues at full

operating rates (Bradley, 2005). However, “resilience depends on choices made before the

disruption than the actions taken on the midst of the disruption” (Sheffi, 2005b). Therefore, to

enable such a shift towards to desirable state, resilience needs to be designed into supply chain

(Christopher & Peck, 2004). Resilience can be designed into supply chain by developing various

capabilities. Supply chain capabilities are defined as the antecedents of supply chain

vulnerability that impact both the probability of occurrence and the severity of supply chain

disruptions (Sheffi, 2005b; Wagner & Bode, 2006). Based on the existing literature, Jüttner and

Maklan (2011) synthesize four formative capabilities; flexibility, velocity, visibility and

collaboration. In this study, we operationalize SCRES as the extent to which a firm’s ability to

return to normal operations rapidly, and discern potential future event or situations. Therefore,

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from the formative capabilities of SCRES, we consider the impact of velocity and visibility that

constitute supply chain agility on SCRES. Thus, we propose:

P4: Supply chain agility will be positively associated with SCRES.

Figure 13 illustrates a theory driven conceptual model developed based on proposed

relationships.

Cloud Based SCM

Supply Chain Agility

Visibility & Velocity

P1+

P2+

Collaboration

Communication

Integration

P3

P4+SCRES

Anticipation & Adaptability

Figure 13. Proposed relationships

Methodology

Sample and Data Collection

Since this study is exploratory in nature, we develop a survey questionnaire, based on

existing scales, to test the proposed theoretical model. The survey contains questions about the

respondents views on Cloud based SCM - enabled collaboration, communication and integration,

supply chain agility and SCRES. The questionnaire also contains questions asking demographic

information of the respondents. The dependent variable and all independent variables are

measured using a seven point Likert-type scale with endpoints “Strongly Disagree=1” and

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“Strongly Agree=7”. We use the online survey method to collect the data for this study. The

online survey was developed in Qualtrics and a link was emailed to the prospective respondents.

Variables are measured using established scales in the literature. Instead of focusing on a

single industry, this research is focused on developing a broad understanding of cloud based

SCM's on SCRES. Therefore, multiple industries are included in the sampling frame. Potential

respondents for the survey work for organizations that use cloud computing to manage their

supply chain operations. The questionnaire will be sent to cloud based SCM clients of One

Network Enterprises.

Measures

This study uses constructs that were previously measured in the context of supply chain

resilience. Table 16 shows the summary of the constructs used in this study.

Constructs Definitions Measures Source Cloud based SCM –enabled collaboration

The extent to which a firm’s ability to work effectively with other entities for mutual benefit would influence SCRES.

5-item Five items were adopted from Pettit et al., 2013.

Cloud based SCM –enabled communication

The extent to which a firm’s information exchange with its supply chain partners would influence supply chain collaboration.

5-item Five items were adopted from Brandon-Jones et al., 2014.

Cloud based SCM –enabled integration

The extent to which cloud based SCM is used to coordinate and integrate information with partners within their supply chain.

6-item

Three items were developed building upon Brandon-Jones et al. 2014 and Fawcett et al. 2011. One item was adapted from Swafford et al., 2008. One item was adopted from Wieland and Wallenburg, 2013. Another item was developed based on Adams et al. 2014.

Supply Chain Agility- Visibility

The extent to which a firm’s knowledge status of operating assets and the environment would influence SCRES.

5-item

Three items were adopted from Pettit et al., 2013. Two items were adopted from Brandon-Jones et al., 2014.

Supply Chain Agility-Velocity

The extent to which a firm’s speed or quickness with which the firm can engage would influence SCRES.

5-item

Four items were adopted from Swafford et al. 2008, Wieland and Wallenburg, 2013. One item was adapted from Swafford et al., 2008.

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Supply Chain Resilience

The extent to which a firm’s ability to return normal operations rapidly, and discern potential future event or situations.

9-item Fourteen items were developed building upon Pettit et al., 2013.

Table 16. Summary of the constructs and measures

The complete structured questionnaire is presented in the Appendix A.

Conclusions and Future Research

In this study, we propose a theoretical model based on RBV and RV and a review of

supply chain, supply chain resilience, and cloud computing literature.

We suggest that the results of this study can help supply chain managers to better

understand cloud computing's impact on SCRES when making strategic decisions to improve a

firm’s competitive advantage. We aim to empirically test the validity of the constructs and the

framework proposed in this paper.

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CHAPTER 4

ESSAY 3

Abstract

The inadequacies of traditional information sharing in the healthcare sector often lead to

poor demand and inventory visibility. The resulting demand and supply mismatch of healthcare

products may have dire economic and patient care consequences. For instance, a hospital drug

shortage often requires an emergency delivery. This emergency refill is costly and dangerous for

a patient's healing process. In recent years, innovations in information technology (IT) have

provided the catalyst for successfully advancing supply chain collaboration. We examine cloud

computing as an internet-enabled supply chain management system (eSCMS) that enhances

collaborative information sharing in a multi-echelon hospital supply chain. We use systems

theory and system dynamics to develop two conceptual, casual loop diagrams (CLDs); one

representing traditional and the other cloud based information sharing in a hospital supply chain.

CLDs and their equivalent system dynamics (SD) models are used to simulate the performance

of the traditional and cloud based hospital supply chains. We compare the performance metrics

of both models: average inventory levels, lead time, visibility, and unfilled orders. The results

suggest that the use of the cloud based information sharing in a hospital supply chain reduces

inventory levels, reduces actual lead time through demand and inventory visibility, and reduces

delivery delays while increasing overall performance. The findings of this study will help health

care decision makers to better understand the structure and the benefits of embracing cloud based

information systems in their supply chains.

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Introduction

Today’s healthcare supply chains are complex networks of firms that aim to profitably

deliver replenishment supplies in the right quantity, at the right place, and at the right time.

Effective healthcare supply chain capacity planning and inventory management is difficult due to

the uncertain lead times of healthcare supplies (e.g., drugs, devices, and instruments), the lack of

understanding supply chain practices when making procurement decisions, and the difficulty in

predicting patient demand in case of emergencies (Vikram, Prakash, & Amrik, 2012). These

challenges of healthcare supply chain inventory management lead pharmacy departments in

hospitals to carry safety stock, often in excess, to cope with demand uncertainty and supply

bottlenecks. However, building up inventory buffers may result in shortages in health care

supplies due to their short shelf-life (Vikram et al., 2012). Supply shortages may result in

tremendous costs for hospitals and serious implications for patient care. According to Premier (

March 2011) 98 percent of drug shortages increase the cost of obtaining drugs, 89 percent of

drug shortages cause delay or cancellation of a patient care intervention, and 80 percent of drug

shortages result in medication errors and medication safety issues due to substitution of similar

drugs.

Since the 1990s hospitals began to use information technology (IT) systems to enable

Vendor-Managed Inventory (VMI) programs to solve supply shortages (Kelle, Woosley, &

Schneider, 2012). However, the nature of hospital supply chains creates challenges for adopting

and using IT to improve inventory management. The challenges for using and adopting IT

include : 1) cost of adopting IT; 2) highly unpredictable demand in healthcare supplies for

medical procedures due to diversity of patient characteristics; 3) difficulty in inventory tracking

due to the urgency of medical procedures; 4) lack of accountability for healthcare supplies

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managed under VMI programs due to supply expiration and tracking; 5) poor visibility among

hospital supply chain partners due to conflicted, inaccurate, and delayed demand and inventory

information (Bendavid & Boeck, 2011; Volpe, 2012). These IT challenges are consistent with

the Nachtmann’s (2009) healthcare supply chain survey findings. According to 42 percent of

respondents, the lack of data standards, having no visibility and quality of available information

are major IT system challenges in health care supply chains (AHRMM, 2009). To overcome the

challenges of utilizing traditional IT systems for managing healthcare supply chains, hospitals

started adopting cloud computing technology (Low & Chen, 2012)- a dynamic, transparent, and

cost effective information sharing system that instantaneously captures and communicates supply

chain changes..

Cloud computing is an internet based technology (e.g., software as a service) that differs

from traditional, on-premise computer technoloies (e.g., mainframe and client-server based ERP

systems). Cloud computing technology enables “scalable on-demand computing power, rapid

deployment, and reduced support infrastructure, all while facilitating lower cost of ownership”

(Casey et al., 2012). For organizations within a complex supply chain, flexibility and availability

of information are among the greatest benefits of the cloud computing technology (Casey et al.,

2012; Zhou et al., 2012). Therefore, the adoption of cloud computing technology is rapidly

growing among organizations to cope with dynamic changes (Zhou et al., 2012). Using cloud

based information systems in hospital supply chains, hospitals gain flexibility to manage their

own data and obtain available data from their supply chain partners over the web, anytime and

anywhere in the world. However, unlike other industries, hospital CIOs are cognizant of the

long term cost benefits, enhanced security and back-up support provided by cloud computing

technologies (Sullivan, 2013). Thus, hospitals widely invest in private clouds to manage and

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integrate electronic medical records, claims, medication and lab data (Low & Chen, 2012) in

their internal hospital supply chain that consists of patient care units, points of care and hospital

storage, etc. (Rivard-Royer, Landry, & Beaulieu, 2002) instead of using public clouds for their

external supply chain that consist of medical suppliers, manufacturers and distributors. Figure

3.1 shows the difference between internal and external hospital supply chains. The present

research focuses on external hospital supply chains.

Figure 14. Internal and External Hospital Supply Chains

Source: adapted from Arthur Anderson & Co. (1990,p.38)

In this study we use systems theory (Von Bertalanffy, 1950) to develop casual loop

diagrams (CLDs) and their equivalent system dynamics (SD) models to simulate the impact of

cloud-based information sharing on a hospital’s supply chain performance. SD is a modeling

and simulation methodology that provides insights on how dynamic interactions of the physical

process, informational flows, and managerial policies improve system performance over time

(Sterman, 2000). The structure of a system determines its behavior (Sterman, 2000). SD models

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offer a structural illustration for feedback loops, accumulation processes, and delays between

cause and effect (Größler, Thun, & Milling, 2008). In this respect, we develop generalizable

(SD) models for N-echelon hospital supply chains that capture the traditional hospital supply

chain and cloud based hospital supply chain structures. Then, we compare the resulting supply

chain performance of the traditional and cloud based models.

Even though practical industry applications of cloud computing in support of supply

chain operations continue to increase, in academic literature, the utilizing of cloud computing as

eSCMS is still in infancy (Casey et al., 2012). Existing studies regarding cloud computing

mainly focus on the benefits and complications of adopting cloud computing in supply chains

(Buyya et al., 2009; Casey et al., 2012; Cegielski et al., 2012). Additionally, there are studies that

investigate the benefits of cloud based information sharing in internal healthcare supply chains

(Bhattacharya, Ghosh, & Nanda, 2012; Low & Chen, 2012; Swaminathan et al., 2012). Absent

from the literature is how cloud computing impacts supply chain performance, especially in the

healthcare supply chain domain. The contributions of this study to the literature are: 1)

development of conceptual causal loop diagram (CLD) for cloud based hospital supply chain; 2)

use of SD feedback-based structure to demonstrate the impact of cloud computing on hospital

supply chains; and 3) investigation of cloud computing's impact on hospital supply chain's

performance.

In the following sections, first we present a literature review. Second, we develop CLDs

for a traditional hospital supply chain and a cloud based hospital supply chain. The CLDs

identify the relationships between a hospital’s supply chain elements. Third, we discuss the

relationships between the traditional and cloud-based CLDs. Fourth, we develop the CLDs and

its equivalent SD models that compare the performance of a traditional and a cloud based

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hospital supply chain. Finally, we conclude with a discussion on managerial implications, the

limitations of this study, and a future agenda to extend this research.

Literature Review

We performed an extensive review of the extant literature. First, we explored how firms

use information sharing and information technology as coordination mechanisms in supply

chains. This led to how technology is leveraged to implement Vendor Managed Inventory

(VMI) programs. So, we then reviewed the benefits and weaknesses of VMI programs

concenetrating on health care supply chains. We also review the supply chain collaboration

literature to pinpoint the factors for successful collaboration. Next, we evaluate the cloud

computing phenomena and describe a cloud based supply chain. Finally, we discuss the relavant

research in SD supply chain modeling.

Information Sharing and Information Technology (IT)

The dependencies between supply chain partners can be managed by coordination

mechanisms with the intent to improve the supply chain performance. In the literature, there are

comparative studies where a no information sharing policy is compared with a full information

sharing policy to analyze the impact of information sharing on supply chain performance

(Cachon & Fisher, 2000; Lee, So, & Tang, 2000; Yina, Xuejun, Xiande, Jeff Hoi Yan, & Fei,

2012; Yu, Hong, & Cheng, 2001). Hau L. Lee et al. (2000) present a comparative study to

investigate the impact of replenishment lead time. They find that demand information sharing

provides significant inventory reduction and cost savings to the manufacturer. Similarly,Cachon

and Fisher (2000) use a simulation based comparative study to show that there are significant

savings from lead time and batch size reductions. They suggest that these reductions are

facilitated by the implementation of IT as a coordination mechanism in supply chains.

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Implementation of IT enhances the coordination between supply chain partners by seamlessly

linking the production to delivery (Kanda & Deshmukh, 2008). Moreover, coordination

enhanced through IT increases the stability and accuracy of lead time demand forecasting

process (Chatfield et al., 2004). Accurate lead time demand forecasting reduces the bullwhip

effect by minimizing the risk of demand amplification in the supply chain (Angulo, Nachtmann,

& Waller, 2004) which improves the supply chain performance (Ciancimino, Cannella,

Bruccoleri, & Framinan, 2012). The present study builds on the idea of the information sharing

and IT’s positive impact on supply chain performance to propose a model for an emergent IT and

information sharing platform for supply chains, cloud computing, and study its impact on

hospital supply chain performance.

Vendor Managed Inventory (VMI)

In the literature, the benefits of information sharing when making decisions highlight the

significance of Vendor Managed inventory (VMI) whereby the supplier maintains inventory

levels and determines order quantities for its customers. To show the significance of VMI on

reducing the bullwhip effect, Disney and Towill (2003) compare a traditional supply chain

structure with a VMI supply chain structure. They find that adopting VMI reduces the bullwhip

effect if the inventory and sales information is appropriately used by supply chain partners.

Accordingly, Angulo et al. (2004) investigate the impact of sharing inaccurate and delayed

information on the performance improvements achieved by VMI. They suggest that performance

of VMI substantially decreases if shared information is not up-to-date. Later, Sari (2007)

develops a simulation model to evaluate the benefits of VMI under different market conditions.

The results of Sari (2007)’s study show that the retailer needs to provide additional information

to the supplier in order to eliminate the uncertainty. Similarly, Rubiano Ovalle and Crespo

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Marquez (2003) convey a simulation study using system dynamics approach in order to evaluate

the impact of collaboration tools such as VMI, collaborative forecasting (CF), and collaborative

planning (CP) on supply chain performance. They find that the elimination of retailers from

inventory and forecasting decisions results in poor VMI program. They suggest that use of

internet enabled e-collaboration tools as an enabler of information sharing improves the supply

chain performance. Claassen, Van Weele, and Van Raaij (2008) study the impact of the quality

of an IT infrastructure on the success of VMI program. They find that when the quality of an IT

infrastructure is poor, VMI programs lead to improved service levels rather than cost reduction

due to the buyer’s willingness to ensure availability of safety stock. These studies show that

regardless of how promising the theory of VMI may appear, VMI implementations fail because

of sharing outdated, inaccurate and uncertain sales and inventory data.

eSCMS, Collaboration and Trust

Using information technology (IT) as an integrated component of the internet is critical

for the successful supply chain coordination that aims to increase supply chain performance by

minimizing the bullwhip effect (Casey et al., 2012; Liang & Huang, 2006). eSCMS facilitate the

development of more effective and efficient supply chain (Gunasekaran & Ngai, 2004) by

providing seamless collaboration with supply chain partners. However, organizations struggle

with the long term investment and visibility issues of the complex eSCMs (Casey et al., 2012).

Cloud computing supports the advantages of eSCMs and provides solutions to visibility issues of

eSCMs without the burden of long term investment cost. For instance, when an on-premise

Enterprise Resource Planning (ERP) system loses visibility, inventory counts may not include

the in-transit items only days away from arrival. Cloud based supply chain management (SCM)

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bridges this information gap by providing real-time data on intransit milestones and updating it

instantly across the supply chain network (GTNexus, 2012).

Seamless collaboration improves forecasting and planning, which helps to reduce the

stock and stock-outs (Cristina Giménez & Helena R Lourenço, 2008). However, seamless

collaboration is more likely based on the efforts of trust and commitment between supply chain

partners (Cristina Giménez & Helena R Lourenço, 2008). Therefore, supply chain partners need

to “overcome the natural resistance to reveal business secrets” to their partners in order to gain

the benefits of full collaboration (Chou, Tan, & Yen, 2004). Vikram et al. (2012) propose a case

study to explore the various collaboration arrangements to improve management of inventory in

hospital supply chain network. One of their findings highlights the importance of the degree of

trust / commitment between organizations when sharing information. Akkermans, Bogerd, and

Van Doremalen (2004) define trust as “the belief that the other party will act in the firm’s best

interest in circumstances where that other party could take advantage or act opportunistically to

gain at the firm’s expense” (McCutcheon & Stuart, 2000, p.291). When two organizations have

trust/ commitment, they are more likely to engage in collaborative programs (Vikram et al.,

2012). The limited trust between hospitals and the suppliers block entering into any form of

collaborative engagement. For example, during a period of shortage such as an outbreak of

pandemic influenza, hospitals tend to order more than they really need from a supplier, because

they anticipate that they will be getting less anyway. Since all hospitals do so, this strongly

inflates the incoming order. Subsequently, the suppliers know this is happening they tend to

downscale all incoming demand levels. The only way to prevent this increase from happening is

if the buyer can trust the supplier to interpret this order information correctly and if the supplier

can trust the buyer to provide him with correct demand figures (Akkermans et al., 2004).

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Building on Akkermans et al. (2004)’s findings, in the present study we assume a high level of

trust/commitment needs to exist when sharing demand and inventory information among hospital

supply chain partners.

Cloud Computing

The term “cloud computing” emerged in 2007 (Leukel et al., 2011). Cloud computing is

defined as " a connectivity-facilitated virtualized resource (e.g. software, infrastructure, or

platforms) that is dynamically reconfigurable to support various degrees of organizational need,

which allows for optimized systems utilization "(Casey et al., 2012). Cloud computing offers

pools of virtualized resources in the form of software as a service (SaaS), infrastructure as a

service (IaaS), and platform as a service (PaaS). The organizations make customized Service

Level Agreements (SLA) with the cloud service provider to use these hosted resources via

internet (Leukel et al., 2011). Compared to the on-premise traditional information systems cloud

computing provides agility, the ability to adopt and be rapidly deployed; while reducing the

total cost of ownership through pay-per-use (Wu et al., 2013). Therefore, cloud computing offers

a cost advantage for small and medium size enterprises that cannot afford the investment

commitment required for deploying complex, on-premise IT systems (Durowoju et al., 2011).

In addition, cloud computing acts as a control tower that enables rapid high quality data

exchange with external supply chain partners (GTNexus, 2012). Lindner et al. (2010) are the

first to develop a framework to explain the application of cloud computing technology in supply

chains. They introduce the concept of cloud supply chain and define it as "two or more parties

linked by the provision of cloud services, related information and funds". Building on Lindner et

al. (2010), Zhou et al. (2012) develop a conceptual model that identifies the benefits and

challenges of cloud computing in supply chains. Later on, Casey et al. (2012) empirically study

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the decision makers' intention to adopt cloud computing technologies in supply chains. They

suggest that a firm's information requirements and capabilities affect the intention to adopt cloud

computing. To the best of our knowledget he question of how cloud based supply chain can

optimize a hospital supply chain performance has not yet been explored.

Cloud computing with high visibility allows data from an ERP system to be shared by

trading partners across supply chain networks by instantly updating the data. The instant sharing

of accurate and timely demand and inventory information improves visibility (Btandon‐Jones et

al., 2014; Christopher & Lee, 2004; Tang, 2006). Supply chain visibility refers to the clear view

of upstream and downstream information such as inventory positions and levels, demand and

supply conditions, and production and purchasing schedules (Christopher & Peck, 2004). Health

care supply chain managers who use the cloud computing to manage their inventory have greater

visibility into what items need to be ordered to ensure physicians have a strong supply of what

they need (Freund, September 2013). In this study, we focus on how cloud computing influences

demand and inventory visibility, inventory levels, lead times, and unfilled customer orders (i.e.,

customer service).

Systems Theory and System Dynamics

Von Bertalanffy (1950, 1968) first introduced the general systems theory to analyze a

variety of complex operations. General systems theory describes how different parts of complex

operations need to be viewed and analyzed as a whole. The performance of a system is

dependent upon the collaborative performance of individual firms in the system (Bolumole,

Frankel, & Naslund, 2007). Since supply chains are complex systems containing materials and

information flow within and outside of the firms, a systemic view is useful in understanding

supply chain complexity. An important element of a system is its dynamic nature, with

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interactions of sub-systems across their boundaries (Caddy & Helou, 2007). System dynamics

(SD) (Forrester, 1958, 1961) gives a structural explanation on how the dynamic interactions of

the physical process, informational flows, and managerial policies improve system performance

over time (Sterman, 2000). Therefore, (Lane, 1999) defines SD as a structural theory of dynamic

systems that is based on feedback loops, accumulation processes, and delays between cause and

effect (Größler et al., 2008). "The development of System Dynamics models is a process in

which modeling and empirical work take turns, providing a deductive-inductive balance"

(Größler et al., 2008). In this study, we employ the system dynamics approach, based on general

systems theory, to develop SD CLDs to simulate the behaviour of the traditional and cloud based

hospital supply chains.

The first SD supply chain model was developed by Forrester (1958, 1961) to analyze

inventory management in a three-echelon supply chain of an industrial enterprise. Since then

various SD models have utilized to study the operations of systems that change over time under

different policies (Sterman, 2000). SD models are used to capture the production-inventory

order behavior at an aggregate level using feedback-based structures (Venkateswaran & Son,

2007). Rubiano Ovalle and Crespo Marquez (2003) develop SD models to evaluate the e-

collaboration tools to examine its impact on supply chain performance. Using loop dominance

analysis Kamath and Roy (2007) investigate the dynamics of the capacity growth. They find that

delivery delay information has a little effect on capacity augmentation decision. Accordingly,

Wilson (2007) applies the SD approach to investigate the effect of transportation disruptions on

supply chain performance. Wilson (2007) simulates feedback loops of traditional information

system and a VMI system in supply chain to show the impact of these systems on supply chain

performance. Wilson (2007) uses performance measures such as unfilled customer orders,

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maximum and minimum average inventory levels and maximum goods in transit. These studies

suggest that system dynamics modeling is appropriate to study the effects of information sharing

on supply chain performance. In this study, we use average inventory levels, actual lead times

that consists of service lead times and physical lead times, and unfilled orders as comparitive

performance measures.

The General Model

Table 17 shows the notation we use to represent the general structure of an N-echelon

hospital supply chain. We also use this notation to develop the casual loop diagrams (CLDs) and

its equivalent systems dynamics (SD) models for comparing traditional and cloud based hospital

supply networks.

Notation

Indices

t = index of time period, t = 1, …, |T |; e = index for echelons, e = 1,…., |E|; and le = index for locations in echelon e, le = 1, …, |Le|.

Sets

E = set of all echelons; M = set of manufacturers, 𝑀𝑀 ⊆ 𝐸𝐸; and Le = set of locations in the echelon e.

Parameters

𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝑇𝑇 = target delivery delay at location l in echelon e;

𝐷𝐷𝑙𝑙1,𝑡𝑡 = customer demand at location l in echelon 1 at time t; 𝑃𝑃𝑇𝑇𝑙𝑙𝑒𝑒 = minimum order processing time at location l in echelon e; 𝑇𝑇𝑙𝑙𝑒𝑒 = time to average order rate at location l in echelon e; 𝑆𝑆𝑆𝑆𝑙𝑙𝑒𝑒 = safety stock at location l in echelon e; 𝐿𝐿𝑙𝑙𝑒𝑒 = manufacturing lead time at location l in echelon e, 𝑒𝑒 ∈ 𝑀𝑀; 𝐼𝐼𝐽𝐽𝑙𝑙𝑒𝑒 = inventory adjustment time at location l in echelon e; and 𝑊𝑊𝐽𝐽𝑙𝑙𝑒𝑒 = WIP adjustment time at location l in echelon e, 𝑒𝑒 ∈ 𝑀𝑀.

Stock Variables

𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 = inventory levels at location l in echelon e at time t; 𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 = order backlog at location l in echelon e at time t.

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𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 � = expected/forecasted demand at location l in echelon e at time t; and 𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 = work-in-process (WIP) inventory at location l in echelon e at time t;

Flow Variables

𝑃𝑃𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 = production release rate at location l in echelon e at time t; 𝑆𝑆𝑙𝑙𝑒𝑒,𝑙𝑙𝑒𝑒−1,𝑡𝑡 = shipment/supply rate from location le-1 to location le at time t; 𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 = production completion rate at location l in echelon e at time t; 𝐶𝐶𝐷𝐷𝑙𝑙𝑒𝑒 ,𝑡𝑡 = change in expected demand at location l in echelon e at time t; and 𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 = order delivery rate at location l in echelon e at time t.

Auxillary Variables

𝐷𝐷𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 = desired orders at location l in echelon e at time t; 𝑂𝑂𝑙𝑙𝑒𝑒 ,𝑡𝑡 = order rate at location l in echelon e at time t; 𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 = desired shipment rate at location l in echelon e at time t; 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒

𝐴𝐴 = actual delivery delay at location l in echelon e at time t; 𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡 = order fulfillment ratioat location l in echelon e at time t; 𝐶𝐶𝑙𝑙𝑒𝑒,𝑡𝑡 = inventory coverage at location l in echelon e at time t; 𝑀𝑀𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 = maximum shipment rate at location l in echelon e at time t; 𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 = desired inventory at location l in echelon e at time t; 𝐺𝐺𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 = inventory gap – adjustment from inventory at location l in echelon e at time t; 𝐷𝐷𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 = desired quantity of work in process inventory at location l in echelon e at time t; 𝐺𝐺𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 = adjustment for WIP at location l in echelon e at time t, 𝑒𝑒 ∈ 𝑀𝑀; and 𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒 ,𝑡𝑡 = desired production at location l in echelon e at time t, 𝑒𝑒 ∈ 𝑀𝑀.

Table 17. Notation for the general health care supply chain models

Assumptions for traditional hospital supply chains

• Medicine cannot be delivered to the patients immediately. Therefore, there is a backlog

of unfilled orders.

• Shipment rate and order fulfillment rate are numerically equal since the shipment rate

represents physical rate whereas the order fulfillment rate represents an information flow

(Sterman, 2000).

• Manufacturing capacity is unconstrained.

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Assumptions for cloud based hospital supply chains

• Actual orders are equal to the desired orders, implying orders were delivered instantly.

Therefore, there are no or minimal order backlogs.

• Desired order information that includes the desired level of hospital inventory and

expected patients demand is visible for all supply chain partners.

This study uses average inventory levels, unfilled orders, and lead time that have been

previously studied and established as good estimates for simulating supply chain operations (

Bijulal & Venkateswaran, 2008; Bijulal, Venkateswaran, & Hemachandra, 2011; Rubiano Ovalle

& Crespo Marquez, 2003; Wilson, 2007). We consider actual lead time (ALT) that is comprised

of physical lead time (PLT) and system lead time (SLT) (Tyndall & Kane, 2013). PLT refers to

the amount of time it takes to produce and ship an order SLT refers to the amount of processing

time to complete an order. Manufacturing companies are subject to ALTs.

Problem Description

The general structure of the N-echelon traditional and cloud-based hospital supply chains

are shown in Figure 15 and Figure 16, respectively.

Supplier𝑒𝑒=|𝐸𝐸|, 𝑙𝑙e=1

Supplier𝑒𝑒=|𝐸𝐸|, 𝑙𝑙e=2

Supplier𝑒𝑒=|𝐸𝐸|, 𝑙𝑙e=|𝐿𝐿𝑒𝑒|

Manufacturer𝑒𝑒=3, 𝑙𝑙3=1

Manufacturer𝑒𝑒=3, 𝑙𝑙3=2

Manufacturer𝑒𝑒=3, 𝑙𝑙3=|𝐿𝐿3|

Distributor𝑒𝑒=2, 𝑙𝑙2=1

Distributor𝑒𝑒=2, 𝑙𝑙2=2

Distributor𝑒𝑒=2, 𝑙𝑙2=|𝐿𝐿2|

Hospital𝑒𝑒=1, 𝑙𝑙1=1

Hospital𝑒𝑒=1, 𝑙𝑙1=2

Hospital𝑒𝑒=1, 𝑙𝑙1=|𝐿𝐿1|

Patients

N-Echelon Echelon=3 Echelon=2 Echelon=1

Flow of ProductFlow of Information

Figure 15. Information and product flow in an N-echelon, traditional hospital supply chain

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Supplier𝑒𝑒=|𝐸𝐸|, 𝑙𝑙e=1

Supplier𝑒𝑒=|𝐸𝐸|, 𝑙𝑙e=2

Supplier𝑒𝑒=|𝐸𝐸|, 𝑙𝑙e=|𝐿𝐿𝑒𝑒|

Manufacturer𝑒𝑒=3, 𝑙𝑙3=1

Manufacturer𝑒𝑒=3, 𝑙𝑙3=2

Manufacturer𝑒𝑒=3, 𝑙𝑙3=|𝐿𝐿3|

Distributor𝑒𝑒=2, 𝑙𝑙2=1

Distributor𝑒𝑒=2, 𝑙𝑙2=2

Distributor𝑒𝑒=2, 𝑙𝑙2=|𝐿𝐿2|

Hospital𝑒𝑒=1, 𝑙𝑙1=1

Hospital𝑒𝑒=1, 𝑙𝑙1=2

Hospital𝑒𝑒=1, 𝑙𝑙1=|𝐿𝐿1|

Patients

N-Echelon Echelon=3 Echelon=2 Echelon=1

Cloud

Flow of ProductFlow of Information

Figure 16. Information and product flow in an N-echelon, cloud based hospital supply chain

The problem is to determine the impact of cloud based information sharing on a

hospital’s supply chain when a hospital supply chain adopts cloud based information sharing for

its order fullfilment, inventory control, order placement and production supply chain processes.

We develop two SD models to solve this problem. One SD model to represent a cloud based

hospital supply chain and another SD model to represent a traditional hospital supply chain.

The SD models are then used to simulate and compare the performance of a traditional and a

cloud based hospital supply chain. Supply chain performance meterics include lead times,

inventory levels, and order fulfillment capability.

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This study models a traditional and a cloud-based, N-echelon hospital supply chain

(shown in Figures 15 and 16) where patients create demand for medical supplies at a hospital’s

pharmacy department (e=1), the hospital’s most downstream echelon. Pharmaceutical

manufacturers (e=3) commonly use distributors (e=2) to supply the hospitals (e=1) with their

required medical supplies. This research applies to a generalizable, end-to-end hospital supply

chain that extends to the hospital’s suppliers’ suppliers all the way back to the raw material

supplier (e=|E|). For example, Pfizer manufactures (e=3) Atgam®, an injectable penicillin

solution, that is distributed to Dallas-based hospital pharmacy departments (e=1) by BDI Pharma

(e=2). The glass manufacturer that supplies the glass for the bottle manufacturing that will

eventually contain the Ampoule solution used in Atgam® may represent an example of a trading

partner furthest upstream (e=|E|) in a hospital’s supply chain.

Rubiano Ovalle and Crespo Marquez (2003) define a traditional supply chain as “non-

collaborative” due to the excessive delays arising from when in order is placed until an order is

received. These excessive delays are a direct result of the hospital supply chain partners’

inability to timely share critical inventory and demand information. Figure 15 shows the flow of

information and medical supplies between each partner in a traditional hospital supply chain.

Information flows consists of order and demand information.

In the traditional hospital supply chain, the hospital pharmacy departments (l1 = 1,…,

|L2|) receives patient demand information (𝐷𝐷𝑙𝑙1,𝑡𝑡) and patients' orders (𝑂𝑂𝑙𝑙1,𝑡𝑡). The distributors (l2 =

1… |L2| ) receives only hospital order information (𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡) from the hospital pharmacy

departments (l1 = 1,…, |L1|). Accordingly, the pharmaceutical manufacturers (l3 = 1,...,|L3|)

receive only distributor order information. The only information available to all upstream

hospital supply chain partners is order information which includes desired (𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡) and actual

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inventory (𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡) levels, order backlog (𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡), and order (𝐷𝐷𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡) status. The time delays exist

in receiving and processing orders, as well as in knowing the real inventory levels. Upstream

partners do not know the time delays, demand information, and make their decisions based on

only the number of units ordered in each period (Kelle et al., 2012; Rubiano Ovalle & Crespo

Marquez, 2003; Vikram et al., 2012).

Next, the cloud based supply chain is defined as “full collaborative supply chain”

meaning that there is as a scalable emergent eSCM that senses changes in real time and executes

the optimal response by providing information transparency and visibility of real-time demand

data across the hospital supply chain. Figure 16 shows the flow of information and drugs

between each partner for cloud based information sharing.

Unlike traditional supply chain, cloud based supply chain partners share the critical

information including end patient demand information. Cloud based supply chain allows orders

in the supply chain to be visible in real time and processed with minimum communication delays

(Harris, Wang, & Wang, 2015)Cloud computing provides the “ actual” demand information to be

communicated throughout the supply chain by efficiently syncronizing and allowing real-time

visibility and traceability among multiple supply chain partners (Kong et al.). Hospital supply

chain partners can view each other’s demand forecasts, make changes, and agree in a consensus-

based forecast using a web interface (Rubiano Ovalle & Crespo Marquez, 2003). Therefore,

upstream and downstream supply chain partners can make their decisions based on demand

forecasts and number of units ordered in each period.

The Casual Loop Diagrams (CLDs)

The CLDs and their equivalent SD models simulate the performance of the traditional and cloud

based hospital supply chains. The CLDs describe the hospital supply chain’s order fullfilment,

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inevntory control, order placement and production processes. The SD model’s stock and flow

diagrams and SD equations are developed and discussed in Appendix B and Appendix C,

respectively.

In this study, we use (Größler et al., 2008) two-step SD method, conceptual modeling and

experimentation. We use CLDs as the conceptual modeling component to represent the

conceptual feedback structure in order to provide better understanding of a hospital supply

chain’s behavior. We adopted Sterman's (2000) stock management structure to create the CLDs.

Feedback loops of the CLDs are created by causal links between elements of reality (Größler et

al., 2008). Two types of feedback loops are used: balancing (negative) and reinforcing (positive)

loop. We use sytems theory (Von Bertalanffy, 1950) as the theoretical lens to develop the

hospital supply chains CLDs.

The generalized structure of an N-echelon hospital supply chain is essentially a collection

of hospitals, distributors, and manufacturers. Figures 17, 18, and 19 show the hospital,

distributor, and manufacturer CLDs that represent the conceptual feedback structure for a

traditional hospital and a cloud based supply chain. Each arrow in the CLD diagram represents a

causal link or cause and effect relationship between independent (the variable at the tail of the

arrow) and dependent variable (the variable at the head of the arrow). The positive (+) and

negative (-) signs near the arrow head indicates the direction of the cause and effect relationship.

A pair of parallel lines implies delay between the cause and effect. A reinforcing loop enhances

the change (increase/decrease → increase/decrease) and creates a growing effect over time. On

the other hand, a balancing loop opposes the change (increase/decrease → decrease/increase) and

completes balancing act over time (Kamath & Roy, 2007; Sterman, 2000). The variables in the

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boxes express the accumulated quantities (i.e., stocks), dashed arrows show the flow of

information, and solid arrows show the flow of medicine.

Figure 17. The Hospitals’ CLD

Figure 18.The Distributor’s CLD

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Figure 19. The Manufacturer’s CLD

Figure 20 illustrates the CLD diagram of the cloud information sharing setting for a

multi-echelon hospital supply chain. The structure itself is essentially the same as the on-

premise, traditional supply chain with the internet-based information sharing cloud clearly at the

center. Cloud based information sharing, unlike the traditional information sharing, quickly

provides the forecasted patient demand at the hospitals 𝐷𝐷𝑙𝑙1,𝑡𝑡 to all of its trading partners,

including manufacturers and distributors . Hospitals no longer place orders with distributors;

similarly, the distributors no longer place orders with manufacturers, and so on through the

hosptial supply chain. Thus, there are no or minimal 𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 in each echelon (Wilson, 2007). The

products are automatically sent to the distributor and the hospital based on the 𝐷𝐷𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡, demand

𝐷𝐷𝑙𝑙1,𝑡𝑡, and 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 information are instantaneously through cloud computing technology. As a result,

there are minimal 𝑃𝑃𝑇𝑇𝑙𝑙𝑒𝑒 and 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝐴𝐴 .

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Cloud

<ManufacturerShipment Rate>

<DesiredHospital Order> Hospital's

Order Rate

Distributor OrderFullfilment Rate

Distributor DesiredShipment Rate

DistributorShipment Rate

++

DistributorInventory

Manufacturer'sMedicine Delivery

Rate +

DistributorInventory Gap

Desired DistributorInventory

DistributorMaximum Shipment

Rate

+

+

+

+

-

+

DesiredDistributor Order

+

B

BDistributor

Inventory Coverage

+ -

DistributorInventory

AdjustmentTime

+

DistributorSafety Stock

+ Distributor MinimumOrder Processing

Time

-+

+

+

+

<Patients Demand>

+

+

Work In Process(WIP) Inventory

Manufacturer'sInventory

WIP Gap

Desired Production

ManufacturerInventory Gap

Desired WIP

ProductionRelease Rate

ProductionCompletion Rate

+ +

+

-

+

+

+

+

ManufacturingLead Time

-

+-

DesiredManufacturer

Inventory

+

+

+ ManufacturerMaximum Shipment

Rate

ManufacturerShipment Rate

+

+

-

Manufacturer OrderFullfilment Rate (OFR)

DesiredShipment Rate

+

+

B

B B

B

<DesiredDistributor Order>

Distributor'sOrder Rate

WIP AdjustmentTime

+

ManufacturerInventory Adjustment

Time

+

ManufacturerMinimum OrderProcessing Time

+

+

ManufacturerInventoryCoverage

+ -

SupplierShipment Rate

+

+

+

<Patients Demand>

-

+

Hospital OrderFullfilment Rate

Hospital DesiredSupply Rate

Hospital Supply Rate

++

HospitalInventory

Distributor'sMedicine Delivery

Rate +

HospitalInventory Gap

Desired Hospitalinventory

Hospital'sMaximum Supply

Rate

+

+

+

+

-

+

Desired HospitalOrder

+

B

BHospital InventoryCoverage

+ -

HospitalInventory

AdjustmentTime

+

Hospital SafetyStock

+Hospital MinimumOrder Processing

Time

-+

<DistributorShipment Rate> +

+

Patients Demand

Patients' OrderRate

+

Figure 20. CLDs of cloud based hospital supply chain

In the next subsections, we explain the behavior of the various hospital supply chain

processes (order fullfiment, inventory control, order placement and production) in each echelon.

Order fulfillment:

The hospital’s (e=1) CLD (Figure 17) begins with the Patients Demand (𝐷𝐷𝑙𝑙1,𝑡𝑡). The

𝐷𝐷𝑙𝑙1,𝑡𝑡 leads to an increase in Patients Order Rate (𝑂𝑂𝑙𝑙1,𝑡𝑡). When there is not sufficient inventory

in stock to deliver orders immediately, 𝑂𝑂𝑙𝑙1,𝑡𝑡 leads to an increase in Hospital Order Backlog

(𝑂𝑂𝑂𝑂𝑙𝑙1,𝑡𝑡) (Sterman, 2000) which refers to unsatisfied/unfulfilled orders (Venkateswaran & Son,

2007; Wilson, 2007). An increase in 𝑂𝑂𝑂𝑂𝑙𝑙1,𝑡𝑡, triggers Hospital Desired Supply

Rate (𝐷𝐷𝑆𝑆𝑙𝑙1,𝑡𝑡) which refers to the hospital’s target supply rate. 𝐷𝐷𝑆𝑆𝑙𝑙1,𝑡𝑡 confirms that the medicines

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are filled within the certain time frame called Hospital Target Delivery Delay �𝐷𝐷𝐷𝐷𝑙𝑙1𝑇𝑇 �. 𝐷𝐷𝐷𝐷𝑙𝑙1

𝑇𝑇 is

determined by the hospital to meet the patients’ needs on time. If the Hospital Inventory �𝐼𝐼𝑙𝑙1,𝑡𝑡� in

stock is adequate, 𝐷𝐷𝑆𝑆𝑙𝑙1,𝑡𝑡 leads to an increase in the Hospital Supply Rate (𝑆𝑆𝑙𝑙1,𝑡𝑡).

Consequently, 𝑆𝑆𝑙𝑙1,𝑡𝑡 leads to an increase in the Hospital Order Fulfillment Rate Ratio (𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡)

(Kamath & Roy, 2007; Venkateswaran & Son, 2007). Once the orders are fulfilled within 𝐹𝐹𝑙𝑙1,𝑡𝑡 ,

𝑂𝑂𝑂𝑂𝑙𝑙1,𝑡𝑡 and Hospital Delivery Delay (𝐷𝐷𝐷𝐷𝑙𝑙1𝐴𝐴 ), the actual average delay between the placement

and the receipt of the medicine, decrease, respectively. Otherwise, 𝑂𝑂𝑂𝑂𝑙𝑙1,𝑡𝑡 leads to an increase in

the 𝐷𝐷𝐷𝐷𝑙𝑙1𝐴𝐴 . The SD equations for the order fulfillment process are in Appendix C.

Inventory Control:

Sufficient 𝐼𝐼𝑙𝑙1,𝑡𝑡 in stock increases the Hospital Maximum Supply Rate (𝑀𝑀𝑆𝑆𝑙𝑙1,𝑡𝑡).

𝑀𝑀𝑆𝑆𝑙𝑙1,𝑡𝑡 refers to the maximum rate of shipments given the 𝐼𝐼𝑙𝑙1,𝑡𝑡 and Hospital Minimum Order

Processing Time (𝑃𝑃𝑇𝑇𝑙𝑙1). 𝑃𝑃𝑇𝑇𝑙𝑙1 refers to the amount of time it takes between the order is placed

and shipped. Therefore, an increase in 𝑃𝑃𝑇𝑇𝑙𝑙1 leads to a decrease in 𝑀𝑀𝑆𝑆𝑙𝑙1,𝑡𝑡. 𝑀𝑀𝑆𝑆𝑙𝑙1,𝑡𝑡 decreases the

𝑆𝑆𝑙𝑙1,𝑡𝑡 ,since a hospital cannot supply more than 𝑀𝑀𝑆𝑆𝑙𝑙1,𝑡𝑡. On the other hand, an increase in 𝑆𝑆𝑙𝑙1,𝑡𝑡

leads to a decrease in 𝐼𝐼𝑙𝑙1,𝑡𝑡 . To prevent stockouts, it is essential to determine whether a hospital

𝑆𝑆𝑙𝑙1,𝑡𝑡 has sufficient inventory to match with 𝐷𝐷𝑙𝑙1,𝑡𝑡. Hospital Inventory Coverage Ratio (𝐶𝐶𝑙𝑙1,𝑡𝑡)

refers to number of days the hospital could supply at the current 𝑆𝑆𝑙𝑙1,𝑡𝑡 given 𝐼𝐼𝑙𝑙1,𝑡𝑡 which indicates

the service level of a hospital. The lower the 𝐶𝐶𝑙𝑙1,𝑡𝑡, the more a hospital desires an increase in the

𝐼𝐼𝑙𝑙1,𝑡𝑡 level in order to match with the 𝐷𝐷𝑙𝑙1,𝑡𝑡. The inventory level required to maintain the desired

service level of a hospital to provide full and reliable deliveries is called the Desired Hospital

Inventory (𝐷𝐷𝐼𝐼l1,𝑡𝑡). 𝐷𝐷𝑙𝑙1,𝑡𝑡 is estimated by the Expected Patient Demand (𝐸𝐸�𝐷𝐷𝑙𝑙1,𝑡𝑡 �) and depends

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on the Hospital Safety Stock (𝑆𝑆𝑆𝑆𝑙𝑙1) and 𝑃𝑃𝑇𝑇𝑙𝑙1 . An increase in the 𝐷𝐷𝑙𝑙1,𝑡𝑡 over time leads to an

increase in 𝐸𝐸�𝐷𝐷𝑙𝑙1,𝑡𝑡 � and 𝐷𝐷𝐼𝐼𝑙𝑙1,𝑡𝑡, respectively (Georgiadis, Vlachos, & Tagaras, 2006). The

hospital considers Change in Patients’ Demand (𝐶𝐶𝐷𝐷𝑙𝑙1,𝑡𝑡) when setting the 𝐷𝐷𝐼𝐼𝑙𝑙1,𝑡𝑡. 𝐶𝐶𝐷𝐷𝑙𝑙1,𝑡𝑡 refers to

the discrepancy between 𝐸𝐸�𝐷𝐷𝑙𝑙1,𝑡𝑡 �and the 𝑂𝑂𝑙𝑙1,𝑡𝑡 over a time period determined by the Time to

Average Order Rate (𝑇𝑇𝑙𝑙1). The 𝐷𝐷𝑙𝑙1,𝑡𝑡 information is used to generate 𝐸𝐸�𝐷𝐷𝑙𝑙1,𝑡𝑡 � by smoothing the

demand figures with the pervious period’s perceived demand. A hospital seeks to maintain the

𝐷𝐷𝐼𝐼𝑙𝑙1,𝑡𝑡 set equal to the 𝐸𝐸�𝐷𝐷𝑙𝑙1,𝑡𝑡 �(Venkateswaran & Son, 2007). 𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 leads to an increase in the

Hospital Inventory Gap (𝐺𝐺𝐼𝐼𝑙𝑙1,𝑡𝑡), the discrepancy between 𝐷𝐷𝐼𝐼𝑙𝑙1,𝑡𝑡 and 𝐼𝐼𝑙𝑙1,𝑡𝑡. Hospital Inventory

Adjustment Time (𝐼𝐼𝐽𝐽𝑙𝑙1), the time required to take the inventory to the desired level, corrects

𝐺𝐺𝐼𝐼𝑙𝑙1,𝑡𝑡 over certain amount of time. The SD equations for the inventory control process are in

Appendix C.

Order Placement:

As the 𝐺𝐺𝐼𝐼𝑙𝑙1,𝑡𝑡 increases, the hospital's desire to place an order with the distributor for

more medicine, Desired Hospital Order( 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡), increases (Wilson, 2007). In the traditional

information sharing setting, the 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡 in e=1 translates to the Hospital’s Order Rate (𝑂𝑂𝑙𝑙2,𝑡𝑡) in

distributor echelon (e=2). Therefore, 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡 results in shipments to the hospital denoted as

Distributor Shipment Rate (𝑆𝑆𝑙𝑙1,𝑙𝑙2,𝑡𝑡). 𝑆𝑆𝑙𝑙1,𝑙𝑙2,𝑡𝑡 in e=2 translates into Distributor’s Delivery Rate

�𝐷𝐷𝑃𝑃𝑙𝑙2,𝑡𝑡� in e=1. An increase in 𝐷𝐷𝑃𝑃𝑙𝑙2,𝑡𝑡 increases the 𝐼𝐼𝑙𝑙1,𝑡𝑡 and the service level (inventory

coverage) (𝐶𝐶𝑙𝑙1,𝑡𝑡) of the hospital.

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The distributor’s (e=2) CLD (Figure 18) starts with 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡 and 𝑂𝑂𝑙𝑙2,𝑡𝑡. The distributor does

not have access to the 𝐷𝐷𝑙𝑙1,𝑡𝑡 information. Order fulfillment, inventory control and order

placement processes remain the same as e=1 in e=2.

The manufacturer’s (e=3) CLD (Figure 19) starts with 𝐷𝐷𝑂𝑂𝑙𝑙2,𝑡𝑡 and 𝑂𝑂𝑙𝑙3,𝑡𝑡. The

manufacturer’s order fulfillment, inventory control, and order placement processes are the same

as the hospital and distributor’s. In addition, the manufacturers in e=3 perform the following

production process. The SD equations for the order placement process can be found in Appendix

C.

Production:

An increase in 𝐸𝐸�𝐷𝐷𝑙𝑙2,𝑡𝑡 � escalates the manufacturer’s Desired Production (𝐷𝐷𝑃𝑃𝑙𝑙3,𝑡𝑡).

𝐷𝐷𝑃𝑃𝑙𝑙3,𝑡𝑡 leads to an increase in Production Release Rate (𝑃𝑃𝑃𝑃𝑙𝑙3,𝑡𝑡) (Venkateswaran & Son, 2007)

and an increase in 𝑃𝑃𝑃𝑃𝑙𝑙3,𝑡𝑡 leads to an increase in manufacturer’s Work in Process (WIP)Inventory

(𝑊𝑊𝑙𝑙3,𝑡𝑡) (Georgiadis et al., 2006). 𝑊𝑊𝑙𝑙3,𝑡𝑡 accumulates the difference between production starts

𝑃𝑃𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 and Production Completion (𝑃𝑃𝑙𝑙3,𝑡𝑡 ). 𝑊𝑊𝑙𝑙3,𝑡𝑡 increases the Production Completion (𝑃𝑃𝑙𝑙3,𝑡𝑡 ).

WIP Gap (𝐺𝐺𝑊𝑊𝑙𝑙3,𝑡𝑡), the pending production linerefers to the discrepancy between the Desired

WIP (𝐷𝐷𝑊𝑊𝑙𝑙3,𝑡𝑡) and 𝑊𝑊𝑙𝑙3,𝑡𝑡 that is adjusted by WIP Adjustment Time (𝑊𝑊𝐽𝐽𝑙𝑙𝑒𝑒) (Sterman, 2000).

𝐺𝐺𝑊𝑊𝑙𝑙3,𝑡𝑡 modifies 𝑃𝑃𝑃𝑃𝑙𝑙3,𝑡𝑡 to keep up with the 𝑊𝑊𝑙𝑙3,𝑡𝑡 and 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡in line with the 𝐷𝐷𝑊𝑊𝑙𝑙3,𝑡𝑡 level.

Manufacturing Lead Time (𝐿𝐿𝑙𝑙3) decreases the 𝐷𝐷𝑊𝑊𝑙𝑙3,𝑡𝑡 (Venkateswaran & Son, 2007) while

leading to a decrease in 𝑃𝑃𝑙𝑙3,𝑡𝑡 due to the third order delay (𝛿𝛿3) . The 𝑃𝑃𝑙𝑙3,𝑡𝑡 leads to an increase in

Manufacturer's inventory (𝐼𝐼𝑙𝑙3,𝑡𝑡). A sufficient 𝐼𝐼𝑙𝑙3,𝑡𝑡 level decreases 𝐺𝐺𝐼𝐼𝑙𝑙3,𝑡𝑡 (Wilson, 2007). The

lower the 𝐺𝐺𝐼𝐼𝑙𝑙3,𝑡𝑡 , the less 𝐷𝐷𝑃𝑃𝑙𝑙3,𝑡𝑡 (Venkateswaran & Son, 2007), or vice versa. Increased 𝐼𝐼𝑙𝑙3,𝑡𝑡

level also leads to an increase in Maximum Manufacturer Shipment Rate (𝑀𝑀𝑆𝑆𝑙𝑙3,𝑡𝑡) and

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Manufacturer Shipment Rate (𝑆𝑆𝑙𝑙2,𝑙𝑙3,𝑡𝑡), respectively. The SD equations for the order fulfillment

process are shown in Appendix C.

Numerical Study

In this section, we develop a numerical example from a case study report to analyze the

effectiveness of the proposed SD models. Figures 21 and 22 depict a traditional hospital supply

chain network structure and a cloud based hospital supply chain network structure, respectively.

Each of the supply chain structure consists of a manufacturer, a distributor, a hospital and

patients.

Manufacturer𝑒𝑒=3, 𝑙𝑙3=1

Distributor𝑒𝑒=2, 𝑙𝑙2=1

Hospital𝑒𝑒=1, 𝑙𝑙1=1 Patients

Echelon=3 Echelon=2 Echelon=1

Flow of MedicineFlow of Information

Figure 21.Information and medicine flow in a three echelon traditional hospital supply chain

Manufacturer𝑒𝑒=3, 𝑙𝑙3=1

Distributor𝑒𝑒=2, 𝑙𝑙2=1

Hospital𝑒𝑒=1, 𝑙𝑙1=1 Patients

Echelon=3 Echelon=2 Echelon=1

Cloud

Flow of MedicineFlow of Information

Figure 22. Information and medicine flow in a three echelon cloud hospital supply chain

In this numerical study we assume patients stochastically arrive to the hospital in need of

medicine. We use the general SD models and assume that patients demands �𝐷𝐷𝑙𝑙1,𝑡𝑡 � are

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normally distributed with a mean (𝜇𝜇 ) of 10 units per day and a standard deviation (𝜎𝜎) of 5. The

SD models simulate traditional and cloud-based hospital chain chain operations for a period of

500 days. We assume there is no capacity constraint for any supply chain partner. Table 18

shows the parameters and their values that are used in this numerical study for both the

traditional and cloud SD models.When time is equal to 0 all parameters are set to the values

shown in Table 18.

Table 18. Illustrative Parameters for SD models

In the numerical study, we use the Order Fullfiment Ratio (𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡) to determine the

shipment rate (𝑆𝑆𝑙𝑙2,𝑙𝑙3,𝑡𝑡) from the manufacturer to the distributor, the shipment rate (𝑆𝑆𝑙𝑙1,𝑙𝑙2,𝑡𝑡)from

the distributor to the hospital, and the supply rate �𝑆𝑆𝑙𝑙1,𝑡𝑡� from the hospital to the patient. We

apply Sterman’s (2000) research relating Order Fullfiment Ratio (𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡) to the ratio of Maximum

Shipment Rate �𝑀𝑀𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 � and Desired Shipment Rate (𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡) as shown in Table 19.

Parameters

Parameter Values in Traditional Model Parameter Values in Cloud Model

Hospital Distributor Manufacturer Hospital Distributor Manufacturer

𝑫𝑫𝒍𝒍𝟏𝟏,𝒕𝒕 ~𝑵𝑵(𝝁𝝁,𝝈𝝈) 𝑫𝑫𝒍𝒍𝟏𝟏,𝒕𝒕 ~ 𝑁𝑁(5,10)

N/A N/A 𝑫𝑫𝒍𝒍𝟏𝟏 ,𝒕𝒕 ~ 𝑁𝑁(5,10)

N/A N/A

𝑳𝑳𝒍𝒍𝒆𝒆~ 𝑻𝑻𝑵𝑵(𝒎𝒎𝒎𝒎𝒎𝒎,𝒎𝒎𝒎𝒎𝒎𝒎,𝝁𝝁𝑴𝑴,𝝈𝝈𝑴𝑴) N/A N/A 𝐿𝐿𝑙𝑙𝑒𝑒~

𝑇𝑇𝑁𝑁(0.1,20,0.5,3) N/A N/A 𝐿𝐿𝑙𝑙𝑒𝑒~

𝑇𝑇𝑁𝑁(0.001,20,0.05,0.6) 𝑻𝑻𝒍𝒍𝒆𝒆 8 days 8 days 8 days 8 days 8 days 8 days 𝑰𝑰𝑰𝑰𝒍𝒍𝒆𝒆 1 day 1 day 1 day 1 day 1 day 1 day 𝑾𝑾𝑰𝑰𝒍𝒍𝒆𝒆 N/A N/A 1 day N/A N/A 1 day 𝑫𝑫𝑫𝑫𝒍𝒍𝒆𝒆

𝑻𝑻 3 days 3 days 3 days 1 day 1 day 1 day 𝑷𝑷𝑻𝑻𝒍𝒍𝒆𝒆 2 days 2 days 2 days 1 day 1 day 1 day 𝑺𝑺𝑺𝑺𝒍𝒍𝒆𝒆 2 days 2 days 2 days 2 days 2 days 2 days

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𝑴𝑴𝑺𝑺𝒍𝒍𝒆𝒆,𝒕𝒕

𝑫𝑫𝑺𝑺𝒍𝒍𝒆𝒆,𝒕𝒕 𝑭𝑭𝒍𝒍𝒆𝒆,𝒕𝒕

0 0 0.2 0.2 0.4 0.4 0.6 0.58 0.8 0.73 1 0.85

1.2 0.93 1.4 0.97 1.6 0.99 1.8 1 2 1

Table 19. Order fulfillment ratio table

For each trial run of the simulation models we derive the Maximum Shipment Rate

�𝑀𝑀𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 � and the Desired Shipment Rate (𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡); and determine the Order Fullfiment Ratio

(𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡) through Table 19.

Figure 23 shows the order fullfilment ratio as a function of 𝑀𝑀𝑀𝑀𝑙𝑙𝑒𝑒,𝑡𝑡𝐷𝐷𝑀𝑀𝑙𝑙𝑒𝑒,𝑡𝑡

. For instance, when

the derived ratio 𝑀𝑀𝑀𝑀𝑙𝑙𝑒𝑒,𝑡𝑡𝐷𝐷𝑀𝑀𝑙𝑙𝑒𝑒,𝑡𝑡

is equal to 0.6, the lookup value from Table 19 for 𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡 is 0.58.

Figure 23. Order fulfillment as function of inventory

Source: Adapted from Sterman (2000, p.721)

Maximum Shipment Rate/ Desired Shipment Rate (𝑀𝑀𝑆𝑆𝑙𝑙𝑒𝑒 𝑡𝑡/𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒 𝑡𝑡 )

Ord

er F

ulfil

lmen

t Rat

io (𝐹𝐹

𝑙𝑙𝑒𝑒,𝑡𝑡

)

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When shipments are equal to desired shipments ( 𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡), the order fulfillment

ratio equals to 1 ( 𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡 = 1). The derivation of 𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 is shown in Appendix C. Accordingly, we

assume that supply chain partners will ship an order as long as if they have adequete inventory.

They will not delay an order and keep in stock for future orders. Three supply chain

performance metrics are used to compare the system behaviors of both hospital supply chains:

the actual lead time, average inventory levels, and unfilled orders.

Comparing the Traditional and Cloud Based Hospital Supply Chains: Model Run Results and

Analysis

Inventory Level

The hospital supply chain SD models begins with Patient Demand (𝐷𝐷𝑙𝑙1,𝑡𝑡) at a hospital.

The SD models derives a demand forecast for future patient demands at the hospital. The

hospital places an order with the distributor based on the Hospital Desired Order ( 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡). The

distributor does not have access to the actual hospital’s patient demand, the distributor can only

view the hospital’s desired order, ( 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡). The Hospital Desired Order ( 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡) is the

maximum value between the Inventory Gap (𝐺𝐺𝐼𝐼𝑙𝑙1,𝑡𝑡) and the 𝐸𝐸�𝐷𝐷𝑙𝑙1,𝑡𝑡 �.

The distributor now performs a forecast based on the hospital’s 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡, not based on the

actual 𝐷𝐷𝑙𝑙1,𝑡𝑡. Once the forecast is complete, the 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡 translates into the Distributor Desired

Order �𝐷𝐷𝑂𝑂𝑙𝑙2,𝑡𝑡� which represents the maximum of Inventory Gap (𝐺𝐺𝐼𝐼𝑙𝑙1,𝑡𝑡) and the 𝐸𝐸�𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡 �.

The distributor places an order with manufacturer based on 𝐷𝐷𝑂𝑂𝑙𝑙2,𝑡𝑡 . Table 20 compares the

patient demand (𝐷𝐷𝑙𝑙1,𝑡𝑡), hospital desired order ( 𝐷𝐷𝑂𝑂𝑙𝑙1,𝑡𝑡), and distributor desired order �𝐷𝐷𝑂𝑂𝑙𝑙2,𝑡𝑡�

statistics resulting from the 500 day simulation run in both traditional and cloud based hopsital

supply chain.

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Table 20. Demand and desired orders in a traditional and cloud based hospital supply chain

In the traditional hospital supply chain, we observe considerable variability in inventory

levels, especially at the manufacturer (refer to Figure 24). Variability in inventory levels is

common in supply chains that have a traditional, non collaborative information sharing policy

(Rubiano Ovalle & Crespo Marquez, 2003). No stockouts are produced. However, we observe

that stock levels and the variability of stock levels increase as you move upstream in the supply

chain. This observation is consistent with the bullwhip effect. Lee et al. (1997) suggests this

increase concatenated upstream supply chain variability results in excess inventory and is

reduced with improvements in demand forecasting, lead times, order batching, supply shortages

and price fluctuations. These improvments can be achieved through embracing cloud based

technology in a hospital supply chain. From the Figure 25, we observe that the bullwhip effect is

greatly reduced, the variability of inventory levels across the supply chain is reduced compared

to the inventory levels in the traditional setting.

Tra

ditio

nal Min Max Average Std. Deviation

Patient 0 22.52 10.05 4.55

Hospital 0 25.32 10.22 6.63

Distributor 0 43.09 10.34 11.32

Clo

ud Patient 0 22.52 10.06 4.56

Hospital 0 25.62 10.11 4.93

Distributor 0 33.49 10.19 6.89

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In this numerical study, there are two causes of supply chain variability; the non-

zero/uncertain lead times of the supply chain trading partners, and the inaccurate demand

forecasts due to the distributor and manufacturer’s lack of visibility of the actual patient’s

demand 𝐷𝐷𝑙𝑙1,𝑡𝑡. Uncertain lead times and inaccurate forecasts leads to excess inventory; therefore,

we observe high inventory levels at the manufacture, distributor, and hospital. Table 21 presents

the minimum, maximum and average manufacturer (𝐼𝐼𝑙𝑙3,𝑡𝑡 ), distributor (𝐼𝐼𝑙𝑙2,𝑡𝑡) and hospital (𝐼𝐼𝑙𝑙1,𝑡𝑡 )

inventory levels in both the traditional and cloud based hospital supply chains.We observe a

57%, 37%, and 26% reduction in 𝐼𝐼𝑙𝑙3,𝑡𝑡 , 𝐼𝐼𝑙𝑙2,𝑡𝑡, 𝐼𝐼𝑙𝑙1,𝑡𝑡 (30.54,30.33, and 29.66) with the cloud setting

compared to the inventory levels in the traditional setting (71.59, 48.51, and 40.42).

Min Max Average Std. Deviation

Tra

ditio

nal Manufacturer 3.00 232.10 71.59 37.47

Distributor 3.00 94.34 48.51 16.89

Hospital 3.00 67.17 40.42 8.82

Clo

ud

Manufacturer 2.93 50.58 30.54 7.15

Distributor 1.63 61.70 30.33 7.43

Hospital 1.33 43.71 29.66 5.71

Figure 24. Inventory levels in a traditional hospital supply chain

Figure 25. Inventory levels in a cloud based hospital supply chain

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Table 21. Results for inventory levels in traditional and cloud based hospital supply chain SD

The reduction in inventory levels and variability are a direct result of cloud computing,

refer to Figures 3.11 and 3.12 for this comparison. Cloud computing provides a platform for

gathering, interpreting, and disseminating the data instantly. Therefore, cloud computing

executes more accurate statistical demand forecasts for all supply chain partners (Toka et. al,

2011). Accurate demand forecasts lead to a significant decrease in the bullwhip effect by

reducing SLT (Lee et al., 1997).

Lead Time

In this numerical study, we observe that lead time variability also attributes the the

bullwhip effect. Lead times are also subject to the bullwhip effect. Variability increases as you

move up the supply chain with the manufacturer experiencing the highest variability. Variability

in lead times require high levels of inventory to properly react to orders and maintain service

levels (Tyndall & Kane, 2013). Reducing lead time variability significantly decreases inventory

levels, decreases order delays, and improves service levels (Tyndall & Kane, 2013). We observe

that the variability of the can be stabilized by cloud computing. Cloud computing actually

reduces the lead time variability by lowering the processing times across the supply chain,

enhancing information visibility, and providing more accurate demand forecasts. Our results

show a lead time reduction, average 𝐿𝐿𝑙𝑙3, from 2.51 days to 0.5 days when a hospital supply chain

uses cloud based technology. Figures 26 and Figure 27 show the manufacturer’s lead time

variability, 𝐿𝐿𝑙𝑙3, in the traditional and cloud based hospital supply chains.

123

Building up large inventory levels impact delivery delays. We observe that cloud

computing reduces the average actual delivery delays ( 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝐴𝐴 ) by approximately two days at the

manufacturer, distributor, and hospital (shown in Table 22) The 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝐴𝐴 reduction is a result of the

reduction in 𝐿𝐿𝑙𝑙3.

Table 22. Delivery Delays in traditional and cloud based hospital supply chain SD

Unfilled orders

An increase in delivery delays cause order backlogs (the number of unfilled orders).

According to our results, a significant number of orders remain unfulfilled, especially at the

Tra

ditio

nal Manufacturer Distributor Hospital

Average 3.07 3.09 3.10

Std. Deviation 0.68 0.64 0.55

Clo

ud Average 1 1.1 1.1

Std.Deviation 0.3 0.3 0.3

Average 2.51Std. Deviation 1.75

Average 0.5Std Deviation 0.3

Figure 27. Manufacturing lead time in traditional hospital supply chain

Figure 26. Manufacturing lead time in cloud based hospital supply chain

124

manufacturer in traditional hospital supply chain. We observe a 13% increase in unfilled orders

at the manufacturer and at the hospital. According to the results, we observe a small variability in

average number of unfilled orders ( 𝑂𝑂𝑂𝑂𝑙𝑙3,𝑡𝑡=35.57, 𝑂𝑂𝑂𝑂𝑙𝑙2,𝑡𝑡=35.49, 𝑂𝑂𝑂𝑂𝑙𝑙1,𝑡𝑡=30.74) in in traditional

hospital supply chain. Compared to the order backlogs in a traditional hospital supply chain, we

observe more than 65% reduction in the cloud based hospital supply chain. Reduced order

backlogs indicate that incoming orders are fullfiled ontime due to the reduction in delivery

delays. In addition, the unfilled order level escalates to approximately 7% at the manufacturer in

a cloud based hospital supply chain but is still lower than the traditional hospital supply chain.

Table 23 shows the results for order backlogs (𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡) for the manufacturer (𝑂𝑂𝑂𝑂𝑙𝑙3,𝑡𝑡), distributor

(𝑂𝑂𝑂𝑂𝑙𝑙2,𝑡𝑡), and hospital (𝑂𝑂𝑂𝑂𝑙𝑙1,𝑡𝑡) in the traditional and cloud based hospital supply chains.

Table 23. Order Backlogs in traditional and cloud based hospital supply chain SD

Variability in unfilled orders in traditional and cloud based supply chain is shown in Figure 28

and 29, respectively.

Min Max Average Std. Deviation

Tra

ditio

nal Manufacturer 0.70 130.53 35.57 26.64

Distributor 4.64 85.37 35.49 15.89

Hospital 8.60 52.61 30.74 6.14

Clo

ud

Manufacturer 0.23 45.93 11.04 7.37

Distributor 0.94 43.86 10.68 5.18

Hospital 2.36 30.07 10.26 3.42

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The cloud based hospital supply chain sees a reduction in unfilled orders in comparison

to the traditional model. The reduction indicates an increase in the service levels of each supply

chain partner. As service levels increase, hospital supply chain efficiency increases (Rubiano

Ovalle & Crespo Marquez, 2003). There is also a noticeable bullwhip effect present among

backorders with the cloud based supply chain dampening the effect.

Conclusions and Managerial Implications

Sharing accurate and timely data through effective collaboration with supply chain

partners is important to quickly respond to supply chain changes, especially as supply chain

complexity increases. Healthcare supply chains are complex. The lack of accurate and timely

demand and inventory information sharing in a healthcare supply chains can have catastrophic

consequences that might even lead to mortality. IT-enabled, supply chain collaboration provides

a platform for effective information sharing (Brandon‐Jones et al., 2014). To show the

importance and the benefits of IT-enabled, supply chain collaboration, through cloud based

0

10

20

30

40

50

0 50 100

150

200

250

300

350

400

450

OR

DE

R B

AC

KL

OG

LE

VE

LS

DAYSManufacturer Order BacklogDistributor Order BacklogHospital Order Backlog

Figure 29. Order Backlogs in traditional hospital supply chain

Figure 28. Order Backlogs in cloud based hospital supply chain

126

computing, we develop CLDs and their equilavent SD models to evaluate the impact information

sharing has within a hospital supply chain. The SD models simulate hospital supply chain

behavior in a traditional and a cloud based information sharing setting. We use lead time,

inventory, demand and inventory visibility, and customer service levels to compare both supply

chain models,. Our findings suggest that the use of the cloud based information sharing in

hospital supply chains improve demand and inventory visibility, reduce lead time levels and

variability, reduce average inventory levels and variability, and improve customer service Based

on our findings we offer the following business implications:

• Cloud based information sharing improves demand and inventory visibility

throughout a hospital supply chain. With improved visibility hospitals are now

better positioned to make timely, strategic order fulfillment decisions and to

increase responsiveness to fluctuations in patient demand, lead times, and

cusomer expectations.

• Cloud based information sharing improves inventory. With instantaneous

demand and inventory information visibility hospital supply chain partners will

have more timely and accurate demand forecasts, reducing inventory cost and

health care supply shortages.

• Cloud based information sharing between supply chain partners can overcome the

failure of VMI programs by preventing information distortions and delays while

updating, transmitting, and analyzing demand, lead time, inventory, and

fulfillment information.

127

• High levels of trust among health care supply chain partners are needed to

maximize the benefits of cloud computing technology. Once cloud computing is

identified as the reason for improved performance, “supply chain partners will

find themselves in a virtuous cycle of improving supply chain performance

leading to trust which will in turn improve the performance even further”

(Akkermans et al., 2004, p.454).

This paper contributes to the literature by developing a new systems dynamics approach,

feedback-based structure to model and investigate the behavior of hospital supply chains, and to

evaluate the impact of cloud based information sharing systems. We hope that the proposed

methodology will help decision makers to understand the benefits and structure of cloud based

information systems in hospital supply chains.

Limitations and Future Research

This paper has several limitations. First, the study does not use validated empirical data.

For research research, the results and key findings of this study can be further validated using

empirical data. Second, the generalizability of the findings is limited to a hospital supply chain

setting. Applying the SD models to other industry sector will provide further generalizability to

the key findings and results. Next, certain assumptions have been made when simulating in

order to make it easier to interpret the results. In real life, these assumptions may not apply. In

future studies, the proposed model can be extended by eliminating some of the assumptions. In

addition, the behavior of the developed models can be tested in the presence of disasters to study

the resilience of hospital supply chain. Another extension of this research could be the use of

game theory to study agent behaviors, corresponding to different trust levels. Finally, in this

study we use demand and inventory visibility, lead time and lead time variability, unfilled orders

128

and backlog variability, and average inventory levels and variability as performance metrics.

There are other metrics that influence the supply chain performance. In future studies, the

influence of other metrics such as capacity and cost can be tested.

129

APPENDIX A.

Impact of Cloud based SCM on Supply Chain Resilience Survey

Thank you for agreeing to participate in this survey. The survey asks your opinions on Cloud Based

SCM. The term ‘Cloud Based SCM’ refers to an emergent eSCM that senses the changes in real

time and executes the optimal response by providing a platform for collaboration, communication

and integration across the supply chain.

What type of industry does your company belong to?

1. Retail 2. Consumer Products 3. Direct Store Delivery 4. High Tech 5. Automotive and Manufacturing 6. Logistics Service Providers 7. Public Sector & Defense 8. Healthcare and Pharmaceuticals 9. Energy 10. Handheld Devices/Wireless Technologies 11. Other ____________________

What is your job title?

1. CEO/CFO/CIO/President 2. Vice President/ Partner/ Principal /General Manager 3. Manager 4. Planner/Scheduler/Analyst/Buyer 5. Engineer 6. Other ____________________

130

How many employees does your company have?

1. 0-50 2. 51-100 3. 101-200 4. 201-500 5. 501-1000 6. 1001+ Do you currently use a Cloud based SCM system for your supply chain operations?

1. Yes 2. No

Which of the following cloud based solutions does your company use for decision making?

1. Sales and Operations Planning 2. Business Continuity & Risk Management 3. Sustainability 4. Forecasting 5. Demand Signal Repository 6. Demand Management 7. Order Management 8. Vendor Managed Inventory 9. Multi-Tier Replenishment 10. Multi-Tier Inventory Management 11. Demand Driven Deployment 12. Production Planning and Scheduling 13. Procurement 14. Transportation Planning and Optimization 15. Transportation Management 16. Global Trade Management 17. Financial Reconciliation

Which of the following software or applications does your company and supply chain partners currently access via cloud?

1. ERP ( Enterprise resource planning) 2. CRM (Customer relationship management)

131

3. HRM (Human resources management) 4. TMS (Transportation management system) 5. SRM (Supplier relationship management system) 6. SCM (Supply chain management system)

Please indicate the extent to which you agree with the following statements about your firm’s ability to coordinate and integrate information with partners within their supply chain( Strongly Disagree=1, Strongly Agree=7)

11. Our Cloud based SCM system satisfies supply chain communication requirements

12. Our Cloud based SCM system highly integrates information applications within the firm and the supply chain

13. Our Cloud based SCM system provides adequate information system linkages with suppliers and customers

14. Our Cloud based SCM system manages and coordinates supply chain activities

15. Our Cloud based SCM system gives our firm full access to joint planning systems

16. Our Cloud based SCM system captures real time data

1. Our firm exchanges relevant information with its supply chain partners

2. Our firm exchanges timely information with its supply chain partners

3. Our firm exchanges accurate information with its supply chain partners

4. Our firm exchanges complete information with its supply chain partners

5. Our firm exchanges confidential information with its supply chain partners

Please indicate the extent to which you agree with the following statements about your firm's ability to work effectively with other entities for mutual benefit. ( Strongly Disagree=1, Strongly Agree=7)

6. Our firm effectively employs collaborative demand forecasting techniques using shared data

7. Our data flow transparently between supply chain members, with full access by all firms to facilitate collaborative decision making

8. Our firm jointly develops strategic objectives with its supply chain partners

9. Our firm shares its resources to help suppliers improve its capabilities

10. Our firm invests in facilities and equipment at suppliers’ plants and is prepared to share risks with both suppliers and customer

132

Thank you for participating in our study!

Please indicate the extent to which you agree with the following statements about your knowledge of the status of operating assets and the environment. ( Strongly Disagree=1, Strongly Agree=7)

17. Our firm has an information systems that accurately track all operations

18. Our firm has real-time data on location and status of supplies, finished goods, equipment and employees

19. Our firm have a regular interchange of information among our supply chain partners

20. Demand levels are highly visible throughout the supply chain

21. Inventory levels are highly visible throughout the supply chain

Please indicate the extent to which you agree with the following statements about the speed or quickness (degree of responsiveness) with which your firm can engage.( Strongly Disagree=1, Strongly Agree=7)

22. Our firm quickly adapts manufacturing lead times

23. Our firm quickly adapts level of customer service

24. Our firm quickly adjusts delivery capability

25. Our firm quickly improves delivery reliability

26. Our firm quickly responds to changing market needs

Please indicate the extent to which you agree with the following statements about your firm's ability to return to normal operations rapidly. ( Strongly Disagree=1, Strongly Agree=7)

27. Our firm can quickly organize a formal response team of key personnel, both on-site and corporate level

28. Our firm takes an immediate action to mitigate the effects of disruptions, despite the short-term costs

29. Our firm has an effective strategy for communications in a variety of extraordinary situations

30. Our firm is very successful at dealing with crises, including addressing public relations issues

Please indicate the extent to which you agree with the following statements about your firm's ability to discern potential future events or situations.( Strongly Disagree=1, Strongly Agree=7)

31. Our firm effectively uses demand forecasting methods

32. Our firm has a formal risk identification and prioritization process

33. Our firm closely monitor deviations to normal operations, including near misses

34. Our firm has detailed contingency plans and regularly conduct preparedness exercises and readiness inspections

35. Our firm quickly recognizes early warning signals of possible disruptions

133

APPENDIX B

Stock and Flow Diagrams

Hospital

Distributor

Patient Demand

Patients' Order Rate

Hospital OrderBacklog

Hospital DesiredSupply Rate

+

HospitalInventory

HospitalInventory Gap

Desired Hospitalinventory

Hospital MaximumSupply Rate

-

+

+

ExpectedPatientDemand

<Patient Demand>+

+

+

Desired HospitalOrder +

+

B

B

B

Hospital OrderFullfilment Rate

(OFR)

Supply Rate

+

+

HospitalDelivery Delay

+

-

-

Hospital MinimumOrder Processing

Time

-+Hospital

InventoryAdjustment

Time

Change in Patients' Demand

+

-

Distributor'sMedicine Delivery

Rate

Hospital SafetyStock

-

<DistributorShipment

Rate> +

+

Hospital InventoryCoverage

+ -

Hospital TargetDelivery Delay

-

+

Hospital's Time toAverage Order Rate

+

Hospital Order Rate

DistributorOrder Backlog

Distributor DesiredShipment Rate

+

DistributorInventory

DistributorInventory Gap

Desired Distributorinventory

DistributorMaximum Shipment

Rate

-

+

+

ExpectedHospitalDemand

+

+

DesiredDistributor Order+

+

B

B

B

Distributor OrderFullfilment Rate (OFR)

DistributorShipment Rate

+

+

DistributorDelivery Delay

+

-

-

Distributor MinimumOrder Processing

Time

-+DistributorInventory

AdjustmentTime

Change in Hospital's Demand-

Manufacturer'sMedicine Delivery

Rate

DistributorSafety Stock

-

<DesiredHospital Order> +

<Desired Hospital Order>

++

DistributorInventory Coverage

Distributor TargetDelivery Delay

+ -

+

-

<ManufacturerShipment

Rate>+

Distributor's Time toAverage Order Rate

+

134

Manufacturer

Work In Process(WIP) Inventory

Manufacturer'sInventory

ExpectedDistributor

Demand

ManufacturerOrder Backlog

WIP Gap

Desired Production

ManufacturerInventory Gap

Desired WIP+

+

+

Lead Time

+

-

DesiredManufacturer

Inventory

+

+

+

ManufacturerMaximum Shipment

Rate+

Desired Shipment Rate

+

+

+

B

B

B

Change in Distributor's Demand-

<DesiredDistributor Order>

Distributor OrderRate

+

Manufacturer OrderFullfilment Rate (OFR)

ManufacturerDelivery Delay

-+

Manufacturer Shipment Rate

+

+

-

B

ManufacturerMinimum OrderProcessing Time

+

+

ProductionCompletion

Rate

-ProductionRelease Rate

+

+

WIP AdjustmentTime Manufacturer

Inventory AdjustmentTime

+

+

+

<DesiredDistributor Order>

++

ManufacturerInventoryCoverage+ -

Manufacturer TargetDelivery Delay

+

-

SupplierShipment Rate

+

Supplier's Time toAverage Order Rate

+

135

APPENDIX C.

System Dynamics Equations

Order fulfillment

The hospital (e=1) CLD begins with the Patients Demand (𝐷𝐷𝑙𝑙1,𝑡𝑡). The casual loops for the other

types of echelons (e.g., distributors, manufacturers, and suppliers) begin with an incoming order

rate �𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡�.

𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 = �𝐷𝐷𝑙𝑙1,𝑡𝑡,𝐷𝐷𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡∞,

,𝑒𝑒 = 1

1 < 𝑒𝑒 <𝑒𝑒 = |𝐸𝐸|

|𝐸𝐸| (1)

In each echelon, 𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 triggers 𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡. 𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 accumulates the unfilled orders since the orders

cannot be delivered immediately. To ensure the model’s equilibrium, 𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 as follows:

𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 × 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝑇𝑇 , 𝑡𝑡 = 0 (2)

where 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝑇𝑇 takes any value determined by the entities in each echelon.

Equation 3 shows the order backlog for unfilled orders, 𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡, calculation:

𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 = �𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 × 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝑇𝑇 � + 𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 − 𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡 (3)

𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 influences the behavior of the desired shipment/supply rate, 𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 .𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 is calculated as

the ratio of 𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 and 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝑇𝑇 :

𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 =𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒

𝑇𝑇 (5)

𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 also effects the actual average delay, 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝐴𝐴 . 𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒

𝐴𝐴 is determined as the ratio of 𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 and

𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡 :

𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒𝐴𝐴 =

𝑂𝑂𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡

(6)

where 𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡 =𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡. (7)

136

Shipment rate, 𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡, is calculated as follows:

𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 × �𝑇𝑇𝐹𝐹 �𝑀𝑀𝑀𝑀𝑙𝑙𝑒𝑒,𝑡𝑡𝐷𝐷𝑀𝑀𝑙𝑙𝑒𝑒,𝑡𝑡

�� . (8)

where TF refers to the Table for Order Fulfillment. �𝑇𝑇𝐹𝐹 �𝑀𝑀𝑀𝑀𝑙𝑙𝑒𝑒,𝑡𝑡𝐷𝐷𝑀𝑀𝑙𝑙𝑒𝑒,𝑡𝑡

�� represents a function of

𝑀𝑀𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 𝑡𝑡𝑡𝑡 𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 that refers to the fraction of the orders given 𝐷𝐷𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 which is called 𝐹𝐹𝑙𝑙𝑒𝑒,𝑡𝑡.

𝑀𝑀𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 , the maximum rate of shipments can be made given the current 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 level, adjusted by

𝑃𝑃𝑇𝑇𝑙𝑙𝑒𝑒:

𝑀𝑀𝑆𝑆𝑙𝑙𝑒𝑒,𝑡𝑡 =𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡𝑃𝑃𝑇𝑇𝑙𝑙𝑒𝑒

(9)

where minimum order processing time, 𝑃𝑃𝑇𝑇𝑙𝑙𝑒𝑒 is given.

Inventory Control

𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡, the level of finished medicine inventory in stock and initialized as:

𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 (10)

𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 accumulate the difference between the delivery rate, 𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡, and/or production conclusion

rate, and the shipment rate, 𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡, calculated as follows:

𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 = �𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 + 𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 − 𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡,𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 + 𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡−𝑆𝑆𝑙𝑙𝑒𝑒−1,l𝑒𝑒,𝑡𝑡,

𝑒𝑒 = 1,2 𝑒𝑒 = 3 (11)

where 𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡, ∀𝑒𝑒 (12)

and 𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 is shown in equation (27).

𝐶𝐶𝑙𝑙𝑒𝑒,𝑡𝑡 represents the number of days the entities in each echelon could ship at the current 𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡

given their 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 level:

𝐶𝐶𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡

𝑀𝑀𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡 (13)

137

Entities in each echelon try to maintain enough 𝐶𝐶𝑙𝑙𝑒𝑒,𝑡𝑡 to provide adequate service level to their

customers. To maintain 𝐶𝐶𝑙𝑙𝑒𝑒,𝑡𝑡, the entities seeks to keep 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 level sufficient at 𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 by covering

𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 �. Therefore, 𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 is calculated as:

𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 = (𝑃𝑃𝑇𝑇𝑙𝑙𝑒𝑒 + 𝑆𝑆𝑆𝑆𝑙𝑙𝑒𝑒) × 𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 � (14)

Where 𝑃𝑃𝑇𝑇𝑙𝑙𝑒𝑒 and the safety stock, 𝑆𝑆𝑆𝑆𝑙𝑙𝑒𝑒 are assigned predetermined values.

𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 � , demand forecast, initialized when the 𝑡𝑡 = 0 as follows:

𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 � = 𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡, 𝑡𝑡 = 0 (15)

First order exponential smoothing technique is used for 𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 �. Calculation for 𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 � as

follows:

𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 �=𝐷𝐷𝑙𝑙1,𝑡𝑡 + 𝐶𝐶𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 (16)

where 𝐶𝐶𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 , change in demand, is determined as the difference between 𝐷𝐷𝑙𝑙1,𝑡𝑡 and 𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 �

adjusted by the given 𝑇𝑇𝑙𝑙𝑒𝑒 , time to average order rate:

𝐶𝐶𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 =�𝐷𝐷𝑙𝑙1,𝑡𝑡−𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 ��

𝑇𝑇𝑙𝑙𝑒𝑒 (17)

When there is not enough coverage of the 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡, there is a difference between 𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 and 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡

which is referred as 𝐺𝐺𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 , inventory gap, and adjusted by the given 𝐼𝐼𝐽𝐽𝑙𝑙𝑒𝑒 :

𝐺𝐺𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 =(𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡−𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡)

𝐼𝐼𝐼𝐼𝑙𝑙𝑒𝑒 (18)

To minimize the 𝐺𝐺𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 and take the 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 level to the desired level, desired orders, 𝐷𝐷𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 is

determined as follows:

𝐷𝐷𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 = max (0,𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 � + 𝐺𝐺𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡) (19)

𝐷𝐷𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 is constrained to be nonnegative.

As mentioned in equation 1, 𝐷𝐷𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 is equal to 𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 when 𝑒𝑒 < 1 < |𝐸𝐸|.

138

𝐷𝐷𝑂𝑂𝑙𝑙𝑒𝑒,𝑡𝑡 in each echelon results in 𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡 in previous echelon. In each echelon, 𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡 is

calculated as in equation (6) and in each previous echelon 𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡 translates into delivery

rate, 𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡:

𝑆𝑆𝑙𝑙𝑒𝑒−1,𝑙𝑙𝑒𝑒,𝑡𝑡=𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡, 𝑒𝑒 = 𝑒𝑒 − 1 (20)

Production

𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡, desired production rate, adjusts 𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 � to match the 𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 to the 𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡 and is subject to

nonnegativity:

𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝑚𝑚𝑚𝑚𝑚𝑚(0,𝐸𝐸�𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡 � + 𝐺𝐺𝐼𝐼𝑙𝑙𝑒𝑒,𝑡𝑡),∀𝑒𝑒 ∈ 𝑀𝑀 (21)

𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 effects 𝑃𝑃𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡, production start rate. 𝑃𝑃𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡is determined by the 𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 as:

𝑃𝑃𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝑚𝑚𝑚𝑚𝑚𝑚 �0,𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 + 𝐺𝐺𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡�,∀𝑒𝑒 ∈ 𝑀𝑀 (22)

𝐺𝐺𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 , work in process inventory gap, modifies 𝑃𝑃𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 to minimize the difference between

desired work in process inventory, 𝐷𝐷𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 and work in process inventory, 𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡:

𝐺𝐺𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 =𝐷𝐷𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡−𝐷𝐷𝑙𝑙𝑒𝑒,𝑡𝑡

𝐷𝐷𝐼𝐼𝑙𝑙𝑒𝑒 ,∀𝑒𝑒 ∈ 𝑀𝑀 (23)

where 𝑊𝑊𝐽𝐽𝑙𝑙𝑒𝑒 value is given.

𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 accumulates the difference between production release rate 𝑃𝑃𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 and production

completion, 𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡. 𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 is initialized when 𝑡𝑡 = 0 as follows:

𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝐷𝐷𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡, 𝑡𝑡 = 0 (24)

Equation (25) shows the 𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 calculation at each manufacturing location,

𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 =𝐷𝐷𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 + 𝑃𝑃𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 - 𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 . (25)

𝐷𝐷𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 provides a level of 𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 sufficient to yield the 𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 given manufacturing lead time 𝐿𝐿𝑙𝑙𝑒𝑒:

𝐷𝐷𝑊𝑊𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝐿𝐿𝑙𝑙𝑒𝑒 × 𝐷𝐷𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 (26)

139

Production completion rate 𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡, is calculated by the third order delay of the production start

rate (delay 3) 𝛿𝛿3 with the delay time determined by the 𝐿𝐿𝑙𝑙𝑒𝑒:

𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡 = 𝛿𝛿3�𝑃𝑃𝑃𝑃𝑙𝑙𝑒𝑒,𝑡𝑡, 𝐿𝐿𝑙𝑙𝑒𝑒�,∀𝑒𝑒 ∈ 𝑀𝑀 (27)

140

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