Quantitative Models For Supply Chain Risk Analysis From A . PDF

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Quantitative models for supply chain risk analysis from a firm’s perspectivebyArun VinayakA thesis submitted to the graduate facultyin partial fulfillment of the requirements for the degree ofMASTER OF SCIENCEMajor: Industrial EngineeringProgram of Study Committee:Cameron A. MacKenzie, Major ProfessorCaroline C. KrejciScott J GraweThe student author and the program of study committee are solely responsible for thecontent of this dissertation. The Graduate College will ensure this dissertation is globallyaccessible and will not permit alterations after a degree is conferred.Iowa State UniversityAmes, Iowa2017Copyright Arun Vinayak, 2017. All rights reserved.

iiDEDICATIONI dedicate this thesis to my mother and my father for their patience and support while Iwas away from them to complete my studies. I also dedicate this thesis to my sister forencouraging me to succeed since we were kids.

iiiTABLE OF CONTENTSPageACKNOWLEDGMENTS .ivABSTRACT . .vCHAPTER 11GENERAL INTRODUCTION AND REVIEW OF LITERATUREResearch Motivation .Thesis Organization .References .334CHAPTER 2 A QUANTITATIVE MODEL FOR ANALYZING MARKETRESPONSE DURING SUPPLY CHAIN DISRUPTIONS .6Abstract . .Introduction .Literature Review.Model . .Illustrative Example .Conclusions .References .67911172425CHAPTER 3 COST EFFECTIVENESS ANALYSIS OF SUPPLIEREFFECTIVENESS BASED ON SCOR METRICS .29Abstract . .Introduction .Literature Review.Methodology .Illustrative Example .Conclusions .References .29303235535961CHAPTER 5GENERAL CONCLUSIONS .64General Discussion .Recommendations for Future Research .6465APPENDIX A APPROXIMATING VALUE FUNCTIONS IN EXCEL .67

ivACKNOWLEDGMENTSFirstly, I would like to thank my advisor Dr. Cameron MacKenzie for introducing meto the fascinating world of engineering risk analysis and encouraging me to do research rightfrom my first semester. I want to express my gratitude for his continuous support, patience,motivation, and immense knowledge throughout my graduate study and research.I would like to thank my committee members, Dr. Caroline Krejci and Dr. Scott Grawefor the guidance and support that they provided to achieve my academic goals. I would alsolike to express my gratitude for all my colleagues, mentors, and mangers at Tesla during myseven-month internship at the Tesla factory in Fremont, CA.In addition, I want to offer my appreciation to my friends, colleagues, the departmentfaculty and staff for making my time at Iowa State University a wonderful experience, withoutwhom, this thesis would not have been possible.

vABSTRACTSupply chain risk analysis garnered increased attention, both in academia and inpractice, since the early 2000s. Modern production methodologies such as just-in-time and leanmanufacturing, globalized supply chains, shorter product life cycle, and the emphasis onefficiency have increased the risk faced by many supply chains. Managing such risks that isfaced by a supply chain is vital to the success of any company. Currently employed methodslack consideration of market reaction and incorporation of decision maker preferences inmanaging supply chain risk. In this thesis, these two factors are taken into consideration todevelop quantitative methods to analyze supply chain risk.The first study is focused on supply chain risk from the market side in case of a majordisruption. A probabilistic model based on different types of customer behaviors is developedto identify the impact on the firm’s revenue by forecasting the lost revenue in case of aproduction shut down from a disruption event. Results from a simulation of the developedmodel is analyzed to draw useful insights to manage the risk of such an event.The second study is centered on supplier selection. It presents a 5-step framework basedon KPIs derived from the performance metrics of the SCOR (Supply Chain OperationsReference) model. The framework can be used for supplier selection as well as for supplierperformance monitoring as the firm continues to work with the selected supplier. Decisionmakers from a firm can incorporate their own preference within the presented framework todetermine the most preferred supplier and assess the cost effectiveness to select a supplier indifferent scenarios to minimize supply side risk.

1CHAPTER 1. GENERAL INTRODUCTION AND REVIEW OFLITERATURERisk analysis is a critical process and is widely adopted in many sectors ranging frommanufacturing and retail to logistics and military (Bedford and Cooke, 2001). According toKaplan and Garrick (1981), risk is associated with both uncertainty and damage and analyzingrisk consists of answering three questions: 1) What can go wrong? 2) How likely is it that willhappen? and 3) If it does happen, what are the consequences? Getting answers to thesequestions by identifying risk factors, their chances of occurrence, and their consequences,enables a decision maker to devise a plan to manage the risk (Chavas, 2004).Risk analysis can be conducted through both qualitative and quantitative techniquesand a mix of two, ranging from simple brainstorming to more technical computer stimulation(Modarres, 2006). The quantitative techniques for risk analysis use estimation method to findthe probability of loss caused by a certain event and the magnitude of the loss (Modarres,2006). In comparison, qualitative techniques are more flexible and instead of using probability,they usually use more diverse methods to decide the likelihood and impact of risks. Thequalitative techniques are useful in the prioritization of the risk in accordance with theirlikelihood and magnitude of impact (Burtonshaw-Gunn, 2009). The mix of qualitative andquantitative technique use one of the techniques for measuring chances of loss and another onefor amount of loss (Modarres, 2006).Among different quantitative techniques for risk analysis, probabilistic method is mostcommonly used for studying complex technical systems (Käki, Salo & Talluri, 2013). Thebasic technique according to Käki et al. (2013) is to develop a structural model of the system

2under study, identify the key risk factors and measure their probability of occurrence, andfinally conduct a probabilistic analysis to identify the most-risky segments of systems.With the onset of global supply chains and outsourcing of suppliers, supply chains havebecome more complex in the 21st century. Although the cost can be reduced throughoutsourcing suppliers from different parts of the world, it increases the probability of risk aswell as magnitude of loss (Choi and Krause 2006). In addition, the regulations have becomediversified and more complex for a manufacturer to handle without conducting a proper riskanalysis (Sadgrove, 1996). There is also shift in the attitude of clients who have become moredemanding and critical (Sadgrove, 1996). As a result, companies have become more concernedabout risk management than cost management when it comes to supply chain (Simchi-Levi2010).According to Waters (2011), each member of the supply chain is subject to somespecified risks from his own activities, from activities of other members of supply chain andfrom the factors external to the supply chain. From a manufacturer or firm’s perspective, thelosses can range from delay in supplying finished good to market to their total inability tocontinue business. Firms face multiple decision problems where more than one factorinfluences the decision maker’s preferences over the best possible outcome. When faced withsuch complex problems, decision makers often use simplified mental strategies, or heuristicsdue to limited information-processing capacity (Paul & George, 2004). Jüttner (2005) foundthat while there is growing awareness among manufacturers on the growing risk associatedwith supply chain, they still lack proper understanding of what entailed supply chain riskmanagement. Improvement of this understanding and introduction of proper supply riskanalysis practices in manufacturing firm is a critical need of the day.

3Research MotivationThe motivation of this research derives from the need for clear and quantitativemethods to express supply chain risk from the perspective of a firm or a manufacturer so thatin a decision-making process, the firm can weigh risks along with all other costs and benefits.The objectives of this research are as follows:1.To develop a method to quantify the risk faced by a firm or a manufacturer from asevere supply chain disruption with an explicit focus on customer demand.2.To evaluate the extent to which a firm can be penalized from a supplier default leadingto a temporary production shut-down.3.To develop an effective framework for supplier selection and evaluation.4.To derive risk management insights using the developed method and framework.Thesis OrganizationThis thesis contains two research papers that constitutes chapters 2-3. The first paperin chapter 2 attempts to model downstream risk in a supply chain from a firm’s perspectivewhile the second paper in chapter 3 considers the upstream supply chain and presents aframework for supplier selection and evaluation. Both the chapters consist of an abstract,introduction, literature review, methodology, illustrative example, and conclusions.References that correspond to the in-chapter citations are provided at the end of each chapter.All the images and tables are first labeled with the chapter they reside followed by the numberof the graphic within the chapter for clarity. The final chapter consist of general conclusionsand future work.

4ReferencesBedford, T., & Cooke, R. Probabilistic risk analysis: foundations and methods. 2001.Cambridge. Univ. Press, UK.Sheffi, Y., & Rice Jr, J. B. (2005). A supply chain view ofthe resilient enterprise. MIT Sloan Management Review, 47(1), 41.BurtonShAw-Gunn, S. A. (2009). Risk and Financial Management in Construction.Burlington, VT: Ashgate Publishing Company.Chavas, J. P. (2004). Risk Analysis in Theory and Practice. London, UK: Elsevier AcademicPress.Choi, T. Y., & Krause, D. R. (2006). The supply base and its complexity: Implications fortransaction costs, risks, responsiveness, and innovation. Journal of OperationsManagement, 24(5), 637-652.Jüttner, U. (2005). Supply chain risk management: Understanding the business requirementsfrom a practitioner perspective. The International Journal of Logistics Management,16(1), 120-141.Käki, A., Salo, A., & Talluri, S. (2015). Disruptions in supply networks: A probabilistic riskassessment approach. Journal of Business Logistics, 36(3), 273-287.Kaplan, S., & Garrick, B. J. (1981). On the quantitative definition of risk. Risk analysis, 1(1),11-27.Modarres, M. (2006). Risk Analysis in Engineering: techniques, tools, and trends. BocaRaton, FL: CRC press.Paul, G., & George, W. (2004). Decision Analysis for Management Judgment.Simchi-Levi, D. (2010). Operations rules: delivering customer value through flexibleoperations. Cambridge, MA: MIT Press.

5Waters, D. (2011). Supply chain risk management: vulnerability and resilience in logistics(2nd ed.). Philadelphia, PA: Kogan Page Publishers.

6CHAPTER 2. A QUANTITATIVE MODEL FOR ANALYZINGMARKET RESPONSE DURING SUPPLY CHAIN DISRUPTIONSA book chapter accepted for publication in the Springer book Supply Chain RiskManagement: Advanced Tools, Models, and DevelopmentsArun Vinayak, Cameron A. MacKenzieAbstractSupply chain disruptions can lead to firms losing customers and consequently losing profit.We consider a firm facing a supply chain disruption due to which it is unable to deliverproducts for a certain period of time. When the firm is restored, each customer may choose toreturn to the firm immediately, with or without backorders, or may purchase from other firms.This chapter develops a quantitative model of the different customer behaviors in such ascenario and analytically interprets the impact of these behaviors on the firm’s post-disruptionperformance. The model is applied to an illustrative example.Keywords - Supply Chain Risk Management; Supply Chain Disruption; Preparedness;Response; Customer Demand

71. IntroductionSupply chain disruptions have garnered increased attention, both in academia and inpractice, since the early 2000s. Modern production methodologies, globalized supply chains,shorter product life cycle, and the emphasis on efficiency have increased the risk faced bymany supply chains. Managing the risk facing a supply chain is vital to the success of anycompany.Fig. 2.1. A simple supply chain modelA supply chain is an integrated system of companies involved in the upstream anddownstream flows of products, services, finances, and/or information from a source to acustomer (Mentzer et al. 2001). Fig. 2.1 presents a basic supply chain model from the firm’sperspective. A supply chain is characterized by the flow of resources—typically material,information, and money—with the primary purpose of satisfying the

enables a decision maker to devise a plan to manage the risk (Chavas, 2004). Risk analysis can be conducted through both qualitative and quantitative techniques and a mix of two, ranging from simple brainstorming to more technical computer stimulation (Modarres, 2006). The quantitative techniques for risk analysis use estimation method to find