Quantitative models for supply chain risk analysis from a firm’s perspective by Arun Vinayak A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Industrial Engineering Program of Study Committee: Cameron A. MacKenzie, Major Professor Caroline C. Krejci Scott J Grawe The student author and the program of study committee are solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is conferred. Iowa State University Ames, Iowa 2017 Copyright © Arun Vinayak, 2017. All rights reserved. ii DEDICATION I dedicate this thesis to my mother and my father for their patience and support while I was away from them to complete my studies. I also dedicate this thesis to my sister for encouraging me to succeed since we were kids. iii TABLE OF CONTENTS Page ACKNOWLEDGMENTS ......................................................................................... iv ABSTRACT………………………………. .............................................................. v CHAPTER 1 GENERAL INTRODUCTION AND REVIEW OF LITERATURE 1 Research Motivation ............................................................................................ 3 Thesis Organization ............................................................................................. 3 References ......................................................................................................... 4 CHAPTER 2 A QUANTITATIVE MODEL FOR ANALYZING MARKET RESPONSE DURING SUPPLY CHAIN DISRUPTIONS ...................................... 6 Abstract………………………………. ............................................................... 6 Introduction ......................................................................................................... 7 Literature Review................................................................................................. 9 Model………………………………. .................................................................. 11 Illustrative Example ............................................................................................. 17 Conclusions ......................................................................................................... 24 References ......................................................................................................... 25 CHAPTER 3 COST EFFECTIVENESS ANALYSIS OF SUPPLIER EFFECTIVENESS BASED ON SCOR METRICS .................................................. 29 Abstract………………………………. ............................................................... 29 Introduction ......................................................................................................... 30 Literature Review................................................................................................. 32 Methodology ........................................................................................................ 35 Illustrative Example ............................................................................................. 53 Conclusions ......................................................................................................... 59 References ......................................................................................................... 61 CHAPTER 5 GENERAL CONCLUSIONS ......................................................... 64 General Discussion .............................................................................................. 64 Recommendations for Future Research ............................................................... 65 APPENDIX A APPROXIMATING VALUE FUNCTIONS IN EXCEL .............. 67 iv ACKNOWLEDGMENTS Firstly, I would like to thank my advisor Dr. Cameron MacKenzie for introducing me to the fascinating world of engineering risk analysis and encouraging me to do research right from 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 Grawe for the guidance and support that they provided to achieve my academic goals. I would also like to express my gratitude for all my colleagues, mentors, and mangers at Tesla during my seven-month internship at the Tesla factory in Fremont, CA. In addition, I want to offer my appreciation to my friends, colleagues, the department faculty and staff for making my time at Iowa State University a wonderful experience, without whom, this thesis would not have been possible. v ABSTRACT Supply chain risk analysis garnered increased attention, both in academia and in practice, since the early 2000s. Modern production methodologies such as just-in-time and lean manufacturing, globalized supply chains, shorter product life cycle, and the emphasis on efficiency have increased the risk faced by many supply chains. Managing such risks that is faced by a supply chain is vital to the success of any company. Currently employed methods lack consideration of market reaction and incorporation of decision maker preferences in managing supply chain risk. In this thesis, these two factors are taken into consideration to develop quantitative methods to analyze supply chain risk. The first study is focused on supply chain risk from the market side in case of a major disruption. A probabilistic model based on different types of customer behaviors is developed to identify the impact on the firm’s revenue by forecasting the lost revenue in case of a production shut down from a disruption event. Results from a simulation of the developed model 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 based on KPIs derived from the performance metrics of the SCOR (Supply Chain Operations Reference) model. The framework can be used for supplier selection as well as for supplier performance monitoring as the firm continues to work with the selected supplier. Decision makers from a firm can incorporate their own preference within the presented framework to determine the most preferred supplier and assess the cost effectiveness to select a supplier in different scenarios to minimize supply side risk. 1 CHAPTER 1. GENERAL INTRODUCTION AND REVIEW OF LITERATURE Risk analysis is a critical process and is widely adopted in many sectors ranging from manufacturing and retail to logistics and military (Bedford and Cooke, 2001). According to Kaplan and Garrick (1981), risk is associated with both uncertainty and damage and analyzing risk consists of answering three questions: 1) What can go wrong? 2) How likely is it that will happen? and 3) If it does happen, what are the consequences? Getting answers to these questions 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 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 the 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. The qualitative techniques are useful in the prioritization of the risk in accordance with their likelihood and magnitude of impact (Burtonshaw-Gunn, 2009). The mix of qualitative and quantitative technique use one of the techniques for measuring chances of loss and another one for amount of loss (Modarres, 2006). Among different quantitative techniques for risk analysis, probabilistic method is most commonly used for studying complex technical systems (Käki, Salo & Talluri, 2013). The basic technique according to Käki et al. (2013) is to develop a structural model of the system 2 under study, identify the key risk factors and measure their probability of occurrence, and finally 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 have become more complex in the 21st century. Although the cost can be reduced through outsourcing suppliers from different parts of the world, it increases the probability of risk as well as magnitude of loss (Choi and Krause 2006). In addition, the regulations have become diversified and more complex for a manufacturer to handle without conducting a proper risk analysis (Sadgrove, 1996). There is also shift in the attitude of clients who have become more demanding and critical (Sadgrove, 1996). As a result, companies have become more concerned about risk management than cost management when it comes to supply chain (Simchi-Levi 2010). According to Waters (2011), each member of the supply chain is subject to some specified risks from his own activities, from activities of other members of supply chain and from the factors external to the supply chain. From a manufacturer or firm’s perspective, the losses can range from delay in supplying finished good to market to their total inability to continue business. Firms face multiple decision problems where more than one factor influences the decision maker’s preferences over the best possible outcome. When faced with such complex problems, decision makers often use simplified mental strategies, or heuristics due to limited information-processing capacity (Paul & George, 2004). Jüttner (2005) found that while there is growing awareness among manufacturers on the growing risk associated with supply chain, they still lack proper understanding of what entailed supply chain risk management. Improvement of this understanding and introduction of proper supply risk analysis practices in manufacturing firm is a critical need of the day. 3 Research Motivation The motivation of this research derives from the need for clear and quantitative methods to express supply chain risk from the perspective of a firm or a manufacturer so that in 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 a severe 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 leading to 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 Organization This thesis contains two research papers that constitutes chapters 2-3. The first paper in chapter 2 attempts to model downstream risk in a supply chain from a firm’s perspective while the second paper in chapter 3 considers the upstream supply chain and presents a framework 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 number of the graphic within the chapter for clarity. The final chapter consist of general conclusions and future work. 4 References Bedford, 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 of the 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 Academic Press. Choi, T. Y., & Krause, D. R. (2006). The supply base and its complexity: Implications for transaction costs, risks, responsiveness, and innovation. Journal of Operations Management, 24(5), 637-652. Jüttner, U. (2005). Supply chain risk management: Understanding the business requirements from 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 risk assessment 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. Boca Raton, FL: CRC press. Paul, G., & George, W. (2004). Decision Analysis for Management Judgment. Simchi-Levi, D. (2010). Operations rules: delivering customer value through flexible operations. Cambridge, MA: MIT Press. 5 Waters, D. (2011). Supply chain risk management: vulnerability and resilience in logistics (2nd ed.). Philadelphia, PA: Kogan Page Publishers.