In this thesis, Iris Heckmann develops a profound conceptual basis of supply chain risk analytics. She transfers the newly defined concepts for the modelling and operationalization of supply chain risk within simulation and optimization approaches, in order to ease unexpected deviations and disruptions, which are subsumed under the notion of supply chain risk, increasingly aggravating the planning and optimization of supply chains.

One of the many outcomes resulting from the explosion of international trade is access to lower cost production opportunities through outsourcing. This phenomenon has increased the importance of supply chains, the information technology needed to coordinate them and the need for this relatively complex enterprise to be exceptionally well-managed. There are obviously many cost benefits to be had from maintaining a strong and far-reaching supply chain. However, this opportunity to lower costs entails significant risks, such as tsunamis, earthquakes, political unrest, and economic turbulence. This book will introduce concepts and examples of risk in supply chain management, followed by an identification and discussion of an array of quantitative tools (selection methods, risk simulation modeling, and business scorecard analysis) to help manage these risks. Many books are appearing that address various aspects of supply chain risks. No other book known to the author addresses this set of modeling tools as a means of managing this risk.

This book offers a bridge between our current understanding of supply chain risk in practice and theory, and the monumental shifts caused by the emergence of the fourth industrial revolution. Supply chain risk and its management have experienced significant attention in scholarship and practice over the past twenty years. Our understanding of supply chain risk and its many facets, such as uncertainty and vulnerability, has expanded beyond utilizing approaches such as deploying inventory to buffer the initial effects of disruptions. Even with our increased knowledge of supply chain risk, being in the era of lean supply chain practices, digitally managed global supply chains, and closely interconnected networks, firms are exposed as ever to supply chain uncertainties that can damage, or even destroy, their ability to compete in the marketplace. The book acknowledges the criticality of big data analytics in Supply Chain Risk Management (SCRM) processes and provides appropriate tools and approaches for creating robust SCRM processes. Revisiting Supply Chain Risk presents a state-of-the-art look at SCRM through current research and philosophical thought. It is divided into six sections that highlight established themes, as well as provide new insights to developing areas of inquiry and contexts on the topic. Section 1 examines the first step in managing supply chain risk, risk assessment. The chapters in Section 2 encompass resiliency in supply chains, while Section 3 looks at relational and behavioral perspectives from varying units of analysis including consortiums, teams and decision makers. Section 4 focuses on examining supply chain risk in the contexts of sustainability and innovation. Section 5 provides insight on emerging typologies and taxonomies for classifying supply chain risk. The book concludes with Section 6, featuring illustrative case studies as real-world examples in assessing and managing supply chain risk.

This textbook offers a detailed account of analytical models used to solve complex supply chain problems. It introduces a unique risk analysis framework that helps the reader understand the sources of uncertainties and use appropriate models to improve decisions in supply chains. This framework illustrates the complete supply chain for a product and demonstrates the supply chain's exposure to demand, supply, inventory, and financial risks. Step by step, this book provides a detailed examination of analytical methods that optimize operational decisions under different types of uncertainty. It discusses stochastic inventory models, introduces uncertainty modeling methods, and explains methods for managing uncertainty. To help readers deepen their understanding, it includes access to various supplementary material including an online interactive tool in Python. This book is intended for undergraduate and graduate students of supply chain management with a focus on supply chain analytics. It also prepares practitioners to make better decisions in this field.

Risk is of fundamental importance in this era of the global economy. Supply chains must into account the uncertainty of demand. Moreover, the risk of uncertain demand can cut two ways: (1) there is the risk that unexpected demand will not be met on time, and the reverse problem (2) the risk that demand is over estimated and excessive inventory costs are incurred. There are other risks in unreliable vendors, delayed shipments, natural disasters, etc. In short, there are a host of strategic, tactical and operational risks to business supply chains. Supply Chain Risk: A Handbook of Assessment, Management, and Performance will focus on how to assess, evaluate, and control these various risks.

You don’t have to outrun the bear ... you just have to outrun the other guy. Often in business we only have to run a bit faster than our competitors to be successful. The same is true in risk management. While we would always like to anticipate and prevent risk from happening, when risk events do occur being faster, flexible, and more responsive than others can make a world of difference. Supply Chain Risk Management: An Emerging Discipline gives you the tools and expertise to do just that. While the focus of the book is on how you can react better and faster than the others, the text also helps you understand how to prevent certain risks from happening in the first place. The authors detail a risk management framework that helps you reduce the costs associated with risk, protect your brand and reputation, ensure positive financial outcomes, and develop visible, predictable, resilient, and sustainable supply chains. They provide access to a cloud-based, end-to-end supply chain risk assessment "Heat Map" that illustrates the maturity of the chain through the various stages. It should not come as a surprise to anyone that the world is a riskier place than it was just 15 years ago. A survey used to calculate the Allianz Risk Barometer recently concluded for the first time that supply chain risk is now the top concern of global insurance providers. For most organizations this new reality requires major adjustments, some of which will not be easy. This book helps you understand the emerging discipline called supply chain risk management. It explains the relevant concepts, supplies a wide variety of tools and approaches to help your organization stay ahead of its competitors, and takes a look at future directions in risk management—all in a clear, concise presentation that gives you practical advice and helps you develop actionable strategies.

Risk analysis is crucial in stochastic supply chain models. Over the past few years, the pace has quickened for research attempting to explore risk analysis issues in supply chain management problems, while the majority of recent papers focus on conceptual framework or computational numerical analysis. Pioneered by Nobel laureate Markowitz in the 1950s, the mean-risk (MR) formulation became a fundamental theory for risk management in finance. Despite the significance and popularity of MR-related approaches in finance, their applications in studying multi-echelon supply chain management problems have only been seriously explored in recent years. While the MR approach has already been shown to be useful in conducting risk analysis in stochastic supply chain models, there is no comprehensive reference source that provides the state-of-the-art findings on this important model for supply chain management. Thus it is significant to have a book that reviews and extends the MR related works for supply chain risk analysis. This book is organized into five chapters. Chapter 1 introduces the topic, offers a timely review of various related areas, and explains why the MR approach is important for conducting supply chain risk analysis. Chapter 2 examines the single period inventory model with the mean-variance and mean-semi-deviation approaches. Extensive discussions on the efficient frontiers are also reported. Chapter 3 explores the infinite horizon multi-period inventory model with a mean-variance approach. Chapter 4 investigates the supply chain coordination problem with a versatile target sales rebate contract and a risk averse retailer possessing the mean-variance optimization objective. Chapter 5 concludes the book and discusses various promising future research directions and extensions. Every chapter can be taken as a self-contained article, and the notation within each chapter is consistently employed.