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.
