Supply chain risk analysis with mean-variance models: a technical review

Pioneered by Nobel laureate Harry Markowitz in the 1950s, the mean‐variance (MV) formulation is a fundamental theory for risk management in finance. Over the past decades, there is a growing popularity of applying this ground breaking theory in analyzing stochastic supply chain management problems. Nowadays, there is no doubt that the mean‐variance (MV) theory is a well‐proven approach for conducting risk analysis in stochastic supply chain operational models. In view of the growing importance of MV approach in supply chain management, we review a selection of related papers in the literature that focus on MV analytical models. By classifying the literature into three major areas, namely, single‐echelon problems, multi‐echelon supply chain problems, and supply chain problems with information updating, we derive insights into the current state of knowledge in each area and identify some associated challenges with a discussion of some specific models. We also suggest future research directions on topics such as information asymmetry, supply networks, and boundedly rational agents, etc. In conclusion, this paper provides up‐to‐date information which helps both academicians and practitioners to better understand the development of MV models for supply chain risk analysis.