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

Chun Hung Chiu, Tsan Ming Choi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

233 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)489-507
Number of pages19
JournalAnnals of Operations Research
Issue number2
Publication statusPublished - 1 May 2016


  • Mean-variance analysis
  • Review
  • Supply chain management

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research


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