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

Chun-Hung Chiu and Tsan-Ming Choi ()
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Chun-Hung Chiu: Sun Yat-sen University
Tsan-Ming Choi: The Hong Kong Polytechnic University

Annals of Operations Research, 2016, vol. 240, issue 2, No 6, 489-507

Abstract: Abstract 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.

Keywords: Mean-variance analysis; Supply chain management; Review (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (86)

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DOI: 10.1007/s10479-013-1386-4

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