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

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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.

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Notes

  1. This approach is still employed today, see e.g., Chen and Yano (2010), Tuncel and Alpan (2010), Dong and Tomlin (2012), and Gumus et al. (2012).

  2. For the newsvendor problem in the presence of the loss of goodwill stockout opportunity cost, Choi et al. (2008b) and Wu et al. (2009) both provide some more analytical results. In particular, Wu et al. (2009) analytically prove that when demand is distributed following a continuous power distribution, the optimal stocking quantity under an MV newsvendor model will exceed the risk-neutral case’s optimal “fractile” quantity.

  3. Unless otherwise specified, for brevity and consistency, the same notation in this paper will carry the same meaning across models reviewed in the literature.

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Authors and Affiliations

  1. Sun Yat-sen Business School, Sun Yat-sen University, No. 135, West Xingang Road, Guangzhou, 510275, China

    Chun-Hung Chiu

  2. Business Division, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Tsan-Ming Choi

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  1. Chun-Hung Chiu
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Correspondence to Tsan-Ming Choi.

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This research is partially supported by Research Grants Council of Hong Kong under the grant number PolyU5420-10H, and 985 project of Sun Yat-sen University.

We sincerely thank the guest editor and the two anonymous reviewers for their constructive and helpful comments.

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Chiu, CH., Choi, TM. Supply chain risk analysis with mean-variance models: a technical review. Ann Oper Res 240, 489–507 (2016). https://doi.org/10.1007/s10479-013-1386-4

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