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Optimal ordering policies in a multi-sourcing supply chain with supply and demand disruptions-a CVaR approach

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  • Syed Mithun Ali
  • Koichi Nakade

Abstract

In this study, we propose a conditional value at risk (CVaR) model for supply chain disruptions planning of a multi-agent, multi-product supply chain subject to supply and demand disruptions. Our focus is on building and comparing ordering policies under CVaR and expected cost criteria. The proposed formulation is illustrated through some numerical instances. The results present that the CVaR model shows a considerable difference in response policies compared to the expected cost model. Ordering quantities in response to supply and demand disruption are lower in the CVaR model than the expected cost model. In many instances, it is also seen that ordering quantities in response to disruptions tend to become lower when a decision maker becomes more risk-averse. It is expected that the proposed CVaR model would outperform to optimise the supply chain of an organisation, in particular, for the purpose of reducing the risk of high cost.

Suggested Citation

  • Syed Mithun Ali & Koichi Nakade, 2017. "Optimal ordering policies in a multi-sourcing supply chain with supply and demand disruptions-a CVaR approach," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 28(2), pages 180-199.
    • Handle: RePEc:ids:ijlsma:v:28:y:2017:i:2:p:180-199
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    Citations

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    Cited by:

    1. Manu Sharma & Sudhanshu Joshi & Sunil Luthra & Anil Kumar, 2022. "Managing disruptions and risks amidst COVID-19 outbreaks: role of blockchain technology in developing resilient food supply chains," Operations Management Research, Springer, vol. 15(1), pages 268-281, June.
    2. Abhijit Barman & Rubi Das & Pijus Kanti De, 2022. "Logistics and supply chain management of food industry during COVID-19: disruptions and a recovery plan," Environment Systems and Decisions, Springer, vol. 42(3), pages 338-349, September.
    3. Peyman Zandi & Mohammad Rahmani & Mojtaba Khanian & Amir Mosavi, 2020. "Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)," Agriculture, MDPI, vol. 10(11), pages 1-27, October.
    4. Syed Mithun Ali & Asraf Arafin & Md. Abdul Moktadir & Towfique Rahman & Nuzhat Zahan, 2018. "Barriers to Reverse Logistics in the Computer Supply Chain Using Interpretive Structural Model," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(1), pages 53-68, March.

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