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Selection of a dynamic supply portfolio under delay and disruption risks


  • Tadeusz Sawik


The problem of a multi-period supplier selection and order quantity allocation in the presence of supply chain disruption and delay risks is considered. Given a set of customer orders for finished products, the decision-maker needs to decide from which supplier and when to deliver product-specific parts required for each customer order to meet customer requested due date at a low cost or a high service level and to mitigate the impact of supply chain risks. For the selection of risk-neutral or risk-averse dynamic supply portfolio, a scenario-based stochastic mixed integer programming approach is developed. In the scenario analysis, the low probability and high impact supply disruptions are combined with the high probability and low impact supply delays. The risk-neutral portfolio is optimised by minimising expected cost or maximising expected service level. The risk-averse portfolio is optimised by calculating cost- or service-at-risk and minimising conditional cost-at risk or maximising conditional service at risk. The proposed dynamic portfolio approach leads to a time-indexed stochastic MIP formulation with a strong LP relaxation, which has proven to be computationally very efficient. The findings indicate that neglecting potential delay risks in supplier selection may lead to greater supply fluctuations and manufacturing delays.

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  • Tadeusz Sawik, 2018. "Selection of a dynamic supply portfolio under delay and disruption risks," 2018 Papers psa1077, Job Market Papers.
  • Handle: RePEc:jmp:jm2018:psa1077

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    References listed on IDEAS

    1. Park, YoungWon & Hong, Paul & Roh, James Jungbae, 2013. "Supply chain lessons from the catastrophic natural disaster in Japan," Business Horizons, Elsevier, vol. 56(1), pages 75-85.
    2. W.C. Tsai, 2016. "A dynamic sourcing strategy considering supply disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 54(7), pages 2170-2184, April.
    3. Sawik, Tadeusz, 2010. "Single vs. multiple objective supplier selection in a make to order environment," Omega, Elsevier, vol. 38(3-4), pages 203-212, June.
    4. Faiza Hamdi & Ahmed Ghorbel & Faouzi Masmoudi & Lionel Dupont, 2018. "Optimization of a supply portfolio in the context of supply chain risk management: literature review," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 763-788, April.
    5. Tadeusz Sawik, 2017. "A portfolio approach to supply chain disruption management," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1970-1991, April.
    6. Tadeusz Sawik, 2016. "On the risk-averse optimization of service level in a supply chain under disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 98-113, January.
    7. Sawik, Tadeusz, 2011. "Selection of supply portfolio under disruption risks," Omega, Elsevier, vol. 39(2), pages 194-208, April.
    8. Hammami, Ramzi & Temponi, Cecilia & Frein, Yannick, 2014. "A scenario-based stochastic model for supplier selection in global context with multiple buyers, currency fluctuation uncertainties, and price discounts," European Journal of Operational Research, Elsevier, vol. 233(1), pages 159-170.
    9. Sawik, Tadeusz, 2016. "Integrated supply, production and distribution scheduling under disruption risks," Omega, Elsevier, vol. 62(C), pages 131-144.
    10. Mahmut Parlar & David Perry, 1996. "Inventory models of future supply uncertainty with single and multiple suppliers," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(2), pages 191-210, March.
    11. Fattahi, Mohammad & Govindan, Kannan & Keyvanshokooh, Esmaeil, 2017. "Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 176-200.
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    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • O2 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy

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