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The Value of Supply Chain Disruption Duration Information

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  • Mili Mehrotra
  • William Schmidt

Abstract

Companies that experience a disruption in their supply chain often face a difficult decision—either accept the information that they have regarding the duration of the disruption, or invest in collecting better information. This choice is not clear since better information may not be attainable, and if it is attainable, it may not improve operational decision‐making. In light of this dilemma, we collaborate with a multinational division of a Fortune 500 manufacturing firm to develop stochastic linear programming models that quantify the value of disruption duration information. Our models allow us to examine characteristics of the disrupted part that may be associated with the value of better information. We focus on characteristics that are knowable at the outset of the disruption, as those can help the firm decide whether to invest in collecting better information. Using our research partner's supply chain and production data, we find that the value of information can vary materially—from

Suggested Citation

  • Mili Mehrotra & William Schmidt, 2021. "The Value of Supply Chain Disruption Duration Information," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3015-3035, September.
    • Handle: RePEc:bla:popmgt:v:30:y:2021:i:9:p:3015-3035
    DOI: 10.1111/poms.13415
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    References listed on IDEAS

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    2. Li, Zhong-Ping & Wang, Jian-Jun & Perera, Sandun & Shi, Jim (Junmin), 2022. "Coordination of a supply chain with Nash bargaining fairness concerns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    3. Rebecca Stekelorum & Shivam Gupta & Issam Laguir & Sameer Kumar & Subodha Kumar, 2022. "Pouring cement down one of your oil wells: Relationship between the supply chain disruption orientation and performance," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2084-2106, May.
    4. Pervaiz Akhtar & Arsalan Mujahid Ghouri & Haseeb Ur Rehman Khan & Mirza Amin ul Haq & Usama Awan & Nadia Zahoor & Zaheer Khan & Aniqa Ashraf, 2023. "Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions," Annals of Operations Research, Springer, vol. 327(2), pages 633-657, August.

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