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Ordering quantity decisions considering uncertainty in supply-chain logistics operations

Author

Listed:
  • Kim, Hyoungtae
  • Lu, Jye-Chyi
  • Kvam, Paul H.
  • Tsao, Yu-Chung

Abstract

This research seeks to determine the optimal order amount for a retailer given uncertainty in a supply-chain's logistics network due to unforeseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching distribution centers (DCs) and retail stores poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies; this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of a DC's operation policies and model assumptions on a retailer's product-ordering plan and resulting sales profit.

Suggested Citation

  • Kim, Hyoungtae & Lu, Jye-Chyi & Kvam, Paul H. & Tsao, Yu-Chung, 2011. "Ordering quantity decisions considering uncertainty in supply-chain logistics operations," International Journal of Production Economics, Elsevier, vol. 134(1), pages 16-27, November.
    • Handle: RePEc:eee:proeco:v:134:y:2011:i:1:p:16-27
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    References listed on IDEAS

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    Cited by:

    1. Avi Herbon & Konstantin Kogan, 2014. "Time-dependent and independent control rules for coordinated production and pricing under demand uncertainty and finite planning horizons," Annals of Operations Research, Springer, vol. 223(1), pages 195-216, December.
    2. Baruah, Pundarikaksha & Chinnam, Ratna Babu & Korostelev, Alexander & Dalkiran, Evrim, 2016. "Optimal soft-order revisions under demand and supply uncertainty and upstream information," International Journal of Production Economics, Elsevier, vol. 182(C), pages 14-25.
    3. Warsing, Donald P. & Wangwatcharakul, Worawut & King, Russell E., 2019. "Computing base-stock levels for a two-stage supply chain with uncertain supply," Omega, Elsevier, vol. 89(C), pages 92-109.
    4. Gregory A. DeCroix, 2013. "Inventory Management for an Assembly System Subject to Supply Disruptions," Management Science, INFORMS, vol. 59(9), pages 2079-2092, September.

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