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EOQ with independent endogenous supply disruptions

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  • Konstantaras, I.
  • Skouri, K.
  • Lagodimos, A.G.

Abstract

We consider an inventory installation, controlled by the periodic review base stock (S, T) policy and facing a fixed-rate deterministic demand which, if unsatisfied, is backordered. The supply process is unreliable, so supply deliveries may fail according to an independent Bernoulli process; we refer to such failures reflecting the supply service quality and being internal to the supply chain, as endogenous disruptions. We seek to jointly determine the two policy variables, so to minimize long-run average cost. While an approximate model for this problem was recently analyzed, we present an exact analysis, valid for two common accounting schemes for inventory cost evaluation: continuous and end-of-cycle costing. After developing a unified (and exact) average cost model for both costing schemes, the cost for each scheme is analyzed. In both cases, the optimal policy variables and cost prevail in closed-form, having an identical structure to those of EOQ (with backorders). In fact, under continuous costing, the optimal solution reduces to EOQ for perfect supply. Analytical properties, demonstrating the impact of deteriorating supply quality on the optimal policy, are established. Moreover, computations reveal the cost impact of deploying heuristics that either ignore supply disruptions or rely on inaccurate costing information.

Suggested Citation

  • Konstantaras, I. & Skouri, K. & Lagodimos, A.G., 2019. "EOQ with independent endogenous supply disruptions," Omega, Elsevier, vol. 83(C), pages 96-106.
  • Handle: RePEc:eee:jomega:v:83:y:2019:i:c:p:96-106
    DOI: 10.1016/j.omega.2018.02.006
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    References listed on IDEAS

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    1. Salameh, M. K. & Jaber, M. Y., 2000. "Economic production quantity model for items with imperfect quality," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 59-64, March.
    2. Woonghee Tim Huh & Mahesh Nagarajan, 2010. "Technical note ---Linear Inflation Rules for the Random Yield Problem: Analysis and Computations," Operations Research, INFORMS, vol. 58(1), pages 244-251, February.
    3. Z. Bahroun & J-P. Campagne & M. Moalla, 2007. "Cyclic production for cyclic deliveries," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 2(1), pages 30-50.
    4. Skouri, K. & Konstantaras, I. & Lagodimos, A.G. & Papachristos, S., 2014. "An EOQ model with backorders and rejection of defective supply batches," International Journal of Production Economics, Elsevier, vol. 155(C), pages 148-154.
    5. Mahmut Parlar & Defne Berkin, 1991. "Future supply uncertainty in EOQ models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 38(1), pages 107-121, February.
    6. Nils Rudi & Harry Groenevelt & Taylor R. Randall, 2009. "End-of-Period vs. Continuous Accounting of Inventory-Related Costs," Operations Research, INFORMS, vol. 57(6), pages 1360-1366, December.
    7. Taleizadeh, Ata Allah & Khanbaglo, Mahboobeh Perak Sari & Cárdenas-Barrón, Leopoldo Eduardo, 2016. "An EOQ inventory model with partial backordering and reparation of imperfect products," International Journal of Production Economics, Elsevier, vol. 182(C), pages 418-434.
    8. Abraham Grosfeld-Nir & Yigal Gerchak, 2004. "Multiple Lotsizing in Production to Order with Random Yields: Review of Recent Advances," Annals of Operations Research, Springer, vol. 126(1), pages 43-69, February.
    9. Emre Berk & Antonio Arreola‐Risa, 1994. "Note on “future supply uncertainty in EOQ models”," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(1), pages 129-132, February.
    10. Kamran Moinzadeh & Steven Nahmias, 2000. "Adjustment Strategies for a Fixed Delivery Contract," Operations Research, INFORMS, vol. 48(3), pages 408-423, June.
    11. Schmitt, Amanda J. & Snyder, Lawrence V. & Shen, Zuo-Jun Max, 2010. "Inventory systems with stochastic demand and supply: Properties and approximations," European Journal of Operational Research, Elsevier, vol. 206(2), pages 313-328, October.
    12. Hossein Salehi & Ata Allah Taleizadeh & Reza Tavakkoli-Moghaddam, 2016. "An EOQ model with random disruption and partial backordering," International Journal of Production Research, Taylor & Francis Journals, vol. 54(9), pages 2600-2609, May.
    13. Uday S. Rao, 2003. "Properties of the Periodic Review (R, T) Inventory Control Policy for Stationary, Stochastic Demand," Manufacturing & Service Operations Management, INFORMS, vol. 5(1), pages 37-53, February.
    14. Mordechai Henig & Yigal Gerchak, 1990. "The Structure of Periodic Review Policies in the Presence of Random Yield," Operations Research, INFORMS, vol. 38(4), pages 634-643, August.
    15. 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.
    16. Mohebbi, Esmail, 2004. "A replenishment model for the supply-uncertainty problem," International Journal of Production Economics, Elsevier, vol. 87(1), pages 25-37, January.
    17. Srinivas Bollapragada & Thomas E. Morton, 1999. "Myopic Heuristics for the Random Yield Problem," Operations Research, INFORMS, vol. 47(5), pages 713-722, October.
    18. Hauck, Zsuzsanna & Vörös, József, 2015. "Lot sizing in case of defective items with investments to increase the speed of quality control," Omega, Elsevier, vol. 52(C), pages 180-189.
    19. Alamri, Adel A. & Harris, Irina & Syntetos, Aris A., 2016. "Efficient inventory control for imperfect quality items," European Journal of Operational Research, Elsevier, vol. 254(1), pages 92-104.
    20. Lawrence V. Snyder & Zümbül Atan & Peng Peng & Ying Rong & Amanda J. Schmitt & Burcu Sinsoysal, 2016. "OR/MS models for supply chain disruptions: a review," IISE Transactions, Taylor & Francis Journals, vol. 48(2), pages 89-109, February.
    21. Yu-Sheng Zheng, 1992. "On Properties of Stochastic Inventory Systems," Management Science, INFORMS, vol. 38(1), pages 87-103, January.
    22. Snyder, Lawrence V., 2014. "A tight approximation for an EOQ model with supply disruptions," International Journal of Production Economics, Elsevier, vol. 155(C), pages 91-108.
    23. Avinadav, Tal & Henig, Mordecai I., 2015. "Exact accounting of inventory costs in stochastic periodic-review models," International Journal of Production Economics, Elsevier, vol. 169(C), pages 89-98.
    24. Khan, M. & Jaber, M.Y. & Guiffrida, A.L. & Zolfaghari, S., 2011. "A review of the extensions of a modified EOQ model for imperfect quality items," International Journal of Production Economics, Elsevier, vol. 132(1), pages 1-12, July.
    25. Lagodimos, A.G. & Skouri, K. & Christou, I.T. & Chountalas, P.T., 2018. "The discrete-time EOQ model: Solution and implications," European Journal of Operational Research, Elsevier, vol. 266(1), pages 112-121.
    26. Schmitt, Amanda J. & Sun, Siyuan Anthony & Snyder, Lawrence V. & Shen, Zuo-Jun Max, 2015. "Centralization versus decentralization: Risk pooling, risk diversification, and supply chain disruptions," Omega, Elsevier, vol. 52(C), pages 201-212.
    27. Schmitt, Thomas G. & Kumar, Sanjay & Stecke, Kathryn E. & Glover, Fred W. & Ehlen, Mark A., 2017. "Mitigating disruptions in a multi-echelon supply chain using adaptive ordering," Omega, Elsevier, vol. 68(C), pages 185-198.
    28. Lagodimos, A.G. & Christou, I.T. & Skouri, K., 2012. "Computing globally optimal (s,S,T) inventory policies," Omega, Elsevier, vol. 40(5), pages 660-671.
    29. Sven Axsäter, 1996. "Using the Deterministic EOQ Formula in Stochastic Inventory Control," Management Science, INFORMS, vol. 42(6), pages 830-834, June.
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    Keywords

    Single-echelon; EOQD; Stochastic; Uncertainty; Newsvendor;

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