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Managing Inventory with the Prospect of Obsolescence

Author

Listed:
  • Jing-Sheng Song

    (University of California, Irvine, California)

  • Paul H. Zipkin

    (Duke University, Durham, North Carolina)

Abstract

How should inventory management respond when there is a possibility of imminent obsolescence (or, more generally, deteriorating demand)? We use an inventory-control model to address this question. The model incorporates a Markovian submodel to describe the uncertain events leading to obsolescence. These events and their uncertainties come in a variety of patterns. We devote considerable attention to specifying the submodel, and we compare a few alternatives numerically. Also, we compare optimal policies to simpler alternatives, and we investigate the response of the model to parameter changes. Generally, we find that obsolescence does (or should) have a substantial impact in the way inventories are managed. The nature of these effects, moreover, is fairly intricate. It appears that obsolescence cannot be captured in a simpler model through parameter adjustments. These conclusions presume that we cannot dispose of excess stock, either directly or through price promotions; we show also that the disposal option can make the problems of obsolescence more manageable.

Suggested Citation

  • Jing-Sheng Song & Paul H. Zipkin, 1996. "Managing Inventory with the Prospect of Obsolescence," Operations Research, INFORMS, vol. 44(1), pages 215-222, February.
    • Handle: RePEc:inm:oropre:v:44:y:1996:i:1:p:215-222
    DOI: 10.1287/opre.44.1.215
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    Citations

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    Cited by:

    1. Opher Baron & Oded Berman & David Perry, 2010. "Continuous review inventory models for perishable items ordered in batches," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 72(2), pages 217-247, October.
    2. Jochen Schlapp & Moritz Fleischmann & Danja Sonntag, 2022. "Inventory timing: How to serve a stochastic season," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2891-2906, July.
    3. James T. Treharne & Charles R. Sox, 2002. "Adaptive Inventory Control for Nonstationary Demand and Partial Information," Management Science, INFORMS, vol. 48(5), pages 607-624, May.
    4. Pinçe, Çerag & Dekker, Rommert, 2011. "An inventory model for slow moving items subject to obsolescence," European Journal of Operational Research, Elsevier, vol. 213(1), pages 83-95, August.
    5. Pinçe, C. & Dekker, R., 2010. "A Continuous Review Inventory Model with Advance Policy Change and Obsolescence," Econometric Institute Research Papers EI 2009-45, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. van Jaarsveld, Willem & Dekker, Rommert, 2011. "Estimating obsolescence risk from demand data to enhance inventory control--A case study," International Journal of Production Economics, Elsevier, vol. 133(1), pages 423-431, September.
    7. Chad R. Larson & Danko Turcic & Fuqiang Zhang, 2015. "An Empirical Investigation of Dynamic Ordering Policies," Management Science, INFORMS, vol. 61(9), pages 2118-2138, September.
    8. Barron, Yonit, 2016. "Clearing control policies for MAP inventory process with lost sales," European Journal of Operational Research, Elsevier, vol. 251(2), pages 495-508.
    9. Alamri, Adel A. & Syntetos, Aris A., 2018. "Beyond LIFO and FIFO: Exploring an Allocation-In-Fraction-Out (AIFO) policy in a two-warehouse inventory model," International Journal of Production Economics, Elsevier, vol. 206(C), pages 33-45.
    10. Yossi Aviv, 2003. "A Time-Series Framework for Supply-Chain Inventory Management," Operations Research, INFORMS, vol. 51(2), pages 210-227, April.
    11. Li Chen & Jing-Sheng Song & Yue Zhang, 2017. "Serial Inventory Systems with Markov-Modulated Demand: Derivative Bounds, Asymptotic Analysis, and Insights," Operations Research, INFORMS, vol. 65(5), pages 1231-1249, October.
    12. Gen Sakoda & Hideki Takayasu & Misako Takayasu, 2019. "Data Science Solutions for Retail Strategy to Reduce Waste Keeping High Profit," Sustainability, MDPI, vol. 11(13), pages 1-30, June.
    13. Song, Yuyue & Lau, Hoong Chuin, 2004. "A periodic-review inventory model with application to the continuous-review obsolescence problem," European Journal of Operational Research, Elsevier, vol. 159(1), pages 110-120, November.
    14. Cattani, Kyle D. & Souza, Gilvan C., 2003. "Good buy? Delaying end-of-life purchases," European Journal of Operational Research, Elsevier, vol. 146(1), pages 216-228, April.
    15. Xiangwen Lu & Jing-Sheng Song & Amelia Regan, 2006. "Inventory Planning with Forecast Updates: Approximate Solutions and Cost Error Bounds," Operations Research, INFORMS, vol. 54(6), pages 1079-1097, December.
    16. Susan H. Xu & Zhaolin Li, 2007. "Managing a Single-Product Assemble-to-Order System with Technology Innovations," Management Science, INFORMS, vol. 53(9), pages 1467-1485, September.
    17. Awi Federgruen & Min Wang, 2013. "Monotonicity properties of a class of stochastic inventory systems," Annals of Operations Research, Springer, vol. 208(1), pages 155-186, September.
    18. Fleischhacker, Adam J. & Zhao, Yao, 2011. "Planning for demand failure: A dynamic lot size model for clinical trial supply chains," European Journal of Operational Research, Elsevier, vol. 211(3), pages 496-506, June.

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