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Statistical Learning of Service-Dependent Demand in a Multiperiod Newsvendor Setting

Author

Listed:
  • Tianhu Deng

    () (Department of Industrial Engineering, Tsinghua University, Beijing, China 100084)

  • Zuo-Jun Max Shen

    () (Department of Industrial Engineering and Operations Research and Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720; and Department of Industrial Engineering, Tsinghua University, Beijing, China 100084)

  • J. George Shanthikumar

    () (Krannert School of Business, Purdue University, West Lafayette, Indiana 47907)

Abstract

We study an inventory system wherein a customer may leave the seller's market after experiencing an inventory stockout. Traditionally, researchers and practitioners assume a single penalty cost to model this customer behavior of stockout aversion. Recently, a stream of researchers explicitly model this customer behavior and support the traditional penalty cost approach. We enrich this literature by studying the statistical learning of service-dependent demand.We build and solve four models: a baseline model, where the seller can observe the demand distribution; a second model, where the seller cannot observe the demand distribution but statistically learns the demand distribution; a third model, where the seller can learn or pay to obtain the exact information of the demand distribution; and a fourth model, where demand in excess of available inventory is lost and unobserved. Interestingly, we find that all four models support the traditional penalty cost approach. This result confirms the use of a state-independent stockout penalty cost in the presence of demand learning. More strikingly, the first three models imply the same stockout penalty cost, which is larger than the stockout penalty cost implied by the last model.

Suggested Citation

  • Tianhu Deng & Zuo-Jun Max Shen & J. George Shanthikumar, 2014. "Statistical Learning of Service-Dependent Demand in a Multiperiod Newsvendor Setting," Operations Research, INFORMS, vol. 62(5), pages 1064-1076, October.
  • Handle: RePEc:inm:oropre:v:62:y:2014:i:5:p:1064-1076
    DOI: 10.1287/opre.2014.1303
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    File URL: http://dx.doi.org/10.1287/opre.2014.1303
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    References listed on IDEAS

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    1. Vishal Gaur & Young-Hoon Park, 2007. "Asymmetric Consumer Learning and Inventory Competition," Management Science, INFORMS, vol. 53(2), pages 227-240, February.
    2. Turgay Ayer & Oguzhan Alagoz & Natasha K. Stout, 2012. "OR Forum---A POMDP Approach to Personalize Mammography Screening Decisions," Operations Research, INFORMS, vol. 60(5), pages 1019-1034, October.
    3. Mahesh Nagarajan & S. Rajagopalan, 2008. "Inventory Models for Substitutable Products: Optimal Policies and Heuristics," Management Science, INFORMS, vol. 54(8), pages 1453-1466, August.
    4. Ernst, Ricardo & Powell, Stephen G., 1995. "Optimal inventory policies under service-sensitive demand," European Journal of Operational Research, Elsevier, vol. 87(2), pages 316-327, December.
    5. Haim Mendelson & Seungjin Whang, 1990. "Optimal Incentive-Compatible Priority Pricing for the M/M/1 Queue," Operations Research, INFORMS, vol. 38(5), pages 870-883, October.
    6. Hao Zhang, 2010. "Partially Observable Markov Decision Processes: A Geometric Technique and Analysis," Operations Research, INFORMS, vol. 58(1), pages 214-228, February.
    7. Eric T. Anderson & Gavan J. Fitzsimons & Duncan Simester, 2006. "Measuring and Mitigating the Costs of Stockouts," Management Science, INFORMS, vol. 52(11), pages 1751-1763, November.
    8. Xiangwen Lu & Jing-Sheng Song & Kaijie Zhu, 2008. "Analysis of Perishable-Inventory Systems with Censored Demand Data," Operations Research, INFORMS, vol. 56(4), pages 1034-1038, August.
    9. Justin Azadivar & Max Shen & George Shanthikumar, 2010. "Dynamic Inventory Control with Satisfaction-Dependent Demand," Purdue University Economics Working Papers 1249, Purdue University, Department of Economics.
    10. Martin A. Lariviere & Evan L. Porteus, 1999. "Stalking Information: Bayesian Inventory Management with Unobserved Lost Sales," Management Science, INFORMS, vol. 45(3), pages 346-363, March.
    11. Liming Liu & Weixin Shang & Shaohua Wu, 2007. "Dynamic Competitive Newsvendors with Service-Sensitive Demands," Manufacturing & Service Operations Management, INFORMS, vol. 9(1), pages 84-93, June.
    12. Lode Li, 1992. "The Role of Inventory in Delivery-Time Competition," Management Science, INFORMS, vol. 38(2), pages 182-197, February.
    13. 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.
    14. Saif Benjaafar & Ehsan Elahi & Karen L. Donohue, 2007. "Outsourcing via Service Competition," Management Science, INFORMS, vol. 53(2), pages 241-259, February.
    15. Alain Bensoussan & Metin Çakanyıldırım & Suresh P. Sethi, 2007. "A Multiperiod Newsvendor Problem with Partially Observed Demand," Mathematics of Operations Research, INFORMS, vol. 32(2), pages 322-344, May.
    16. Arnab Bisi & Maqbool Dada & Surya Tokdar, 2011. "A Censored-Data Multiperiod Inventory Problem with Newsvendor Demand Distributions," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 525-533, October.
    17. George E. Monahan, 1982. "State of the Art---A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms," Management Science, INFORMS, vol. 28(1), pages 1-16, January.
    18. Li Chen, 2010. "Bounds and Heuristics for Optimal Bayesian Inventory Control with Unobserved Lost Sales," Operations Research, INFORMS, vol. 58(2), pages 396-413, April.
    19. B. L. Schwartz, 1970. "Optimal Inventory Policies in Perturbed Demand Models," Management Science, INFORMS, vol. 16(8), pages 509-518, April.
    20. Tava Lennon Olsen & Rodney P. Parker, 2008. "Inventory Management Under Market Size Dynamics," Management Science, INFORMS, vol. 54(10), pages 1805-1821, October.
    21. James D. Dana, Jr. & Nicholas C. Petruzzi, 2001. "Note: The Newsvendor Model with Endogenous Demand," Management Science, INFORMS, vol. 47(11), pages 1488-1497, November.
    22. Noah Gans, 2002. "Customer Loyalty and Supplier Quality Competition," Management Science, INFORMS, vol. 48(2), pages 207-221, February.
    23. Benjamin L. Schwartz, 1966. "A New Approach to Stockout Penalties," Management Science, INFORMS, vol. 12(12), pages 538-544, August.
    24. Nicole DeHoratius & Adam J. Mersereau & Linus Schrage, 2008. "Retail Inventory Management When Records Are Inaccurate," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 257-277, November.
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    1. repec:eee:ejores:v:267:y:2018:i:2:p:612-627 is not listed on IDEAS
    2. Alain Bensoussan & Pengfei Guo, 2015. "Technical Note—Managing Nonperishable Inventories with Learning About Demand Arrival Rate Through Stockout Times," Operations Research, INFORMS, vol. 63(3), pages 602-609, June.

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