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Dynamic Assortment with Demand Learning for Seasonal Consumer Goods

Author

Listed:
  • Felipe Caro

    () (Anderson School of Management, University of California, Los Angeles, California 90095)

  • Jérémie Gallien

    () (Sloan School of Management, Massachusetts Institute of Technology, 30 Wadsworth Street, Cambridge, Massachusetts 02142)

Abstract

Companies such as Zara and World Co. have recently implemented novel product development processes and supply chain architectures enabling them to make more product design and assortment decisions during the selling season, when actual demand information becomes available. How should such retail firms modify their product assortment over time in order to maximize overall profits for a given selling season? Focusing on a stylized version of this problem, we study a finite horizon multiarmed bandit model with several plays per stage and Bayesian learning. Our analysis involves the Lagrangian relaxation of weakly coupled dynamic programs (DPs), results contributing to the emerging theory of DP duality, and various approximations. It yields a closed-form dynamic index policy capturing the key exploration versus exploitation trade-off and associated suboptimality bounds. In numerical experiments its performance proves comparable to that of other closed-form heuristics described in the literature, but this policy is particularly easy to implement and interpret. This last feature enables extensions to more realistic versions of the motivating dynamic assortment problem that include implementation delays, switching costs, and demand substitution effects.

Suggested Citation

  • Felipe Caro & Jérémie Gallien, 2007. "Dynamic Assortment with Demand Learning for Seasonal Consumer Goods," Management Science, INFORMS, vol. 53(2), pages 276-292, February.
  • Handle: RePEc:inm:ormnsc:v:53:y:2007:i:2:p:276-292
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    File URL: http://dx.doi.org/10.1287/mnsc.1060.0613
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    References listed on IDEAS

    as
    1. Uday S. Karmarkar, 1987. "The Multilocation Multiperiod Inventory Problem: Bounds and Approximations," Management Science, INFORMS, vol. 33(1), pages 86-94, January.
    2. Garrett van Ryzin & Siddharth Mahajan, 1999. "On the Relationship Between Inventory Costs and Variety Benefits in Retail Assortments," Management Science, INFORMS, vol. 45(11), pages 1496-1509, November.
    3. Alain Bultez & Philippe Naert, 1988. "SH.A.R.P.: Shelf Allocation for Retailers' Profit," Marketing Science, INFORMS, vol. 7(3), pages 211-231.
    4. Brezzi, Monica & Lai, Tze Leung, 2002. "Optimal learning and experimentation in bandit problems," Journal of Economic Dynamics and Control, Elsevier, vol. 27(1), pages 87-108, November.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Tang, Christopher S., 2010. "A review of marketing-operations interface models: From co-existence to coordination and collaboration," International Journal of Production Economics, Elsevier, vol. 125(1), pages 22-40, May.
    2. Stephen Chick & Martin Forster & Paolo Pertile, 2017. "A Bayesian decision theoretic model of sequential experimentation with delayed response," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1439-1462, November.
    3. repec:eee:ejores:v:263:y:2017:i:3:p:768-781 is not listed on IDEAS
    4. Ahuja, Vishal & Birge, John R., 2016. "Response-adaptive designs for clinical trials: Simultaneous learning from multiple patients," European Journal of Operational Research, Elsevier, vol. 248(2), pages 619-633.
    5. Ankur Goel & Genaro J. Gutierrez, 2011. "Multiechelon Procurement and Distribution Policies for Traded Commodities," Management Science, INFORMS, vol. 57(12), pages 2228-2244, December.
    6. Kevin Glazebrook & Joern Meissner & Jochen Schurr, 2012. "How big should my store be? On the interplay between shelf-space, demand learning and assortment decisions," Working Papers MRG/0021, Department of Management Science, Lancaster University, revised Dec 2012.
    7. repec:spr:annopr:v:257:y:2017:i:1:d:10.1007_s10479-016-2204-6 is not listed on IDEAS
    8. Niño Mora, José & Jacko, Peter, 2009. "An index for dynamic product promotion and the knapsack problem for perishable items," DES - Working Papers. Statistics and Econometrics. WS ws093111, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. repec:spr:annopr:v:241:y:2016:i:1:d:10.1007_s10479-013-1312-9 is not listed on IDEAS
    10. Felipe Caro & Victor Martínez-de-Albéniz, 2012. "Product and Price Competition with Satiation Effects," Management Science, INFORMS, vol. 58(7), pages 1357-1373, July.
    11. Talebian, Masoud & Boland, Natashia & Savelsbergh, Martin, 2014. "Pricing to accelerate demand learning in dynamic assortment planning for perishable products," European Journal of Operational Research, Elsevier, vol. 237(2), pages 555-565.
    12. repec:eee:jouret:v:85:y:2009:i:1:p:71-83 is not listed on IDEAS
    13. Felipe Caro & Victor Martínez-de-Albéniz, 2010. "The Impact of Quick Response in Inventory-Based Competition," Manufacturing & Service Operations Management, INFORMS, vol. 12(3), pages 409-429, January.

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