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How big should my store be? On the interplay between shelf-space, demand learning and assortment decisions


  • Kevin Glazebrook

    (Department of Management Science, Lancaster University Management School)

  • Joern Meissner

    (Department of Logistics, Kuehne Logistics University)

  • Jochen Schurr

    (Department of Management Science, Lancaster University Management School)


A fundamental decision every merchant has to make is on is how large his stores should be. This is particularly true in light of the drastic changes retail concepts have seen in the last decade. There has been a noticeable tendency, particularly for food and convenience retailers, to open more and smaller stores. Also, there has been a well-documented recent shift in paradigm in apparel retailing with the so called fast-fashion business model. Short lead times have resulted in flexibility that allows retailers to adjust the assortment of products offered on sale at their stores quickly enough to adapt to popular fashion trends. Based on revised estimates of the merchandise's popularity, they then weed out unpopular items and re-stock demonstrably popular ones on a week-by-week basis. However, despite the obvious similarity of reliance on better demand learning, fashion-fashion retailers like Zara have opted to do exactly the opposite as groceries and opened sizable stores in premium locations. This paradox has not been explained in the literature so far. In this paper, we aim to calculate the profit of a retailer in such a complicated environment with demand learning and frequent assortment decisions in particular in dependence of the most valuable resource of a retailer: shelf-space. To be able to achieve this, we extend the recent approaches in the management literature to handle the sequential resource allocation problems that arises in this context with a concurrent need for learning. We investigate the use of multi-armed bandits to model the assortment decisions under demand learning, whereby this aspect is captured by a Bayesian Gamma-Poisson model. Our model enables us to characterize the marginal value of shelf-space and to calculate the optimal store size under learning and assortment decisions. An extensive numerical study confirms that the store size choices observed in real life can be explained by the varying length of selling seasons different retailers face.

Suggested Citation

  • 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.
  • Handle: RePEc:lms:mansci:mrg-0021

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    References listed on IDEAS

    1. Yang, Ming-Hsien & Chen, Wen-Cher, 1999. "A study on shelf space allocation and management," International Journal of Production Economics, Elsevier, vol. 60(1), pages 309-317, April.
    2. Hariga, Moncer A. & Al-Ahmari, Abdulrahman & Mohamed, Abdel-Rahman A., 2007. "A joint optimisation model for inventory replenishment, product assortment, shelf space and display area allocation decisions," European Journal of Operational Research, Elsevier, vol. 181(1), pages 239-251, August.
    3. Felipe Caro & Jérémie Gallien, 2007. "Dynamic Assortment with Demand Learning for Seasonal Consumer Goods," Management Science, INFORMS, vol. 53(2), pages 276-292, February.
    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.
    5. Yücel, Eda & Karaesmen, Fikri & Salman, F. Sibel & Türkay, Metin, 2009. "Optimizing product assortment under customer-driven demand substitution," European Journal of Operational Research, Elsevier, vol. 199(3), pages 759-768, December.
    6. Marcel Corstjens & Peter Doyle, 1981. "A Model for Optimizing Retail Space Allocations," Management Science, INFORMS, vol. 27(7), pages 822-833, July.
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    More about this item


    retailing; assortment planning; multi-armed bandit; store size;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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