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Shelf Space Allocation for Specific Products on Shelves Selected in Advance

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  • Kateryna Czerniachowska
  • Marcin Hernes

Abstract

Purpose: The aim of the research is to develop the shelf space allocation model for specific products on shelves selected in advance, based on the practical retail requirements. Retailers and manufacturers may impose particular conditions for the appearance of products on the shelf based on its package type, brand, price, form and size. Shelf space allocation must be in line with the store positioning strategy of the retailer. Design/Methodology/Approach: This paper proposed dynamic programming to solve the profit maximization problem on small problem sizes considering extra allocation parameters such as capping and nesting. The distribution of shelf space to products has a direct effect on the competitiveness of the retail store. Findings: The paper presented major profit differences between shelf space allocation without capping and nesting parameters and including them. The computational experiments were performed to test the additional gains received with the usage of capping and nesting parameters. Practical Implications: This research provided qualitative insights for the retailers by comparing the profit gained with and without the capping and nesting allocation possibilities proposed in the model. Originality/Value: In this paper the basic shelf space allocation problem was simplified with selection of the shelf to place the products in advance, next the basic shelf space allocation model was extended with capping and nesting on-shelf allocation methods.

Suggested Citation

  • Kateryna Czerniachowska & Marcin Hernes, 2021. "Shelf Space Allocation for Specific Products on Shelves Selected in Advance," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 316-334.
    • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:3:p:316-334
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    References listed on IDEAS

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    1. Yang, Ming-Hsien, 2001. "An efficient algorithm to allocate shelf space," European Journal of Operational Research, Elsevier, vol. 131(1), pages 107-118, May.
    2. Hansen, Jared M. & Raut, Sumit & Swami, Sanjeev, 2010. "Retail Shelf Allocation: A Comparative Analysis of Heuristic and Meta-Heuristic Approaches," Journal of Retailing, Elsevier, vol. 86(1), pages 94-105.
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    4. Wongkitrungrueng, Apiradee & Valenzuela, Ana & Sen, Sankar, 2018. "The Cake Looks Yummy on the Shelf up There: The Interactive Effect of Retail Shelf Position and Consumers’ Personal Sense of Power on Indulgent Choice," Journal of Retailing, Elsevier, vol. 94(3), pages 280-295.
    5. Pisinger, David, 1995. "A minimal algorithm for the multiple-choice knapsack problem," European Journal of Operational Research, Elsevier, vol. 83(2), pages 394-410, June.
    6. Raymond R. Burke, 2006. "The Third Wave of Marketing Intelligence," Springer Books, in: Manfred Krafft & Murali K. Mantrala (ed.), Retailing in the 21st Century, pages 113-125, Springer.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Retailing; decision making/process; merchandising; shelf levels; product package;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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