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When does cross-space elasticity matter in shelf-space planning? A decision analytics approach


  • Schaal, Kai
  • Hübner, Alexander


Continuous product proliferation and scare shelf space require a thorough understanding of customer demand effects when planning product allocation to retail shelves. In this context, cross-space demand effects describe the impact of a change in the space assigned to one item, on the demand of other items. This effect is complex and costly to measure and it is complicated to integrate into decision modeling and solution approaches. The tremendous amount of possible product interlinks results in both a large number of possible combinations to be tested, and non-linear models. Nevertheless, there is a growing body of decision models that integrate cross-space effects. However, current research has not investigated whether cross-space elasticities have any impact at all on optimal shelf decisions. It is therefore unclear whether future research on the empirical measurement and the development of optimization models is economically meaningful and justified.

Suggested Citation

  • Schaal, Kai & Hübner, Alexander, 2018. "When does cross-space elasticity matter in shelf-space planning? A decision analytics approach," Omega, Elsevier, vol. 80(C), pages 135-152.
  • Handle: RePEc:eee:jomega:v:80:y:2018:i:c:p:135-152
    DOI: 10.1016/

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    References listed on IDEAS

    1. Khouja, Moutaz, 1999. "The single-period (news-vendor) problem: literature review and suggestions for future research," Omega, Elsevier, vol. 27(5), pages 537-553, October.
    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.
    3. Mark G. Brown & Jong-Ying Lee, 1996. "Allocation of shelf space: A case study of refrigerated juice products in grocery stores," Agribusiness, John Wiley & Sons, Ltd., vol. 12(2), pages 113-121.
    4. Eisend, Martin, 2014. "Shelf space elasticity: A meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 168-181.
    5. Hübner, Alexander & Kuhn, Heinrich & Kühn, Sandro, 2016. "An efficient algorithm for capacitated assortment planning with stochastic demand and substitution," European Journal of Operational Research, Elsevier, vol. 250(2), pages 505-520.
    6. 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.
    7. Narendra Agrawal & Stephen A. Smith, 1996. "Estimating negative binomial demand for retail inventory management with unobservable lost sales," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(6), pages 839-861, September.
    8. Zhao, Ju & Zhou, Yong-Wu & Wahab, M.I.M., 2016. "Joint optimization models for shelf display and inventory control considering the impact of spatial relationship on demand," European Journal of Operational Research, Elsevier, vol. 255(3), pages 797-808.
    9. repec:dau:papers:123456789/1757 is not listed on IDEAS
    10. Yang, Ming-Hsien, 2001. "An efficient algorithm to allocate shelf space," European Journal of Operational Research, Elsevier, vol. 131(1), pages 107-118, May.
    11. Hübner, Alexander & Schaal, Kai, 2017. "A shelf-space optimization model when demand is stochastic and space-elastic," Omega, Elsevier, vol. 68(C), pages 139-154.
    12. Hansen, Pierre & Heinsbroek, Hans, 1979. "Product selection and space allocation in supermarkets," European Journal of Operational Research, Elsevier, vol. 3(6), pages 474-484, November.
    13. Ahmed Ghoniem & Bacel Maddah & Ameera Ibrahim, 2016. "Optimizing assortment and pricing of multiple retail categories with cross-selling," Journal of Global Optimization, Springer, vol. 66(2), pages 291-309, October.
    14. A. Gürhan Kök & Marshall L. Fisher, 2007. "Demand Estimation and Assortment Optimization Under Substitution: Methodology and Application," Operations Research, INFORMS, vol. 55(6), pages 1001-1021, December.
    15. Hasmukh Gajjar & Gajendra Adil, 2010. "A piecewise linearization for retail shelf space allocation problem and a local search heuristic," Annals of Operations Research, Springer, vol. 179(1), pages 149-167, September.
    16. Irion, Jens & Lu, Jye-Chyi & Al-Khayyal, Faiz & Tsao, Yu-Chung, 2012. "A piecewise linearization framework for retail shelf space management models," European Journal of Operational Research, Elsevier, vol. 222(1), pages 122-136.
    17. Pierre Desmet & Valérie Renaudin, 1998. "Estimation of Product Category Sales Responsiveness to Allocated Shelf Space," Post-Print halshs-00143451, HAL.
    18. Stephen A. Smith & Narendra Agrawal, 2000. "Management of Multi-Item Retail Inventory Systems with Demand Substitution," Operations Research, INFORMS, vol. 48(1), pages 50-64, February.
    19. Hübner, Alexander H. & Kuhn, Heinrich, 2012. "Retail category management: State-of-the-art review of quantitative research and software applications in assortment and shelf space management," Omega, Elsevier, vol. 40(2), pages 199-209, April.
    20. Andrew Lim & Brian Rodrigues & Xingwen Zhang, 2004. "Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization," Management Science, INFORMS, vol. 50(1), pages 117-131, January.
    21. Hübner, Alexander & Schaal, Kai, 2017. "An integrated assortment and shelf-space optimization model with demand substitution and space-elasticity effects," European Journal of Operational Research, Elsevier, vol. 261(1), pages 302-316.
    22. 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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