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Determining profitable products in the retail market with consideration of cash limitation and exhibition periods

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  • Kiani, Gholam Hossain

Abstract

In the selection of profitable products, consumer preferences and retailer constraints in products supply must be considered. When data mining algorithms are used to discover the consumer's preferences from transaction database, the results may be biased, if the exhibition period of the products has not be considered. In this study a new method is proposed to adjust the support and confidence coefficients of traditional association rule mining algorithms such as Apriori or FP-growth taking into consideration of common exhibition periods. On the supply side, the retailer may have some limitations in terms of buying and supplying products in the store. In the most recent researches, only the shelf space constraint has been considered. In this study, financing as an important constraint in the retail market and the opportunity cost of money are imported in the selection of profitable products.

Suggested Citation

  • Kiani, Gholam Hossain, 2020. "Determining profitable products in the retail market with consideration of cash limitation and exhibition periods," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
  • Handle: RePEc:eee:joreco:v:55:y:2020:i:c:s0969698919310719
    DOI: 10.1016/j.jretconser.2020.102079
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    References listed on IDEAS

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    1. Bahareh Rahmati & Mohammad Karim Sohrabi, 2019. "A Systematic Survey on High Utility Itemset Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1113-1185, July.
    2. Düsterhöft, Tobias & Hübner, Alexander & Schaal, Kai, 2020. "A practical approach to the shelf-space allocation and replenishment problem with heterogeneously sized shelves," European Journal of Operational Research, Elsevier, vol. 282(1), pages 252-266.
    3. 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.
    4. Alain Bultez & Philippe Naert, 1988. "SH.A.R.P.: Shelf Allocation for Retailers' Profit," Marketing Science, INFORMS, vol. 7(3), pages 211-231.
    5. Hekimoğlu, Mustafa & Sevim, Ismail & Aksezer, Çağlar & Durmuş, İpek, 2019. "Assortment optimization with log-linear demand: Application at a Turkish grocery store," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 199-214.
    6. Evan E. Anderson & Henry N. Amato, 1974. "A Mathematical Model for Simultaneously Determining the Optimal Brand-Collection and Display-Area Allocation," Operations Research, INFORMS, vol. 22(1), pages 13-21, February.
    7. 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.
    8. Marcel Corstjens & Peter Doyle, 1981. "A Model for Optimizing Retail Space Allocations," Management Science, INFORMS, vol. 27(7), pages 822-833, July.
    9. 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.
    10. 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.
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