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A method for estimating stock-out-based substitution rates by using point-of-sale data

Author

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  • Selçuk Karabati
  • Bariş Tan
  • Ömer Öztürk

Abstract

Empirical studies in retailing suggest that stock-out rates are quite high in many product categories. Stock-outs result in demand spillover, or substitution, among items within a product category. Product assortment and inventory management decisions can be improved when the substitution rates are known. In this paper, a method is presented to estimate product substitution rates by using only Point-Of-Sale (POS) data. The approach clusters POS intervals into states where each state corresponds to a specific substitution scenario. Then available POS data for each state is consolidated and the substitution rates are estimated using the consolidated information. An extensive computational analysis of the proposed substitution rate estimation method is provided. The computational analysis and comparisons with an estimation method from the literature show that the proposed estimation method performs satisfactorily with limited information.

Suggested Citation

  • Selçuk Karabati & Bariş Tan & Ömer Öztürk, 2009. "A method for estimating stock-out-based substitution rates by using point-of-sale data," IISE Transactions, Taylor & Francis Journals, vol. 41(5), pages 408-420.
    • Handle: RePEc:taf:uiiexx:v:41:y:2009:i:5:p:408-420
    DOI: 10.1080/07408170802512578
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    Cited by:

    1. Tan, Baris & Karabati, Selcuk, 2013. "Retail inventory management with stock-out based dynamic demand substitution," International Journal of Production Economics, Elsevier, vol. 145(1), pages 78-87.
    2. 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.
    3. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    4. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    5. Çömez-Dolgan, Nagihan & Fescioglu-Unver, Nilgun & Cephe, Ecem & Şen, Alper, 2021. "Capacitated strategic assortment planning under explicit demand substitution," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1120-1138.
    6. Transchel, Sandra & Buisman, Marjolein E. & Haijema, Rene, 2022. "Joint assortment and inventory optimization for vertically differentiated products under consumer-driven substitution," European Journal of Operational Research, Elsevier, vol. 301(1), pages 163-179.
    7. Patxi J. Bernales & Yongtao Guan & Harihara Prasad Natarajan & Patricia Souza Gimenez & Mario Xavier Alvarez Tajes, 2017. "Less Is More: Harnessing Product Substitution Information to Rationalize SKUs at Intcomex," Interfaces, INFORMS, vol. 47(3), pages 230-243, June.

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