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On Size Substitution and Its Role in Assortment and Inventory Planning

Author

Listed:
  • Yi-Chun Akchen

    (School of Management, University College London, London E14 5AB, United Kingdom)

  • Felipe Caro

    (Anderson School of Management, University of California, Los Angeles, California 90095)

Abstract

Problem definition : How should (apparel) retailers manage product sizes? For example, if most customers wearing a given shoe size, such as 9.5, are willing to accept a half-size up or down, is it necessary for a retailer to carry that size at all? Additionally, although identical products in different sizes are treated as distinct stock-keeping units in inventory management, they are often aggregated for assortment and strategic planning. However, there is no theoretical justification for this approach. In this paper, we address the fundamental questions about size management that have remained largely unexplored in the operations literature. Methodology/results : We propose a choice model where each customer forms a consideration set based on the in-stock availability of products of her best-fit size and adjacent sizes. Using a real-world data set from a large footwear retailer, we show that nearly 25% of the unmet demand caused by stockouts spills over to adjacent sizes. We further solve the assortment and inventory optimization problems under the proposed choice model. Our findings demonstrate that the optimal assortment remains unchanged, regardless of the likelihood that customers might purchase adjacent sizes. We utilize this finding and further show that inventory policies that ignore size substitution can be (asymptotically) optimal when the demand rate is high or the selling horizon is long. We also propose a mixed-integer program to determine inventory levels that account for size substitution and achieve higher profits in low-demand settings. Managerial implications : We show that the prevalent size-aggregation approach employed in apparel retail operations is sensible in high-demand settings, such as e-commerce. In contrast, when the expected demand over the selling horizon is low, size substitution can be relevant and should be considered in stocking decisions.

Suggested Citation

  • Yi-Chun Akchen & Felipe Caro, 2026. "On Size Substitution and Its Role in Assortment and Inventory Planning," Manufacturing & Service Operations Management, INFORMS, vol. 28(2), pages 624-642, March.
  • Handle: RePEc:inm:ormsom:v:28:y:2026:i:2:p:624-642
    DOI: 10.1287/msom.2023.0674
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    References listed on IDEAS

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    1. Ali Aouad & Vivek Farias & Retsef Levi, 2021. "Assortment Optimization Under Consider-Then-Choose Choice Models," Management Science, INFORMS, vol. 67(6), pages 3368-3386, June.
    2. Jörg Rieskamp & Jerome R. Busemeyer & Barbara A. Mellers, 2006. "Extending the Bounds of Rationality: Evidence and Theories of Preferential Choice," Journal of Economic Literature, American Economic Association, vol. 44(3), pages 631-661, September.
    3. Marshall Fisher & Ramnath Vaidyanathan, 2014. "A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations," Management Science, INFORMS, vol. 60(10), pages 2401-2415, October.
    4. Jason Acimovic & Stephen C. Graves, 2015. "Making Better Fulfillment Decisions on the Fly in an Online Retail Environment," Manufacturing & Service Operations Management, INFORMS, vol. 17(1), pages 34-51, February.
    5. Guillermo Gallego & Richard Ratliff & Sergey Shebalov, 2015. "A General Attraction Model and Sales-Based Linear Program for Network Revenue Management Under Customer Choice," Operations Research, INFORMS, vol. 63(1), pages 212-232, February.
    6. Wen, Xin & Choi, Tsan-Ming & Chung, Sai-Ho, 2019. "Fashion retail supply chain management: A review of operational models," International Journal of Production Economics, Elsevier, vol. 207(C), pages 34-55.
    7. Vivek F. Farias & Srikanth Jagabathula & Devavrat Shah, 2013. "A Nonparametric Approach to Modeling Choice with Limited Data," Management Science, INFORMS, vol. 59(2), pages 305-322, December.
    8. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    9. Garrett van Ryzin & Gustavo Vulcano, 2015. "A Market Discovery Algorithm to Estimate a General Class of Nonparametric Choice Models," Management Science, INFORMS, vol. 61(2), pages 281-300, February.
    10. Pol Boada-Collado & Victor Martínez-de-Albéniz, 2020. "Estimating and Optimizing the Impact of Inventory on Consumer Choices in a Fashion Retail Setting," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 582-597, May.
    11. Vineet Goyal & Retsef Levi & Danny Segev, 2016. "Near-Optimal Algorithms for the Assortment Planning Problem Under Dynamic Substitution and Stochastic Demand," Operations Research, INFORMS, vol. 64(1), pages 219-235, February.
    12. Joonkyum Lee & Vishal Gaur & Suresh Muthulingam & Gary F. Swisher, 2016. "Stockout-Based Substitution and Inventory Planning in Textbook Retailing," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 104-121, February.
    13. Siddharth Mahajan & Garrett van Ryzin, 2001. "Stocking Retail Assortments Under Dynamic Consumer Substitution," Operations Research, INFORMS, vol. 49(3), pages 334-351, June.
    14. Andrés Musalem & Marcelo Olivares & Eric T. Bradlow & Christian Terwiesch & Daniel Corsten, 2010. "Structural Estimation of the Effect of Out-of-Stocks," Management Science, INFORMS, vol. 56(7), pages 1180-1197, July.
    15. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, Enero-Abr.
    16. Garrett van Ryzin & Siddharth Mahajan, 1999. "On the Relationship Between Inventory Costs and Variety Benefits in Retail Assortments," Management Science, INFORMS, vol. 45(11), pages 1496-1509, November.
    17. Dorothée Honhon & Vishal Gaur & Sridhar Seshadri, 2010. "Assortment Planning and Inventory Decisions Under Stockout-Based Substitution," Operations Research, INFORMS, vol. 58(5), pages 1364-1379, October.
    18. 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.
    19. Juan José Miranda Bront & Isabel Méndez-Díaz & Gustavo Vulcano, 2009. "A Column Generation Algorithm for Choice-Based Network Revenue Management," Operations Research, INFORMS, vol. 57(3), pages 769-784, June.
    20. A. Charnes & W. W. Cooper, 1962. "Programming with linear fractional functionals," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 9(3‐4), pages 181-186, September.
    21. H.D. Block & Jacob Marschak, 1959. "Random Orderings and Stochastic Theories of Response," Cowles Foundation Discussion Papers 66, Cowles Foundation for Research in Economics, Yale University.
    22. Paat Rusmevichientong & David Shmoys & Chaoxu Tong & Huseyin Topaloglu, 2014. "Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters," Production and Operations Management, Production and Operations Management Society, vol. 23(11), pages 2023-2039, November.
    23. Srikanth Jagabathula & Dmitry Mitrofanov & Gustavo Vulcano, 2024. "Demand Estimation Under Uncertain Consideration Sets," Operations Research, INFORMS, vol. 72(1), pages 19-42, January.
    24. Huber, Joel & Payne, John W & Puto, Christopher, 1982. "Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(1), pages 90-98, June.
    25. Felipe Caro & Jérémie Gallien, 2010. "Inventory Management of a Fast-Fashion Retail Network," Operations Research, INFORMS, vol. 58(2), pages 257-273, April.
    26. Stephen A. Smith & Dale D. Achabal, 1998. "Clearance Pricing and Inventory Policies for Retail Chains," Management Science, INFORMS, vol. 44(3), pages 285-300, March.
    27. Felipe Caro & Jérémie Gallien, 2012. "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research, INFORMS, vol. 60(6), pages 1404-1422, December.
    28. Songtao Li & Lauren Xiaoyuan Lu & Susan Feng Lu & Simin Huang, 2023. "Estimating the Stockout-Based Demand Spillover Effect in a Fashion Retail Setting," Manufacturing & Service Operations Management, INFORMS, vol. 25(2), pages 468-488, March.
    29. Dorothée Honhon & Sridhar Seshadri, 2013. "Fixed vs. Random Proportions Demand Models for the Assortment Planning Problem Under Stockout-Based Substitution," Manufacturing & Service Operations Management, INFORMS, vol. 15(3), pages 378-386, July.
    30. Amos Tversky & Itamar Simonson, 1993. "Context-Dependent Preferences," Management Science, INFORMS, vol. 39(10), pages 1179-1189, October.
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