On Size Substitution and Its Role in Assortment and Inventory Planning

Published Online:https://doi.org/10.1287/msom.2023.0674

References

  • Acimovic J, Graves SC (2015) Making better fulfillment decisions on the fly in an online retail environment. Manufacturing Service Oper. Management 17(1):34–51.LinkGoogle Scholar
  • Akchen Y-C, Mišić VV (2021) Assortment optimization under the decision forest model. Preprint, submitted March 25, https://arxiv.org/abs/2103.14067v1.Google Scholar
  • Alom S, Basu S, Basu P, Joshi R (2024) Shipment policy and its impact on coordination of a fashion supply chain under production uncertainty. Transportation Res. Part E Logist. Transportation Rev. 192:103778.CrossrefGoogle Scholar
  • Aouad A, Farias V, Levi R (2021) Assortment optimization under consider-then-choose choice models. Management Sci. 67(6):3368–3386.LinkGoogle Scholar
  • Aouad A, Levi R, Segev D (2018) Greedy-like algorithms for dynamic assortment planning under multinomial logit preferences. Oper. Res. 66(5):1321–1345.LinkGoogle Scholar
  • Block HD, Marschak J (1959) Random orderings and stochastic theories of response. Technical report, Cowles Foundation for Research in Economics, Yale University, New Haven, CT.Google Scholar
  • Boada-Collado P, Martínez-de-Albéniz V (2020) Estimating and optimizing the impact of inventory on consumer choices in a fashion retail setting. Manufacturing Service Oper. Management 22(3):582–597.LinkGoogle Scholar
  • Bront JJM, Méndez-Díaz I, Vulcano G (2009) A column generation algorithm for choice-based network revenue management. Oper. Res. 57(3):769–784.LinkGoogle Scholar
  • Campo K, Gijsbrechts E, Nisol P (2000) Towards understanding consumer response to stock-outs. J. Retailing 76(2):219–242.CrossrefGoogle Scholar
  • Caro F, Gallien J (2010) Inventory management of a fast-fashion retail network. Oper. Res. 58(2):257–273.LinkGoogle Scholar
  • Caro F, Gallien J (2012) Clearance pricing optimization for a fast-fashion retailer. Oper. Res. 60(6):1404–1422.LinkGoogle Scholar
  • Charnes A, Cooper WW (1962) Programming with linear fractional functionals. Naval Res. Logist. Quart. 9(3–4):181–186.CrossrefGoogle Scholar
  • Che H, Chen X, Chen Y (2012) Investigating effects of out-of-stock on consumer stockkeeping unit choice. J. Marketing Res. 49(4):502–513.CrossrefGoogle Scholar
  • Chen Y-C, Mišić VV (2022) Decision forest: A nonparametric approach to modeling irrational choice. Management Sci. 68(10):7090–7111.LinkGoogle Scholar
  • Claypoole C (2019) What is the markup percentage for retail clothing? Accessed December 5, 2025, https://smallbusiness.chron.com/markup-percentage-retail-clothing-80777.html.Google Scholar
  • Deng Y, Li Y, Song J-SJ (2022) A unified parsimonious model for structural demand estimation accounting for stockout and substitution. Preprint, submitted June 12, https://dx.doi.org/10.2139/ssrn.4134738.Google Scholar
  • Ergin E, Gümüş M, Yang N (2022) An empirical analysis of intra-firm product substitutability in fashion retailing. Production Oper. Management 31(2):607–621.CrossrefGoogle Scholar
  • Farias VF, Jagabathula S, Shah D (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.LinkGoogle Scholar
  • Farra E (2020) What is the right price for fashion? Accessed December 5, 2025, https://www.vogue.com/article/what-is-the-right-price-for-fashion.Google Scholar
  • Fisher M, Vaidyanathan R (2014) A demand estimation procedure for retail assortment optimization with results from implementations. Management Sci. 60(10):2401–2415.LinkGoogle Scholar
  • Gallego G, Ratliff R, Shebalov S (2015) A general attraction model and sales-based linear program for network revenue management under customer choice. Oper. Res. 63(1):212–232.LinkGoogle Scholar
  • Goyal V, Levi R, Segev D (2016) Near-optimal algorithms for the assortment planning problem under dynamic substitution and stochastic demand. Oper. Res. 64(1):219–235.LinkGoogle Scholar
  • Honhon D, Seshadri S (2013) Fixed vs. random proportions demand models for the assortment planning problem under stockout-based substitution. Manufacturing Service Oper. Management 15(3):378–386.LinkGoogle Scholar
  • Honhon D, Gaur V, Seshadri S (2010) Assortment planning and inventory decisions under stockout-based substitution. Oper. Res. 58(5):1364–1379.LinkGoogle Scholar
  • Huber J, Payne JW, Puto C (1982) Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis. J. Consumer Res. 9(1):90–98.CrossrefGoogle Scholar
  • Jagabathula S, Rusmevichientong P (2019) The limit of rationality in choice modeling: Formulation, computation, and implications. Management Sci. 65(5):2196–2215.AbstractGoogle Scholar
  • Jagabathula S, Mitrofanov D, Vulcano G (2024) Demand estimation under uncertain consideration sets. Oper. Res. 72(1):19–42. LinkGoogle Scholar
  • Kök AG, Fisher ML (2007) Demand estimation and assortment optimization under substitution: Methodology and application. Oper. Res. 55(6):1001–1021.LinkGoogle Scholar
  • Lee J, Gaur V, Muthulingam S, Swisher GF (2016) Stockout-based substitution and inventory planning in textbook retailing. Manufacturing Service Oper. Management 18(1):104–121.LinkGoogle Scholar
  • Li S, Lu LX, Lu SF, Huang S (2023) Estimating the stockout-based demand spillover effect in a fashion retail setting. Manufacturing Service Oper. Management 25(2):468–488.LinkGoogle Scholar
  • Liang AJ, Jasin S, Uichanco J (2021) Assortment and inventory planning under dynamic substitution with MNL model: An LP approach and an asymptotically optimal policy. Preprint, submitted February 12, https://dx.doi.org/10.2139/ssrn.3739047.Google Scholar
  • Mahajan S, van Ryzin G (2001) Stocking retail assortments under dynamic consumer substitution. Oper. Res. 49(3):334–351.LinkGoogle Scholar
  • McLachlan GJ, Krishnan T (2007) The EM Algorithm and Extensions (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Musalem A, Olivares M, Bradlow ET, Terwiesch C, Corsten D (2010) Structural estimation of the effect of out-of-stocks. Management Sci. 56(7):1180–1197.LinkGoogle Scholar
  • Pennesi D (2021) A foundation for cue-triggered behavior. Management Sci. 67(4):2403–2419.LinkGoogle Scholar
  • Rieskamp J, Busemeyer JR, Mellers BA (2006) Extending the bounds of rationality: Evidence and theories of preferential choice. J. Econom. Lit. 44(3):631–661.CrossrefGoogle Scholar
  • Rusmevichientong P, Shmoys D, Tong C, Topaloglu H (2014) Assortment optimization under the multinomial logit model with random choice parameters. Production Oper. Management 23(11):2023–2039.CrossrefGoogle Scholar
  • Smith SA, Achabal DD (1998) Clearance pricing and inventory policies for retail chains. Management Sci. 44(3):285–300.LinkGoogle Scholar
  • Talluri K, van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33.LinkGoogle Scholar
  • Topaloglu H (2013) Joint stocking and product offer decisions under the multinomial logit model. Production Oper. Management 22(5):1182–1199.CrossrefGoogle Scholar
  • Train KE (2009) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Tversky A, Simonson I (1993) Context-dependent preferences. Management Sci. 39(10):1179–1189.LinkGoogle Scholar
  • van Ryzin G, Mahajan S (1999) On the relationship between inventory costs and variety benefits in retail assortments. Management Sci. 45(11):1496–1509.LinkGoogle Scholar
  • van Ryzin G, Vulcano G (2015) A market discovery algorithm to estimate a general class of nonparametric choice models. Management Sci. 61(2):281–300.LinkGoogle Scholar
  • Wen X, Choi T-M, Chung S-H (2019) Fashion retail supply chain management: A review of operational models. Internat. J. Production Econom. 207:34–55.CrossrefGoogle Scholar
  • Zhang J, Ma W, Topaloglu H (2025) Technical note—Leveraging the degree of dynamic substitution in assortment and inventory planning. Oper. Res. 73(3):1248–1259.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.