Is Assortment Selection a Popularity Contest?

A Study of Assortment, Return Policy, and Pricing Decisions of a Retailer
  • Aydın Alptekinoğlu
  • Alex Grasas
  • Elif Akçalı
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 131)


Should retailers take product returns into account when choosing their assortments? And, when doing so, should they consider assortment selection as a popularity contest – by carrying products that they think will be popular among consumers? Or, is there ever a case for carrying eccentric products – those that are least likely to be purchased by a typical consumer? In search of answers to these questions, we explore in this chapter the interactions between product assortment, return policy, and pricing decisions of a retailer. We consider a category of horizontally differentiated products delivered in two alternative supply modes: make-to-order (MTO) and make-to-stock (MTS). In the MTO mode, products are supplied after demand materializes, whereas in the MTS mode, the retailer stocks products prior to the selling season. Underlying our demand model, consumer choice behavior follows a nested multinomial logit model, with the first stage involving a product choice, and the second stage involving a keep-or-return decision. We show that the structure of the optimal assortment strongly depends on both the return policy, which we parameterize by refund fraction (percentage of price refunded upon return) and the supply mode (MTO vs. MTS). For relatively strict return policies with a sufficiently low refund fraction, it is optimal for the retailer to offer most eccentric products in the MTO mode, and a mix of most popular and most eccentric products in the MTS mode. For relatively lenient return policies, on the other hand, conventional thinking applies: the retailer selects most popular products. We also numerically study three extensions of our base model to incorporate: (1) endogenous price, (2) endogenous refund fraction, and (3) multiple periods. We demonstrate that interesting aspects of our results regarding strict return policies prevail under all of these extensions.


Return Policy Reverse Logistics Product Return Selling Season Popular Product 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alptekinoğlu A, Corbett CJ (2008a) Leadtime – variety tradeoff in product differentiation. Working paper, SMU, Dallas, TexasGoogle Scholar
  2. Alptekinoğlu A, Corbett CJ (2008b) Mass customization vs. mass production: Variety and price competition. Manufacturing Service Operating Management 10(2):204–217CrossRefGoogle Scholar
  3. Anderson SP, de Palma A, Thisse JF (1992) Discrete choice theory of product differentiation. MIT Press, Cambridge, MassachusettsGoogle Scholar
  4. Aydin G, Ryan JK (2000) Product line selection and pricing under the multinomial logit choice model. Working paper, Purdue University, West Lafayette, IndianaGoogle Scholar
  5. Blanchard D (2005) Moving forward in reverse. Logistics Today 46(7):7–8Google Scholar
  6. Borle S, Boatwright P, Kadane JB, Nunes JC, Shmueli G (2005) The effect of product assortment changes on customer retention. Marketing Science 24(4):616–622CrossRefGoogle Scholar
  7. Cachon GP, Kök AG (2007) Category management and coordination in retail assortment planning in the presence of basket shopping consumers. Management Science 53(6):934–951CrossRefGoogle Scholar
  8. Cachon GP, Terwiesch C, Xu Y (2005) Retail assortment planning in the presence of consumer search. Manufacturing Service Operating Management 7(4):330–346CrossRefGoogle Scholar
  9. Cargille B, Fry C, Raphel A (2005) Managing product line complexity. OR/MS Today 32(3):34–41Google Scholar
  10. Dekker R, Fleischmann M, Inderfurth K, van Wassenhove LN (2004) Reverse logistics: Quantitative models for closed-loop supply chains. Springer-Verlag, New YorkGoogle Scholar
  11. Emmons H, Gilbert SM (1998) The role of returns policies in pricing and inventory decisions for catalogue goods. Management Science 44(2):276–283CrossRefGoogle Scholar
  12. Enright T (2003) Post-holiday logistics. trafficWORLD, January 20Google Scholar
  13. Gaur V, Honhon D (2006) Product variety and inventory decisions under a locational consumer choice model. Management Science 52(10):1528–1546CrossRefGoogle Scholar
  14. Grasas A, Alptekinoğlu A, Akçalı E (2008) When to carry eccentric products? Optimal assortment under product returns. Working paper, University of Florida, Gainesville, FloridaGoogle Scholar
  15. Guide VDR, Jr, Souza GC, van Wassenhove LN, Blackburn JD (2006) Time value of commercial product returns. Management Science 52(8):1200–1214CrossRefGoogle Scholar
  16. Hopp WJ, Xu X (2005) Product line selection and pricing with modularity in design. Manufacturing Service Operating Management 7(3):172–187CrossRefGoogle Scholar
  17. Kim J, Allenby GM, Rossi PE (2002) Modeling consumer demand for variety. Marketing Science 21(3):229–250CrossRefGoogle Scholar
  18. Li Z (2007) A single-period assortment optimization model. Production Operating Management 16(3):369–380Google Scholar
  19. Maddah B, Bish EK (2007) Joint pricing, assortment, and inventory decisions for a retailer’s product line. Naval Research Logistics 54(3):315–330CrossRefGoogle Scholar
  20. McFadden D (1978) Modelling the choice of residential location. North-Holland Publishing Company, AmsterdamGoogle Scholar
  21. Mostard J, Teunter R (2006) The newsboy problem with resalable returns: a single period model and case study. European Journal of Operational Research 169(1):81–96CrossRefGoogle Scholar
  22. Olavson T, Fry C (2006) Understanding the dynamics of value-driven variety management. MIT Sloan Management Review 48(1):63–69Google Scholar
  23. Pasternack BA (1985) Optimal pricing and return policies for perishable commodities. Marketing Science 4(2):166–176CrossRefGoogle Scholar
  24. Robert CP, Casella G (1999) Monte Carlo statistical methods. Springer-Verlag, New YorkGoogle Scholar
  25. Rogers DS, Tibben-Lembke RS (1998) Going backwards: Reverse logistics trends and practices. University of Nevada report, Center for Logistics Management. Reverse Logistics Executive Council.
  26. Shulman JD, Coughlan AT, Savaskan RC (2008) Optimal restocking fees and information provision in an integrated demand-supply model of product returns. Working paper, University of Washington, Seattle, WashingtonGoogle Scholar
  27. Smith S, Agrawal N (2000) Management of multi-item retail inventory systems with demand substitution. Operations Research 48(1):50–64CrossRefGoogle Scholar
  28. Stock J, Speh T, Shear H (2006) Managing product returns for competitive advantage. MIT Sloan Management Review 48(1):57–62Google Scholar
  29. van Riper T, Nolan K (2008) The toughest holiday returns. Forbes, January 14.
  30. van Ryzin G, Mahajan S (1999) On the relationship between inventory costs and variety benefits in retail assortments. Management Science 45(11):1496–1509CrossRefGoogle Scholar
  31. Xie J, Gerstner E (2007) Service escape: Profiting from customer cancellations. Marketing Science 26(1):18–30CrossRefGoogle Scholar
  32. Yalabik B, Petruzzi NC, Chhajed D (2005) An integrated product returns model with logistics and marketing coordination. European Journal of Operational Research 161(1):162–182CrossRefGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • Aydın Alptekinoğlu
    • 1
  • Alex Grasas
    • 2
  • Elif Akçalı
    • 3
  1. 1.SMU Cox School of BusinessDallasUSA
  2. 2.Economics and BusinessUniversitat Pompeu FabraBarcelonaSpain
  3. 3.Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA

Personalised recommendations