IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

A Modeling Framework for Category Assortment Planning

Listed author(s):
  • Juin-Kuan Chong


    (The NUS Business School, National University of Singapore, Singapore 119260)

  • Teck-Hua Ho


    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Christopher S. Tang


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

The complexity of managing a category assortment has grown tremendously in recent years due to the increased product turnover and proliferation rates in most categories. It is an increasingly difficult task for managers to find an effective assortment due to uncertain consumer preferences and the exponential number of possible assortments. This paper presents an empirically based modeling framework for managers to assess the revenue and lost sales implication of alternative category assortments. Coupled with a local improvement heuristic, the modeling framework generates an alternative category assortment with higher revenue. This framework, which consists of a category-purchase-incidence model and a brand-share model, is calibrated and validated using 60,000 shopping trips and purchase records. Specifically, the purchase-incidence model predicts the probability of an individual customer who purchases (and who does not purchase) from a given product category during a shopping trip. The no-purchase probability enables us to estimate lost sales due to assortment changes in the category. The brand-share model predicts which brand the customer chooses if a purchase incidence occurs in the category. Our brand-share model extends the classical Guadagni and Little model (1983) by utilizing three new brand-width measures that quantify the similarities among products of different brands within the same category. We illustrate how our modeling framework is used to reconfigure the category assortment in eight food categories for five stores in our data set. This reconfiguration exercise shows that a reconfigured category assortment can have a profit improvement of up to 25.1% with 32 products replaced. We also demonstrate how our modeling framework can be used to gauge lost sales due to assortment changes. We find the level of lost sales could range from 0.9% to 10.2% for a period of 26 weeks.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Article provided by INFORMS in its journal Manufacturing & Service Operations Management.

Volume (Year): 3 (2001)
Issue (Month): 3 (January)
Pages: 191-210

in new window

Handle: RePEc:inm:ormsom:v:3:y:2001:i:3:p:191-210
Contact details of provider: Postal:
7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA

Phone: +1-443-757-3500
Fax: 443-757-3515
Web page:

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

in new window

  1. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
  2. Jeongwen Chiang, 1991. "A Simultaneous Approach to the Whether, What and How Much to Buy Questions," Marketing Science, INFORMS, vol. 10(4), pages 297-315.
  3. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
  4. Gérard P. Cachon & Marshall Fisher, 2000. "Supply Chain Inventory Management and the Value of Shared Information," Management Science, INFORMS, vol. 46(8), pages 1032-1048, August.
  5. Marshall L. Fisher & Christopher D. Ittner, 1999. "The Impact of Product Variety on Automobile Assembly Operations: Empirical Evidence and Simulation Analysis," Management Science, INFORMS, vol. 45(6), pages 771-786, June.
  6. McAlister, Leigh, 1982. " A Dynamic Attribute Satiation Model of Variety-Seeking Behavior," Journal of Consumer Research, Oxford University Press, vol. 9(2), pages 141-150, September.
  7. P. K. Kannan & Gordon P. Wright, 1991. "Modeling and Testing Structured Markets: A Nested Logit Approach," Marketing Science, INFORMS, vol. 10(1), pages 58-82.
  8. Moore, William L. & Lehmann, Donald R. & Pessemier, Edgar A., 1986. "Hierarchical representations of market structures and choice processes through preference trees," Journal of Business Research, Elsevier, vol. 14(5), pages 371-386, October.
  9. Udo Wagner & Alfred Taudes, 1986. "A Multivariate Polya Model of Brand Choice and Purchase Incidence," Marketing Science, INFORMS, vol. 5(3), pages 219-244.
  10. Pradeep K. Chintagunta, 1993. "Investigating Purchase Incidence, Brand Choice and Purchase Quantity Decisions of Households," Marketing Science, INFORMS, vol. 12(2), pages 184-208.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:inm:ormsom:v:3:y:2001:i:3:p:191-210. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.