Growth Projections and Assortment Planning of Commodity Products Across Multiple Stores: A Data Mining and Optimization Approach

X. Bai, S. Bhattacharjee, Boylu, F., R.D. Gopal, “Growth Projections and Assortment Planning of Commodity Products across Multiple Stores: A Data Mining and Optimization Approach”, INFORMS Journal on Computing, Vol. 27, No. 4, Fall 2015, pp.619-635.

Posted: 4 Jan 2019

See all articles by Sudip Bhattacharjee

Sudip Bhattacharjee

University of Connecticut - Department of Operations & Information Management; US Census Bureau

Xue Bai

affiliation not provided to SSRN

Fidan Boylu

affiliation not provided to SSRN

Ram D. Gopal

University of Connecticut - Department of Operations & Information Management

Date Written: January 6, 2015

Abstract

Product assortment and availability are important determinants of sales success for firms of industrial commodity products. Well-known pricing and promotion strategies for differentiated products do not translate well to such products where price is closely tied to the cost of the products. Consequently, firms with multiple stores of commodity products are faced with the problem of product assortment that incorporates varying geographic and demographic conditions of locations they serve. The paper presents a model for assortment planning and optimization for multiple stores of a company. The novelty of our approach is twofold: first, it deploys data mining techniques to identify sales pattern information across multiple stores through existing sales data across segments and across stores; second, it identifies the optimal product assortment for each store and permits analyses of assortment efficiency evaluation among all existing stores. Our model first finds frequent itemsets based on association rule analysis and prunes them using a novel conflict resolution method. It then incorporates the identified product combinations into the development of the optimization formulation. Our methodology offers solutions that have important implications on product assortment, including complements versus substitutes and product bundling, and sheds lights on product planning and assortment strategies in general. A data set from an industry leading plastics manufacturer and retailer in the United States is used to demonstrate our model.

Keywords: data mining; assortment planning; association rule mining; product variety; sales efficiency

Suggested Citation

Bhattacharjee, Sudip and Bai, Xue and Boylu, Fidan and Gopal, Ram D., Growth Projections and Assortment Planning of Commodity Products Across Multiple Stores: A Data Mining and Optimization Approach (January 6, 2015). X. Bai, S. Bhattacharjee, Boylu, F., R.D. Gopal, “Growth Projections and Assortment Planning of Commodity Products across Multiple Stores: A Data Mining and Optimization Approach”, INFORMS Journal on Computing, Vol. 27, No. 4, Fall 2015, pp.619-635., Available at SSRN: https://ssrn.com/abstract=3297097

Sudip Bhattacharjee (Contact Author)

University of Connecticut - Department of Operations & Information Management ( email )

2100 Hillside Road, U-1041
Storrs, CT 06269-1041
United States

HOME PAGE: http://users.business.uconn.edu/sbhattacharjee

US Census Bureau ( email )

4600 Silver Hill Road
D.C., WA 20233
United States

Xue Bai

affiliation not provided to SSRN

Fidan Boylu

affiliation not provided to SSRN

Ram D. Gopal

University of Connecticut - Department of Operations & Information Management ( email )

368 Fairfield Road
Storrs, CT 06269-2041
United States

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