Data mining-based algorithm for assortment planning

Praveen Ranjan Srivastava, Satyendra Sharma and Simran Kaur

Journal of Management Analytics, 2020, vol. 7, issue 3, 443-457

Abstract: With increasing varieties and products, management of limited shelf space becomes quite difficult for retailers. Hence, an efficient product assortment, which in turn helps to plan the organization of various products across limited shelf space, is extremely important for retailers. Products can be distinguished based on quality, price, brand, and other attributes, and decision needs to be made about an assortment of the products based on these attributes. An efficient assortment planning improves the financial performance of the retailer by increasing profits and reducing operational costs. Clustering techniques can be very effective in grouping products, stores, etc. and help managers solve the problem of assortment planning. This paper proposes data mining approaches for assortment planning for profit maximization with space, and cost constraints by mapping it into well-known knapsack problem.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/23270012.2020.1725666 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjmaxx:v:7:y:2020:i:3:p:443-457

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjma20

DOI: 10.1080/23270012.2020.1725666

Access Statistics for this article

Journal of Management Analytics is currently edited by Li Xu

More articles in Journal of Management Analytics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().