Data mining-based algorithm for assortment planning  

作　　者：Praveen Ranjan Srivastava Satyendra Sharma Simran Kaur 

机构地区：[1]Information Technology,Indian Institute of Management(IIM)Rohatak,Rohtak,India [2]Operation and Supply Chain,Birla Institute of Technology and Science(BITS)Pilani,Pilani,Rajasthan,India [3]Computer Science,Birla Institute of Technology and Science(BITS)Pilani,Pilani,Rajasthan,India

出　　处：《Journal of Management Analytics》2020年第3期443-457,共15页管理分析学报（英文）

摘　　要：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.

关 键 词：Retail assortment SKU rationalization data mining 

分 类 号：F42[经济管理—产业经济]