TY - JOUR
T1 - A data mining approach to product assortment and shelf space allocation
AU - Chen, Mu-Chen
AU - Lin, Chia Ping
PY - 2007/5/1
Y1 - 2007/5/1
N2 - In retailing, a variety of products compete to be displayed in the limited shelf space since it has a significant effect on demands. To affect customers' purchasing decisions, retailers properly make decisions about which products to display (product assortment) and how much shelf space to allocate the stocked products (shelf space allocation). In the previous studies, researchers usually employed the space elasticity to optimize product assortment and space allocation models. The space elasticity is usually used to construct the relationship between shelf space and product demand. However, the large number of parameters requiring to estimate and the he non-linear nature of space elasticity can reduce the efficacy of the space elasticity based models. This paper utilizes a popular data mining approach, association rule mining, instead of space elasticity to resolve the product assortment and allocation problems in retailing. In this paper, the multi-level association rule mining is applied to explore the relationships between products as well as between product categories. Because association rules are obtained by directly analyzing the transaction database, they can generate more reliable information to shelf space management.
AB - In retailing, a variety of products compete to be displayed in the limited shelf space since it has a significant effect on demands. To affect customers' purchasing decisions, retailers properly make decisions about which products to display (product assortment) and how much shelf space to allocate the stocked products (shelf space allocation). In the previous studies, researchers usually employed the space elasticity to optimize product assortment and space allocation models. The space elasticity is usually used to construct the relationship between shelf space and product demand. However, the large number of parameters requiring to estimate and the he non-linear nature of space elasticity can reduce the efficacy of the space elasticity based models. This paper utilizes a popular data mining approach, association rule mining, instead of space elasticity to resolve the product assortment and allocation problems in retailing. In this paper, the multi-level association rule mining is applied to explore the relationships between products as well as between product categories. Because association rules are obtained by directly analyzing the transaction database, they can generate more reliable information to shelf space management.
KW - Data mining
KW - Multi-level association rules
KW - Shelf space management
KW - Zero-one integer programming
UR - http://www.scopus.com/inward/record.url?scp=33751527220&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2006.02.001
DO - 10.1016/j.eswa.2006.02.001
M3 - Article
AN - SCOPUS:33751527220
VL - 32
SP - 976
EP - 986
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 4
ER -