Joint shelf design and shelf space allocation problem for retailers

Abstract

Effective design of planograms in a retail store can improve the visual representation of products on shelves. Existing approaches, however, assume that the shelf length and height are fixed, which can result in unused space on the planogram and suboptimal assignment of product facings, both resulting in reduced revenue for the retailer. To address this real-world challenge, we introduce the joint shelf design and shelf space allocation (JSD-SSA) problem, which determines the optimal shelf design along with product placement considering product family constraints. We propose a decomposition-based approach, which first partitions the planogram area considering product families and then allocates products in that family to the partitioned area. Our novel hybrid approach relies on Particle Swarm Optimization for partitioning and Constraint Programming for product assignment. We also propose two metrics; one to measure variation in product shapes within and between product families, and another to measure the space tightness of a planogram. Experiments indicate that shorter shelf lengths can increase retailers’ profit by up to 37% depending on the product-family shape variation. Higher within-family shape variation can result in higher revenue. Additionally, if product and planogram dimensions share a common factor or multiple, then more compact planograms can be designed, in turn reducing unused space and increasing a retailer's profit. Our approach is general enough to handle gondola, peg, bin, and mixed type of shelving typically found in retail stores.