A data-driven approach to grocery store block layout

Retailers are a major component of almost any supply chain and are the interface between customers and goods. A ubiquitous and important retailing segment is grocery stores, yet almost no analytical work in the block design can be found in the literature. This paper uses a data-driven approach coupled with optimization to address block layout in grocery stores with the participation of Migros, the largest retailer in Turkey. The goal is to develop an effective analytical method for solving realistic grocery store block layout problems considering data which describes revenue generation and adjacency of departments. Historic market basket data is used to characterize certain important aspects that relate to customer sales and these are used in a tabu search meta-heuristic to find layouts which are likely to enhance revenue. To consider the objectives of revenue and adjacency simultaneously, a bi-objective approach is used. A set of non-dominated designs is generated for a decision maker to consider further and the generated designs have been validated with a detailed stochastic simulation model and by the marketing experts at Migros. According to the computational results and the feedback from the industry partner, this approach is both effective and pragmatic for a data-driven, analytic design of grocery store block layouts. Layout designs which improve revenues and desired adjacencies relative to the existing store layouts are identified. While this paper focuses on a single retailer, the approach is general and given that grocery layout is similar worldwide, the method and results should be easily translatable to other retailers.