Problems and Opportunities of Applied Optimization Models in Retail Space Planning

Shelf space analytics have received great attention among researchers for years. The allocation of different products with a certain number of facings (a facing corresponds to one visible unit of the product on the shelf) to limited shelf space is a prime example of applying quantitative methods for a practical use case. Some approaches are of a more generic nature in order to create a basic understanding of the possibilities of decision support models while others aim to investigate specific impacts of different factors (e.g., cross-space elasticity, cost functions). A specific set of approaches provides applicable decision support tools that can be used to determine (almost) final planograms for retail stores. Retailers are subjected to continuous development alongside the many improvements provided by the latest research. The requirements for shelf space planning in practice are increasing with new technologies (whether computational power or data availability) and impacts from different fields of research (marketing, food chemistry, logistics, etc.). When shelf space models are created these specifications should be considered carefully in order to provide beneficial solutions. The intention of researchers and retailers conducting shelf space planning might differ from case to case. We have been able to gain much experience during a collaborative research project with one of Europe’s largest food retailers over the course of three years, drawing intensive comparisons between scientific work and practical requirements. This chapter will share the key findings from related processes based on our experience during this collaboration where several optimization models were developed and actually applied to stores. The goal is to provide insights from practice and point to new issues so that both retailers and researchers gain valuable information.