SHELF SPACE ALLOCATION IN RETAILING: A LITERATURE REVIEW

Abstract

Efficient allocation of shelf space is vital for attracting customers and maximizing profits in the retail industry, particularly given limited display areas. This paper offers a comprehensive review of shelf-space allocation (SSA) modeling, optimization techniques, and relevant case studies from 1969 to 2023. We categorize the literature into five key areas: first, mathematical optimization, which includes deterministic, uncertain, dynamic, and joint optimization models that form the foundation of SSA literature and enhance decision-making in complex retail environments. Second, we explore data mining techniques, demonstrating how retailers can implement consumer preference insights and purchasing patterns to transition from intuition-based decisions to data-driven strategies. Third, the case studies section illustrates real-world applications of SSA, highlighting the challenges and successes faced by retailers. Fourth, we synthesize methodologies, evaluating various SSA approaches through empirical studies that identify best practices and guide efficient resource utilization. Finally, we outline significant gaps in current research and suggest directions for future inquiry, encouraging ongoing exploration of innovative methods to improve shelf-space strategies. By systematically addressing these categories, this paper aims to provide a clearer understanding of the complexities of SSA, ultimately contributing to both academic discourse and practical applications within the retail sector.