Effect of Customer Travel Behavior on Grid Layout and Shelf -- A Solver-Free Heuristic for Store-wide Shelf Space Allocation -- In-Store Tra c Density Estimation -- A Simulation Based Tool to Guide Periodic Changes in a Supermarket Layout -- Data-Driven Analytical Grocery Store Design -- Optimizing Stock-Keeping Unit Selection for Promotional Display Space at Grocery Retailers -- Merchandise Placement Optimization -- Problems and Opportunities of Applied Optimization Models in Retail Space Planning.

This edited volume presents state-of-the-art research that can leverage large-scale sensory data collected in grocery/retail stores where a single customer visit may generate nearly 10,000 data points. For decades, retail shelf space optimization has been confined to the analysis of product allocation decisions over a limited number of shelves, often taken in isolation. Such models incorporated interesting concepts relating to space and cross-space elasticity in the design of planograms. Although useful, these models have not addressed the bigger picture of planning store shelf space in a more holistic manner. It is important to note that the space planning analytics in the book are particularly important in an era where e-commerce is on the rise and brick-and-mortar retailing is declining and experiencing severe crises (the retail apocalypse). This is the first research-oriented book that examines novel problems in store space analytics, triggered by modern-day sensory technologies, customer trackers, and transactional tools (point-of-sales, etc.). In fact, such transformative technologies have prompted the development of new and exciting business practices, accompanied by the need for powerful data-driven models and analyses in retail shelf space and layout planning. The book will facilitate developing algorithms and decision tools that allow a better leverage of the data collected from these mediums.