Causal-Based Rack Layout Optimization in Retail: Incorporating Agent-Based Modeling and Causal Discovery

Rack layout design and optimization is a main research problem in the realm of retail management. To optimize the rack layouts considering customers' preferences and routing behaviors against different layouts, agent-based models (ABM) have been developed and applied. However, applying conventional analysis methods to analyze the model may not be sufficient to proactively propose and evaluate explainable layout patterns that optimize pre-defined metrics. In this work, we extend a causal-based ABM analysis method to enable a causal understanding of ABM models across multiple scenarios and incorporate it to a real-world data calibrated model that simulates the customers' in-store traffic for rack layout optimization. By elucidating the causal relations and changes among customers' preferences, movements, and layout patterns, we demonstrated that the incorporated model and analysis method can enable causal explanation empowered rack layout optimization for improving the customer experience and store revenue, compared with conventional ABM analysis methods.