Optimal Retail Location: Empirical Methodology and Application to Practice
Posted: 24 Sep 2016 Last revised: 3 Jun 2019
Date Written: May 6, 2017
We empirically study the spatio-temporal location problem motivated by an online retailer that uses the Buy-Online-Pick-Up-In-Store fulfillment method. Customers pick up their orders from trucks parked at specific locations on specific days, and the retailer's problem is to determine where and when these pick-ups occur. Customer demand is influenced by the convenience of pick-up locations and days. We combine demographic and economic data, business location data, and the retailer's historical sales and operations data to predict demand at potential locations. We introduce a novel procedure that combines machine learning and econometric techniques. First, we use a fixed effects regression to estimate spatial and temporal cannibalization effects. Then, we use a random forests algorithm to predict demand when a particular location operates in isolation. Based on the predicted demand, we solve the spatio-temporal integer program using quadratic program relaxation to find the optimal pick-up location configuration and schedule. We estimate a revenue increase of at least 42% from the improved location configuration and schedule.
Keywords: Empirical Operations, Location, Scheduling, Retailing, Machine Learning, Optimization
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