Optimal Retail Location: Empirical Methodology and Application to Practice

30 Pages Posted: 24 Sep 2016 Last revised: 7 May 2017

See all articles by Chloe Glaeser

Chloe Glaeser

University of Pennsylvania, The Wharton School, Operations & Information Management Department, Students

Marshall Fisher

University of Pennsylvania - Operations & Information Management Department

Xuanming Su

University of Pennsylvania - Operations & Information Management Department

Date Written: May 6, 2017

Abstract

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

Suggested Citation

Glaeser, Chloe and Fisher, Marshall and Su, Xuanming, Optimal Retail Location: Empirical Methodology and Application to Practice (May 6, 2017). Available at SSRN: https://ssrn.com/abstract=2842064 or http://dx.doi.org/10.2139/ssrn.2842064

Chloe Glaeser (Contact Author)

University of Pennsylvania, The Wharton School, Operations & Information Management Department, Students ( email )

Philadelphia, PA 19104
United States

Marshall Fisher

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

Xuanming Su

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
436
rank
61,523
Abstract Views
1,565
PlumX Metrics