Using Transactions Data to Improve Consumer Returns Forecasting

37 Pages Posted: 9 Jan 2020

See all articles by Guangzhi Shang

Guangzhi Shang

Florida State University - College of Business

Erin C. McKie

Ohio State University (OSU) - Department of Management Sciences

Mark Ferguson

University of South Carolina - Department of Management Science

Michael Galbreth

University of Tennessee, Knoxville - Haslam College of Business

Date Written: September 10, 2019

Abstract

Although generous return policies have been shown to have marketing benefits, such as a higher willingness to pay and a higher purchase frequency, counterbalancing these benefits is an increased volume of consumer returns, which presents significant operational challenges for both retailers and original equipment manufacturers (OEMs). Since accurate return forecasts are inputs into strategic and tactical decision support tools for operations managers, advancements in forecast accuracy can yield better-managed returns. The forecasting approach developed in this paper incorporates transaction-level data, such as purchase and return timestamps, and predicts future return quantities using a two-step “predict–aggregate” process. To enhance the generalizability of our framework, we test it on two distinct datasets provided by a bricks-and-mortar electronics retailer and an online jewelry retailer. We find that our approach demonstrates significant forecasting error reduction, in the range of 10% to 20%, over benchmark models constructed from common industry practice and existing literature. As our approach leverages the same data inputs as existing models, it can be easily adapted by practitioners. We also consider a number of extensions to generalize our approach into contexts such as restricted return time window, new product returns, and inflated same-day returns. Last, we discuss broad implications of return forecast accuracy improvements in the areas such as inventory management, staffing level, reverse logistics, and return recovery decisions.

Keywords: Consumer Returns, Closed-Loop Supply Chains, Retail Operations, Forecasting

Suggested Citation

Shang, Guangzhi and McKie, Erin and Ferguson, Mark and Galbreth, Michael, Using Transactions Data to Improve Consumer Returns Forecasting (September 10, 2019). Available at SSRN: https://ssrn.com/abstract=3505035 or http://dx.doi.org/10.2139/ssrn.3505035

Guangzhi Shang

Florida State University - College of Business ( email )

423 Rovetta Business Building
Tallahassee, FL 32306-1110
United States

Erin McKie

Ohio State University (OSU) - Department of Management Sciences ( email )

United States

HOME PAGE: http://fisher.osu.edu/people/mckie.5

Mark Ferguson (Contact Author)

University of South Carolina - Department of Management Science ( email )

United States

Michael Galbreth

University of Tennessee, Knoxville - Haslam College of Business ( email )

453 Haslam Business Building
Knoxville, TN 37996-4140
United States

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