Forecasting Sales and Return Products for Retail Corporations and Bridging Among Them

Abstract

The purpose of this study is to show how we can bridge sales and return forecasts for every product of a retail store by using the best model among several forecasting models. Managers can utilize this information to improve customer's satisfaction, inventory management, or re-define policy for after sales support for specific products. The authors investigate multi-product sales and return forecasting by choosing the best forecasting model. To this aim, some machine learning algorithms including ARIMA, Holt-Winters, STLF, bagged model, Timetk, and Prophet are utilized. For every product, the best forecasting model is chosen after comparing these models to generate sales and return forecasts. This information is used to classify every product as “profitable,” “risky,” and “neutral,” The experiment has shown that 3% of the total products have been identified as “risky” items for the future. Managers can utilize this information to make some crucial decisions.