High-frequency forecasting for grocery point-of-sales: intervention in practice and theoretical implications for operational design

Abstract

Food waste in grocery supply chains may exceed one third of the total volume, depending on the category. To address this problem effectively, grocery retailers are introducing automated systems for more efficient store replenishment and dynamic pricing. The stock keeping unit (SKU) and store level forecast is pivotal in these operations management solutions, but operationally challenging. Large grocery retailers have millions of SKU-store combinations that depending on the operational application would need to be forecasted on a weekly, daily, hourly, or even 15-min frequency. However, in grocery it is challenging to account for demand variation at high frequencies without introducing manual decisions into the process of forecast model configuration. To investigate the limits of current practice and explore opportunities of technology-enabled change, we explore how an advanced forecasting method for electricity demand, called TBATS, can automate daily forecasting for grocery store replenishment. Adopting an interventionist approach, we explore the implications for the design of the operational process in the operational setting provided by the case company. We find that TBATS can produce high frequency base forecasts for the SKU-store level accurately for a period exceeding 3 months. This finding points to the opportunity of shifting operational focus from recalculating forecasts to monitoring forecast errors. Introducing variable, even indefinite re-training frequencies for forecasting models is a significant change of the forecasting process for situations where monitoring requires less computation than retraining, potentially reducing the time and cost associated with increasing the forecast frequency.