Intraday Retail Sales Forecast: An Efficient Algorithm for Quantile Additive Modeling

TL;DR

This paper introduces a fast, stable quantile additive modeling algorithm for intraday retail sales forecasting, enabling real-time shelf replenishment decisions based on accurate demand predictions.

Contribution

The paper presents a novel, computationally efficient quantile additive model algorithm tailored for intraday sales forecasting in retail environments.

Findings

Accurately estimates intraday sales for various products.

Demonstrates suitability for real-time shelf replenishment.

Achieves high computational efficiency for dynamic forecasting.

Abstract

With the ever increasing prominence of data in retail operations, sales forecasting has become an essential pillar in the efficient management of inventories. When facing high demand, the use of backroom storage and intraday shelf replenishment is necessary to avoid stock-out. In that context, the mandatory input for any successful replenishment policy to be implemented is access to reliable forecasts for the sales at an intraday granularity. To that end, we use quantile regression to adapt different patterns from one product to the other, and we develop a stable and efficient quantile additive model algorithm to compute sales forecasts in an intradaily context. Our algorithm is computationally fast and is therefore suitable for use in real-time dynamic shelf replenishment. As an illustration, we examine the case of a highly frequented store, where the demand for various alimentary products is accurately estimated over the day with the help of the proposed algorithm.