Utilizing artificial neural networks to predict demand for weather-sensitive products at retail stores

TL;DR

This paper develops neural network-based demand prediction models for weather-sensitive retail products, aiming to improve inventory management during extreme weather events using historical Walmart sales data.

Contribution

It introduces a systematic approach employing artificial neural networks to forecast demand for weather-sensitive products, enhancing accuracy over traditional methods.

Findings

Neural networks improve demand prediction accuracy.

Models effectively capture demand spikes during weather events.

Enhanced inventory management reduces stockouts and overstocking.

Abstract

One key requirement for effective supply chain management is the quality of its inventory management. Various inventory management methods are typically employed for different types of products based on their demand patterns, product attributes, and supply network. In this paper, our goal is to develop robust demand prediction methods for weather sensitive products at retail stores. We employ historical datasets from Walmart, whose customers and markets are often exposed to extreme weather events which can have a huge impact on sales regarding the affected stores and products. We want to accurately predict the sales of 111 potentially weather-sensitive products around the time of major weather events at 45 of Walmart retails locations in the U.S. Intuitively, we may expect an uptick in the sales of umbrellas before a big thunderstorm, but it is difficult for replenishment managers to predict the level of inventory needed to avoid being out-of-stock or overstock during and after that storm. While they rely on a variety of vendor tools to predict sales around extreme weather events, they mostly employ a time-consuming process that lacks a systematic measure of effectiveness. We employ all the methods critical to any analytics project and start with data exploration. Critical features are extracted from the raw historical dataset for demand forecasting accuracy and robustness. In particular, we employ Artificial Neural Network for forecasting demand for each product sold around the time of major weather events. Finally, we evaluate our model to evaluate their accuracy and robustness.