Forecasting sales with Bayesian networks: a case study of a supermarket product in the presence of promotions

TL;DR

This paper demonstrates how Bayesian Networks can effectively forecast promotional sales by integrating various qualitative and quantitative factors, providing a valuable tool for strategic promotional planning in retail.

Contribution

It introduces a Bayesian Network model for sales forecasting during promotions, combining multiple factors and validating its effectiveness with empirical data from a supermarket case study.

Findings

Bayesian Networks improve sales forecast accuracy during promotions.

The model effectively integrates price, promotion type, and location factors.

Results support BNs as a valuable tool for strategic promotional decisions.

Abstract

Sales forecasting is the prerequisite for a lot of managerial decisions such as production planning, material resource planning and budgeting in the supply chain. Promotions are one of the most important business strategies that are often used to boost sales. While promotions are attractive for generating demand, it is often difficult to forecast demand in their presence. In the past few decades, several quantitative models have been developed to forecast sales including statistical and machine learning models. However, these methods may not be adequate to account for all the internal and external factors that may impact sales. As a result, qualitative models have been adopted along with quantitative methods as consulting experts has been proven to improve forecast accuracy by providing contextual information. Such models are being used extensively to account for factors that can lead to a rapid change in sales, such as during promotions. In this paper, we aim to use Bayesian Networks to forecast promotional sales where a combination of factors such as price, type of promotions, and product location impacts sales. We choose to develop a BN model because BN models essentially have the capability to combine various qualitative and quantitative factors with causal forms, making it an attractive tool for sales forecasting during promotions. This can be used to adjust a company's promotional strategy in the context of this case study. We gather sales data for a particular product from a retailer that sells products in Australia. We develop a Bayesian Network for this product and validate our results by empirical analysis. This paper confirms that BNs can be effectively used to forecast sales, especially during promotions. In the end, we provide some research avenues for using BNs in forecasting sales.