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Sales forecasting is the prerequisite for a lot of managerial decisions suchas production planning, material resource planning and budgeting in the supplychain. Promotions are one of the most important business strategies that areoften used to boost sales. While promotions are attractive for generatingdemand, it is often difficult to forecast demand in their presence. In the pastfew decades, several quantitative models have been developed to forecast salesincluding statistical and machine learning models. However, these methods maynot be adequate to account for all the internal and external factors that mayimpact sales. As a result, qualitative models have been adopted along withquantitative methods as consulting experts has been proven to improve forecastaccuracy by providing contextual information. Such models are being usedextensively 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 toforecast promotional sales where a combination of factors such as price, typeof promotions, and product location impacts sales. We choose to develop a BNmodel because BN models essentially have the capability to combine variousqualitative and quantitative factors with causal forms, making it an attractivetool for sales forecasting during promotions. This can be used to adjust acompany's promotional strategy in the context of this case study. We gathersales data for a particular product from a retailer that sells products inAustralia. We develop a Bayesian Network for this product and validate ourresults by empirical analysis. This paper confirms that BNs can be effectivelyused to forecast sales, especially during promotions. In the end, we providesome research avenues for using BNs in forecasting sales.

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