  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.
