  Reliable demand forecasts are critical for the effective supply chain
management. Several endogenous and exogenous variables can influence the
dynamics of demand, and hence a single statistical model that only consists of
historical sales data is often insufficient to produce accurate forecasts. In
practice, the forecasts generated by baseline statistical models are often
judgmentally adjusted by forecasters to incorporate factors and information
that are not incorporated in the baseline models. There are however systematic
events whose effect can be effectively quantified and modeled to help minimize
human intervention in adjusting the baseline forecasts. In this paper, we
develop and test a novel regime-switching approach to quantify systematic
information/events and objectively incorporate them into the baseline
statistical model. Our simple yet practical and effective model can help limit
forecast adjustments to only focus on the impact of less systematic events such
as sudden climate change or dynamic market activities. The proposed model and
approach is validated empirically using sales and promotional data from two
Australian companies. Discussions focus on a thorough analysis of the
forecasting and benchmarking results. Our analysis indicates that the proposed
model can successfully improve the forecast accuracy when compared to the
current industry practice which heavily relies on human judgment to factor in
all types of information/events.
