Using Machine Learning and Demand Sensing to Enhance Short Term Forecasting for CPGs
Consumer packaged goods companies (CPGs) account for some of the biggest industries in the world, providing essential items on a regular basis. Supply chain management at CPGs is complex because several products are supplied through multiple channels and distribution methods. Products follow complex order patterns characterized by promotional events, seasonal influences, natural disasters, and so on. Given this complexity, it is crucial to generate accurate short term forecasts of order quantities that reflect the realistic demand for products. Such forecasts enable companies to drive an efficient supply chain response to improve customer service. This paper uses machine learning along with traditional time series forecasting models to generate enhanced weekly and daily forecasts by using historical demand signal data and point of sale data. The model first creates enhanced weekly forecasts, and then breaks down enhanced weekly forecasts into daily forecasts. For weekly forecasts, a combination of a traditional time series forecasting model and a neural network is used to create a productwise forecast. This model combination allows for capturing the complex weekly order patterns and provides an accurate forecast of product demand. Weekly forecasts are divided into daily forecasts using an ensemble of three models: a seasonal model, a trend model, and a neural network model. The paper discusses the methodology behind this approach, along with shortterm forecasting results.