When it comes to demand forecasting, incorporating out-of-stock (OOS) data is a complex subject that we first addressed in a prior blog post. It covered the three primary methods for incorporating OOS when developing your demand forecast: exclusion, imputation, and flagging.Read Now >
Demand planning is a well-established industry process for determining the level of future demand, so production, logistics, and other teams can plan their activities to meet that demand. However, what is perhaps less clear is how a process known as demand sensing is being adopted by progressive and innovative firms to complement demand planning and build a competitive edge in today’s fast-moving consumer goods market.Read Now >
Out-of-stock (OOS) events present a real conundrum to consumer goods companies when forecasting demand. On the one hand, you can’t simply ignore previous OOS events if you hope to prevent them in the future, but on the other hand, determining the “true demand” if out-of-stocks had not caused lost sales is a complex undertaking.Read Now >
As everyone in the consumer goods industry knows, demand forecasting is a demanding business. A serious attention to detail is required to achieve best-in-class accuracy levels.Read Now >
Forecasting is the process of estimating future demand in order to help make better-informed business decisions today. Across consumer goods brands, teams use forecasts to more intelligently allocate resources, optimize inventory, and maximize revenue.
However, companies vary widely in their forecast accuracy. According to the Institute of Business Forecasting & Planning (IBF), the retail industry averages a 30% error rate when forecasting products one month in advance, and “Best-in-Class Forecast Errors” is even a term in the IBF glossary.Read Now >
Consumer goods companies rely on forecasts to support inventory planning and distribution across their sales channels. Building accurate demand forecasts requires more than just an understanding of the latest machine learning techniques; it also requires the right data and an understanding of the potential costs of incorrect estimates.Read Now >