Look closer at out-of-stocks to improve forecast accuracy

Jun 12, 2019  |  5 min read

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.

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4 ways demand sensing and planning work together

Mar 21, 2019  |  4 min read

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.

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How to incorporate out-of-stocks (OOS) in forecasting

Mar 19, 2019  |  4 min read

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.

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The role of seasonality in demand forecasting success

Feb 28, 2019  |  3 min read

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.

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What makes a demand forecast best-in-class

Feb 19, 2019  |  4 min read

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.

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Demand forecasting mistakes in the retail industry

Dec 27, 2018  |  4 min read

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.

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