In many organisations, it is very common to use the statistical forecast as a baseline forecast.  Understanding and analysing of historical data are very important to generate a reliable statistical forecast. In most of the cases, demand planners rely on their team’s experience and conduct visual check (graphs) to understand historical sales pattern such as seasonality, trend, etc. SAP adds another feature in its SAP IBP software under Forecast Automation application, and through this application, demand planners can analyse historical demand data statistically without being too mathematical. In this blog, I will walk you through how it works and discuss some of its advantages and drawbacks of it.

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During my career I have worked as both a Demand and Supply planner, working across FMCG, Fresh Produce and Cosmetics industries. When it comes to creating baseline forecasts for New Product Introduction (NPI) or phasing out forecasts for End of Life (EoL), planners find it challenging to collect relevant information from various stake holders and spend more time crunching data sets than creating meaningful forecasts.

This blog will discuss the features of SAP's Integrated Business Planning (IBP) Product lifecycle application within the Demand module and how it will help planners to overcome these challenges and help create a more accurate base line forecast. Enjoy the read!

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