For some time now, SAP have been clear with their intention to develop forecast automation via machine learning capability and functionality. With the 1811 release of SAP Integrated Business Planning (IBP), SAP are really starting on their machine learning journey by leveraging content within their Predictive Analytics Library (PAL) and have introduced Gradient Boosting of Decision Trees (GBDT) to the available set of statistical forecasting algorithms within the IBP for demand license.

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Forecast Accuracy defines how accurate the forecast works against the actual sales and is usually defined in percentage terms as;

Forecast Accuracy = 1 – Forecast Error

Forecast Error determines the deviation between the forecast and the actual demand/sales.

As Evan Esar’s saying goes "An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today".

So, to be able to react quickly, it is very important to understand why this deviation has occurred. As there are various Error Measures available in software tools such as SAP APO or SAP IBP, it is vital to understand which error measure is applicable under what circumstances.

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In a previous blog (Should you be Demanding IBP?), I highlighted some of the high level differences between SAP APO Demand Planning and SAP Integrated Business Planning (IBP) for demand. 

With the impending release of IBP v6.3 due later this year, I intend to go under the bonnet of the IBP for demand statistical forecasting engine to see if all that glisters is really gold.

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