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#REDIRECT [[Demand forecasting#Calculating demand forecast accuracy]] {{R from merge}} {{R to section}}
{{Copyedit|article|date=January 2008}}
==Supply Chain forecasting==
Understanding and predicting customer demand is vital to manufacturers and distributors to avoid stock-outs and maintain adequate inventory levels. While forecasts are never perfect, they are necessary to prepare for actual demand. In order to maintain an optimized inventory and effective supply chain, accurate demand forecasts are imperative.
==Calculating the accuracy of supply chain forecasts==
Forecast accuracy in the supply chain is typically measured using the [[Mean absolute percentage error|Mean Absolute Percent Error]] or MAPE. However, there are confusions between the statistical definition of MAPE and its application among Supply Chain Planners. Statistically [[Mean absolute percentage error|MAPE]] is defined as the average of percentage errors. Most practitioners however define and use the MAPE as the Mean Absolute Deviation divided by Average Sales. You can think of this as a volume weighted MAPE. In some references, this is also referred to as the MAD/Mean ratio.
==Definition of forecast error==
''Demand Forecast Error is the deviation of the actual realized demand quantity from the Forecasted quantity. The denominator for the Error calculation has been debated in the literature as whether to use the acutal demand or the forecasted quantity.''
We take absolute values of the error because the magnitude of the error is more important than the direction of the error. The Forecast Error can be bigger than Actual or Forecast but NOT both. Error above 100% implies a zero forecast accuracy or a very inaccurate forecast..
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| <math>Error (%) = \frac {|(Actual - Forecast)|} {Forecast} </math>
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| <math> Accuracy (%) = \left ( 1 - Error (%) \right ) </math>
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==How do you define Forecast Accuracy?==
The forecast error needs to be calculated on Actual as base.
There are other alternate forms of forecast errors used namely [[Mean percentage error|Mean Percent Error]], [[Root mean squared error|Root Mean Squared Error]], [[Tracking signal|Tracking Signal]] and [[Forecast bias|Forecast Bias]].
==Simple methodology for MAPE==
An alternate methodology to calculate forecast error is to add the sum of the absolute errors and divide by either the forecast or the realized quantity.
== See also ==
*[[Demand forecasting]]
*[[Optimism bias]]
*[[Reference class forecasting]]
==External links==
* [http://www.demandplanning.net/documents/dmdaccuracywebVersions.pdf Mechanics of calculating forecast accuracy]
* [http://www.robjhyndman.com/papers/mase.pdf Alternate Forecast Measures]
* [http://home.ubalt.edu/ntsbarsh/Business-stat/otherapplets/MeasurAccur.htm Forecast Accuracy Calculations]
* [http://www.DemandPlanning.net Demand Planning.Net Resource]
* [http://www.forecastworldindex.com Indices Forecast]
[[Category:Supply chain management]]
[[Category:Statistical forecasting]]
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