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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 [[customer]] demand is key to any [[manufacturer]] to make and keep sufficient long-lead inventory so that customer orders can be correctly met. Forecasts are never perfect but are valuable in better preparedness for the actual demand. Accurate and timely demand plans are a vital component of an effective [[supply chain]]. Although revenue forecast accuracy is important for corporate planning, forecast accuracy at the [[SKU]] level is critical for proper allocation of resources.
==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 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)|} {Demand} </math>
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| <math> Accuracy (%) = \left ( 1 - Error (%) \right ) </math>
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==How do you define Forecast Accuracy?==
What is the impact of Large Forecast Errors? Is Negative accuracy meaningful? Regardless of errors much higher than 100% of the actual demand or forecast demand, we interpret accuracy as a number between 0% and 100%. Either a forecast is perfect (100%) or relatively accurate or inaccurate or just plain incorrect (0%). So we constrain accuracy to be between 0 and 1.
If actual quantity is identical to forecast => 100% accuracy.
If Error > 100%, then this implies forecast Accuracy = 0%.
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 calulate 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-personal.buseco.monash.edu.au/~hyndman/papers/mase.pdf Alternate Forecast Measures]
* [http://home.ubalt.edu/ntsbarsh/Business-stat/otherapplets/MeasurAccur.htm Forecast Accuracy Calculations]
[[Category:Supply chain management]]
[[Category:Statistical forecasting]]
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