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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 Percent Error or MAPE. However, there are confusions between the statistical definition of MAPE and its application among Supply Chain Planners. Statistically 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.
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 Percent Error, Root Mean Squared Error, Tracking Signal and 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
References
Hyndman, R.J., Koehler, A.B (2005) " Another look at measures of forecast accuracy", Monash University.