Calculating demand forecast accuracy is the process of determining the accuracy of forecasts made regarding customer demand for a product.
Importance of forecasts
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. 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. This is in effect a volume weighted MAPE. This is also referred to as the MAD/Mean ratio.
Calculating forecast error
The forecast error needs to be calculated using actual sales as a base. There are several forms of forecast error calculation methods 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.