Calculating demand forecast accuracy
||It has been suggested that this article be merged into Forecast error. (Discuss) Proposed since January 2012.|
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.
A simpler and more elegant method to calculate MAPE across all the products forecasted is to divide the sum of the absolute deviations by the total sales of all products.
This calculation (Sum (Actual - Forecast) / Sum (Actual)) is also known as WAPE, Weighted Absolute Percent Error. Another interesting option is the weighted MAPE = Sum(weight*ABS((A-F))/Sum(weight*A) where A=Actual & F=Forecast. The advantage of this meassure is that could weight errors, so you can define how to weighted for your relevant business, ex gross proffit or ABC. The only problem is that for seasonal products you will create and indetermined result when sales = 0 and that is not symetrical, that means that you can be much more unnacurate if sales are higher than if they are lower than the forecast. So sMAPE is also used to correct this, it is known as symmetric Mean Absolute Percentage Error.
Last but not least, for intermitent demand patterns none of the above are really usefull. So you can consider MASE (Mean Absolute Scaled Error) as a good KPI to use in those situations, the problem is that is not as intuitive as the ones mentioned before. You can find an interesting discussion here: http://datascienceassn.org/sites/default/files/Another%20Look%20at%20Measures%20of%20Forecast%20Accuracy.pdf
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..
- Hyndman, R.J., Koehler, A.B (2005) " Another look at measures of forecast accuracy", Monash University.
- Hoover, Jim (2009) "How to Track Forecast Accuracy to Guide Process Improvement", Foresight: The International Journal of Applied Forecasting.