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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.
Experts suggest that research featuring multiple hypotheses tested under ideal conditions would be accurate indicators of the accuracy of forecasting methods..[1]
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..
Reducing forecast error
Reference class forecasting was developed to reduce forecast error and increase forecast accuracy.[2]
See also
References
- ^ J. Scott Armstrong. "Findings from Evidence-based Forecasting: Methods for Reducing Forecast Error" (PDF). International Journal of Forecasting (forthcoming).
{{cite web}}: line feed character in|title=at position 42 (help) - ^ "Curbing Optimism Bias and Strategic Misrepresentation in Planning: Reference Class Forecasting in Practice." European Planning Studies, vol. 16, no. 1, January 2008, pp. 3-21.
- Chockalingam, Mark (2001) "Tracking and Measurement of Demand Forecast Accuracy and Implications for Safety stock Strategies", DemandPlanning.Net
- 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.