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#REDIRECT [[Demand forecasting#Calculating demand forecast accuracy]] {{R from merge}} {{R to section}}
{{Merge to|Forecast error|discuss=Talk:Calculating_demand_forecast_accuracy#Merger_proposal_with_.27Forecast_Error.27|date=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 percentage error|Mean Absolute Percent Error]] or MAPE. 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. 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.
==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 percentage error|Mean Percent Error]], [[Root mean squared error|Root Mean Squared Error]], [[Tracking signal|Tracking Signal]] and [[Forecast bias|Forecast Bias]]..
== Reducing forecast error ==
[[Reference class forecasting]] was developed to increase forecasting accuracy by framing the forecasting problem so as to take into account available distributional information.<ref>[http://www.sbs.ox.ac.uk/centres/bt/Documents/Curbing%20Optimism%20Bias%20and%20Strategic%20Misrepresentation.pdf Flyvbjerg, B., 2008, "Curbing Optimism Bias and Strategic Misrepresentation in Planning: Reference Class Forecasting in Practice." European Planning Studies, vol. 16, no. 1, January, pp. 3-21.]</ref> Daniel Kahneman, winner of the Nobel Prize in economics, calls the use of reference class forecasting "the single most important piece of advice regarding how to increase accuracy in forecasting.”<ref>Daniel Kahneman, 2011, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux), p. 251</ref>
== See also ==
*[[Consensus forecasts]]
*[[Demand forecasting]]
*[[Optimism bias]]
*[[Reference class forecasting]]
==References==
{{Reflist}}
* Chockalingam, Mark (2001) [http://www.demandplanning.net/documents/dmdaccuracywebVersions.pdf "Tracking and Measurement of Demand Forecast Accuracy and Implications for Safety stock Strategies"], DemandPlanning.Net
* Chockalingam, Mark (2011) [http://demandplanning.net/DemandMetricsExcelTemp.htm "Demand Metrics Diagnostics Template"], DemandPlanning.Net
* Hyndman, R.J., Koehler, A.B (2005) [http://www.robjhyndman.com/papers/mase.pdf " Another look at measures of forecast accuracy"], Monash University.
* Hoover, Jim (2009) [http://forecasters.org/pdfs/foresight/Foresight_ForecastAccReprint.pdf "How to Track Forecast Accuracy to Guide Process Improvement"], Foresight: The International Journal of Applied Forecasting.
==External links==
* [http://home.ubalt.edu/ntsbarsh/Business-stat/otherapplets/MeasurAccur.htm Forecast Accuracy Calculations]
[[Category:Supply chain management]]
[[Category:Statistical forecasting]]
[[Category:Demand]]
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