Revision as of 10:22, 18 January 2017 edit Vickysardar243 (talk \| contribs) 6 edits m →Calculating the accuracy of supply chain forecasts ← Previous edit		Revision as of 10:44, 18 January 2017 edit undo Materialscientist (talk \| contribs) Edit filter managers, Autopatrolled, Checkusers, Rollbackers, Administrators 2,037,595 edits m Reverted edits by Vickysardar243 (talk) to last version by David.moreno72 Next edit →
Line 13: Last but not least, for intermittent demand patterns none of the above are really useful. 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 For a normal distribution, Mean Absolute Deviation (MAD) is about 0.8 times the standard deviation.In some cases with intermittent demand patterns, for example spare parts inventory, it is found that the MAD to standard deviation ratio is either more or very less than 0.8. So, statistically, in some scenarios, intermittent demand need not follow normal distribution due to the occurrence of zero values for many time intervals. You can find the supporting discussion here: ~~https://www.researchgate.net/publication/309731283_Study_of_Bias_Associated_with_forecasting_of_Intermittent_Demands~~ ==Calculating forecast error==

Calculating demand forecast accuracy: Difference between revisions