Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. For the example you give it is indeed correct that 10 units error for demand of 90 is slightly worse than 10 units error for demand of 100. But once you understand how to interpret, one might be enough. MAPE is asymmetric and reports higher errors if the forecast is more than the actual and lower errors when the forecast is less than the actual.

One solution is to first segregate the items into different groups based upon volume (e.g., ABC categorization) and then calculate separate statistics for each grouping. Request a Demo of The Arkieva Supply Chain Software Suite Start Now Enjoyed this post? The difference between At and Ft is divided by the Actual value At again. Melde dich bei YouTube an, damit dein Feedback gezÃ¤hlt wird.

A few of the more important ones are listed below: MAD/Mean Ratio. You try two models, single exponential smoothing and linear trend, and get the following results: Single exponential smoothing Statistic Result MAPE 8.1976 MAD 3.6215 MSD 22.3936 Linear trend Statistic Result MAPE Whether it is erroneous is subject to debate. Last but not least, for intermittent demand patterns none of the above are really useful.

The Forecast Error can be bigger than Actual or Forecast but NOT both. So we constrain Accuracy to be between 0 and 100%. Follow us on LinkedIn or Twitter and we will send you notifications on all future blogs. Contents 1 Importance of forecasts 2 Calculating the accuracy of supply chain forecasts 3 Calculating forecast error 4 See also 5 References Importance of forecasts[edit] Understanding and predicting customer demand is

When we talk about forecast accuracy in the supply chain, we typically have one measure in mind namely, the Mean Absolute Percent Error or MAPE. Mean Absolute Percent Error (MAPE) is a very commonly used metric for forecast accuracy.Â The MAPE formula consists of two parts: M and APE. By using this site, you agree to the Terms of Use and Privacy Policy. MAPE delivers the same benefits as MPE (easy to calculate, easy to understand) plus you get a better representation of the true forecast error.

Either a forecast is perfect or relative accurate or inaccurate or just plain incorrect. For this reason, consider using Mean Absolute Deviation (MAD) alongside MAPE, or consider weighted MAPE (more on these in a separate post in the future). This post is part of the Axsium Retail Forecasting Playbook, a series of articles designed to give retailers insight and techniques into forecasting as it relates to the weekly labor scheduling VerÃ¶ffentlicht am 13.12.2012All rights reserved, copyright 2012 by Ed Dansereau Kategorie Bildung Lizenz Standard-YouTube-Lizenz Mehr anzeigen Weniger anzeigen Wird geladen...

Small wonder considering weâ€™re one of the only leaders in advanced analytics to focus on predictive technologies. Planning: »Budgeting »S&OP Metrics: »DemandMetrics »Inventory »CustomerService Collaboration: »VMI&CMI »ABF Forecasting: »CausalModeling »MarketModeling »Ship to Share For Students MAPE and Bias - Introduction MAPE stands for Mean Absolute Percent Error - Please help improve this article by adding citations to reliable sources. The statistic is calculated exactly as the name suggests--it is simply the MAD divided by the Mean.

So, they are different, at least at the definition level. Solutions Sales Forecasting SoftwareInventory Management SoftwareDemand Forecasting SoftwareDemand Planning SoftwareFinancial Forecasting SoftwareCash Flow Forecasting SoftwareS&OP SoftwareInventory Optimization SoftwareProducts Vanguard Forecast ServerDemand Planning ModuleSupply Planning ModuleFinancial Forecasting ModuleBudgeting ModuleReporting ModuleAdvanced AnalyticsVanguard SystemBusiness However, there is a lot of confusion between Academic Statisticians and corporate Supply Chain Planners in interpreting this metric. On-Premise Supply Chain Software: And the Winner Isâ€¦.

SMAPE. This, however, is also biased and encourages putting in higher numbers as forecast. There are several forms of forecast error calculation methods used, namely Mean Percent Error, Root Mean Squared Error, Tracking Signal and Forecast Bias.. Email: Please enable JavaScript to view.

WÃ¤hle deine Sprache aus. For reporting purposes, some companies will translate this to accuracy numbers by subtracting the MAPE from 100. A fair comparison would have been if actual demand were 100 units in both cases but forecasts were 90 and 110 respectively. MAPE does not provide a good way to differentiate the important from not so important.

Wird geladen... There is a very long list of metrics that different businesses use to measure this forecast accuracy. Of course you can measure it instead at aggregate levels, but as you correctly state the MAPE paints a very rosy picture when you do this. By using this site, you agree to the Terms of Use and Privacy Policy.

Very good papers. There are a slew of alternative statistics in the forecasting literature, many of which are variations on the MAPE and the MAD. HinzufÃ¼gen Playlists werden geladen... Most academics define MAPE as an average of percentage errors over a number of products.

Fax: Please enable JavaScript to see this field. The MAD/Mean ratio tries to overcome this problem by dividing the MAD by the Mean--essentially rescaling the error to make it comparable across time series of varying scales.