He has also held positions as head of sales and marketing for Gloria Jean’s Coffees and Domino’s Pizza for the Cyprus market. Before that, he worked for one of the biggest distribution and 3PL organizations in the Middle East, Transmed Overseas, directly managing supply chain operations for companies such as P&G, Clorox, Danone, Kellogg’s and Duracell. Video explaining how to interpre the error metrics here: Download a copy of the Excel workbook here: MAP MSE MAPE-V2.Before joining Muscat University as senior lecturer in the Faculty of Transport & Logistics, Can worked as a consultant (Middle East & Africa) for a Netherlands-based inventory optimization company. View the video explaining the workbook here: Here is an image of the Excel workbook you can download: SMAPE is the forecast minus actuals divided by the sum of forecasts and actuals as expressed in this formula: SMAPE self-limits to an error rate of 200%, reducing the influence of zero or near-zeros observations.
Symmetric Mean Absolute Percent Error (SMAPE) is an alternative to Mean Absolute Percent Error (MAPE) when there are zero or near-zero values in your actual observations. SMAPE, Symmetric Mean Absolute Percent Error, can be used where there are zero or near-zeros values in the actual data. MAPE should not be used if there are zeros or near-zeros in the actual data. Mean Absolute Percentage Error (MAPE) is the average of absolute errors divided by actual observation values. RMSE is used to convert MSE back into the same units as the actual data. The Root Mean Square Error (RMSE) is the square root of the MSE. The MSE is the sum of the squared errors divided by the number of observations. It penalizes larger errors because squaring larger numbers has a greater impact than squaring smaller numbers. Mean square error (MSE) is probably the most commonly used error metric. MAD is the same as MAE, Mean Absolute Error. The Mean Absolute Deviation (MAD) is the sum of absolute differences between the actual value and the forecast divided by the number of observations. With Excel 2016 or later, this is easy to do. To optimize your forecast, whether moving average, exponential smoothing or another form of a forecast, you need to calculate and evaluate MAD, MSE, RMSE, and MAPE.
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