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Rick Glover on LinkedIn described his calculation of BIAS this way: Calculate the BIAS at the lowest level (for example, by product, by location) as follows: BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units. If the forecast is greater than actual demand than the bias is positive (indicates over-forecast).What is bias in forecasting?
It is an average of non-absolute values of forecast errors. If it is negative, a company tends to over-forecast; if positive, it tends to under-forecast. In the machine learning context, bias is how a forecast deviates from actuals. In new product forecasting, companies tend to over-forecast.How to correct forecast error?
Once bias has been identified, correcting the forecast error is quite simple. It can be achieved by adjusting the forecast in question by the appropriate amount in the appropriate direction, i.e., increase it in the case of under-forecast bias, and decrease it in the case of over-forecast bias.How do you calculate forecast accuracy?
Forecast Accuracy = 1 - ([Asolute Variance] / SUM ([Forecast])) Put the first 3 columns and the first measure into a table. Put the second measure into a card visualization. Your Forecast Accuracy will work in your table as well for the forecast accuracy of each material.