Search Results for: forecasting

… determined by the demand pattern of the item being forecasted. Some items are easy to forecast, and some are difficult. For example, a company with many intermittent demand items can’t match a company’s forecast accuracy with a large percentage of high-volume items in its database.
Issue #2: Forecasting Competence
Most companies tend to put minimal resources into forecasting. Even the most prominent companies have quite small demand planning departments. Therefore, forecasting in a way that matches or exceeds the average forecast accuracy of other companies is not much of an accomplishment.
Issue #3: Forecast Error Measurement Differences …

… to calculate than MAD or RMSE.
Because how sMAPE is calculated is that it is squared, the error is not proportional — meaning that more substantial errors become much more substantial as they are squared. Any error measurement that is not proportional makes it intuitively more difficult to understand than a proportional measure. How is a non-forecasting-focused person supposed to interpret nonproportional forecast errors? Our brain is designed to understand proportionality.

For example, the Richter Scale is a logarithmic scale, which allows a relatively small number of values to represent large magnitudes in difference. However, it also means humans

… to MAD, RMSE uses squared values. Because RMSE is squared, the error is not proportional — meaning that more significant errors become much more prominent as they are squared.
Any error measurement that is not proportional makes it intuitively more difficult to understand than a proportionate measure. How is a non-forecasting-focused person supposed to interpret nonproportional forecast errors? Our brain is designed to understand proportionality.

For example, the Richter Scale is a logarithmic scale, which allows a relatively small number of values to represent large magnitudes in difference. However, it also means humans cannot calculate the difference between different …

… uses absolute values, so one does not understand the bias. But because MAD is squared, the error is not proportional — meaning that larger errors become much more significant as they are squared. Any error measurement that is not proportional makes it intuitively more difficult to understand than a proportional measure. How is a non-forecasting-focused person supposed to interpret non-proportional forecast errors? Our brain is designed to understand proportionality.

For example, the Richter Scale is a logarithmic scale, which allows a relatively small number of values to represent large magnitudes in difference. However, it also means humans cannot

… calculation, MAPE was our favorite method.
Yet, after several years, the problems with MAPE continued to surface. And they are problems that all standard forecast error measurements have in common.
These include the following:

They are difficult to weigh. Any forecast error aggregated beyond the product location combination must be weighed to make any sense.
They make comparisons between different forecasting methods overly complicated.
They lack the context of the volume of the demand history or the price of the forecasted product, meaning that the forecast errors must be provided with context through another formula.
They are difficult to …

… a steady-state of error.
When a forecasting application produces a forecast error, it may provide multiple forecast error calculations (MAD, MAPE, MSE, etc..). It is invariably for a single forecast. As we cover in the article Forecast Error Myth #1: One Can Rely on The Forecast Error Measurement in Forecasting Applications, the forecast error measurement functionality in nearly all forecasting applications is extremely limited. Forecasting applications are designed to produce a non-comparative forecast error at a product location combination.
For All of the Myths, See the Following Table
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Forecasting Error Myths
Select the link below …

Executive Summary

The standard forecast error measurements are universally accepted.
What is rare is to ask the question of whether they direct companies to improve forecast accuracy.

Video Introduction: Forecast Error Myth #4: Most Forecast Error Measurement Supports Identifying How to Improve Forecast Accuracy
https://youtu.be/qMYYwHOFMvw
Text Introduction (Skip if You Watched the Video)
Forecast error calculation comes typically from accepting one of the standard forecast error calculation methods. Often, companies think they are finished with all of the decisions as soon as they select a method. And the forecasting literature, software vendors, and consulting firms do little …

… is an alien concept. It may be possible that Sales should get zero input into the Final forecast, or it should receive input and be the primary input, but only for three product categories. Which multiple stream forecast error measurement, no one knows.
The Problem of Forecast Error Measurement Within Forecasting Applications
Now that we have gone through the potential measurability based upon the availability of forecast data, we return to the subject we sped past back when discussing Option #3, which is how to perform the forecast error measurements of multiple forecast inputs.
A logical approach is to begin …