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Measure Forcast Accuracy

Best Way to Measure Forcast Accuracy

When it comes to evaluating how accurate forecasts are, businesses can use three standard methods. Understanding exactly what these methods mean and in what scenarios they’re most useful allows planners to choose the best method for their business.

Forecasting provides a barometer of current performance trends; it can help sales leaders understand if their sales team is falling short of the target and needing intervention. Forecasts are essential for making critical decisions about staffing, deals, and spending on critical accounts. These forecasts must be data-driven insights to reflect your company’s current performance trends accurately. Sales leaders can use the following insights to improve forecast accuracy.

Forecast accuracy is not always easy, particularly when sales leaders do not have the data to make accurate forecasts. Yet, high-quality insights are often essential to ensuring your prediction is correct. Here is a list of the best ways to measure forecast accuracy.

Best Way to Measure Forecast Accuracy

Percent difference or Percentage Error

The percentage difference or Percentage Error is a statistical measure that shows how close the forecast was to the actual data instead of the predicted value (the weather forecast in this example). This statistic is often used in weather forecasting because of its direct link to weather forecast accuracy.

Percent Difference is a measure of how close a future forecast is to the actual value, or inversely, a future forecast that is further away from actual is more comparable to a zero percent difference. Percentage error is another measure of forecast accuracy, which may be easier for some people to understand than the percent difference.

Standard Deviation

Standard deviation is a great way to measure the accuracy of a forecast. In this interactive data visualization, the standard deviation is shown in two different ways:

  1. As dotted lines
  2. As error bars around the point forecast (line)

The top chart shows the measurements of poplar trees. The dotted lines show the variation in the magnitudes of a one-day forecast. The error bars show each day’s one-day forecast plus or minus its Standard Deviation from the mean. The bottom chart shows the data for poplar trees analyzed using two dimensions.

Standard deviation is a statistical measure of how much variation exists from the mean. It also measures how consistently data elements occur relative to their mean value. It is a valuable tool because higher values of standard deviation are often associated with increased forecast accuracy and lower values with decreased accuracy.

Correlation Coefficient

The correlation coefficient is a popular statistical method for measuring the forecast performance of a particular indicator. It is one-half of the expression known as the Coefficient of Determination or R2, which is used in statistics to describe the variance in the data explained by the model.

The correlation coefficient (r) measures the extent to which two variables are related. A correlation coefficient of 1 indicates that there is a perfect positive linear relationship between the variables and a correlation coefficient of 0 indicates that there is no linear relationship between the variables. Values near 1 or -1 indicate a strong correlation, while values near 0 indicate weak correlations.

Conclusion

Forecast quality is about understanding what ultimately drives forecast accuracy. And for demand forecasting, three key factors directly impact the attained forecast accuracy. These are quality of demand data, the characteristics of your product portfolio, and the structure of your operations. However, to set an effective demand management strategy, it is essential to know where each factor fits into your overall function and how they come together to drive forecast interpretation and performance calibration. The responses to these questions will also help you assess forecast quality.

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