# How To Measure Forecast Accuracy Metrics (With Tips)

What are forecast accuracy metrics? Forecast accuracy metrics are measurements that show the reliability of a forecast, which is a prediction of future trends based on historical data. These types of metrics measure the forecast error

forecast error
In statistics, a forecast error is the difference between the actual or real and the predicted or forecast value of a time series or any other phenomenon of interest.

https://en.wikipedia.org › Forecast_error

, which is the difference between an actual value and its expected forecast.

Demand forecasting and demand planning are terms often used interchangeably. However, they are two entirely distinct ideas that complement one another. A demand forecast is a projection of future demand made in the context of business. It is typically based on a combination of historical data, forecasts of future sales activity, marketing initiatives, consumer behavior (think of shopping holidays like Black Friday and Christmas), and even the weather. A forecast, in its simplest form, looks to predict future demand for a good or service. Demand planning, on the other hand, covers the entire picture. Demand planners translate the forecast into supply chain action, making sure that customers get the products they need when they need them and that there are enough workers available to make this happen efficiently.

## Why is it important to track forecast accuracy?

Companies may find it useful to monitor their forecast accuracy over time to determine their average forecast error. Understanding the forecast error can help companies use forecasts to effectively predict future trends. Monitoring forecast accuracy is crucial because it can:

## What are forecast accuracy metrics?

Metrics that gauge the accuracy of forecasts, which are predictions of future trends based on historical data, are known as forecast accuracy metrics. The forecast error, or the difference between an actual value and its expected forecast, is measured by these metrics. Many businesses measure their average forecast accuracy as a key performance indicator (KPI) to aid in business decision-making. Businesses can, for instance, forecast customer demand using forecast accuracy metrics, which can help them control inventory levels and guarantee customer satisfaction.

## How to measure forecast accuracy

The actions you can take to gauge forecast accuracy within your company are listed below:

### 1. Gather forecasts

Gather the forecasts you want to measure over a specific time period to start. For instance, you can get the forecasts for each of the six months if you want to assess how well a company predicts demand over a six-month period. These statistics are produced by forecasting teams in some businesses, while inventory managers are used in other ones. Work with the forecasting team members to collect these records so you can assess the forecasts’ accuracy.

### 2. Measure actual trends

Determine the actual trends so you can contrast the numbers with the forecasts after you have the forecasts for the time period you want to measure. For instance, you can work with the sales team to obtain monthly sales figures if you want to compare the actual sales of a product to the demand accuracy forecast. To assess the precision and consistency of the company’s forecasts, having both the actual and forecasted numbers is crucial.

### 3. Choose a metric

While there are many ways to measure forecast accuracy, two popular approaches can give you insightful information based on your data. These two methods are:

The average of forecast errors in percentage terms is measured by the mean absolute percentage error (MAPE). Because many people can comprehend forecast accuracy in terms of percentages, it is a useful accuracy metric to employ. For instance, a MAPE of 3% indicates that the actual and projected data differed by 3%. Typically, a lower MAPE indicates a higher forecast accuracy. There are four parts to the MAPE formula:

The formula determines each forecast’s absolute percent error before averaging those values. Heres the MAPE formula for determining forecast accuracy:

MAPE is (1/n) times ((actual – forecast) / actual) multiplied by 100.

Here is an illustration of a data set that displays the actual and anticipated values over a three-month period in order to determine the MAPE:

Month**Actual**Forecast**Absolute error*January504510%February809012. 5%March605016. In this case, you can figure out the absolute percent error for each month by deducting the forecast from the actual amount, dividing by the actual amount, and multiplying it by 100. The result is the MAPE, which is displayed as 6.0%. Heres the calculation for each month:*.

February equals ((80 – 90) / (80 – 90) x 100 = 12 January = (((50 – 45) / 50) x 100 = 10% ((60 – 50) / 60) x 100 = 16 for March. 6%.

After that, by adding the total of the absolute errors and multiplying it by the sample size, you can determine the average value of the absolute percent error. The formula looks like this:

MAPE = (1/3) x Σ(10 + 12. 5 + 16. 6) = 13%.

Similar to the MAPE, the mean absolute deviation (MAD) measures the average of the forecast errors in units. When comparing actual figures and anticipated forecasts for a single item, this can be a useful metric to use. For instance, you can use MAD to calculate the forecast error for the quantity of goods sold in comparison to the predicted demand. When calculating the forecast error for items with low numbers, it is also useful. The MAPE formula’s values are included in the MAD formula as well. It looks like this:

MAD = (1/n) x Σ(actual – forecast)

Here is an illustration of how to determine the MAD for a set of data spanning three months:

By deducting the forecasted amount from the actual value, you can calculate the absolute error for each month in this example: Month**Actual**Forecast**Absolute error*January30255February50455March504010. Heres the absolute error for each month:*.

February is 50-45, which equals 5, and March is 50-40, which equals 10.

The mean of the absolute error can then be determined by adding the sums together and dividing by the sample size. Heres how that equation looks:

Mean = (5 + 5 + 10) / 3 = 6. 66.

The MAD can then be determined by dividing the mean by the sample size:

MAD = 1/3 x 6.66 = 2.22

### 4. Make calculations

Make the calculations using the forecast and actual values once you’ve decided on the approach you want to take. Consider using a spreadsheet program that uses formulas to calculate large amounts of data automatically if you have a lot of data sets to measure. Even though you might be able to perform some calculations manually, especially if you’re dealing with data from a few months or less, using a computer program is frequently much faster for these measurements.

### 5. Identify accuracy trends

Continually tracking forecast accuracy over a predetermined time frame, like a year, is useful for gaining perspective on the business’s long-term operations. Select a regular period of time to gauge forecast accuracy that you can easily recall, like the end of each month. Prepare reports that include information on forecast accuracy and distribute them to managers, leaders, or stakeholders of the company to assist them in making business decisions. You can monitor forecasting progress and make adjustments to achieve continued accuracy by tracking accuracy trends over time.

## Tips for measuring forecast accuracy

Here are some pointers for effectively measuring and utilizing metrics for forecast accuracy:

### Isolate exceptions

When working with data, it’s crucial to identify metrics that could have an impact on the forecasts’ average accuracy. By changing the data averages, these exceptions can affect your forecast’s accuracy, so eliminating them can help you create a more accurate forecast. You can take that month out of the data set and analyze it separately, for instance, if you’re analyzing the forecast accuracy for a time period that includes a month when a business experienced an unexpected rise in demand.

### Use data for feedback

You can evaluate a company’s forecasts for anticipated trends by tracking your forecast accuracy over time. Monitoring this data can show forecasting teams how accurate they have become over time, which can encourage them to keep creating reliable forecasts. Additionally, it can assist these teams in determining how to enhance their forecasts in order to meet accuracy standards. Giving this feedback based on data can assist a business in continuing to increase the accuracy of its forecasts.

### Compare industry data

You can learn about trends in your industry by analyzing your forecast accuracy and comparing it to that of similar companies. This comparison can show you where you need to make forecasting improvements to better meet market expectations. For instance, you can assess how well your company assesses expected demand by comparing the accuracy of your demand forecast with that of similar businesses. If your industry lacks these benchmarks, you can monitor your own forecast accuracy development over time to gauge your progress in this area.

## FAQ

What is the most common metric for forecast accuracy?

The most commonly used metric for evaluating forecast accuracy is called MAPE (Mean Absolute Percentage Error). It falls under scale-independent percentage errors that can be used to compare series on various scales.

What is a forecast accuracy?

Forecast accuracy measures how well sales leaders foresee future sales (both long- and short-term). Making important decisions about short-term spending and deals for key accounts requires accurate sales forecasts.

Which of the following is a metric to measure the accuracy of your forecast?

MAPE (Mean Absolute Percentage Error) and WAPE (Weighted Absolute Percentage Error) are the most frequently used metrics to assess the forecast’s accuracy.

Which is better MAD or MAPE?

MAPE is used for high volume/relatively consistent and regular demand pattern, whereas MAD is used for low volume/sporadic demand pattern. Additionally, you can model a change from MAD to MAPE in the growth phase and back to MAD in the declining phase if you’re doing lifecycle planning.