How to calculate forecast accuracy and forecast error

What is forecast accuracy and forecast error?

One way to check the quality of your demand forecast is to calculate its forecast accuracy, also called forecast error. The forecast accuracy calculation shows the deviation of the actual demand from the forecasted demand. If you can calculate the level of error in your previous demand forecasts, you can factor this into future ones and make the relevant adjustments to your planning.

In this post we show you how to measure the accuracy of your forecasts, by calculating forecast error, and then discuss why it’s important to do so.

Forecast accuracy/forecast error calculations

There are a number of formulas that inventory planners can use to calculate forecast accuracy / forecast error. These range from the fairly simple to the quite complex. Two of the most common forecast accuracy / error calculations are MAD – the Mean Absolute Deviation and MAPE – the Mean Absolute Percent Error.

Let’s take a closer look at both:

1. MAD forecasting calculation

A common way to work out forecast error is to calculate the Mean Absolute Deviation (MAD). This shows the deviation of forecasted demand from actual demand, in units.

The MAD calculation takes the absolute value of the forecast errors (difference between actual demand and the forecast) and averages them over the forecasted time periods. ‘Absolute value’ means that even when the difference between the actual demand and forecasted demand is a negative number, it becomes a positive. So 25 divided by 4 is 6.25.

The MAD calculation works best when using it on one product, as the demand error is not proportional. If you use it on items with different volumes, the result will be skewed by those with heavier volumes.

2. MAPE forecasting calculation

Another fairly simple way to calculate forecast error is to find the Mean Absolute Percent Error (MAPE) of your forecast. Statistically MAPE is defined as the average of percentage errors. The MAPE formula consists of two parts: M and APE. The formula for APE is the difference between you actual and forecasted demand as a percentage:

With APE calculated for each period, you then calculate the mean of all percentage errors.

Since MAPE is a measure of error, high numbers are bad and low numbers are good.

There are other forecast accuracy calculations that you can use, but make sure you find the most appropriate method for your needs, as it’s important to understand how accurate your forecasting is for a number of reasons that we will now discuss.

Using forecast error data for better demand predictions

Once you have your forecast error calculations, you need to ensure you act on the data. Smart inventory planners will use their forecast error stats to refine their forecasting processes and improve overall forecasting accuracy. More accurate forecasts will then help improve their inventory purchasing and planning.

Here are a number of ways this can be done:

1. Mitigate the risk of future forecasting accuracy: The forecast error calculation provides a quantitative estimate of the quality of your past forecasts. If you can calculate the level of error in your previous demand forecasts, you can factor this risk into future forecasts. If you can determine how uncertain a forecast is for a given future business period, you can make the necessary adjustments to your inventory management rules, such as increasing safety stock levels and adjusting re-order points to cover the uncertain periods of demand.

2. Prioritise questionable forecasts: Identifying and prioritising items with a high forecast error allows to you give them dedicated attention. You can closely monitor their future demand and adjust stock levels accordingly.

3. Refine and improve forecast accuracy: If you consistently see high forecast error rates this is an indication that the demand forecasting technique you’re using needs to be reviewed and improved.

Measuring forecast accuracy/forecast error with automation

Some Enterprise Resource Planning (ERP) and Warehouse Management Systems (WMS) will have the functionality to automatically calculate forecast errors. But beware, every system will have a different level of complexity, so be sure to understand yours and account for its limitations. For example, is your system interrogating every SKU? What calculation is it using to forecast error? Is it adjusting stock parameters based on the results?

If you’re finding that your current inventory management system has limitations, consider investing in an inventory optimisation plug-in. Inventory optimisation software will work in collaboration with an ERP, WMS or inventory management tool to provide statistical demand forecasting functionality. You can then save time carrying out complex calculations and instead make informed inventory management decisions, based on accurate data.

If you find it a challenge to achieve forecasting accuracy with your current systems and processes, contact the EazyStock team today. Our demand forecasting software gives you advanced inventory management capabilities that you can utilise to improve the day-to-day running of your business – fast.