Calculating Forecast Accuracy & Forecast Error

How to calculate demand forecast accuracy and forecast error

Mark Chapman

3 min read

The importance of demand forecasting accuracy

In supply chain management it’s important to be able to measure the accuracy of your demand forecasts. Inaccurate demand forecasting can lead to the accumulation of excess stock or, the reverse: issues with product availability. Both are unwelcome problems for inventory planners!

Ensuring forecasting accuracy should be a key responsibility for any conscientious inventory planner. In short, accurate demand forecasting helps you:

  • Improve customer satisfaction
    Customers have zero tolerance for out of stock scenarios. Ensuring stock availability keeps positive reviews flowing.
  • Optimise inventory levels
    Setting safety stock levels based on accurate forecasts will prevent stockouts without holding excess stock.
  • Manage supplier lead times
    By giving suppliers a forecast of your annual inventory needs, they can plan to meet your delivery deadlines.
  • Prevent lost revenue
    Out-of-stock scenarios lead to lost sales, not only for that product, but for companion items too.


What is forecast accuracy?

One way to check the quality of your demand forecast is to calculate its forecast accuracy, also called forecast error. Forecast accuracy is 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.

Calculating forecast error

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, from the fairly simple to the quite complex. Two of the most common forecast accuracy / error calculations include MAPE – the Mean Absolute Percent Error and MAD – the Mean Absolute Deviation.

Let’s take a closer look at both:

A 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.

1. MAPE formula

The MAPE formula consists of two parts: M and APE. The formula for APE is:


APE calculation

You then calculate the mean of all percentage errors over a given time period.


MAPE forecast error calculation


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

2. MAD formula

Another 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. It takes the absolute value of forecast errors and averages them over the forecasted time periods.


MAD forecast accuracy calculation


There’s a wealth of further forecast accuracy calculations that can be used to work out forecast error. 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.


Improving forecasting accuracy

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.


Improve forecast accuracy 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.


Whitepaper Managing Demand Volatility Through Inventory Optimisation


Mark Chapman

3 min read

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