# Calculating Forecast Error to Enhance Demand Accuracy

When working in supply chain management, you must be able to make accurate demand forecasts. If your predictions are inaccurate, you might have excess or insufficient stock, which is detrimental to your business. On the other hand, you will significantly improve customer satisfaction, inventory levels, and supplier lead times when you prioritize forecast accuracy, which equates to fewer problems and more profit.

# What is Forecast Error?

To determine forecast accuracy, you will first want to calculate forecast error. Forecast error is calculated by subtracting actual demand from a forecasted demand. It is helpful to determine the error level in your previous demand forecasts because this data can pinpoint how to adjust to ensure better accuracy in the future. Below, we showcase select popular methods to calculate forecast error.

# Calculations for Forecast Error

There are several forecast error formulas that inventory planners use to make their decisions. Some are simple, while others are pretty complex. We will discuss two more common forecast accuracy calculations: the MAPE and the MAD.

## No. 1: MAPE Formula

MAPE stands for Mean Absolute Percent Error. This formula comprises two parts: one to retrieve the mean (M) and one to retrieve the absolute percent error (APE). The MAPE formula is:

(Actual Demand – Forecasted Demand) / Actual Demand) x 100

With this formula, you can calculate the mean of all percentage errors that occur during a given period. MAPE is a measure of error, so a lower percentage should be the desired outcome.

MAD stands for Mean Absolute Deviation. The MAD Formula shows how forecasted demand differs from actual demand, measured in units. For example, if the actual demand is 90, but your demand forecast is 95, your absolute error (or deviation) is 5. Calculate the deviations for multiple periods and then take the mean of those values to produce your error forecast.

Beyond the MAPE and MAD formulas, you can use many forecast error calculations. Regardless of which you choose to use, forecast error is critical to your business’s health, so be sure to use at least one!

# The Benefits of Improving Forecast Accuracy

Forecast error data helps inventory planners perfect inventory purchasing. Here’s how:

Reduce Risk of Future Forecast Errors: With the forecast error calculation, you receive a numerical estimate of your past forecast quality. This number can help you factor risk into future projections and make the necessary adjustments to your inventory management process, such as increasing the stock buffer or adjusting reorder points to cover gaps.

Prioritize Questionable Forecasts: If you find a high forecast error for one of your products, you will want to pay special attention to that product. Closely monitor these products, look at their future demand, and adjust stock levels as needed. Forecast error values pinpoint which products need such attention.

Refine and Improve Forecast Accuracy: If you consistently realize high forecast error rates, there is an underlying problem. You may need to update your forecasting technique to alleviate the issue and ensure the accuracy of future forecasts.