What Is MAPE and How Is It Calculated?
Understand a core metric for evaluating the accuracy of predictions. Learn how to calculate, interpret, and apply this essential forecasting tool.
Forecasting future events and trends is a fundamental activity for businesses across various sectors. Accurate predictions are invaluable for informed decision-making, whether predicting sales, managing inventory levels, or planning financial budgets. Evaluating the reliability of these predictions requires robust metrics to quantify how well forecasts align with actual outcomes. One widely recognized metric used for this purpose is the Mean Absolute Percentage Error, often referred to as MAPE.
Understanding MAPE
Mean Absolute Percentage Error (MAPE) serves as a forecasting accuracy metric, expressing the average magnitude of error in forecasts as a percentage.
The “Mean” aspect indicates that an average is taken across multiple data points, providing a single summary figure for forecast performance. “Absolute” signifies that the calculation uses the absolute difference between forecasted and actual values, ensuring that positive and negative errors do not cancel each other out. This approach focuses on the size of the error, regardless of its direction.
The “Percentage Error” component normalizes the error by dividing it by the actual value, then multiplying by 100 to express it as a percentage. This percentage representation allows for direct comparison of forecast accuracy across different datasets or products, even if they have vastly different scales or units. For example, a 10% error in forecasting the sales of a high-value item can be directly compared to a 10% error in forecasting a low-value item, providing a relative measure of accuracy.
Calculating MAPE
The fundamental formula for MAPE is: (1/n) Σ (|Actual – Forecast| / Actual) 100. Here, ‘n’ represents the number of data points, ‘Actual’ is the observed value, and ‘Forecast’ is the predicted value.
To illustrate, consider a scenario where a company forecasts sales for three products. For Product A, the actual sales were 100 units, and the forecast was 90 units. For Product B, actual sales were 200 units, with a forecast of 210 units. Product C had actual sales of 50 units, forecasted at 45 units.
First, calculate the absolute percentage error for each product. For Product A, this is (|100 – 90| / 100) 100 = 10%. For Product B, it’s (|200 – 210| / 200) 100 = 5%. For Product C, the calculation yields (|50 – 45| / 50) 100 = 10%. Finally, the MAPE is the average of these individual percentage errors: (10% + 5% + 10%) / 3 = 8.33%. This result indicates the average percentage deviation of the forecasts from the actual sales.
Interpreting MAPE Results
A lower MAPE value generally indicates a more accurate forecast, meaning the predictions are closer to the actual observed values. Conversely, a higher MAPE suggests that the forecasts are less accurate and deviate significantly from reality. For instance, a MAPE of 5% implies that, on average, the forecasts are off by 5% from the actual outcomes.
The determination of what constitutes a “good” or “bad” MAPE value is highly dependent on the specific industry, the nature of the data being forecasted, and the business context. In some stable industries with predictable demand, a MAPE below 10% might be considered acceptable, while in highly volatile markets, a MAPE of 20-30% could still be viewed as reasonable due to inherent uncertainties. Businesses often establish their own internal benchmarks based on historical performance or industry standards to evaluate the effectiveness of their forecasting efforts. Comparing the current MAPE against these benchmarks helps in understanding whether forecasting accuracy is improving or deteriorating over time.
Where MAPE is Used
In supply chain management, MAPE is frequently used to assess the accuracy of demand forecasts, which directly impacts inventory levels and production schedules. Accurate demand forecasting helps businesses optimize stock, reduce holding costs, and prevent stockouts, thereby improving operational efficiency.
Financial planning and analysis departments commonly employ MAPE to evaluate the accuracy of revenue or expense forecasts. This application is crucial for budget planning, financial reporting, and making informed investment decisions.
Retailers utilize MAPE to gauge the precision of sales forecasts for different product categories or store locations, aiding in merchandise planning and promotional strategy development. Furthermore, in marketing, MAPE can help assess the effectiveness of advertising campaigns by comparing projected sales lifts against actual results.