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What are the pros and cons of using MAPE (mean absolute percentage error) as a forecast accuracy metric?

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If you are involved in budgeting and forecasting, you probably know how important it is to measure the accuracy of your forecasts. But how do you choose the best metric to evaluate your performance and identify areas for improvement? One common option is MAPE, or mean absolute percentage error, which calculates the average of the absolute values of the percentage errors between the actual and forecasted values. However, MAPE is not a perfect solution and has some advantages and disadvantages that you should be aware of. In this article, we will discuss the pros and cons of using MAPE as a forecast accuracy metric and how to use it effectively.

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Ben Gapinski

Sr. Director of Supply Chain at Walmart
Abhijeet R.

Certified FRM || UBS || Credit Suisse || Completed CFA Level 1
Daniel Paulusma

Father. Partner. Lifelong Learner. Passionate about effecting change for the betterment of future generations.

1 What is MAPE and how to calculate it

MAPE is a simple and intuitive metric that expresses the forecast error as a percentage of the actual value. For example, if you forecasted 100 units and the actual demand was 120 units, the percentage error is (120-100)/120 = 16.67%. The absolute value of this error is 16.67%, regardless of whether the forecast was too high or too low. To calculate MAPE, you need to add up the absolute percentage errors of all the observations and divide by the number of observations. For example, if you have four observations with percentage errors of 10%, -15%, 5%, and -20%, the MAPE is (10+15+5+20)/4 = 12.5%.

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Ben Gapinski

Sr. Director of Supply Chain at Walmart
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MAPE and Bias often tend to get used interchangeably. However when aggregating across large value sets or long timelines it is important to focus on where variability is occurring to measure accurately. MAPE will not tell if you are under or over forecasted, but bias could overstate your forecast accuracy during regular intervals of over and under indexing. It is useful to either use a combination of measures, or ensure that you select the one most closely aligned to the intended purpose in practical use.

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2 Pros of using MAPE

One of the main benefits of using MAPE is that it is easy to understand and communicate. MAPE tells you how much your forecasts deviate from the actual values on average, in percentage terms. This can help you compare your performance across different products, markets, or time periods, as long as the actual values are not too close to zero. MAPE can also help you set realistic and achievable goals for your forecasting process and monitor your progress over time. For example, you can use MAPE to benchmark your forecasts against industry standards or historical data and identify areas where you can improve.

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Daniel Paulusma

Father. Partner. Lifelong Learner. Passionate about effecting change for the betterment of future generations.
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The main benefit of MAPE is that it expresses the forecasting error in relative terms, putting the error into perspective. An absolute error of 10 for a forecast of 50 is much different than for a forecast of 1,000.

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3 Cons of using MAPE

However, MAPE also has some limitations and drawbacks that you should be aware of. One of the main issues with MAPE is that it can be skewed by outliers or extreme values. For example, if you have one observation with a very large percentage error, it can inflate the MAPE and make your overall performance look worse than it actually is. Conversely, if you have one observation with a very small percentage error, it can deflate the MAPE and make your overall performance look better than it actually is. Another problem with MAPE is that it can be biased by the scale of the actual values. For example, if you have two observations with the same absolute error, but different actual values, the one with the smaller actual value will have a larger percentage error and contribute more to the MAPE. This means that MAPE can penalize forecasts that are accurate for large values and reward forecasts that are inaccurate for small values.

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Abhijeet R.

Certified FRM || UBS || Credit Suisse || Completed CFA Level 1
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This is particularly not a good measure where the value is oscillating around zero. This would create an outsized MAPE, even though the general trend may be followed.

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Shanka Wickramasinghe

Helping global businesses to modernize finance and accounting operations, Senior Finance Director, project management, value creation and strategic business acumen. Talks about #Finance #Leadership #EPM #FCC #Oracle
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In my experience, MAPE emphasizes on the absolute percentage deviation, disregarding the direction of the errors presenting challengers in variance analysis situations where over-forecasting and under-forecasting carry distinct implications

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4 How to use MAPE effectively

MAPE can be an effective forecast accuracy metric, but you should use it with caution and context. Remember that MAPE is a relative measure, not an absolute one; it can give you a general idea of your forecast accuracy, but it does not tell you the direction, magnitude, or distribution of the errors. Additionally, MAPE can be misleading or meaningless when the actual values are very small or zero. To get a more comprehensive picture of your forecast accuracy, use MAPE with other metrics such as MAE, MSE, or RMSE. Additionally, consider excluding or adjusting observations with small or zero values, or use a different metric such as MASE.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the pros and cons of using MAPE (mean absolute percentage error) as a forecast accuracy metric?

1

2

3

4

5

1 What is MAPE and how to calculate it

2 Pros of using MAPE

3 Cons of using MAPE

4 How to use MAPE effectively

5 Here’s what else to consider

Budgeting & Forecasting

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What are the pros and cons of using MAPE (mean absolute percentage error) as a forecast accuracy metric?

1

2

3

4

5

1 What is MAPE and how to calculate it

2 Pros of using MAPE

3 Cons of using MAPE

4 How to use MAPE effectively

5 Here’s what else to consider

Budgeting & Forecasting

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