MENU
Consider the following data comparing actual demand versus forecast demand:
Month Actual Demand Forecast Demand
January 100,000 80,000
February 105,000 90,000
March 110,000 100,000
April 70,000 100,000
May 90,000 110,000
June 100,000 90,000
What is the Mean Annual Percent Error (MAPE) for the six months of data?
To calculate the Mean Absolute Percentage Error (MAPE), we follow these steps:
1. Find the absolute error for each month.
2. Convert the absolute error to a percentage of the actual demand for each month.
3. Find the average of these percentage errors over the six-month period.
Here's the calculation:
* January: 100,00080,000100,000100=20%left| frac{100,000 - 80,000}{100,000} right| times 100 = 20%100,000100,00080,000100=20%
* February: 105,00090,000105,000100=14.29%left| frac{105,000 - 90,000}{105,000} right| times 100 = 14.29%105,000105,00090,000100=14.29%
* March: 110,000100,000110,000100=9.09%left| frac{110,000 - 100,000}{110,000} right| times 100 = 9.09%110,000110,000100,000100=9.09%
* April: 70,000100,00070,000100=42.86%left| frac{70,000 - 100,000}{70,000} right| times 100 = 42.86%70,00070,000100,000100=42.86%
* May: 90,000110,00090,000100=22.22%left| frac{90,000 - 110,000}{90,000} right| times 100 = 22.22%90,00090,000110,000100=22.22%
* June: 100,00090,000100,000100=10%left| frac{100,000 - 90,000}{100,000} right| times 100 = 10%100,000100,00090,000100=10%
MAPE=(20+14.29+9.09+42.86+22.22+10)6=19.41%20%MAPE = frac{(20 + 14.29 + 9.09 + 42.86 + 22.22 + 10)}{6} = 19.41% approx 20%MAPE=6(20+14.29+9.09+42.86+22.22+10)=19.41%20%
Thus, the MAPE for the six months of data is approximately 20%. Reference:
* Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2006). Operations Management for Compet-itive Advantage. McGraw-Hill/Irwin.
* Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.