Explanation:
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.