Understanding Mean Absolute Percentage Error (MAPE) in Demand Planning
By Intelichain’s Team
MAPE quantifies the accuracy of a forecast by comparing the forecasted values to the actual values and expressing the error as a percentage. This metric is particularly useful because it provides a clear, easily understandable measure of forecast accuracy that is not dependent on the scale of the data.
Importance of MAPE
- Standardization: By expressing errors as percentages, MAPE standardizes forecast errors across different datasets, making it easier to compare performance.
- Interpretability: The percentage format of MAPE is intuitive and easy to understand for stakeholders at all levels.
- Improvement Tracking: MAPE allows organizations to track improvements in forecasting methods over time by providing a consistent metric for evaluation.
- Benchmarking: Companies can benchmark their forecast accuracy against industry standards or competitors using MAPE, facilitating continuous improvement.
Calculating MAPE
Index:
$$ \sum_{i=1}^n $$
Parameters:
Actual demand at period \( i \), \( A_i \)
Demand forecast for period \( i \), \( F_i \)
MAPE:
$$ \text{MAPE} = \frac{100}{n} \times \sum_{i=1}^n \left| \frac{F_i - A_i}{A_i} \right| $$
Example:
Consider a company that forecasts monthly demand for a product. Over three months, the actual demands were 100, 120, and 80 units, while the forecasted demands were 110, 115, and 90 units. The MAPE calculation would be:
MAPE Calculation:
$$ \text{MAPE} = \frac{100}{3} \times \sum_{i=1}^3 \left| \frac{F_i - A_i}{A_i} \right| $$
$$ \text{MAPE} = \frac{100}{3} \times \left( \left| \frac{110 - 100}{100} \right| + \left| \frac{115 - 120}{120} \right| + \left| \frac{90 - 80}{80} \right| \right) $$
$$ \text{MAPE} = \frac{100}{3} \times \left( 0.1 + 0.0417 + 0.125 \right) $$
$$ \text{MAPE} = \frac{100}{3} \times 0.2667 = 8.89\% $$
Summary
MAPE is a crucial metric in demand planning that provides a clear and standardized measure of forecast accuracy. By focusing on MAPE, organizations can enhance their forecasting processes, reduce errors, and make more informed decisions. Implementing strategies to improve MAPE not only boosts forecast accuracy but also leads to better inventory management, cost savings, and improved customer satisfaction.
