MAPE (Mean Absolute Percentage Error): complete guide in January 2026

Mean Absolute Percentage Error (MAPE) is a widely used metric in forecasting that expresses prediction errors as percentages, making it accessible for non-technical stakeholders to understand, as they do not need to consider units or scales. MAPE is advantageous for comparing accuracy across different scales and products, with values under 10% considered excellent and 10-20% acceptable. However, MAPE has significant limitations: it fails with zero actual values, treats over-forecasts and under-forecasts asymmetrically, and is distorted by low-volume items. Weighted MAPE (WMAPE) is suggested as a better alternative for diverse product portfolios, as it weights errors by actual demand volume. MAPE is used in various sectors such as demand planning, supply chain forecasting, and regression analysis, though it may require alternatives like WMAPE or RMSE for more insightful evaluations of forecast quality. The article also highlights how to calculate MAPE using Python's sklearn library and discusses the importance of regular model retraining and monitoring to maintain forecast accuracy in AI systems.