High MAPE Does Not Always Mean Bad Forecasting

In a recent sales conversation with a potential client looking for a planning solution, they expressed dissatisfaction with the MAPE produced from their test data. To provide context, the organization is a fast-growing, young company with a large and expanding SKU base and limited historical data. In such environments, forecast accuracy metrics—particularly percentage-based measures—often appear inflated during early planning cycles.

While MAPE is a widely used and easy-to-understand measure of forecast accuracy, it performs best when demand patterns are relatively stable and sufficient historical data exists. For organizations experiencing rapid growth, frequent product introductions, and evolving demand signals, MAPE requires continuous iteration before it begins to reflect meaningful improvements in forecast quality.

Several factors inherent to the client’s operating environment directly impact MAPE. High demand volatility driven by growth, seasonality, and promotions increases forecast error. Limited demand history and new product launches reduce the statistical reliability of early forecasts. A sizable SKU portfolio—especially when forecasts are evaluated at a granular SKU-location level—naturally amplifies percentage-based errors. Data quality challenges, including missing history, stockouts that mask true demand, and master data inconsistencies, further contribute to elevated MAPE.

In addition, longer forecast horizons introduce greater uncertainty, while supply constraints, allocation rules, and capacity limitations distort historical demand signals. Manual forecast overrides and planner bias can add variability, and forecasting models that are not well-suited to intermittent or rapidly changing demand patterns may exaggerate percentage errors during early planning cycles.

Given these considerations, early-stage MAPE should be interpreted with caution and not viewed in isolation. To provide a more balanced and representative view of forecast performance, MAPE is best complemented with alternative accuracy measures. Metrics such as WAPE (Weighted Absolute Percentage Error) provide a more stable view by weighting error against total demand, particularly in high-SKU environments. MAE (Mean Absolute Error) offers clear insight into forecast error in absolute units, which is often more actionable for operations. RMSE (Root Mean Squared Error) helps identify large forecast misses by penalizing significant deviations, making it useful for model evaluation. Additionally, forecast bias and tracking signals are critical for understanding the direction and consistency of error, ensuring accuracy improvements are not masking systematic over- or under-forecasting.

By combining MAPE with these complementary measures, organizations gain a more complete and practical understanding of forecast performance—one that aligns with their growth stage, data maturity, and operational complexity—rather than relying on a single metric that may be misleading in isolation.