Certified Production & Operations Manager Exam Practice 2025 – Complete Study Guide

In forecasting, 'bias' refers to a systematic error in predictions where values tend to consistently deviate in one direction—either overestimating or underestimating the actual outcomes. This means that if a forecast consistently misses the actual result due to overestimation, it indicates a positive bias, while consistent underestimation suggests a negative bias.

Recognizing bias is crucial for improving forecasting accuracy, as it highlights a persistent deviation from the true values, whether that trend is towards overestimating outcomes or underestimating them. By identifying the direction and nature of the bias, forecasters can adjust their models or forecasts accordingly to mitigate the impact of this systematic error. This understanding emphasizes that bias in forecasting might manifest as either consistent overestimation, consistent underestimation, or both types of errors occurring across different predictions. Thus, the most comprehensive understanding of bias captures both the possibilities of over and underestimation, accounting for any systematic tendencies present in the forecasting model.