Final answer:
The incorrect statement about bias in forecasts is that bias is calculated based on the mean absolute percent error (MAPE). Bias is actually a measure of whether a forecast consistently overestimates or underestimates values, separate from error measurements like MAPE.
Step-by-step explanation:
The statement that is not accurate about bias in forecasts is: Bias is calculated based on the mean absolute percent error (MAPE). Bias in forecasting is a measure of systematic error, indicating the tendency of a forecast to consistently be above or below the actual values. It does not equate to the sum of forecast errors which is often a measure of accuracy or cumulative error. Persistent positive bias suggests that forecasts are regularly less than the actual figures, thus underestimating them. Conversely, a persistent negative bias indicates that forecasts tend to exceed the actual values, representing an overestimation.
Bias may indeed suggest a change in the demand pattern, and it can have serious implications for decision-making. Understanding bias helps in improving forecasting models and reduces the potential negative impacts of forecasting errors. Bias can originate from misinformation, outdated models, or even cognitive biases, such as confirmation or anchoring bias. It is crucial to differentiate between random errors and bias; bias implies a consistent deviation from actual values due to systematic issues, while random errors arise from unpredictability and lack of information. Forecasting accuracy can be enhanced significantly with a careful analysis and correction of bias in the forecasts.