What function allows you to improve or correct the statistical forecast in Maestro?

Reviewing the accuracy of the historical forecast is essential for improving or correcting the statistical forecast in Maestro. This function enables users to assess how well previous forecasts aligned with actual outcomes by examining forecasting errors and pinpointing patterns or recurring issues. By understanding these discrepancies, users can make data-driven adjustments to enhance future forecasts.

In statistical forecasting, analyzing historical accuracy is crucial; it provides insights into how various factors may have impacted past predictions. This process can reveal whether the forecasting model employed needs adjustments or whether the inputs were flawed. Proper evaluation of forecast accuracy ultimately leads to refining methodologies and improving overall forecasting performance.

In contrast, other alternatives do not contribute effectively to the refinement of forecasts. For instance, modifying historical sales data directly could introduce biases or inaccuracies rather than enhancing the forecast. Adjusting supply plans based on past data does not directly improve the statistical forecast itself, but rather responds to past performance. Creating new forecasts without historical context can lead to unreliable predictions, as past data is vital for accurately calibrating future forecasts.