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Cumulative actual demand in relation to cumulative forecast is essential for evaluating the accuracy and effectiveness of forecasting methods. When actual demand does not align closely with the forecast, the variances observed can indicate potential trends or patterns that may inform future forecasts.

For example, if actual demand consistently exceeds the forecast, this may signal an upward trend in demand that needs to be accounted for in future forecasts. Conversely, if actual demand falls significantly short of the forecast, it might indicate that the forecast is overly optimistic, necessitating adjustments to prevent future discrepancies.

These variances provide valuable insights into the reliability of the forecasting process, allowing supply chain professionals to make informed adjustments and improve future forecasting accuracy. By understanding the relationship between actual demand and forecasts, organizations can mitigate the risk of bias and develop more robust forecasting methodologies.