As a demand planner, what can you achieve with historical disaggregation rates?

Evaluating historical disaggregation rates allows demand planners to refine and adjust disaggregation profiles and logic effectively. When demand data is disaggregated, it is typically broken down from a higher level (like product families or categories) into more granular levels (like specific SKUs or locations). By analyzing these historical rates, demand planners can identify trends in how demand has shifted or fluctuated over time.

This understanding enables them to make informed adjustments to disaggregation profiles, ensuring that the demand forecast reflects actual historical behavior more accurately. As market conditions and consumer preferences change, being able to tweak the disaggregation logic based on historical data allows for improved accuracy in demand planning, leading to better inventory management and meeting customer demands more effectively.

Other options pertain to areas that, while related to demand planning, do not specifically leverage historical disaggregation rates in the same direct manner as adjusting profiles and logic does. Statistical methods in forecasting, for instance, can apply to a broader range of data analysis techniques, while creating new market strategies focuses on broader market insights rather than the detailed breakdown of historical data. Similarly, production scheduling management is a separate process that may utilize demand forecasts but does not directly relate to the manipulation of historical disaggregation rates.