Automating the creation of attribute-based forecasts at the SKU level is called proportional profile planning. From storage capacity options in laptop computers to diameters of PVC pipe, from blade sizes on ceiling fans to the colors of summer skirts, generating more accurate sub-SKU forecasts has a major impact on sourcing, production planning, and meeting actual demand throughout the product life cycle. Proportional profile planning dives deeper into inventory forecasting to optimize your supply chain performance.

Many companies create unconstrained demand plans at the category or product level, but have insufficient resources to break the plans into accurate attribute-based forecasts based on finish, style, color, size, gender, region, speed, power, material type, trim level, configuration, and more. This task often falls on the shoulders of the sourcing and supply functions rather than the demand planners. This paper presents ways in which planners can automate proportional profile planning to hand off more accurate, detailed demand plans to their sourcing and supply planning teams. Four powerful techniques can remove the aggravation from disaggregation:
- Extend demand planning to a SKU-level forecast using existing sales history data to automatically generate profiles. Adopt a solution that can scale to handle any number of sub-SKUs and attribute tiers.
- Develop the ability to forecast by attribute early in the planning cycle, so that SKU-level forecasting is part of up-front demand planning, not left until the supply/sourcing plans are created.
- Create proportional profiles for item attributes at each stage of the product life cycle, so that inventory matches business goals from launch to retirement.