This is like giving your demand planners a very smart co-pilot that can read all your plans, history, and assumptions, then challenge, refine, and stress-test your demand forecast before it’s locked in.
Traditional demand planning is slow, spreadsheet-heavy, and highly dependent on individual experts. It often underuses available data and makes it hard to run multiple scenarios quickly, leading to inaccurate forecasts, higher inventory costs, and missed sales. This framework uses generative AI as a structured ‘co-planner’ to make the process faster, more consistent, and more data-driven.
Embedded in company-specific demand-planning workflows, tied to proprietary sales, promotion, and supply data, and refined with planners’ feedback over time, making the co-planner increasingly tailored and hard to replicate.
Hybrid
Vector Search
Medium (Integration logic)
Context window cost and latency when grounding the co-planner in large volumes of historical demand, promotions, and external signals across many products and markets.
Early Majority
Positions generative AI not as a fully autonomous forecaster, but as a structured ‘co-planner’ layered on top of existing demand-planning and time-series models, with clear governance, human-in-the-loop review, and integration into established S&OP processes—targeted initially at consumer and retail-style demand environments.