Demand Forecast Reconciliation Agent
Reconciles statistical demand forecasts with sales, marketing, and field input signals to produce a single consensus forecast for planning.
Demand forecasts often diverge across departments — sales holds one number, statistical models produce another, and marketing promotional plans aren't factored in consistently — leaving planners to manually reconcile spreadsheets in lengthy S&OP meetings
These meetings frequently run long because discrepancies are discovered live rather than surfaced beforehand, and the final consensus number is often a political compromise rather than a data-driven one
Promotions, new product launches, and known supply disruptions get missed by pure statistical models, causing systematic over- or under-forecasting
This agent continuously reconciles all forecast inputs, flags material discrepancies before meetings happen, and proposes a statistically grounded consensus number with documented adjustments, cutting prep time and improving forecast accuracy
The agent pulls statistical baseline forecasts from the demand planning system alongside sales rep pipeline input, marketing promotional calendars, and historical forecast-accuracy data by SKU and region. An LLM parses free-text notes from sales and marketing (e.g., "large account ramping up in Q4", "promo delayed to November") and converts them into quantified forecast adjustments. It then runs a reconciliation pass that weights each input source by its historical accuracy, flags SKUs where inputs diverge beyond a configured threshold, and produces a proposed consensus forecast with a rationale trail for planners to review before the S&OP meeting.
Collect Forecast Inputs
- Pull statistical baseline forecast by SKU/region
- Ingest sales pipeline and account-level input
- Pull marketing promotional calendar and spend plans
- Gather historical forecast accuracy by source
Quantify Qualitative Input
- Parse sales/marketing free-text notes with LLM
- Convert qualitative signals into volume adjustments
- Tag adjustments with confidence and source
- Flag conflicting signals for review
Reconcile and Propose Consensus
- Weight inputs by historical source accuracy
- Flag SKUs with material forecast divergence
- Generate proposed consensus forecast per SKU
- Document rationale for each adjustment
Track Accuracy and Refine Weights
- Compare consensus forecast to actual demand post-period
- Calculate forecast accuracy (MAPE/bias) by source and SKU
- Adjust source weighting for future reconciliation runs
- Publish accuracy scorecard to planning leadership