demand-forecast-explainer

You can explain ML forecasts in plain English by uploading current forecast data and an optional previous baseline; the system computes variance, identifies drivers, and generates structured, actionable narratives for leadership.

Demand driver analysis works by comparing current forecasts against an optional previous baseline to identify what changed, by how much, and why, ranking business-context drivers like promotions and seasonality to produce clear explanations.

Yes, you can generate tailored forecast explanations for VP/SVPs, Directors, and Demand Planners, providing appropriate detail levels ranging from high-level executive summaries to granular driver analysis for demand planning automation.

Yes, this approach supports weekly, monthly, or quarterly demand planning across individual SKUs or broader categories, ingesting forecast data to translate machine learning variance into structured executive explanations.

The best way to translate inventory planning automation data into executive insights is to compute forecast variance against a baseline, rank demand drivers like promotions and seasonality, and output plain-English explanations with recommended actions.

A baseline is needed to explain forecast changes because it provides the reference point for computing variance, allowing the system to quantify what changed, identify the underlying drivers, and determine confidence implications for executive decision-making.