T7 builds hierarchical, segmented demand forecasts tied to a dollarised scoreboard your CFO actually reads — not just a MAPE number your data team defends.
A single global model rarely wins across velocity classes, promotions and new-product introductions.
MAPE improvements don't translate to inventory decisions without a working-capital view — so forecasts sit in dashboards nobody acts on.
We segment SKUs by velocity, volatility and lifecycle, then choose a model class per segment — statistical for stable, gradient-boosted for promoted, analogue-priors for new products.
Forecasts feed a weekly working-capital dashboard: excess inventory carrying cost, markdown risk, stockout lost sales and expedited freight — split by category and region.
Continuous evaluation with backtesting, drift monitoring and automated retraining keeps the system honest.
SKU segmentation, exogenous feature engineering (weather, price, promo, calendar) and a baseline model per segment.
Bottom-up forecasts reconciled hierarchically across SKU → category → region → total.
Weekly dashboard translating forecast error into working-capital impact for the S&OP meeting.
Ideally 24 months at daily grain per SKU. We handle sparse and intermittent demand with dedicated model classes and analogue priors for new products.
Yes — forecasts publish to SAP IBP, o9, Kinaxis, Anaplan or a custom S&OP process via API or scheduled export.
The dollarised scoreboard is designed for finance leadership: forecast error → working capital → P&L impact, weekly.
Industries where this workflow ships, the insights behind it, and the tradeoffs to weigh.
AI for power, oil, gas and renewables
AI-native retail & commerce
AI for hotels, restaurants and cloud kitchens
A 5-point MAPE improvement rarely moves an executive. Translating forecast accuracy into inventory, working capital, and lost-sale dollars is what wins the room.
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