Once your data is unified in Core, planning stops being guesswork. Planner forecasts demand per SKU, aligns your teams on one agreed plan, and turns that plan into replenishment and procurement you can act on, no six-figure planning suite required.
Statistical and machine-learning models learn the seasonality, trend, and demand patterns in your order history, then push SKU-level forecasts straight into planning, so purchasing stops being a guess.
Bring statistical forecasts, sales input, and operational constraints into a single consensus plan. Track how accurate each cycle was, see where bias creeps in, and tighten the numbers every period.
Planner turns demand forecasts into reorder points, safety stock, and suggested order quantities, then surfaces over- and understocked SKUs so you act before they become a problem.
No single model fits every product. Planner trains a pool of established statistical and machine-learning models on your history and lets them compete — the winner forecasts that SKU until something beats it.
Every enabled model is cross-validated against your own order history, SKU by SKU — no synthetic benchmarks.
The most accurate model wins each SKU. Slow sellers with spiky, intermittent demand get specialist treatment instead of a force-fit.
A naive benchmark always runs alongside. A model that can’t beat the baseline never ships — your forecast can’t get worse than common sense.
Searches seasonal and non-seasonal ARIMA configurations automatically and picks the best fit for each series.
Exponential smoothing that selects its own error, trend, and seasonality structure per SKU.
Triple exponential smoothing — the classic workhorse for level, trend, and seasonal patterns.
Decomposes each series to model long-term trend and short-term dynamics separately.
Gradient-boosted trees over lagged and rolling demand features, with calendar signals built in.
Gradient-boosted trees built for large panels of series, trained across your whole catalogue at once.
A per-SKU weighted blend of the top backtesting candidates — used when the blend beats any single model.
The honest baseline every champion has to beat — it keeps the sophisticated models accountable.
Forecast, agree, replenish, and review, all on top of the data you already run in Core.
Statistical and ML models chosen per series for accuracy.
Align sales, ops, and finance on a single agreed plan.
Track bias and error over time, and improve every cycle.
Reorder points and suggested quantities, ready to action.
Safety stock, ABC analysis, and over/understock alerts.
Budgets, approval thresholds, and spend visibility.
Book a demo and we'll run your own history through the forecasting engine and show you the replenishment plan it produces.