S&OP-grade forecasting, replenishment and scenario planning.

AI Supply Chain Planner

T7 builds supply-chain planners that combine hierarchical forecasting with scenario simulation — so planners spend time deciding, not spreadsheeting.

The problem

Spreadsheet-driven S&OP lags demand signals by weeks; stock-outs and dead stock swing margin every quarter.

Legacy APS tools don't handle promotions, weather and new-SKU cold-start well.

How T7 solves it

Hierarchical forecasts at SKU × location × day with promotional, weather and event features; new-SKU cold-start via similar-item embeddings.

Scenario simulator lets planners test what-if changes to promo, price or lead time.

Recommendations write back to SAP IBP, o9, Kinaxis or your APS of choice.

The workflow

Step 1

Forecast

Nightly hierarchical forecasts across SKU/store/DC with promo + weather features.

Step 2

Plan

Replenishment + allocation recommendations respecting lead time, MOQ and shelf-life.

Step 3

Simulate

Planners test scenarios before committing; system tracks forecast vs. actual on every run.

Outcomes we ship for

25%
Forecast error reduction
30%
Stock-out reduction on top SKUs
$Ms
Working capital released

The stack

NixtlaProphetXGBoostPyTorch ForecastingSnowflakeSAP IBP / o9

Frequently asked questions

Do you replace SAP IBP / o9 / Kinaxis?

No — we plug into them. AI improves forecast and recommendation quality; the APS remains system of record.

How much history do you need?

2+ years for seasonal categories; 6–12 months plus similar-item cold-start works for newer SKUs.

How is accuracy tracked?

Live MAPE per hierarchy level with alerts on regressions; monthly QBRs with planners and demand review.

Ready to Build Your AI Product?

Talk to a senior AI consultant from T7 about your industry, workflow, or product idea. Free, no commitment — reply within one business day.

  • · AI feasibility & architecture review
  • · Product / MVP roadmap
  • · Integration & automation strategy