Demand Forecasting You Can Run Today, Not in 12 Months

Those are enterprise platforms designed for global Fortune 500 supply chains, with 6 to 18 month deployments and 6-figure annual licenses. Our service is fractional: model plus people, days not quarters, suited to teams under $1B revenue or pilots inside larger organizations. We give you the same calibrated forecasts and S&OP cadence without the platform cost or rip-and-replace project.

Minimum 24 months of historical demand at the granularity you want to forecast (SKU, product family, channel, or location). A simple two-column CSV with date and value works for the pilot. For ongoing operation we connect to your ERP, WMS, or BI source (SAP, NetSuite, Dynamics, Odoo, Shopify, BigQuery, etc.) so the forecast refreshes on its own.

Depends on data quality and demand volatility, but a typical lift over a naive or moving-average baseline is a 15 to 40 percent MAPE reduction. Industry benchmarks show ML models cutting forecast error by ~18 percent and lifting service levels by ~7 percent. We report MAPE, WAPE, and bias from day one against your incumbent baseline so the lift is auditable.

We blend statistical baselines (ETS, ARIMA, Theta) with gradient boosting and neural sequence models (LSTM, Temporal Fusion Transformer) and pick per-series via cross-validated backtests. Promotional uplift, new-product launches, weather, and macro indicators come in as exogenous regressors. The model choice is transparent in the report so your planners can challenge it.

Promotions are modeled with uplift regressors trained on past campaigns. New products use analog matching and Bayesian priors so you get a forecast on day one even without history. Seasonality is detected automatically (weekly, monthly, yearly) and the model widens its confidence band where historical volatility is high. Planner overlays on top of any of this are saved with an audit trail.

The forecast is the input to your weekly or monthly S&OP review. We push the baseline plus alternative scenarios into your existing spreadsheets, BI dashboard, or planner UI; planners overlay adjustments; the consensus number flows to ERP via CSV or API. Slack and Teams alerts fire on exceptions (e.g. forecast deviates from actuals beyond a threshold) so the cycle becomes exception-driven instead of every-line review.

AWS, region of your choice (eu-central-1 by default for EU data residency, or us-east-1 for North America). Data is encrypted at rest and in transit. Access is locked to a per-customer role; we do not pool data across clients and we do not train shared models on your numbers. We sign an NDA before discovery and a DPA before any production data leaves your perimeter.

Two phases. Pilot is a fixed fee for 1 to 2 weeks of work covering discovery, model build, and a backtest report. Once you decide to keep it, we move to a monthly retainer that covers recalibration, planner support, exception monitoring, and model upgrades. No per-SKU licensing, no platform fee. Cheaper than enterprise platforms and cancellable monthly.

Yes. We have delivered AI and automation in CPG, food and beverage, pharma, retail and e-commerce, manufacturing, and distribution. The forecasting model is industry-agnostic; what changes per industry is the set of regressors (e.g. weather for retail apparel, holiday calendars for F&B, lead-time variability for pharma). The 1 to 2 week pilot is short enough to validate fit on your specific data before you commit.