Forecasting-as-a-Service for Supply Chain
Demand Forecasting You Can Run Today, Not in 12 Months
Drop your historical demand data, get a calibrated forecast with confidence intervals in minutes. We embed it into your S&OP cycle in days, at a fraction of Blue Yonder, o9, or Kinaxis.
Who Benefits in Supply Chain?
From supply chain directors and demand planners to CFOs and IT leaders, our forecasting service replaces spreadsheet planning with AI-grade forecasts you can defend. Standard MAPE, WAPE, and bias reporting plus exception-driven S&OP integration make it safe to deploy across product families while CSV and API connectivity fits your existing ERP, WMS, and BI stack.
Supply Chain Director / VP Operations
Problem: Stockouts and overstock eat margin. Planners run consensus on spreadsheets and the forecast is stale before it ships.
Get: calibrated demand forecasts with confidence intervals, automated S&OP cadence, defensible inventory targets, and exception alerts. ROI in days not quarters with a 1 to 2 week pilot.
Head of S&OP / Demand Planner
Problem: Slow consensus cycle, manual statistical models, no AI lift over naive baselines.
Get: AI baseline plus planner overlay with full audit trail, weekly or monthly cadence, MAPE, WAPE, and bias reporting from day one, exception-driven review so planners focus on what changed.
CFO / Finance Director
Problem: Forecast variance hits cash and EBITDA. You can't justify a Blue Yonder or o9 TCO for the volume you have.
Get: predictable inventory, lower working capital, transparent accuracy KPIs, and a fixed-fee pilot. Cheaper than enterprise platform license, faster than a 12-month deploy.
CIO / IT Director (manufacturing, distribution)
Problem: Enterprise forecasting platforms mean a 12-month deploy with a dedicated integration team and a vendor lock-in you can't unwind.
Get: CSV-in, CSV-out plus API. Embeds with existing ERP, WMS, and BI. EU or US data residency, no rip-and-replace, and our team operates the model with you.
See an example, then run one on your data
Real forecast on a noisy 4-year monthly demand series. Click "Try with my data" below to upload your own. The chart updates with your result.
Run a forecast on your data
We’ll send a 6-digit code to your work email so we can rate-limit the compute. The page stays where it is the whole time.
Example forecast on a noisy 4-year monthly demand series with one anomaly month (2023-03). Model captures seasonality and trend, widens confidence band where historical volatility is high. MAPE 14.2% on a held-out tail backtest.
Our Forecasting-as-a-Service Process
From a one-week discovery to ongoing operation, we run the model with your planners and integrate forecasts into your S&OP cycle with measurable accuracy and clear KPIs.
01
Discovery (free, 2 to 3 days)
Workshop with planners and ops. We map demand drivers, SKU hierarchy, S&OP cadence, and ERP source-of-truth. Define success metrics (MAPE, WAPE, bias, stockout rate).
02
Forecast pilot (3 to 5 days)
We ingest 24+ months of history, generate baseline forecast with confidence intervals, planners validate. Accuracy reported transparently against naive and your incumbent baseline.
03
S&OP integration (5 to 10 days)
Wire forecasts into your monthly or weekly cycle. Slack and Teams alerts on exceptions, CSV and API export to ERP, planner overlay UI for adjustments.
04
Run and improve (ongoing)
Monthly recalibration, planner overlay UI, model upgrades as data grows. Our team operates the model with you. Cheaper than enterprise platform license.
How It Works
From CSV upload to embedded S&OP. Six steps to turn historical demand into calibrated forecasts you can defend in front of your board.
Upload monthly or weekly demand history
AI generates calibrated forecast with confidence intervals
Planners validate, overlay, override (audit trail)
Exception alerts on Slack or Teams
Export to ERP via CSV or API
Monthly recalibration as data grows
Frequently Asked Questions
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.