Forecasts that show their uncertainty, so planners can decide how much to hedge.
Models combine sales history, promotions, prices, seasonality and known events to forecast demand at the level you plan at, with a range rather than a single number — and planners review, adjust and sign off the plan.
- 01Assemble history and drivers
- 02Generate forecasts with ranges
- 03Flag unusual items
- 04Review and adjust
- 05Publish and track accuracy
The workflow
Demand planners, supply and inventory teams, and the category or finance managers who use the forecast to set orders, staffing and budgets.
- Forecasts live in spreadsheets built by one analyst and updated by hand.
- Promotions and new products are forecast by gut feel.
- A single number hides how uncertain the forecast is, so safety stock is set by habit.
- Planners override forecasts without a record of why, so nobody learns from the overrides.
Assemble history and drivers
SystemSales, stock-outs, prices, promotions, calendars and any external signals you choose are cleaned and joined at planning level.
Generate forecasts with ranges
AIModels produce forecasts per item, location and period, each with a likely range and the main drivers behind it.
Flag unusual items
SystemItems with large changes, low confidence or recent data problems are flagged for planner attention.
Review and adjust
PersonPlanners review flagged items, apply known information the model lacks, and record a reason for each override.
Publish and track accuracy
SystemThe approved plan is sent to ordering and planning systems, and accuracy of both the model and the overrides is tracked over time.
Where people stay in control
Planners own the final plan. They see the forecast range and drivers, can override any number, and each override is recorded with a reason. Accuracy is reported separately for model forecasts and adjusted forecasts, so the team can see where judgment adds value and where it does not.
- Forecast error by item group and horizon, against the current method
- Bias, meaning consistent over- or under-forecasting
- Stock-outs and excess inventory for forecast items
- Planner time spent per cycle and share of items overridden
Data and integrations
- At least two years of sales history at the planning level, where available
- Stock-out and availability records, so lost sales are not read as low demand
- Promotion, pricing and calendar data
- ERP or planning system access for publishing the plan
Realistic boundaries
- Forecasts cannot anticipate events with no precedent in the data; planners add that knowledge.
- New products and sparse items need proxy methods and wider ranges.
- Accuracy targets are set from a backtest on your own data, not quoted in advance.
How we build it
- Machine Learning & Predictive AnalyticsForecasting, classification, anomaly detection and recommendation models trained on your data and evaluated against the decisions they support.
- Data EngineeringData architecture, pipelines, quality and access that make your data usable for reporting, operations and AI.
- Data Analytics & BIAgreed metric definitions, dashboards and analysis designed around the decisions your teams actually make.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
- Retail & CommerceCustomer service, catalog operations, demand planning and personalization — connected to the commerce platforms and order systems you already run.
- ManufacturingMaintenance, visual quality inspection, knowledge transfer and connected operations — designed around plant-floor conditions and the systems that run production.
- Logistics & Supply ChainShipping documents, exception handling, planning and visibility across carriers, warehouses and partner systems that were never designed to talk to each other.
- Energy & UtilitiesAsset operations, field knowledge, reliability planning and customer service — designed for critical infrastructure, long-lived assets and strict security boundaries.
Questions buyers ask
We run a backtest — forecasting past periods using only the data available at the time — and compare error against your current method on the same items and horizons before anything goes live.
Yes, and they should when they know something the model does not. Overrides are logged with a reason and their accuracy is tracked, which helps the team decide where to trust the model. See our predictive modeling service.
Map this workflow with us.
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