Operations and supply chain
Demand Forecasting
Probabilistic demand forecasts for every item and location, with exceptions sent to planners and value measured on service, inventory, and waste.
The problem
Forecasts trained on sales read stockouts as low demand, so they under-order exactly where they failed before. A single number hides the uncertainty that replenishment needs, and planner overrides go unmeasured in both directions.
Who owns it
The demand-planning lead. Planners manage exceptions, and sales and operations planning reconciles the hierarchy.
What it learns from
- Sales and orders
- Inventory and stockout signals
- Product and location master data
- Point-of-sale sell-through
- The promotion and price calendar
- Weather and events, where relevant
How it works
Predict, decide, act, measure
The same four steps in every solution, in this order, each one visible to the team that owns it.
- 01
Predicts demand, with its uncertainty
Global models across every series forecast each item and location with a range, corrected for demand lost to stockouts and reconciled from item to category to network. New items borrow from similar ones.
- 02
Decides what needs a planner
An exception policy flags forecasts that break from plan or from override history, recommends safety stock, and marks projected overstock for markdown review. Planner overrides are tracked and scored for the value they add.
- 03
Acts in the planning system
Forecasts, ranges, and exceptions land in the ERP or demand-planning system and in sales and operations planning. Planners work exceptions instead of producing every number.
- 04
Measures forecast value added
Forecast value added is measured against a naive baseline and against planner overrides. The business effect on service level, stockouts, inventory turns, and waste is measured with a phased rollout by category or region.
What it moves
The business levers
Working capital
Less inventory held for the same service level.
Availability
Fewer stockouts, and fewer sales lost to them.
Waste and obsolescence
Less product written off, and fewer expedited orders.
Planner time
Planners manage exceptions and add market knowledge instead of generating numbers.
Proof
How the return is proven
The return is the change in inventory, availability, and waste in the categories running the forecast against those not yet running it, stated against the return model agreed up front. Bias by segment and promotion-window misses are reported beside it.
What you see: the ROI readout
Every solution produces the same readout, assembled from the system's own records rather than built by hand.
Correctness
The checks the pipeline passed on your own data: leakage, point-in-time, and entity resolution.
Incrementality
The incremental effect against the holdout or equivalent design, with its confidence interval.
Return
The measured return against the investment, set beside the return model agreed before work started.
Guardrails
Complaints, cost, margin, fairness, and contact limits, each shown against its bounds.
Audit extract
Every score, action, and outcome, with the model and policy versions behind it.
Governance
What is governed
Access, approvals, and the audit trail are part of the solution, not a review step added at the end.
Identity-scoped access
Planners see the categories and regions they own, through your single sign-on.
Approval gates
Changes to segments and the planning hierarchy need the planning lead's approval.
Audit trail
Every override is logged, and every forecast version can say what changed since the last cycle.
Financial controls
Where forecasts feed planning commitments, the handoff follows your financial controls.
Why it holds up
Trained on your data, framed by the outcome
This solution trains models on your own data, checks every stage from data to decision, and is measured against the outcome it was built for. Because its predictive power is measured, it also shows the drivers behind the metric, so your team can fix causes as well as act on scores.
Why solutions hold upStart with one decision
Bring the problem and the systems your team works in. We scope the first solution with you, agree how its return will be measured, and run it in production with your people.