Operations and supply chain
Replenishment & Inventory Action
Draft orders that respect every constraint, released under the policy you choose for each segment, measured in service, turns, and waste.
The problem
Accurate forecasts lose their value in planner queues when nobody acts on them. Draft orders that ignore supplier minimums or pack sizes lose a planner's trust at once, and items optimized one at a time amplify swings up the supply chain.
Who owns it
Supply-chain operations, who own the release policy. Replenishment analysts work the queue, and finance owns open-to-buy.
What it learns from
- The demand forecasts and their ranges
- ERP master data
- Supplier lead times, quoted and actual
- Order, release, and edit history
- Shelf capacity and open-to-buy
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
Computes what to order from the forecast
Service targets for each segment read the matching point in the forecast range, never the average. Supplier minimums, pack multiples, shelf capacity, and open-to-buy are validated against master data before a draft is shown.
- 02
Decides release through a policy per segment
For each segment you choose: drafts a planner reviews, one-click release, or automatic release within spend and deviation caps with a kill-switch. Flagged items always escalate, and moving a segment to more autonomy requires evidence of release quality.
- 03
Acts in the ERP
Draft purchase orders, transfers, safety-stock updates, and markdown triggers land as reviewable tasks in the ERP workflow and go to suppliers on release. Every quantity carries its forecast, service target, and constraint trace.
- 04
Measures with a phased rollout
Categories running the action layer are compared with the current ordering process on service, stockouts, turns, waste, and expedite costs. Draft acceptance and edit distance show whether planners trust the orders.
What it moves
The business levers
Inventory cash
Working capital released as stock matches demand more closely.
Expedite and waste
Fewer rush orders and less aged stock.
Availability
The right stock in the right place, so forecast quality reaches the shelf.
Planner time
Analysts review exceptions and edits instead of building every order.
Proof
How the return is proven
The return is the change in service, turns, waste, and expedite costs against the current ordering process, stated against the return model agreed up front. Acceptance rate and edit distance are reported beside it as the measures of trust.
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
Queues and release rights are scoped by identity through your single sign-on.
Approval gates
Release policy is set per segment, and any move to more autonomy needs finance approval with the evidence attached.
Audit trail
Every draft, edit, and release is logged.
Segregation of duties
The identity that recommends an order is never the identity that releases it, at every level of autonomy, with spend caps on ordering.
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.