Connect and prepare
Bring in the relevant signals
- Inventory position
- Demand forecast
- Lead times
- Service policy
- Shelf life
- Supply and capacity
Release working capital without weakening service.
Teams receive prioritized inventory actions with service, cash, obsolescence, supply, and demand consequences visible together.
The operating experience
See how the outcome changes—not another analytics project.
The business user receives a ranked, explainable operating view. Data preparation, model selection, back-testing, governance, monitoring, and evidence remain underneath it.
Aevah Data ScienceInventory optimization
Illustrative interface · Enterprise scopeConsequential question
Owner review requiredWhere should we change inventory before service or cash is put at risk?
Priority scopeWhat changedProposed actionState
01Distribution node 03 · Core line
Service exposure is rising inside lead time
Advance replenishmentEvidence ready02Distribution node 07 · Slow line
Working capital is trapped above policy
Review transfer opportunityReview required03Retail region West · Seasonal line
Shelf life narrows the available response
Constrain allocationMonitorAnalytical drivers · relative influence
Safety-stock optimization
Exception risk
Allocation
Multi-echelon analysis
Aevah recommendation
Prioritize replenishment, allocation, transfer, and policy changes using service, cash, shelf-life, and supply consequences together.
Assumptions, confidence, and constraints attachedIllustrative operating experience—not a customer result. Actual signals, recommendations, acceptance measures, authority, and evidence are established for each bounded implementation.
Outcome to improve
Protect availability while reducing avoidable working capital, expedites, shortages, and obsolescence.
- Accountable owner
- Supply-chain or finance executive
- Required participants
- Supply chain · Planning · Procurement · Finance · Commercial
The Aevah evidence standard
Value is defined before the model is built.
Aevah does not manufacture an ROI number after delivery. The baseline, owner, measures, observation period, and evidence boundary are agreed before production work begins.
- 01 · Baseline
Name the current operating burden
Record how the decision is made today, how long it takes, where confidence breaks down, and which economic or operating measures already exist.
Produces · Current-state evidence - 02 · Acceptance
Define value before building
Agree the accountable owner, minimum useful data, action boundary, adoption signal, measurement horizon, and evidence required to continue.
Produces · Acceptance contract - 03 · Production
Observe the decision in use
Retain the model version, assumptions, confidence, recommendation, human challenge, approval, intervention, and exceptions inside the operating record.
Produces · Decision evidence - 04 · Outcome
Report where the conclusion stops
Compare observed performance with the accepted baseline, disclose constraints, and make an explicit accept, refine, pause, or expand decision.
Produces · Executive evidence decision
Decision contractOwner · baseline · boundary · measure · evidence
Compare recommended policies and interventions against service, inventory, cash, exception, and realized outcome history in a bounded product-location scope.
Service levelWorking capitalStockoutsObsolescenceExpedite cost
Operational data science behind this outcome
What Aevah assembles to produce measurable operating change.
Each capability below exists because a production outcome requires it. The business user experiences better performance—not the engineering, data-management, modeling, application, and governance handoffs behind it.
Trust and understand
Establish governed business context
- Product-location identity
- Inventory policy
- Supply relationships
- Demand uncertainty
- Action history
Build and validate
Model and back-test the decision
- Safety-stock optimization
- Exception risk
- Allocation
- Multi-echelon analysis
- Scenario simulation
Decide and deliver
Put the answer into the work
Ranked replenishment, allocation, transfer, and policy actions with expected service and cash impact, ownership, approval, and evidence.
Act and govern
Keep authority and traceability attached
- Supply-chain or finance executive
- Policy and permission
- Human confirmation and exception handling
- Compare recommended policies and interventions against service, inventory, cash, exception, and realized outcome history in a bounded product-location scope.
Measure and learn
Know whether it worked
- Service level
- Working capital
- Stockouts
- Obsolescence
- Expedite cost
The platform return
Improve this outcome. Preserve the operational data science behind it.
The inventory optimization First Flight can establish reusable business context, quality rules, analytical assets, and evidence for the next business priority.
What this can build
- Product-location identity
- Inventory policy
- Supply relationships
- Demand uncertainty
- Action history
Implementation path
Qualify one bounded production outcome.
A bounded implementation around one consequential operating area. Pre-packaged use cases carry a 30-day target after agreed data, access, ownership, and environment prerequisites are staged.
The target is not a universal delivery guarantee. Custom use cases and unstaged prerequisites require a separately agreed plan.Prerequisites to stage
- Named decision owner
- Representative prior decisions
- Source and definition access
- Agreed action and approval boundary
Evidence to agree
Compare recommended policies and interventions against service, inventory, cash, exception, and realized outcome history in a bounded product-location scope.
A practical next step
Build the value case for working capital.
Create a preliminary brief with the outcome, accountable owner, current burden, minimum useful data, analytical path, action boundary, measures, and bounded route into production.
Build this Value Brief