From business priorities to operational decisions.
We combine advisory work, decision science and engineering around a specific business workflow. Each engagement starts with accountable owners, operational constraints and an agreed measurement baseline.
From source systems to a decision-ready foundation.
Data governance, integration and Medallion Architecture form part of the same engagement as the decision model. Establish usable evidence before scaling AI.
Explore data governance & lakehouse services →
Operations & production alignment
Connect order commitments, dynamic costs and finite capacity to make production plans that reflect real operating constraints.
Explore this solution →The management question
Which orders should we accept, at what margin, and with which delivery commitments when capacity is constrained?
What we deliver
An OmniSolve order–cost–schedule model, ATP/CTP scenarios and a capacity-feasible plan with explanations of binding constraints.
How value is measured
On-time delivery · contribution margin · schedule adherence · capacity utilization
Supply chain & procurement resilience
Balance service levels, inventory and supply risk across procurement, replenishment and allocation decisions.
Explore this solution →The management question
How much inventory should each location hold, and how should sourcing change when demand or lead times become uncertain?
What we deliver
Multi-echelon inventory policies, supplier lead-time scenarios, replenishment recommendations and disruption alternatives.
How value is measured
Fill rate · inventory days · stockouts · procurement cost · recovery time
Energy & low-carbon operations
Coordinate energy assets, demand and commercial constraints within a shared energy–carbon–finance model.
Explore this solution →The management question
How should storage, on-site generation and flexible demand operate as prices, carbon objectives and asset limits change?
What we deliver
Dispatch and demand-response scenarios, storage operating policies and investment sensitivities with explicit assumptions.
How value is measured
Energy cost · peak demand · emissions · asset degradation · project cash flow
Cash flow & working capital
Bring finance and operations onto the same view of liquidity, inventory, receivables and payment timing.
Explore this solution →The management question
Which operating choices protect liquidity without weakening fulfillment or supplier relationships?
What we deliver
Rolling liquidity scenarios, AR/AP and inventory trade-offs, cash-conversion-cycle analysis and risk sensitivities.
How value is measured
Cash conversion cycle · liquidity buffer · forecast error · overdue receivables
Enterprise AI & decision governance
Move from an AI experiment to a governed workflow, with private deployment, human approvals and traceable execution.
Explore this solution →The management question
Which decisions can AI support, who remains accountable, and what must be explained before a recommendation is executed?
What we deliver
A use-case assessment, decision model, deployment design, approval rules and an auditable recommendation-to-execution workflow.
How value is measured
Decision cycle time · approval adherence · traceability · operational exceptions
Data governance & lakehouse foundations
Build the data foundation for enterprise decisions. AEVO connects governance, data lakes and Medallion pipelines to analytics, AI and decision optimization.
Explore this solution →The management question
Can teams reconcile orders, stock and capacity across systems, trust their freshness and explain which records informed a decision?
What we deliver
A scoped governance model, source inventory, Bronze/Silver/Gold pipelines, documented data products and a reviewed interface to the Decision Engine.
How value is measured
Freshness against agreed limits · reconciliation exceptions · lineage coverage · pipeline recovery · decision-input readiness
Built around your operating environment.
We scope ERP, MES, WMS, finance and energy-system interfaces with your team. Recommendations can begin in advisory mode, progress to approval-based execution, and become automated only within agreed controls. KPIs are agreed per engagement; improvements depend on data, constraints and adoption.
-
Start with the operating reality.
Bring orders, capacity, stock and business rules into one defined decision context. Check what is known, what is missing and who owns the data.
Orderbook · capacity · stock -
Understand what may change.
Forecasts describe possible demand and uncertainty. They inform the decision; a predicted number alone does not choose the operating plan.
Expected demand · uncertainty range -
Compare what is feasible.
Evaluate alternatives against capacity, availability, service and budget constraints. Make the cost of each trade-off visible before choosing a plan.
Feasible plans · explicit trade-offs -
Show the reasoning behind the plan.
Connect the recommendation to its inputs, assumptions and binding constraints. The team should be able to understand what would change the recommendation.
Rationale · assumptions · alternatives -
Keep responsibility with people.
Route the proposed plan to the responsible team, record approval or changes, and follow its outcomes. Human review remains part of the decision process.
Decision owner · approval · follow-up
A disciplined path from insight to execution.
Frame the decision
Agree on business owners, data boundaries, constraints and the current performance baseline.
Validate in shadow mode
Compare model recommendations against existing decisions before changing live operations.
Integrate with control
Connect agreed systems, approval gates and exception handling with your operating teams.
Monitor value over time
Track agreed KPIs, review model behavior and adapt as business conditions change.
From business questions to decisions you can explore.
Explore the Enterprise Decision Engine with sample data. See how business priorities, constraints and recommended actions come together.
Decision EngineThe public decision engine has eight modules and uses sample data. Production planning and energy optimization are additional enterprise capabilities. Demonstrations illustrate scenarios, not customer results.
How the Decision Engine works →Demand & inventory
Explore how demand changes affect replenishment, shortage risks and inventory levels.
Working capital
Compare the effects of receivables, payables and inventory on the cash conversion cycle.
Supply chain risk
Review supplier concentration, delivery variability and sourcing alternatives.
Start with a decision that matters.
Tell us where cost, capacity, risk or cash is limiting performance. We will help define a focused evaluation before committing to a wider transformation.