Industrial Intelligence A structured map of industrial AIA structured map of industrial AI
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Supply Chain & Procurement · Concept
Data & Classical Analytics / Mathematical Optimization
Concept-stage supply feasibility screening
Research Support low effect
Core capability
The system computes the best feasible production or logistics plan under real constraints, helping improve throughput, delivery reliability, and cost efficiency.
How it works
Business rules, capacities, deadlines, and resource limits are encoded mathematically, and the solver computes the best feasible plan instead of leaving planners to resolve trade-offs manually.
Application here
Simple models screen concept alternatives for rough supply feasibility and cost before development proceeds further.
Business impact
This helps avoid committing to concepts that are likely to face major sourcing or cost problems downstream.
Limitations
Early supply data is rough, so results are directional only. Supplier-specific constraints and emerging supply risks may still be missed.
In production
This is already a real production capability in many companies: the system helps build better plans for production, logistics, and resources under real business limits.
Research
The direction of travel is toward systems where a planner describes the problem in business language and the software helps turn that into a solvable planning model much faster than today.
Examples
A framework for supply chain optimization for modular manufacturing with production feasibility analysis
Description: Chemical‑process case study in which a mixed‑integer supply‑chain optimization model screens modular plant architectures early in design, embedding production‑feasibility constraints to flag infeasible configurations before detailed engineering.



Modular supply chain optimization considering demand uncertainty to manage risk
Description: Industrial‑gas supply‑chain case where optimization models at concept stage evaluate multiple modular plant–network structures under demand and risk scenarios, rejecting options that violate capacity or logistics constraints before capital is committed.



Sustainable two‑stage supply chain management: A quadratic optimization approach
Description: Manufacturing case study using a quadratic optimization model to select early supply‑chain configurations that simultaneously meet cost, capacity, and emissions constraints, discarding infeasible concepts before material‑sourcing decisions.
https://www.sciencedirect.com/science/article/pii/S0098135420306621https://aiche.onlinelibrary.wiley.com/doi/10.1002/aic.17367https://www.sciencedirect.com/science/article/pii/S2192440622000168
Sources
Dassault DELMIA Quintiq — ; SAP Integrated Business Planning —
https://www.3ds.com/products/delmia/quintiqhttps://www.sap.com/products/scm/integrated-business-planning.html