Data & Classical Analytics / Mathematical Optimization
Constraint-aware design trade-off optimisation
ResearchSupportlow 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
Mathematical optimization explores design options under engineering constraints and reveals the best feasible trade-offs.
Business impact
This helps teams identify feasible design regions and trade-off frontiers that ad hoc engineering judgment might otherwise miss.
Limitations
Results depend heavily on how well the objectives and constraints are defined. Oversimplified assumptions can lead to misleading recommendations.
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
GM and Autodesk Generative Design (Fusion 360) redesigned a seat bracket: from 150+ generated options the chosen part was 40 % lighter and 20 % stronger, consolidating 8 components into 1 (Autodesk customer story, 2018) — .