Data & Classical Analytics / Mathematical Optimization
Prototype procurement and trial material planning
LiveCorehigh 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
Optimization models allocate suppliers and order timing for pilot builds under material and capacity constraints.
Business impact
This helps reduce prototype procurement lead time and cost by finding a better supplier-material-timing combination.
Limitations
It assumes supplier data is reasonably accurate, which is often not true at prototype stage. Small pilot quantities may also conflict with supplier minimum-order requirements.
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
Toyota applies MILP-based procurement optimisation for prototype materials, reducing pilot-batch lead times by 15–20 % (Toyota Engineering, internal). Dassault DELMIA Quintiq is used for pilot launch planning — .