Data & Classical Analytics / Mathematical Optimization
Portfolio and resource scenario optimization
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 models compare portfolio alternatives under resource and budget constraints and identify which combination performs best against the chosen objectives.
Business impact
This makes resource allocation decisions more transparent and repeatable and exposes trade-offs that intuition alone would often miss.
Limitations
Results are only as good as the assumptions and data behind them. The model cannot capture politics, informal constraints, or missing resource data.
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
Shell uses Gurobi for refinery portfolio scenario planning and CAPEX allocation across sites, reducing scenario modelling time from weeks to hours (Gurobi customer story). Air Liquide applies IBM CPLEX for multi-plant capacity allocation — .