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Supply Chain & Procurement · Production
Data & Classical Analytics / Mathematical Optimization
Supply and production planning optimisation
Live Core high 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 plan material flow, production slots, and routing to minimize cost while meeting delivery commitments.
Business impact
This can reduce inventory cost and improve on-time delivery in complex multi-product manufacturing environments.
Limitations
Results depend on accurate demand, lead-time, and capacity data. Sudden disruptions still require human intervention, and system integration can be difficult.
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
Using Mathematical Optimization to Solve Complex Business Challenges
Description: Air France applied mathematical optimization to fleet scheduling, assigning aircraft, gates, and crew under constraints like flight hours and capacity to minimize fuel costs, achieving 1% annual savings worth millions. Mondelez International used it for product shipment scheduling, reducing planning time by 92%.



Kaneka: Production Planning Optimization
Description: Kaneka deployed MathCutting, powered by mathematical optimization, at its Shiga Plant to automate polyimide film cutting plans. It selects materials and sequences under demand fluctuations and production constraints, reducing planning time, material waste, equipment downtime, and improving delivery and productivity.



Making Business Decisions with Mathematical Optimization
Description: FedEx uses mixed-integer programming in its Global Supply Chain Model for production location, warehouse consolidation, and transportation routing to cut costs while ensuring service levels; it has saved over $10 million. New York ISO optimizes power generation dispatch for cost-effective electricity provision.
https://hbr.org/sponsored/2024/09/using-mathematical-optimization-to-solve-complex-business-challengeshttps://www.gurobi.com/resources/case-studies/kaneka-production-planning-optimizationhttps://www.dataversity.net/articles/making-business-decisions-with-mathematical-optimization/
Sources
Gurobi — Amazon case study — ; Siemens Opcenter APS —
https://www.gurobi.com/case_studies/amazon/https://plm.sw.siemens.com/en-US/opcenter/advanced-planning-scheduling/