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Supply Chain & Procurement · Service
Data & Classical Analytics / Mathematical Optimization
Service logistics and spare inventory optimisation
Live Adjacent medium 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 balance spare-parts inventory across service locations to reduce both stockouts and excess carrying costs.
Business impact
This can improve parts availability while lowering inventory cost across the service network.
Limitations
It assumes reasonably stable demand and shared data. Sudden fleet-wide issues or organizational silos can reduce effectiveness.
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
8 Successful Supply Chain Management Case Studies
Description: AGCO, an agricultural machinery manufacturer, implemented a transport management system and logistics control tower for inbound supply. This optimized freight rates, shipment scheduling, and carrier payments across Europe, cutting freight costs by 18% initially and 3-5% yearly thereafter.



Using Mathematical Optimization to Solve Complex Business Challenges
Description: Air France used mathematical optimization for fleet scheduling, assigning aircraft to flights under constraints like crew hours and gates. Achieved 1% annual fuel savings (millions of dollars) by minimizing costs and delay propagation in operations.



Management of spare parts for efficient maintenance: a case study
Description: A dairy sector company optimized its spare parts warehouse using mathematical models to balance stock levels across locations. Reduced stockouts and excess inventory costs while supporting maintenance under demand variability and constraints.
https://www.logisticsbureau.com/7-mini-case-studies-successful-supply-chain-cost-reduction-and-management/https://hbr.org/sponsored/2024/09/using-mathematical-optimization-to-solve-complex-business-challengeshttps://www.sciencedirect.com/science/article/pii/S2405896324008747
Sources
PTC Servigistics — ; Syncron Service Parts Planning —
https://www.ptc.com/en/products/servigisticshttps://www.syncron.com/solutions/service-parts-planning/