Multi-Echelon Inventory Optimisation – MRO Slow Mover Centralisation
A leading Maintenance, Repair, and Operations (MRO) spare parts distributor, managing a vast portfolio of highly valuable but infrequently requested components (slow movers), faced the common dilemma of balancing service levels with excessive inventory holding costs.
Their existing strategy involved localising these slow movers across numerous regional hubs to ensure fast service, resulting in high aggregate safety stock, redundant stock-outs, and significant capital tied up in slow-moving inventory across the network. The business challenge was to quantitatively assess a centralisation strategy for these slow-moving parts.
Management needed a definitive answer to the question: would the inevitable increase in transportation costs associated with shipping parts further and faster from a central location be offset by the holistic benefits of reduced aggregate safety stock and improved overall service level for critical spare parts?
The analysis required modelling the complex inventory dynamics across multiple echelons simultaneously.
The Solution: Multi-Echelon Inventory Optimization (MEIO)
The client engaged Sophus to model the holistic supply chain impact of centralising slow movers using the Sophus X platform. The solution was built around a Multi-Echelon Inventory Optimisation (MEIO) engine that could model uncertainty and service level targets across all locations, from the single central hub to the dozens of regional stocking points and end-user maintenance sites.
The MEIO Digital Twin incorporated several critical data streams:
- Demand Variability: Highly variable and intermittent demand patterns for each slow mover SKU at the regional level were modelled to accurately calculate the required safety stock under both centralised and localised scenarios.
- Inventory Costs: The model captured the precise cost of capital tied up in inventory, warehouse space utilisation, and obsolescence risk associated with the slow movers.
- Logistics Network: All transportation costs, transit times, and service levels were defined across the middle-mile linehaul and final mile delivery legs, enabling the model to calculate the exact cost premium of shipping from a central site.
Sophus X ran multiple what-if scenarios, comparing the current localised strategy against various degrees of centralisation, allowing the MEIO engine to simultaneously recommend optimal safety stock levels and facility stocking strategies.
Key Insights and Outcomes
The MEIO analysis provided the definitive financial justification for the strategic network change, proving that a holistic view was essential for managing slow movers:
Positive ROI Despite Increased Transport
The core finding was that, even with a projected 15% increase in annual transportation cost due to more frequent, longer-distance shipments from the central hub, the financial benefit of the inventory reduction more than offset this rise. The model demonstrated that the centralisation strategy led to a 25% reduction in aggregate safety stock, freeing up millions in working capital.
Service Level Improvement
By pooling demand variability at a single central location, the inventory coverage improved dramatically. The centralised strategy achieved the target 98% service level with significantly less total inventory than the localised network, where fragmentation led to sub-optimal service and stock-outs.
Future-Proofing the Network
The MEIO model provided the MRO company with a clear, quantified policy for managing all future SKU introductions: high-velocity items remain localised, while slow-moving, high-value spares are automatically directed to the central hub. This framework ensures future capital expenditure is optimally deployed and removes ambiguity from stocking decisions.
The comprehensive MEIO study transformed the MRO company’s approach to stock management, shifting focus from minimising local costs to optimising the total cost of ownership across the entire, multi-echelon network.
