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Multiechelon Spare Parts Inventory Optimization for Offshore Wind Farm Maintenance: A Decision-Support Framework for Engineering Managers
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DOI：10.1002/we.70130.png)
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Abstract
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Offshore wind farm operations depend on timely access to critical spare parts, yet maintenance planning is constrained by long procurement lead times, weather-limited site access, and geographically distributed storage facilities. This study presents a practically oriented multiechelon spare parts inventory planning framework that supports engineering managers in making coordinated stocking decisions across distribution centers, local depots, and offshore sites. The proposed Spare Parts Inventory Policy (SPIP) integrates demand requirements, storage capacity, budget limitations, and service reliability targets into a unified planning model that remains transparent and practically interpretable for managerial use. Variability in component failures and offshore accessibility is incorporated through parameterized demand and service-level inputs, allowing managers to evaluate trade-offs without introducing unnecessary model complexity. Rather than introducing a new optimization algorithm, the contribution lies in translating offshore wind operational realities into a linear programming–based decision-support framework that can be implemented with Python using the PuLP optimization package. A case study of a 60-turbine offshore wind farm covering five classes of critical components demonstrates that the framework can reduce annual spare-parts expenditures by approximately 27% relative to current industry planning practices while maintaining required service reliability. Scenario and sensitivity analyses further reveal how budget constraints, storage capacity, and service targets influence cost and inventory allocation decisions. The results provide actionable insights for maintenance planning, depot sizing, and inventory budgeting, offering a practical decision-support approach for improving cost efficiency and maintenance readiness in offshore wind operations.
Keywords:
decision-support
engineering management
multiechelon inventory
offshore wind farm
service-level optimization
spare parts management
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