Maintenance Optimization
of Legacy Army Assets
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Improving operational availability and reducing sustainment costs using mission-driven MBSE and MADE, transforming fragmented data into actionable insight, enabling engineers to predict failures, optimise maintenance strategies, and drive measurable improvements in platform readiness.
Availability
Costs
Validation
Knowledge Base
Sustaining Legacy Platforms Under Real Operational Conditions
The OEM was tasked with sustaining a class of main battle tanks at defined operational availability levels. However, improving availability required deeper insight into how mission profiles and environmental conditions influenced maintenance outcomes.
This meant moving beyond static assumptions and understanding how real-world usage patterns, terrain, and operating intensity were driving failure behaviour and maintenance demand across the platform.
Without this level of understanding, maintenance decisions risked being misaligned with actual operational needs—resulting in unnecessary interventions in some areas, and unplanned failures in others. The OEM needed a way to connect operational data with engineering insight, enabling a shift from reactive maintenance to a proactive, model-driven approach that could continuously adapt and improve platform readiness.
Data-Driven Sustainment Optimization Using MADE
Analysis of maintenance data to identify failing components. By examining historical maintenance records across the fleet, the OEM was able to pinpoint components and subsystems that were consistently underperforming or failing more frequently than expected. This provided a clear, data-driven starting point for targeted improvements, focusing effort where it would deliver the greatest impact on availability and cost reduction.
Evaluate changes to maintenance frequency and approach. Rather than relying on static schedules, the OEM used data-driven insights to refine when and how maintenance should be performed. This enabled a shift toward smarter, condition informed maintenance, reducing downtime, minimising wasted effort, and ensuring resources were focused where they delivered the greatest operational benefit.
Assess mission and environment effects on costs and performance. By incorporating mission profiles and environmental conditions into the analysis, the OEM was able to understand how real-world usage influenced system behaviour, failure rates, and maintenance demand. This provided a more accurate view of lifecycle costs and enabled better informed decisions around maintenance planning and resource allocation.
Validate design and maintenance recommendations. Proposed changes were rigorously assessed using the model to ensure they delivered measurable improvements in performance, availability, and cost. This provided confidence that both maintenance adjustments and design modifications were technically sound, operationally effective, and aligned with the OEM’s sustainment objectives.
“MADE gave the OEM a structured way to validate maintenance strategies, identify poor performers, and communicate defensible recommendations backed by operational data.”
Measurable Results. A Model That Keeps Delivering.
Increased Operational Availability
Improved capability readiness of the platform through better-informed maintenance strategies.
Reduced Sustainment Costs
Mission profile and maintenance cost relationships were modelled to support cost reduction decisions.
Reusable Knowledge Base
Failures and associated maintenance actions can now be reused across the life of the platform and contract.
See What MADE Can Do for Your Sustainment Program
Talk to a PHM Technology RAMS MBSE specialist and discover how model-based sustainment can transform your operations.
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