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MADe

Engineering decisions better MADe

The Maintenance Aware Design environment (MADe) is an engineering decision support solution that is used to consistently identify and mitigate engineering risks based on their potential technical, capability and economic consequences.

The asset is represented in a simulation model that defines how it functions; how, when and why it can fail; and identifies the integration and dependencies between electronic, hydraulic, mechanical and pneumatic systems. The MADe analysis capabilities are used to determine appropriate mitigation for the potential impacts of failures on availability and support costs (e.g. change in maintenance approach, design change, change in usage characteristics, etc.).

MADe provides the right information and concurrent analysis to efficiently make function specific engineering decisions that are technically valid and economically justified – at every stage of the product lifecycle.

The MADe solution

MADe enables an integrated ‘systems engineering’ approach to asset supportability. A model based solution, MADe can be utilised throughout the system lifecycle from concept design to operation. The key benefit is in the early identification and potential mitigation of risks in the design process meaning that you can “Design for Support” instead of just ‘supporting a design’ (as has been the traditional model). The optimal maintenance approach and regime can be developed, reviewed and optimized for availability, safety and cost performance based on the most current system configuration.

Why use MADe

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Life Extension

Identify the costs and mitigate the risks involved in extending asset availability

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Prognostics and Health Management (PHM)

Identify and validate the diagnostic capabilities required to support PHM

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Condition Based Maintenance (CBM)

Minimise supportability costs and maximise availability with effective CBM capability

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Performance Based Logistics

Providing visibility of supportability costs and risks for the negotiation of PBL contract

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Maintenance Optimization

Model the supportability aspects of equipment to reduce costs and improve availability

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