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The Maintenance Optimization (MO) uses AI tool that enables enterprises in various industries to analyze and optimize their maintenance strategy in order to reduce costs and increase the availability. The methodology can be implemented in the design phase where one has more decision freedom or also in existing facilities to reduce the maintenance cost.
The MO enables to define maintenance concepts, calculate their costs and optimize them to reduce cost and increase the availability.
The optimization can automatically recommend how to achieve the following:
- Optimal spares and resources for a desired availability
- Optimal stock locations and transportation for spares (exchange, central
or forward)
- Optimal Repair Level – which parts should be discard and which should
be repaired
- Minimize the corrective/preventive/predictive maintenance tasks for
a required availability
- Bundling of several maintenance tasks to reduce the cost
What-if analysis can be performed and alternative optimized maintenance concepts can be compared to select the optimal one.
The MO can use predicted/vendor failure rate distribution for each part and can also analyze the field data to use the real field failure rate distribution.
In many case-studies the MO solution will make transition from corrective maintenance (using run-to-fail strategy) to preventive/predictive maintenance. It usually increases the availability dramatically while reducing the costs of 39% in average.
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