Predictive Maintenance

State-of-the-art prediction systems have become a universal cure for pruning unwarranted OpEx (Operating Expense) in asset-intensive industries. Unlike the maintenance regimes, including reactive/corrective overhauls, PPM (periodic preventive maintenance) & even preemptive service strategies, predictive maintenance depicts an upfront, all-inclusive approach to asset health management.

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Predictive maintenance transforms data into valuable insights & advanced analytics, enhancing asset reliability. The core predictive maintenance methodology is the advanced remediation of unforeseeable events with asset-centered, multisource, and real-time intuitions.

Challenges Faced by the Customers

Unforeseen Downtime
Sudden/unplanned breakdown of any asset is crucial to the operational workflow. This always calls for downtime root cause analysis, and the cost of ownership of the asset keeps increasing with multiple downtime episodes. Predictive maintenance increases asset lifetime with lower long-term operational costs while ensuring full-time asset visibility. Assets themselves convey potential failures before they happen.
Irrational Periodic Preventive Maintenance
24x7 monitoring of the assets at predefined intervals for unnecessary replacement of components when they still have life is a conundrum faced by many industries. This increases the running cost of equipment and resources engagement to be deployed for the planned maintenance tasks. Resultantly, the planned downtime increases. Instead, with predictive maintenance, things take an intelligent turn as the smart replacement of parts is carried out only, which is self-indicated by the machines based on their running conditions.
Excessive Inventory
To maintain the assets working uninterruptedly, the industries must make serious upfront commitments for inventory management. Therefore, stock obsolescence and degradation are understood implications for maintaining stock to meet unexpected breakdown challenges. With predictive maintenance, only those parts are to be procured in advance, which is indicated as fast-moving by the intuitive data insights of the asset functioning.
Disjointed Resource Allocation
Workforce distribution is always done by the PPM cycles of the assets. In order, to meet the predefined OEM (Original Equipment Manufacturer) guidelines for PPM cycles, the resources are aligned to see if all functional conditions of help are met as per the service manual. This adds a burden to the resources segment of any organization. On the other end, predictive maintenance frees resources from unnecessary preventive maintenance practices whereby the assets are only maintained when they proactively declare any anomaly themselves.
Unforeseen Downtime
Sudden/unplanned breakdown of any asset is crucial to the operational workflow. This always calls for downtime root cause analysis, and the cost of ownership of the asset keeps increasing with multiple downtime episodes. Predictive maintenance increases asset lifetime with lower long-term operational costs while ensuring full-time asset visibility. Assets themselves convey potential failures before they happen.
Irrational Periodic Preventive Maintenance
24x7 monitoring of the assets at predefined intervals for unnecessary replacement of components when they still have life is a conundrum faced by many industries. This increases the running cost of equipment and resources engagement to be deployed for the planned maintenance tasks. Resultantly, the planned downtime increases. Instead, with predictive maintenance, things take an intelligent turn as the smart replacement of parts is carried out only, which is self-indicated by the machines based on their running conditions.
Excessive Inventory
To maintain the assets working uninterruptedly, the industries must make serious upfront commitments for inventory management. Therefore, stock obsolescence and degradation are understood implications for maintaining stock to meet unexpected breakdown challenges. With predictive maintenance, only those parts are to be procured in advance, which is indicated as fast-moving by the intuitive data insights of the asset functioning.
Disjointed Resource Allocation
Workforce distribution is always done by the PPM cycles of the assets. In order, to meet the predefined OEM (Original Equipment Manufacturer) guidelines for PPM cycles, the resources are aligned to see if all functional conditions of help are met as per the service manual. This adds a burden to the resources segment of any organization. On the other end, predictive maintenance frees resources from unnecessary preventive maintenance practices whereby the assets are only maintained when they proactively declare any anomaly themselves.

In a nutshell:

Predictive maintenance leads to significant improvements in managing unexpected breakdowns while minimizing repair and maintenance cycles. With preconfigured data insights, the machines predict failures to ensure quick favorable actions.

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