Within maintenance Operational Efficiency is the capability of an enterprise to deliver products or services to its customers in the most cost-effective manner possible while still ensuring the high quality of its products, service and support.
A company needs to minimize redundancy and waste while leveraging the resources that contribute most to its success and utilizing the best of its workforce, technology and business processes to achieve Operational Efficiency. The reduced internal costs that result from operational efficiency enable a company to achieve higher profit margins or be more successful in highly competitive markets.
The implementation of Predictive Maintenance is one of the strongest strategies to achieve operational efficiency.
Corrective maintenance is maintenance that is performed in order to rectify and repair faulty systems and equipment. The purpose of corrective maintenance is to restore broken down systems.
Corrective maintenance is triggered when a technician sees something that is about to break or will affect the overall performance of a piece of equipment. It can still be repaired or restored without incurring downtime.
If corrective maintenance is not scheduled, the problem may become an emergency maintenance work order down the road and result in halted production lines, interruption in service, or unhappy customers.
Predictive Maintenance is a solution that incorporates corrective maintenance but improves upon it as the AI-powered system notices irregularities and highlights which asset is causing the issue.
For optimal performance, all factory machinery needs to be maintained and companies often have maintenance agreements in place, usually with the original machine manufacturers or their approved service network. Whilst useful, these agreements are largely service-interval schedules, which don’t take into account actual usage and do little to prevent unplanned downtime. This is the widely-used preventative and reactive maintenance model.
Predictive maintenance is more proactive, analysing the huge volumes of available machinery data to give you a better understanding of the ongoing health of your machines and pre-empt failure. This maintenance approach opens up opportunistic possibilities in machinery management, allowing you to adapt and enhance your existing maintenance arrangements. By analysing data to predict when machinery will break down, companies can remove surprise failures and reduce downtime, scheduled maintenance and the routine replacement of parts that may in-fact be perfectly healthy.
READ ABOUT PREDICTIVE MAINTENANCE
Download the “Harness The Power of Prediction: Maximize ROI With The Right Condition Monitoring Solution” Whitepaper to learn:
- The obstacles manufacturers need to overcome
- How the arrival of smart technologies has helped tackle some of these challenges
- The potential impact predictive maintenance can have on ROI
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WATCH HOW PREDICTIVE MAINTENANCE WORKS
SKF, Siemens, GE, Schneider Electric, Nissan, Tata Steel, Smufit Kappa and many more…
- 85% improvement in downtime forecasting accuracy
- 50% reduction in unplanned machine downtime
- 55% increase in maintenance staff productivity
- 40% reduction in maintenance costs