Operational efficiency is the ratio between an output gained from the business and the input to run a business operation. When improving operational efficiency, the output to input ratio improves. These inputs are typically money, people or time/effort.
Typically it 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.
PREDICTIVE MAINTENANCE: THE BEST OPERATIONAL EFFICIENCY TOOL
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 OPERATIONAL EFFICIENCY & 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