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Avoid Unplanned Breakdowns and Reduce Overall Maintenance Costs
Reliability is the lifeblood of vehicles and equipment. If an aircraft is sitting idle because of unplanned maintenance or if a vehicle breaks down while a government employee is on the road, then not only do costs increase, but opportunities are missed and most importantly lives can be endangered. New technologies powered by sophisticated analytical tools can predict equipment failures and prevent unforeseen breakdowns, increasing reliability and reducing costs. These analytical tools rely on data collection, modeling and analysis to identify equipment that is likely to fail, before it fails. At the same time, these tools can prevent unnecessary or too-frequent maintenance, also reducing expenses.
A proactive approach to maintenance uses advanced analytics to predict failures, which reduces overall costs while avoiding unplanned breakdowns and maximizing up time.
Today, most enterprises employ either a reactive or a preventive approach to maintenance. A reactive method repairs an asset only after it breaks, so there is almost always a cost associated—both the downtime of the out-of-service equipment, and the expense of keeping back-ups on hand and/or expediting parts. A preventive scheme repairs assets at specific suggested intervals, which is sometimes too soon and other times too late, both of which increase costs. It is not unusual for the costs associated with traditional approaches, like the ones noted above, to consume 20 percent of operating budgets—yet unplanned breakdowns still happen too often.
Federal agencies with significant investment in physical assets such as fleets of vehicles, can reap the benefits of using sensor data to understand the performance of those assets, identifying both collective performance and outliers that perform better, or worse, than the norm. The monitoring and sensing devices capture real-time physical characteristics, such as vibration, temperature and pressure, which can be downloaded periodically to a database. By comparing the data against an underlying engineering definition of optimal measurements, it is possible to identify the root cause of breakdowns and maintenance problems.
Sophisticated mathematical models can be formulated and employed to better isolate and comprehend the “leading indicators” of failures and establish action thresholds. The completed models predict the timing and likelihood of future failures, identifying potential breakdowns before they occur. This enables technicians to take action and head off unplanned breakdowns and failures, a significant advantage over traditional approaches. The result―agencies spend less of their operating budget to assets operational while also extending serviceable life of equipment.
Accenture’s approach to predictive asset maintenance reduces unplanned maintenance and increases up time. The key to this approach is a six-step process.
July 9, 2012
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