One of the biggest benefits of using an Enterprise Asset Management (EAM) system is the ability to review historical work order data as well as other recorded asset information to more accurately project the asset lifecycle. Having a realistic expectation of the point of replacement or retirement allows for relatively accurate capital budget planning. The critical element for projecting capital budgets is the quality of information being gathered.
An Opposing Viewpoint
In reviewing a recent slideshow titled The Whole of Life the author attempted to suggest that current systems relying on routine data collection and traditional preventive maintenance programs are unable to provide accurate information for capital planning. This is not even remotely accurate. It would be far more accurate to say that any system is only as good as the information that is input (from asset detail set up to work order descriptions and results). This is commonly called the Garbage In Garbage Out (GIGO) effect. A properly implemented EAM will set up the correct framework for data collection.
I do agree that there is a difference between data and knowledge (slides 25,26). However, the data captured becomes part of a knowledge base that is critical for knowledge transfer. The trick is to capture data so that it can be assimilated then integrated into tools enabling intelligent decisions. Knowledge transfer is an increasingly high priority issue because of the aging population of maintenance management professionals.
Gathering Valid Data
As previously mentioned, the real issue is how to collect valid data that can be used for accurate capital budget planning as well as knowledge transfer. Needed data can be collected in a variety of ways which encompass preventive, predictive and other reliability or conditioned based maintenance. Referring to the slide presentation again, regardless of the terms used the primary objective is to increase asset lifecycles, operate more efficiently and be able to schedule everything from preventive maintenance to inspections to repair with confidence as well as be labor efficiency.
It is important to note that it would be rare for any one methodology to be solution for manufactures, utilities or other industries. Organizations need to make use of any tools that best addresses their pain. Finally, predictive maintenance may not be a good stand alone method. Predictive maintenance is part of a quality preventive maintenance solution.
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