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For asset-intensive organizations, the maturity of their maintenance practices is a key determinant of their ability to operate reliably, without interruption, profitably. Investments in improving maintenance practices, processes, and systems can have a large return. The foundation for predictive maintenance (PdM) is building and maintaining a ‘digital thread’ for each asset—i.e., a full-lifecycle, digitally-connected approach to asset management, connecting all of the data and systems for each asset, from concept/design to manufacturing, service, and ultimately end-of-life/recycle.
With predictive maintenance (PdM), equipment is serviced more in line with actual wear and tear and need for service, while reducing unexpected outages. It brings the multiple advantages of making fewer scheduled maintenance repairs or replacements, using fewer maintenance resources (including spare parts and supplies), while simultaneously reducing failures. A well-run predictive maintenance program can have a dramatic impact on OEE (Overall Equipment Effectiveness). It impacts each of the three components of OEE: higher availability (uptime), increased performance (% of full design speed/output being achieved), and higher quality (first pass yield). Implementing a functional predictive maintenance program can reduce equipment breakdowns by 70%-75%, reduce maintenance costs by 25%-30%, reduce downtime by 35%-45%, and increase production by 20%-25%. In this report you will find:
- Maintenance Maturity Model – outlining the journey from reactive maintenance, through preventative, condition-based, predictive, prescriptive, and ultimately autonomous maintenance.
- Performance improvements you can expect from implementing predictive maintenance.
- How to assess your current state of readiness for predictive maintenance, including your systems, data, and organizational readiness.
- Digital thread concept, including all of the systems involved throughout the lifecycle of a maintained asset, the role of each system in supporting predictive maintenance, and the types of data passed between systems.
- How predictive maintenance supports demand management, particularly for service parts and MRO materials.
- Use case examples of predictive maintenance for:
- Extractive equipment (oil & gas, mining, agriculture, lumber, fishing)
- Manufacturing plant and warehouse material handling systems
- Transportation fleets
- Facilities management/building systems
- PdM’s Role in risk management and sustainability.
- Business model evolution and new services enabled by PdM.
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