Predictive Maintenance: Achieving Level 4 Maturity
0 likes133 views
Predictive maintenance uses sensor data and algorithms to predict equipment health and potential failures, with four maturity levels identified by PwC. Most manufacturing companies currently operate at levels 1 and 2, with only 11% achieving level 4 maturity, which combines IoT and machine learning for actionable insights. Key success factors for improving predictive maintenance include data availability, technology, budget, and organizational culture.
Predictive Maintenance: Achieving Level 4 Maturity
- 1. Predictive Maintenance: Achieving Level 4 Maturity
- 2. What is predictive maintenance? It is a type of condition-based maintenance that uses predictive algorithms from equipment’s sensors to predict system health and the possibility of future breakdowns.
- 3. PwC identifies 4 levels of maturity in predictive maintenance based on the increase in data that companies use to predict failures.
- 4. Visual inspection Instrument inspections Big data analytics to drive decision making Level 1 Level 4 Level 2&3 Real-time condition monitoring
- 5. Level 4 is the conflation of IoT and machine learning techniques to generate vast amounts of data and then use it to identify meaningful patterns and generate actionable insights to improve asset availability.
- 6. According to the survey conducted by PwC, most manufacturing companies are at the Level 1 and 2.
- 7. “Only 11% have already achieved level 4.” 11%
- 8. Companies use a combination of resources, capabilities and tools to achieve level 4 maturity levels. Resources Capabilities Tools
- 9. Key elements of the combination are skilled technicians, standard software tools and maintenance logs. Skilled Technicians Software Tools Maintenance Logs
- 10. Going a step further, a few of them rely on “reliability engineers and data scientists, statistical software packages, and external sources.” Reliability Engineers Data Scientist Statistical Software Packages External Sources
- 11. Most respondents, according to PwC, are “quite ambitious about improving their predictive maintenance maturity.”
- 12. Uptime improvement is the key reason they say for achieving level 4 maturity in predictive maintenance.
- 13. Other reasons are cost reductions, lifetime extension for aging assets and the reduction of safety, health, environment and quality risks.
- 14. They mentioned “availability of data” as a critical success factor “followed by technology, budget, and culture”.
- 15. Learn how FieldCircle can help you build on these critical success factors to make your predictive maintenance plan work for you. Visit www.fieldcircle.com for more information.