Have you navigated your predictive maintenance maturity level? Navigating the maturity level of your predictive maintenance model could help you identify the new requirements of your business to utilize the data more effectively. Check out the post to explore more about predictive maintenance maturity level and how you can improve on the levels to increase your operational effectiveness.

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.