Most industry research into IIoT trends relies on insights gained from senior management and decision makers.
Below is a high-level summary of the key points from the study:
Current State of Predictive Maintenance
There is little discontent with current Predictive Maintenance systems. Traditional Predictive Maintenance including vibration monitoring, oil residue analysis, and thermal imaging still dominate and manual statistical modeling such as Excel has not been replaced by more advanced technologies.
The Outlook for Smart Factory Technologies
O&M professionals expect that Automated Failure Reporting and Automated Repair Scheduling are most likely to be widely adopted over the next five years. There are limited expectations for the deployment of Robotics Assisted Repair and Drone/Robotics Assisted Inspection.
Perspectives on IIoT Predictive Maintenance
O&M professionals are less enthusiastic about IIoT for Predictive Maintenance than senior management. Part of this is attributed to the “hype” that resonates less with the Maintenance and Reliability workers who are responsible for implementation.
Implementation of IIoT for Preventive Maintenance
The most significant inhibitor of IIoT for Predictive Maintenance deployment is a skill shortage of Big Data Scientists and a lack of understanding of Machine Learning.
Impact of IIoT Predictive Maintenance
Overall, O&M professionals have a positive view of IoT Predictive Maintenance. Improvements to Operational Equipment Efficiency (OEE) are widely expected. Furthermore, most survey respondents believe that utilizing and analyzing the data in real-time will allow better decision making.
Summary and Conclusion
In general, O&M professionals have adopted a wait-and-see approach and are not getting swept up in the enthusiasm about Industry 4.0. They see the potential value but are concerned about deployment challenges.
To read the full study, please visit this page.
Written by Arnav Jalan, Emory University.