The Future of IIoT Predictive Maintenance
Updated: Oct 1, 2019
In April 2013, at the Hanover Messe conference in Germany, the guiding principles of Industrie 4.0 or Industry 4.0 were released. Over the past five years, Industry 4.0 has moved from German government policy to executive-level strategy across the globe. Today we are in the third wave: active implementation.
Industry 4.0 is considered the fourth Industrial Revolution, and industry analysts have forecasted a significant and broad economic impact. The application of the Industrial Internet of Things (IIoT), Artificial Intelligence and Machine Learning to industrial maintenance or Predictive Maintenance 4.0 is a core element of Industry 4.0.
The Emory University Future of IIoT Predictive Maintenance research study was designed to identify the gaps between the high-level strategic and business drivers of change and the reality of implementation. For this purpose, we interviewed Maintenance and Reliability professionals responsible for Predictive Maintenance in their organizations.
This study’s goal is to provide a field perspective on the following topics:
The current state of Predictive Maintenance in industrial plants
The level of satisfaction with current Predictive Maintenance systems
IIoT Maintenance systems most likely to be adopted within the next five years
The extent to which the Digital Twin is likely to be deployed
The disconnect between executives and O&M professionals responsible for implementation
Reasons for delays in investments in new IIoT Predictive Maintenance solutions
Factors blocking the implementation of IIoT Predictive Maintenance solutions
The likely impact of IIoT Predictive Maintenance on current O&M practices
For this study, 103 O&M professionals were surveyed across Europe, North America, and Asia Pacific. A combination of quantitative research (online survey) and in-depth interviews were used. In addition, feedback was solicited in public forums in Asset Maintenance LinkedIn groups.
Six Emory University students participated in the research and writing of this report: Arnav Jalan and Nathan Brooks (project co-leads) and Dilsher Dhupia, Ian Goldstein, Hannah Laifer and Sabiha Officewala.
Summary of Findings
In 2017 and 2018 alone, significant advances in cognitive analytics have been applied to the discipline of Predictive Maintenance. In parallel, Industry 4.0 has been embraced by the senior management of worldwide industrial facilities.
Our research indicates a growing chasm between the potential for PdM4.0 and the reality in today’s industrial plants. We found no urgency to upgrade legacy Maintenance and Reliability practices from the 1970’s and 1980’s. Microsoft Excel is still the default analytics tool.
Concerns that are raised about PdM4.0 and Maintenance 4.0 stem from practical considerations regarding the feasibility of deployment and the lack of resources. O&M professionals view PdM4.0 positively but expect an incremental change in the form of improvements to existing systems and processes.
This report analyzes the following topics:
Current State of Predictive Maintenance: IIoT for Predictive Maintenance is still in its infancy. Despite the promise of PdM4.0, there is little discontent with current Predictive Maintenance systems. Traditional Predictive Maintenance, including vibration monitoring, oil residue analysis, and thermal imaging, still dominates, and manual statistical modeling such as Excel has not been replaced by more advanced technologies.Outlook for Industry 4.0 Maintenance 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. The Digital Twin concept is not widely known by O&M professionals and is not forecast to play a major role in industrial plants within the next five years.
Perspectives on IIoT Predictive Maintenance: O&M professionals are less enthusiastic about IIoT for Predictive Maintenance than is senior management. Part of this is attributed to the “hype” that resonates less with the Maintenance and Reliability workers who are responsible for implementation. Almost 40% of respondents in the online survey cite a lack of IIoT strategy as a reason for delays in adoption. In the long term, there is an expectation that the perceived ROI from IIoT Predictive Analytics will justify the expenditures.
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 Industry 4.0. The complexity of software and access to sensor data are considered less significant factors affecting stalled deployment.
Impact of IIoT Predictive Maintenance: Overall, O&M professionals have a positive view ofIoT 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 for better decision making. From an organizational perspective, there are only limited concerns that the roles and responsibilities of O&M professionals will change. In general, there was not much support for the outlook that IIoT Predictive Maintenance will force the convergence of Information Technology and Operational Technology organizations.
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