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The myriad articles covering digital transformation typically gloss over the O&M employee’s role. The vision of the smart, autonomous factory is a far more compelling and sexy topic than the nitty-gritty of how the vision is to be integrated with existing production and maintenance practices.

Earlier this year, we sponsored a research study of O&M employees that was conducted by Emory University. The purpose of this study was to learn about the future of IIoT and Machine Learning based Predictive Maintenance.

This article will cover the two topics that the Emory Research study did not address:

  1. To what extent are O&M employees concerned that Industry 4.0 / Maintenance 4.0 will hurt their careers?
  2. How can plant executives address these concerns?

Why Do Workers Fear Change?

At a basic level, resistance to change is an accepted norm. According to a report in Harvard Business Review, there are 10 reasons why workers resist change:

  • Loss of control. When others dictate change, workers may feel that their sense of self-determination is at risk.
  • Excess uncertainty. The fear of the unknown, regardless of the rationale or merits for change.
  • Opposition to sudden change. Decisions imposed without prior communication and warnings are likely to face resistance.
  • Fear of the unfamiliar. Too much change happening at once leads to distraction and confusion.
  • When current employees feel they must justify past practices.
  • Lack of confidence in competencies. Concern that their skillset will become obsolete.
  • More work and responsibilities. With change comes more hard work with uncertain recognition.
  • Ripple effects. Disruption can interfere with the work of many stakeholders affected by the change.
  • Past resentments. It can be challenging to gain cooperation from stakeholders who have past resentments.
  • Sometimes the threat is real. Some workers can pay a real price, such as in the form of lost employment.

Although some of these reasons why workers resist change apply to Maintenance 4.0, it is important to avoid oversimplifying the concerns of O&M employees. In our assessment, much resistance is based on rational, legitimate concerns about the change’s practicality.

O&M Concerns About Maintenance 4.0

Based on the Emory research, O&M employees have reached an overall consensus about the vast potential impact of Maintenance 4.0. In other words, O&M employees recognize the vision of digitalization.

According to the research, O&M employees expect Maintenance 4.0 to improve Overall Equipment Efficiency (OEE). They agree that utilizing Big Data in real time will allow for better decision making.

O&M employees focused on specific deployment roadblocks that were mostly unrelated to personal, job-related motivations. Their concerns related to the application of Machine Learning to industrial maintenance.

At best, some are likely to have studied statistics at some point in their formal education. However, they lack training and expertise in the rapidly evolving Big Data science.

Although the shortage of data scientists in the workforce has been well-documented, it is the solution that gives rise to concern. Gartner coined the phrase “Citizen Data Scientist” – someone whose primary job function is outside of statistics, but who generates models for advanced analytics.

Based on several interviews, we have identified an underlying fear that O&M employees will be expected to analyze Machine Learning output.

There is also a recognition that current Data Governance practices do not support a plant-wide application of Machine Learning to Predictive Maintenance.Depoyment IOT PdM


How Can Plant Management Mitigate O&M Employees’ Concerns?

The first step in addressing O&M concerns is to acknowledge the human element of adapting to change. This requires honest, consistent communications from executives to plant-level employees responsible for deployment.

Ideally, O&M employees should be included in the planning process. Ideally, O&M stakeholders should be included in formalizing the business and functional requirements for Maintenance 4.0 solutions.

Management recognizes that O&M employees are not data scientists. They are also not Citizen Data Scientists. Therefore, Maintenance 4.0 technologies should be selected based on their ability to forecast evolving asset degradation and failure with as minimal plant-level input as possible.

Within the data science community, in the last two years, we have witnessed significant innovation in Automated Machine Learning or AutoML. AutoML reduces many of the repetitive and labor-intensive Machine Learning tasks.

Finally, industrial plants should look to the OEMs that manufacture the machinery within their industrial plants. As Industry 4.0 evolves, OEMs are likely to embed Predictive Analytics capabilities within machinery, adding value for their end users. If OEMs can bundle analytics, the result would reduce the burden at the plant level.

Summary and Conclusion

Technology advancements are expected to transform industrial maintenance. Most O&M employees recognize the challenges ahead and will play a critical role in implementing change. At the same time, it is unrealistic – and unwise – to expect that O&M employees will become experts in Big Data. Tools and technologies requiring minimal input from plant-level employees are necessary to scale Machine Learning for Predictive Maintenance.