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The Impact of Maintenance 4.0 on OEMs

A Combined Study by Emory University and Presenso

Introduction

The industrial sector is adjusting to the new reality of Industry 4.0 and Maintenance 4.0. The Original Equipment Manufacturers (OEMs) that supply the machinery to operate industrial plants are being forced to re-assess their business models and product offerings.

The Emory University study of the Impact of Maintenance 4.0 on OEMs was designed to gain insights from OEMs’ customers (industrial plant Operations & Maintenance employees). The report is a follow-up to the 2018 research conducted with Emory, entitled “The Future of IIoT Predictive Maintenance.”

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:

• Costs and benefits of Hardware as a Service
• Expected changes to current maintenance practices
• The extent to which the Digital Twin is likely to be embedded in OEM’s offerings
• Risk factors for both OEMs and O&M organizations
• The likely impact of Hardware as a Service on current O&M practices

For this study, 123 O&M professionals were surveyed across Europe and North America. A combination of quantitative research (online survey) and in-depth interviews was used.

Summary of Findings
The O&M community recognizes that the underlying drivers of Maintenance 4.0 (Machine Learning, automation, etc.) will have a significant impact on both the supply chain and their own job roles. Although there are no widespread concerns about job security, there is an understanding that the status quo will change as OEMs assume a more significant role in maintenance activities.

This report analyzes the following topics.
Adoption of New Models for OEMs: The sensors embedded in industrial equipment generate sensor data that has the potential to provide insights into asset performance. Today, only 85% of assets remain connected. However, this is changing due to factors such as the relatively low cost of connectivity and edge analytics. The result is that OEMs are expected to play an increasingly important role by analyzing the sensor data generated from the equipment they manufacture. This is likely to affect both OEMs’ business and financial models. There is strong support for the notion that OEMs will monitor sensor data, but less support for the notion that OEMs will assume responsibility for the tasks that plant O&Ms are currently performing.

Too Soon to Tell Who Benefits from New Models: O&M professionals provided mixed feedback on the likely beneficiary of new OEM models. Many remain undecided. It is also apparent that larger and well-financed OEMs are likely to benefit at the expense of smaller OEMs that lack the ability to extend their service offerings.
Overall Positive Impact on Asset Performance: The confluence of new Maintenance 4.0 applications and the shift from O&M to OEM responsibility for asset maintenance is expected to improve asset efficiency, availability and useful life.

Lack of OEM Expertise in Machine Learning: In our other studies, O&Ms stated concerns about their in-house Machine Learning capabilities. O&Ms have extended these concerns to the OEMs. They consider a lack of technical expertise in Big Data to be a significant constraint.
Digital Twin Adoption: In the previous Emory research, only 4% of O&M respondents forecast widespread use of the Digital Twin within the next 5 years. In this research, approximately half of respondents indicated that in the future some or most OEMs will bundle a Digital Twin.

1.0 The Changing Role of OEMs
Many OEMs are evaluating or implementing Hardware as a Service or HaaS. With the HaaS model, OEMs lease equipment to industrial plants and maintain overall ownership. As the asset owner, the OEM assumes responsibility for asset reliability and maintenance.


One of the drivers that enable HaaS is the OEM’s ability to access the Big Data generated from the sensors embedded in its equipment. By applying Machine Learning to this data, the OEM can detect signs of evolving asset failure from remote monitoring facilities and then dispatch technicians before asset breakdown.

The highest ranked statement relating to the changing OEM role was that over the next 5 years, “OEMs will monitor the data from embedded sensors.” The overall score was 7.0/9.0, with 9.0 being the score for “Strongly Agree.” Over 43% of respondents stated that they Strongly Agreed, while only 3% of respondents Strongly Disagreed. The scenario that OEMs monitor data from embedded sensors is the first step towards a HaaS model, although this does not necessarily mean that OEMs will assume full maintenance responsibility. At the very least, it indicates that OEMs are likely to be more engaged with analytics, even if overall ownership of maintenance remains within existing O&M organizations.

Not surprisingly, there was a drop off in agreement with the statement that OEMs will assume responsibility for Predictive Maintenance (6.2/9.0 versus 7.0/9.0 for data monitoring). Although the percentage of respondents who Strongly Disagreed did not change significantly, the percentage of respondents who Strongly Agreed with this statement was only 27%. The proportions of respondents who moderately agreed or were neutral were 37% and 19%, respectively.

With respect to the question of the adoption of the HaaS model (“OEMs will lease equipment to industrial plants instead of selling”), there is less consensus. Twenty-three percent of respondents disagreed (Strongly or Moderately), 21% were neutral and a further 56% agreed (Strongly or Moderately).

Finally, in terms of “OEMs will perform maintenance roles currently performed by plants’ O&M employees,” a split emerged between respondents, 35% of whom disagreed (Strongly or Moderately) and 45% of whom agreed (Strongly or Moderately). A further 20% were neutral. This reflects the fact that a significant number, though not a plurality, of O&M employees predict a shift of maintenance activities to OEMs.

2.0 Uncertainty About Who Benefits from Hardware as a Service
There is no emerging consensus about who will be the primary beneficiary of Hardware as a Service, although few see the industrial plants as the primary beneficiary. Most respondents (52%) indicated that both the industrial plant and OEM would benefit equally, while an additional 10% were unsure or selected “Other.”

Hardware as a service

It should be noted that 26% of respondents indicated that OEMs would be the primary beneficiary, which is significantly higher than the 12% who selected the industrial plant.

What can we learn from this data? Although most O&M employees are unsure about who benefits from Hardware as a Service, the overwhelming assumption is that the benefits will not be accrued to the industrial plants.

3.0 HaaS Will Lead to an Improvement in Asset Performance
O&M employees believe that HaaS will be a positive influence on the health of industrial assets. However, there is no agreement about the balance of power between OEM and industrial plants.

With respect to who within the OEM sector will benefit, 58% of respondents agreed that HaaS will hurt OEMs that cannot compete in terms of product and service, while 18% disagreed with this statement. Because HaaS requires OEMs to maintain ownership of the industrial equipment and to provide maintenance services, the expectation is that some OEMs will lack the financial flexibility or execution capabilities to compete against more established players.

Hardware as a ServiceSixty-five percent of respondents agreed with the statement that the result of HaaS is the exit of OEMs that cannot compete in terms of both product and service. Although it is too early to predict the long-term impact on pricing, the potential for monopolistic or duopolistic scenarios should not be discounted.

In terms of asset performance, the following is a ranking of statements from highest to lowest:
• It will improve operational efficiency and asset availability. (6.6/9.0)
• It will lead to a meaningful extension of asset useful life. (6.6/9.0)
• There will be an improvement in Operational Safety and Health. (6.0/9.0)
• In the long run, the cost of equipment ownership will be reduced. (6.0/9.0)
What does this data indicate? The shift in responsibility from industrial plant to OEM is a net positive from an asset health and reliability perspective but is less likely to improve the cost of ownership.

4.0 OEM Risk Factors
Risks arise when OEMs expand to new service areas that are not within their traditional core competencies. Although O&M employees lack first-hand knowledge of OEM internal capabilities, a significant percentage of survey respondents assumed that OEMs will not be able to execute due to a lack of technical experience (Big Data). Twenty percent of survey respondents believe that this scenario is a high-risk one, whereas only 6% thought the risk was low.
With respect to the risk of OEMs assuming responsibility for Predictive Maintenance activities, we were surprised to find that the risk of it displacing industrial plant O&M employees was relatively low (3.1/5.0).

HaaS

5.0 Digital Twin Adoption
In the last few years, significant hype has surrounded the Digital Twin. To some extent, this has been muted by GE’s lackluster performance in this market. There is a growing recognition that deploying the Digital Twin on existing equipment is not economically feasible because it requires investment in resources to manually re-create a virtual model of an industrial asset and to train the algorithm on its behavior.

digital twin adoption

If OEMs can bundle the Digital Twin with new industrial equipment, manual work is not required to build and train the virtual model. Based on this logic, it is understandable that 45% of respondents expect that some OEMs will bundle a Digital Twin with their equipment and that an additional 14% indicated that most OEMs will bundle the Digital Twin.

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Michael Dreszer

Michael Dreszer

Michael Dreszer is a Junior at Emory University. He is studying for a Bachelors of Business Administration (BBA) in Finance.