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How do O&M professionals feel about the impact of IIoT on equipment manufacturers?
When evaluating the impact of IIoT, the role of the Original Equipment Manufacturer is often overlooked.

A new study by Emory University students and Presenso is designed to gain an understanding of the how OEM’s will adjust to the changes brought about by IIoT.

Below is a high-level summary of the key points from the study:

OEMs Expected to Adopt New Operating and Business Models:  Industrial equipment is embedded with sensors that generate Big Data that can provide insights into asset performance. Today, only a small percentage of assets are connected and most operational data is not analyzed. In the future, with relatively low-cost access to connectivity and the availability of cloud and edge-based analytics, OEMs will be able to access sensor data and apply Machine Learning to this data.

The impact on the supply chain is potentially far reaching from both a business model and operating model. There is strong support for the idea 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.

Hardware as a Service

Too Soon to Tell Who Benefits from New Models: From an O&M perspective, it is not clear who will be the ultimate beneficiary of new OEM models. However, one conclusion is 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: New Maintenance 4.0 technologies applications and the shift from O&M to OEM responsibility for asset maintenance is expected to result in improved operational performance of industrial assets. These include operational efficiency, Operational Safety and Health (OSH) and the cost of equipment ownership.
Lack of OEM Expertise in Machine Learning: In our other studies, O&M employees indicated that a lack of in-house Machine Learning capabilities would likely inhibit or slow the adoption of these solutions. When asked about OEMs’ internal Machine Learning capabilities, the O&M employees indicated that a lack of technical expertise in Big Data on the part of OEM is likely to a significant constraint.


Digital Twin Adoption: In the previous Emory / Presenso research project, only a small minority of O&M respondents forecast widespread use of the Digital Twin within the next 5 years. In this report, 59% of respondents indicated that in the future some or most OEMs will bundle a Digital Twin. At the same time, a relatively high number (28%) of respondents were not even familiar with the Digital Twin concept.