A recent study, “The Future of IIoT Predictive Maintenance,” was conducted by Emory University and Presenso. The study was designed to gain the perspective of plant-level employees on issues relating to deployment and the expected impact of IIoT PdM on plant operations. (Download the full report at https://plnt.sv/1812-BPI.) Chris McNamara, content director of our sister publication Smart Industry, sat down for a chat recently with Eitan Vesely, CEO of Presenso, to get his insights on the report findings.
SI: How does the perception of digital transformation among the plant-floor staff differ from that of senior management?
EV: Although we deliberately did not interview executives for this report, there is a clear difference between plant-level employees and the senior management that I frequently encounter.
Senior management tends to be enthusiastic about the financial and operational potential of digital transformation. Plant-level employees have adopted a very pragmatic approach to IIoT, bordering on cynical. Most O&M professionals have not seen a compelling need for Industry 4.0 practices and expect change to be more incremental.
There is also an acknowledgement of the gap between expectations from plant employees and management. There was much stronger agreement with the notion that senior executives recognize the potential of predictive analytics relative to facility staff.
SI: How important is addressing that divide in terms of optimally adopting these new technologies?
EV: I don’t see the divide, per se, as preventing or delaying deployment. At the same time, given the fact that many industries are dealing with labor shortages, IIoT-based deployments cannot be mandated without providing sufficient resources.
In general, plant employees are more accepting of incremental change, such as the automation of workflows. If IIoT predictive maintenance simply provides new insights to trigger existing maintenance activities, then it will be adopted easily.
However, if plants need to hire data scientists and retrain O&M employees, then more resistance is likely. Ultimately, it’s a function of how IIoT for predictive maintenance is defined and the extent to which tools are easy to understand and use.