At this point, many manufacturers have completed basic proof of concepts including visualizing maintenance parameters, assessing performance and constituting organizational functions. Fortunately, quite a few manufacturers have realized documentable successes.
However, when it’s time to scale, IIoT initiatives often hit a brick wall. The question is how can manufacturers expand pilot successes to leverage deeper AI-driven programs capable of introducing more predictive and preventive practices that stretch across the enterprise. Of course, this represents a significant step since most proof of concepts focus on singular use cases, such as the ability to visualize and address a key production problem like an ongoing vibration-related failure.
When proof of concept time is over, organizations often struggle with addressing the needed shift in focus, explains Prasad Satyavolu, CDO & Global Head of Innovation for Cognizant’s Manufacturing, Logistics, Energy & Utilities Business Unit. “Instead of just focusing on maintenance as a subject, manufacturers need to look at plant effectiveness and its broader integration into supply chain,” he says. “It is about expanding focus to include all the entire operation. This is necessary to realize a more holistic level that impacts order fulfillment, better visibility and better forecasting.”
