Manufacturers globally are now recognizing the disruptive potential of IoT technology. IoT’s value proposition stands on the availability of massive IoT data. According to Gartner by 2025, IoT devices are expected to generate 73.1 zettabytes of data, compounding four times from 18.3 zettabytes in 2019. For all this data to be useful, manufacturers must be able to aggregate all data into a Single Pane of Glass. In doing so, users can then gain actional insights that allow for visualizing and real-time monitoring, for example seeing the monetary savings potential from equipment failure before it results in a cascading effect that would stall production or disrupt other subsystems within the infrastructure resulting in a negative cost impact. In manufacturing plants, the maintenance cost of industrial equipment accounts for nearly 70% of the overall cost of production.

For years, manufacturing companies relied on time-based equipment maintenance, where the machine’s age decided maintenance requirements. Older equipment had more frequent maintenance cycles. However, a recent study by ARC found globally that only 18% of equipment failed due to age while 82% of equipment failures are random. This indicates the ineffectiveness of time-based maintenance and affirms the need to dynamically predict machine failures to lower maintenance costs.

Instead of relying on how old the machine is, maintenance teams can leverage the use of IoT sensor technology to model baseline performance characteristics. Analytics can then use this data to predict potential faults to intervene promptly. Overall, IoT is rapidly changing many parts of the manufacturing supply chain, with the vast amounts of data being collected, it is imperative that you outfit your operations with the analytical ability to detect, diagnose, predict, advise, and optimize its performance.