The IIoT will have a big impact on industrial productivity and cost savings in the year ahead. Already, 62% of the industrial-manufacturing sector is using IoT technology in operations, and that number is almost sure to rise in the coming months. That’s why it seems like everyone’s talking about the IIoT right now.
However, not everyone is talking about it the same way. Technology fans, analysts and the media often focus on IIoT devices’ technical specifications and capabilities, which are fascinating and exciting. However, the managers I talk with at plants around the country focus on how internet-connected sensors can solve the specific problems they face. Based on those discussions about their goals, here’s how I think the IIoT will help manufacturers in 2020.
PREDICTIVE MAINTENANCE WILL REDUCE DOWNTIME
A lot of manufacturing plants have a major maintenance challenge: equipment that’s been in use for decades. When these aging compressors, condensers, conveyors and motors break, manufacturers often have to fabricate replacement parts on-site because no one stocks them anymore. When breakdowns happen without warning, line stoppages can last for days or weeks until new parts are made and installed. Predictive maintenance (PdM) enabled by remote wireless sensors can alert anagers whenever a machine is operating outside its normal parameters. By predicting failure well ahead of time, this IIoT application gives companies advance notice to start fabricating replacement parts. That can reduce unplanned shutdowns and increase uptime by up to 20%, according to Deloitte. Over time, as the sensor network collects more data for analysis, the predictions become more refined and precise, allowing for even better planning.
“As the sensor network collects more data for analysis, the predictions become more refined and precise, allowing for even better planning.”
— Sam Cece
SENSORS WILL SUPPORT THE TRANSFER OF WORKER KNOWLEDGE
One way that factories without PdM try to stay ahead of equipment failures is by using the insights of their most experienced people. There’s a generation of factory workers who’ve been on the job for two or three decades. Some of these people know the equipment they work with so well that they can diagnose problems by sound. Now many of those workers are reaching retirement age, and you transfer sensory knowledge about individual pieces of equipment—insights that took ears to develop—to new employees before that knowledge is lost? Wireless sensor data can help. For example, new hires can shadow senior workers to learn how they monitor and manage the equipment. During that time, the newer worker gets two sets of information about how the machines operate. The first is from the senior worker, who can diagnose equipment by changes in sound or vibration. The second is from the machine’s sensor data. By correlating the sensor data with input from senior workers, new hires can see what the sensor readouts look like when equipment is working well and when there’s trouble brewing. This can help bridge the knowledge gap between soonto-retire employees and newer team members, preventing breakdowns and reducing unplanned downtime.
“With that data, managers were able to work with shift leads to resolve the issue without a multimillion-dollar expenditure.”
— Sam Cece
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