Industrial internet of things. Industrie 4.0. Industrial internet. Smart manufacturing. Call it what you will, but the addition of automation, internet connectivity and analytics to manufacturing and industrial processes is driving efficiencies, optimizing operations and revolutionizing business models.
Industrial IoT applications are coming soon to a utility plant or manufacturing facility near you R12; if they haven’t arrived already. In fact, 86% of respondents to a 2017 Bsquare survey said they already have IIoT technologies in place; another 12% plan to add them within a year.
There is a plethora of success stories: Stanley Black & Decker, manufacturer of industrial tools and household hardware, used IIoT in its multiproduct manufacturing plants to increase equipment effectiveness by 24%, improve labor utilization from 80% to 92%, and up throughput by 10%.
Velenje Coal Mine — Slovenia’s largest coal mine, which provides one-third of Slovenian electricity — uses industrial IoT applications to improve predictive maintenance, resulting in 22% fewer production delays and 16% lower maintenance costs.
Aluminum producer Aluminum of Greece improved its smelting processes using digital twins (digital replicas of physical counterparts) and IIoT. The company lowered its raw material consumption and energy consumption, leading to a 1% increase in operations efficiency and $970 million savings in production, with a $936 million increase in output and $464 million savings in operations and maintenance costs.
The uses are there and the benefits are widely touted, though they aren’t guaranteed. The same Bsquare survey found most IIoT deployments are in the least-mature stages, with only 28% of respondents automating actions across internal systems and only 7% using edge analytics.
Why the disparity?
The benefits of industrial IoT applications are many, but two major hindrances prevent widespread adoption: a lack of agreed-upon IIoT standards (although they’re on their way!) and a skills gap between IT and operational technology (though the data scientist might be the key to bridging the chasm).