3 Experts Weigh in on the Most Mature Industrial IoT Use Cases
IoT World Today asked three industry experts to evaluate IoT technology in manufacturing. Resoundingly, all agreed that IoT use cases enabling predictive maintenance are the most mature in the space. Read on to learn what they say. (Responses have been lightly edited for clarity).
October 23, 2018
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Javier Diaz, IIoT team leader in Aingura IIoT said: “The company Aingura IIoT builds CNC machine-tools for powertrain manufacturing in the automotive sector. Therefore, my thoughts are oriented to sectors where production asset availability is critical, requiring more than 95 percent uptime, such as in the automotive sector. The cost of downtime can easily hit $50,000 per hour. Unexpected downtimes are one of the most critical aspects where new technologies, such as IIoT, help to improve.” Diaz, who is co-leader of the Smart Factory Machine Learning for Predictive Maintenance testbed with the Industrial Internet Consortium (IIC), added: “This could be summarized as IIoT-based maintenance, in which a complete IIoT architecture from the sensors, to the advanced analytics is used to provide those actionable insights, in terms of failure prediction, degradation monitoring, etc., that could help customers to reduce downtime. We understand that maintenance-based IIoT applications are mature enough to solve specific problems within the manufacturing industry. For example, there are solutions to monitor the tool wear or breakage, predict ball-bearing failures, ball-screws degradation, etc.”
Ian Hughes, senior analyst, 451 Research said: “The core use cases for IIoT in manufacturing are those that start speeding up the analysis of data that plants already have, such as engaging with data from a Data Historian directly. This is more of an organizational challenge than a technical one, and falls into the battle tensions between OT and IT. Predictive maintenance and condition-based monitoring tend to be the initial use cases from an OT perspective as that is an increased efficiency for what is already being done. For some manufacturing, there is also the use case of measuring and improving product quality, instrumenting the output not just the plant line.”
Eddie Lee, director of global industry marketing at Moxa, and co-chair for the Energy Task Group, the Industrial Internet Consortium (IIC) said: “From my experience, the most mature and most common IoT use case in manufacturing centers around predictive maintenance. To be fair, predictive maintenance use cases can be found across multiple industry sectors outside of traditional manufacturing including oil and gas, transportation and traditional electric utilities, as well as smart grid. Regardless of industry, the common denominator stems from the benefit of addressing an obvious pain point, along with measuring the effectiveness of the IoT application and its inherent ROI. In manufacturing, the ability to use machine learning and AI to identify trends and derive insights to predict and prevent downtown is a prime example. Many power utilities such as Duke Energy have implemented IoT predictive maintenance programs and have more than four years of data and insights to be able to calculate their downtime and cost avoidance savings. Digital oilfield applications for predictive maintenance are also more mature since the oil and gas industry was forced to perform “smarter” and more efficiently due to the drop in oil prices several years ago. Implementing IoT for predictive maintenance became more than a competitive advantage, it was almost a necessity in order for survival.”
Eddie Lee, director of global industry marketing at Moxa, and co-chair for the Energy Task Group, the Industrial Internet Consortium (IIC) said: “From my experience, the most mature and most common IoT use case in manufacturing centers around predictive maintenance. To be fair, predictive maintenance use cases can be found across multiple industry sectors outside of traditional manufacturing including oil and gas, transportation and traditional electric utilities, as well as smart grid. Regardless of industry, the common denominator stems from the benefit of addressing an obvious pain point, along with measuring the effectiveness of the IoT application and its inherent ROI. In manufacturing, the ability to use machine learning and AI to identify trends and derive insights to predict and prevent downtown is a prime example. Many power utilities such as Duke Energy have implemented IoT predictive maintenance programs and have more than four years of data and insights to be able to calculate their downtime and cost avoidance savings. Digital oilfield applications for predictive maintenance are also more mature since the oil and gas industry was forced to perform “smarter” and more efficiently due to the drop in oil prices several years ago. Implementing IoT for predictive maintenance became more than a competitive advantage, it was almost a necessity in order for survival.”
IoT World Today asked three industry experts to evaluate IoT technology in manufacturing. Resoundingly, all agreed that IoT use cases enabling predictive maintenance are the most mature in the space. Read on to learn what they say. (Responses have been lightly edited for clarity).
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