INTERNET OF THINGS: IT TAKES TWO (OR MORE) TO TANGO

Improving the manufacturing process 

Another widely used application for IoT is in manufacturing. Typical manufacturing applications of IoT are predictive maintenance, remote status monitoring, and production measurement. Preddio is a technology and solutions company that designs and manufactures industrially compliant electronics, firmware, end-user applications, and analytics. The company partners with original equipment manufacturers (OEMs) and service providers, assisting them with digital transformation of their existing equipment and business using Preddio’s Simplicity IoT platform. “The Preddio platform is optimized for rapid transformation, moving OEM equipment makers from their traditional manual approach to a digital solution,” said Aaron Ganick, CEO and co-founder of Preddio. 

Because Preddio is focused on rapid deployment of OEM solutions, the company partnered with Davra to handle the back-office infrastructure. Preddio’s Simplicity IoT platform leverages key features of the Davra infrastructure to integrate, share, and analyze data and to present it to different departments throughout the company in a meaningful form. “The workforce in a modern industrial operation ranges from individuals on the plant floor to C-level executives, all of whom are essential to delivering positive outcomes,” Ganick explained. 

Although modern manufacturing equipment is tied into production control systems, most of these assets are not monitored for malfunctions or optimal performance. “Several of our end users in the mining industry, for example, were operating thousands of pumps in some extremely harsh conditions,” noted Ganick. The pumps were failing at a high rate, and the companies did not know why. “We installed Preddio sensors, and within a few hours, alarms were generated and spikes in data were clearly visible,” Ganick said. 

Root-cause analysis jointly enabled by the Preddio and Davra platforms revealed that the problem was caused by unexpected hardening of the slurry carried through pumps and injected into the equipment. “Once a change was made in the production process to heat the compound before being pumped, the vibration disappeared, and the life of the pump increased,” Ganick said. 

Teamwork gets results 

In combination with Davra, the Preddio platform allows this type of problem to be analyzed simultaneously by a team of experts across a wide range of disciplines. “We were all able to convene around a common view of the situation, some of us from thousands of miles away,” added Ganick. “We looked at the data and confirmed it with the customer. Their equipment operator called in their process engineer, and, within a few days, the process was fixed.” 

IoT technology has evolved in the past few years, but most equipment OEMs and service providers are not able to develop a full solution on their own. Through its close relationship with Davra, as well as its own capabilities, Preddio has been able to work with OEM companies to accelerate digital transformations while minimizing the risk. “We never market IoT as a technology for technology’s sake,” commented Ganick. “What is important is to focus on the purpose—solving problems for customers by enabling the best digital transformation possible.” 

From diagnosis to prediction, and the digital twin 

The predictive capabilities enabled by IoT have advanced through the use of machine learning. With the large volumes of data available, patterns can be detected that would not be revealed by monitoring individual pieces of equipment. “Initially, customers were happy just to visualize the data provided by sensors,” said Paul Glynn, CEO of Davra, “but now they want to be able to predict problems before they occur and prevent them from happening.” The Davra platform can trace the history of data from a machine and based on that history, predict likely outcomes. 

“Digitization via IoT was already well underway, but COVID accelerated its use,” Glynn commented. “Many people who have left their jobs since COVID emerged were very experienced and might previously have been involved in collecting data in the field and analyzing it. Now, data is collected by sensors and analyzed on an IoT platform.” 

Once incoming data is digitized, it is possible to construct a “digital twin” of a factory (or a smart city or other entity), which provides a virtual image. The concept is not new—it was formally introduced in 2002 for product lifecycle management and had been in use by NASA almost a half-century earlier. The digital twin model allows simulation of a system’s performance. The virtual model can be used to monitor performance, spot bottlenecks, and look for opportunities for innovation. 

The ability of digital twins to measure and predict performance or maintenance needs will contribute to the growth in this market. Gartner reported that 24% of organizations with IoT solutions in production or progress use digital twins, with 42% planning to do so within a few years. According to Research and Markets, the digital twin market was $3.2 billion in 2020, and will reach $184.5 billion by 2030, for an annual growth rate of about 50%.

Organizations want to be able to look at the broader picture of their data, which can be challenging, since many systems with sensors do not communicate with each other. “Users don’t want to have to look at multiple applications,” Glynn said. “They want to see the results all together, normalized, and shared— whether it’s from cameras or sensors— and be able to make sense of it in an integrated way.”