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IIoT Solves Problems Previously Considered Unsolvable

At a conference I attended, the keynote speaker observed that today is the first time in history that tools in our personal lives are better than those in our work lives. Through mobility and the pervasiveness of the Internet, we can book an Uber, change the thermostat in our homes, and even locate our children from anywhere in the world. But we cannot be confident that the boilers or seals or pumps at our multibillion dollar plants are not going to fail prematurely.

That speaker was correct, but I do not think it will be this way for long.

The Internet of Things (IoT) that drives much of our personal lives is not only invading the world of manufacturing, it is becoming the world of manufacturing. I predict that within 10 years, every modern manufacturing facility will have taken advantage of the Industrial Internet of Things (IIoT). The corporations that do it sooner will improve competitiveness and increase revenues faster.

Early on, manufacturers questioned the value of the IIoT. Skeptics are a shrinking minority.

Key issues were consistent regarding the three areas where they expect to benefit from IIoT technologies—eliminating unexpected downtime, reducing off-spec production, and enterprise supply chain integration.

Early adopters of digitization are achieving excellent results with profitability gains of multimillions of dollars. Mineral processing companies have centralized process knowledge and provided collaborative support to remote locations. Refineries have increased overall equipment effectiveness by 1 to 2 percent. Chemical companies have reduced inventories and improved customer responsiveness. Paper companies have solved key knowledge retention issues.

Although exposure to and interest in IIoT is growing rapidly, what may still be unclear is how to implement it and how to get started now. There are three important aspects to adapting the IIoT at a facility. If you get all three right, you can extract huge value.

The first step is data consolidation. Disparate systems of data need to be integrated in an asset model to apply predictive equipment analytics. This includes process data in the distributed control system, asset data, plant environment data, and data stranded in unconnected systems, such as analyzers or rotating equipment. Next, you need to be able to move that data, protected by top cybersecurity, from the individual plant or unit into the enterprise system. There, you can use the advanced process analytics capabilities and expertise that exist across the organization to identify trends.

Those first two are the more easily accomplished of the three tasks. The third step is where IIoT value is created by very few IIoT suppliers. It is the ability to use deep process and equipment domain knowledge and transform analyzed data and trends into meaningful actions.

Make no mistake, the value the IIoT brings is not about the amount of data generated—there is already an enormous amount of data available—rather it is about what is being done with that data. It is about predictive modeling and prevention. It is about combining that diagnostic knowledge with proven technologies to help predict and prevent failure. Ultimately, it is about plants that can self-diagnose problems before they happen. It is about increased run time, products that consistently meet specifications, and fully integrated supply chains that can run more efficiently with real-time visibility.

It is about solving problems that were previously considered unsolvable.

A version of this article also was published at InTech magazine

Andrew Hird
Andrew Hird
Andrew Hird is vice president & general manager digital transformation at Honeywell Process Solutions. Andrew has been involved in the process industries for more than 20 years, holding engineering, sales, marketing, sales management, and general management positions.

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