SEP-OCT 2018

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A large steel manufacturer wanted to use its operational data to improve its competitive position. By concentrating on condition-based maintenance and product quality, the manufacturer increased equipment availability by 13 percent and boosted the per- centage of "prime" product from 76 to 91 per- cent, while slashing the percentage of mainte- nance conducted in the more costly reactive mode from 80 percent of the total to 20 percent. On paper, the company looks like a success story for Industry 4.0 or a similar Internet of Things (IoT) initiative, but this story is older than either of those buzzwords. The above ex- ample comes from a presentation by Dofasco (now part of ArcelorMittal) in 2002. The phrase "Industrie 4.0" would not be coined at Han- nover Messe for another nine years. The automation industry has always liked buzzwords and standards. People debate the definition of concepts like big data, IoT, ma chine learning, Industry 4.0, digital transformation, and digital twins, but they are all a single effort to drive change by increasing two factors: scope and scale. By increasing scope (the number of data sources) and scale (the amount of data col- lected), companies broaden and deepen the information they tap into for a more accurate, actionable picture of reality. The greater the scale and scope, the greater the opportunity to increase the productivity, efficiency, safety, and ultimately the health of your business. What is different about what is occurring to day versus 2002, or even 1992? From one perspec- tive, it is business as usual. Good engineers are never happy with just the data they have on hand. They should always be (and usually are) seeking out new ways to do things better and looking for numbers that can either support their hypotheses or prove them wrong. On the other hand, everything is different. Moore's Law, artificial intelligence, increasing global competition, and a changing economic environment have created a new business land- scape. Producers now have to be more sensitive to the demands of suppliers, regulatory bodies, and their customers, and there is a new premi- um on intelligence, efficiency, and agility. Com- panies can no longer simply be manufacturers. They have to be software companies and manu- facturers. The scope and scale of your data have fundamentally changed how industry operates. Industry 4.0 is well documented. In this article, I will use it as a model to describe the real-world requirements of businesses and how data archi- tectures need to be designed to meet those goals. Quick history of historians The new demands being wrought by increases in scope and scale have sparked an evolution in historians and data infrastructures. Early data systems collected information from individual assets. Later, those systems were expanded and enhanced to collect data across plants and entire enterprises. And right now, data archi- tectures are being implemented that will allow enterprises to seamlessly exchange information with each other to improve supply chains. Sev- en years ago, many large industrial companies would have blanched at the idea of sharing their data or putting it on the cloud. Now, most are studying ways to build digital communities. Likewise, the community of users of this in for- mation is expanding, moving from engineers and FAST FORWARD l Digital transformation is about one thing: gaining control over the expanding scope and scale of data generated by industrial operations. l The key of success is to control expenditures by implementation, not goals. l Transparency, usability, and interoperability of data between systems becomes increasingly important as sharing information becomes the norm. 34 INTECH SEPTEMBER/OCTOBER 2018 WWW.ISA.ORG Scale scope By J. Patrick Kennedy, PhD and

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