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JUL-AUG 2019

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INTECH JULY/AUGUST 2019 13 COVER STORY re sources," says Aziz. "The breakneck speed of advancement in the Indus- trial AI/ML space over the last three years affords a unique advantage for these newcomers. They can skip many of the expensive intermediate steps (e.g., significant investments in data aggregation infrastructure, dash- boards, and monitoring centers) and gain the same AI benefits as the sav- vier early adopters." Aziz says most industrial AI initia- tives fall into three categories. AI for assets includes equipment automa - tion, equipment stabilization, and equipment health. AI for processes includes yield maximization through efficiency gains, automation and sta - bilization across multiple assets or spanning multiple flows, and quality improvement. AI for operational excel - lence and/or business agility includes energy cost optimization, predictive maintenance, logistics and scheduling, research and development, and more. AI for assets IBM Watson IoT helps organizations make smarter decisions about asset management by combining IoT data with cognitive insights driven by AI. IBM's Maximo enterprise asset man- agement (EAM) system uses Watson IoT technology to make better deci- sions about critical physical assets in industrial plants—whether they are discrete machines, complex function- al asset systems, or human assets. One Maximo user, Ivan de Loren- zo, is outage planning manager for Che niere Energy, a Houston-based liquefied natural gas producer. He says that, with the software, "we have better information on assets and maintenance activity, and more so - phisticated tools and mechanisms for managing it all. The result is greater operational control and accountabil - ity, especially when it comes to plan- ning and scheduling." AI-based asset life-cycle and main- tenance management solutions like Maximo use real-time data collec- tion, diagnostic, and analysis tools to ex tend an asset's usable life cycle. Use of the software also improves overall maintenance best practices; meets increasingly complex health, safety, and environmental requirements; and controls operational risk by embed- ding risk management into everyday business processes. IBM says EAM also helps "control the brain drain among employees fac- ing retirement by [putting] into place proven workflows and enforced best practices that capture the knowledge and critical skills of long-time em- ployees." Such a system also helps a reduced workforce to work more ef fi- ciently and cost effectively "by using the captured intellectual experience of skilled workers in a format easily dis- persed in a wide range of languages." AI for processes AI systems are being used to improve whole processes as well as indus- trial assets. In an MIT Technology Re- view Insights publication produced in conjunction with IBM, Raytheon senior principal systems engineer Chris Finlay describes the benefits of replacing document-based informa- tion exchange with an AI-compatible digital platform to support engineer- ing and design. "Once you start to capture things digitally, you can start to exploit machine learning or AI al- gorithms," he says. "You can start to reduce development costs because you can automate tasks that you were doing by hand." Joe Schmid, director of world- wide sales for IBM Watson Internet of Things, says, "In the engineering pro- cess, you define what you want to do, design it, build it, test it, and prove that you've done it. The key is integrating those steps. But integrating is hard." Customers that Schmid has worked with are often good at one part of the process, such as design, but they do not integrate design into the life cy cle. "When they need to change goals or specs, it's all in people's heads," he says. "That doesn't work anymore with the complex systems we have today. One engineer can't have an entire system in their head. That's when errors pop up." The goal of AI for engineering pro- cesses is to create an integrated "sys- tem of systems," a closed loop that runs from the requirements phase of product development to real-time monitoring of how consumers are using the product, and then deploy AI systems to analyze the data and leverage that knowledge to improve the product, says Dibbe Edwards, vice president of IBM Watson IoT connect- ed products offerings. In another example, global build- ing materials company Cemex is on an industry 4.0 journey toward en- hanced standardized operations using AI. The ultimate goals are increased efficiencies, reduced fuel and energy consumption, better quality, reduced costs, and improved decision making. The company announced in March Petuum's Atif Aziz says, "Typical AI-driven improvements provide savings or value- added improvements ranging from 2 percent to 7 percent to many multiples after that." In his experience, such extremely high gains require the following criteria: • strong sponsorship from the C suite • effective change management • leveraging an ecosystem; not trying to do everything in-house • significant collaboration between subject-matter experts and AI/data science teams. n AI project success criteria By 2022, more than 80 percent of enterprise IoT projects will have an AI component—up from less than 10 percent today. —Gartner

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