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JAN-FEB 2018

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in house than predictive analytics ap- plications. Process data is often very manufacturing specific, whereas tools and techniques for predictive ana - lytics are not necessarily dependent on close knowledge of processes. As a result, predictive analytics can be a back-office function or contracted out to software or platform vendors. Standards landscape Rapidly improving factory connectivity is the underlying infrastructure that allows both predictive analytics and process improvement. There is a huge demand for access to usable data, and a maturing data standards landscape in manufactur- ing that helps service that demand. More and more devices in the factory have a hardware or software plug for getting data out as well as just in. The data egress was initially very low-level signals, but software libraries and APIs help abstract from otherwise generic voltages, and semantic vocabularies like MTConnect standardize and supply additional con- text from one device to the next. The data models in MTConnect are most widely implemented in discrete manufacturing equipment, but have been used on every- thing from vending machines to discrete sensors to personnel data. The standards ecosystem for manufac- turing data extends well beyond MTCon- nect, which solves a relatively small and narrow problem for analytics. National manufacturing policies, including Plat- form Industrie 4.0 (Germany), Made in China 2025, and Make in India/Digital India, explicitly call out standards as en- abling technology, in large part because clean and coherent data is required for the most promising next-generation applica- tions. The broadest layer of the ecosystem is occupied by reference architectures like the Reference Architecture Model for Industrie 4.0 (RAMI) or the In dustrial Internet Consortium's Industrial Internet Reference Architecture. These reference models specify functional areas that need industry (or cross-industry) collabora- tion and coordination to be successfully addressed. In many cases, standards bodies are working to directly integrate stan - dards. For MTConnect, this includes implementation guidelines for ISA-95/ B2MML on device integration with higher level enterprise planning and management systems. It also includes the OPC UA/MTConnect compan - ion specification, originally released in 2012 and currently being updated for a new version expected in the first half of 2018. The MTConnect devel - opment road map includes expanded device and asset models (e.g., robot - ics, additive, programmable grippers or work holding, and file transfer), but also covers integration with QIF quality standards and expanded functionality by supporting UPnP discovery. Much more to do Analytics is an area of tremendous op- portunity for manufacturing and au- tomation. Basic connectivity and data collection are increasingly the norm, and new applications that go well be- yond status reporting are rapidly be- ing commercialized. Meanwhile, the standards ecosystem for manufactur- ing data is evolving to serve demand for increasingly complex needs. Semantic definitions provided by MTConnect are part of the puzzle, but industrial policy in major manufacturing countries and global consortia are working to minimize duplicated effort. Everyone in manufac- turing and automation should know that analytics are coming to the industry in a big way. Now it is time to get educated on how the hype is fast becoming reality. n ABOUT THE AUTHOR Russ Waddell (rwaddell@amtonline.org) is the managing director for the MTCon- nect Institute and is responsible for day- to-day business operations. He also sits on the technical steering committee for the MTConnect standard. He previously worked at AMT as an industry economist, providing statistical research for sales and marketing in the manufacturing technol- ogy industry. He holds a BA in economics from The College of William and Mary. View the online version at www.isa.org/intech/20180203. FACTORY AUTOMATION INTECH JANUARY/FEBRUARY 2018 21 Predictive analytics, unlike process improvement, uses tools and techniques that are not specific to manufacturing. It is often a back-office function or is subcontracted to software or platform vendors. RESOURCES Industrial Analytics 2016/2017 https://digital-analytics-association.de/doku- mente/Industrial%20Analytics%20Report%20 2016%202017%20-%20vp-singlepage.pdf 2016 Global Manufacturing Competitiveness Index www2.deloitte.com/global/en/pages/manufac- turing/articles/global-manufacturing-competi- tiveness-index.html Reference Architecture Model for Industrie 4.0 www.plattform-i40.de/I40/Redaktion/EN/Down- loads/Publikation/rami40-an-introduction.html Industrial Internet Reference Architecture www.iiconsortium.org/IIRA.htm

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