InTech

MAY-JUN 2018

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INTECH MAY/JUNE 2018 11 COVER STORY A ccording to the big data analytics cov er story in the November/Decem- ber 2015 issue of InTech (www.isa.org/ intech/20151201), "Manufacturing firms have the benefit and curse of technology refresh rates typically measured in decades, and tech- nology selection processes typically measured in months or years. This is a benefit, because the technology we buy is built to last and pro- vides a return on investment well beyond that of other industries. It is also a curse. Since we do not get new technology very often, when we get the chance we often bite off more than we can chew, are paralyzed by fear of the unknown, or seek out the new shiny toy." Big data is no longer new, but as the quote points out, it takes more than availability for new technologies to improve process manufac- turing organizations. Implementing new tech- nologies requires innovative products, oppor- tunity for adoption, and applied use by skilled personnel. As a result, while big data may be entrenched and accepted in less constrained environ- ments, it is only now leaving its introductory stage for most process manufacturing organi- zations. Early adopters have deployments and have achieved success, and many companies are evaluating or in- cluding new analytics projects on their road maps, but the single most popular term in articles on big data and the manufacturing in- dustry is "opportunity." As in, the opportunity is out there, but there is still too much: l data (mostly stored in process historians) and too little insight l hard work instead of easy access to innovation l unplanned downtime l time spent cleansing and modeling data before analytics can even begin Ask process engineers what their most com- monly used analytics tool is, and for most the an- swer is a 30-year-old, single-user, heavyweight- client piece of software called a spreadsheet. Ask managers about their data environments, and they will answer in terms of process historians, asset management systems, and other silos. So big data, for many companies, is still out there as a promise, waiting for the intersection of innovation and opportunity to bring new in sights and improved production outcomes to plants and organizations. And no industry has more need to create value from big data. Manu- facturing plants generate twice as much data as any other vertical market, according to McK- insey and Company research from the seminal report that launched big data awareness—and hype—back in 2011 (figure 1). With this much data comes a corresponding opportunity for FAST FORWARD l Advances in technology enable more effective use of big data, which has been around for decades in the process industries. l Many process industry firms use old tools, usually spreadsheets, to derive insights from their big data, with unsatisfactory results. l Advanced analytics solutions address spreadsheet shortcomings in four areas: context, self-service, deployment, and ease of use. Figure 1. No industry produces more big data than manufacturing, creating a huge opportunity for improvement through advanced analytics. Source: McKinsey Annual new data stored by sector, 2010 Petabytes Manufacturing* Government Banking Communications and media Retail Professional services Securities and investment services Healthcare Education Insurance Transportation Wholesale Utilities Resource industries Consumer and recreational services Construction Manufacturing already generates more data than any other sector * Discrete manufacturing constitutes 1072 petabytes; Process manufacturing 740 petabytes 911 773 776 424 397 336 375 276 273 256 245 207 166 116 87 1,812

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