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JUL-AUG 2019

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they are strictly advisory and are thus not suited for inclusion in closed- loop control systems. l Diagnostic analytics seek to iden- tify why something happened based on analysis of historical data, often called root-cause analysis. As de scrip- tive analytics are to reports, diagnos- tic analytics are to spreadsheets as engineers combine, contextualize, and perform calculations on data to uncover cause and effect in processes and units. l Predictive analytics help engineers identify what will likely happen based on real-time and historical data, enabling corrective action to be taken before an undesirable out- come. Benefits include avoiding unplanned downtime, optimizing maintenance schedules, and im prov- ing quality or yields. l Prescriptive analytics aim to opti- mize outcomes by informing plant employees of their best actions based on existing conditions. In a closed- loop system, prescriptive analytics can automate asset or process adjust - ments based on a predefined set of conditions. In an open-loop system, prescriptive analytics inform engi - neers of desired actions. The future of analytics: Three developments Against the backdrop of DRIP, with ever more data coming soon and elevated pressure to gain faster insights of all types for improved production, there are three important trends that will define the future of analytics as experi- enced in process manufacturing envi- ronments. 1. Recognition of employee empower- ment through self-service analyt- ics. The reason spreadsheets have enjoyed their run of success as the primary tool for analytics is that they are accessible to the employees who know the questions to ask. The ap - proach of information technology (IT) personnel without industrial knowl - edge generating or automating ana- lytics or insights is proving short lived, and deservedly so. It simply does not work in complex and rapidly changing environments with extensive interac - tion among variables. An example of the importance of a self-service approach can be found in a recent McKinsey & Company report. "Value emerges as a combination of the tool and the people who operate it. Yet we have seen too many cases where that simple truth has been forgotten in the wave enthusiasm for a new ap- proach. Advanced solutions often fail not because they produce erroneous results, but because the workforce does not understand, or trust, those results." Technology investments are necessary, but not sufficient to achieve productiv- ity improvements, the authors write. To succeed, it is essential for manufactur- ers to invest in their people. In process industries, such as oil and gas, chemical, refining, pharmaceuti- cal, and food and beverage, engineers are the most important group of ana- lytics users. They have the required ex perience, expertise, and history with the plant and processes. Self-service analytics let engineers work at an ap- plication level with productivity, em- powerment, interaction, and ease-of- use benefits (figure 3). In the future, the universe of analytics users will expand beyond engineers to operators, execu- tives, and accountants—all of whom will also benefit. 2. The emergence of advanced analytics. This new class of analytics speaks to the inclusion of cognitive computing technologies into the visualization and calculation offerings that have been used for years to accelerate insights for end users. McKinsey defines advanced analytics solutions this way: "[Advanced analytics solutions] . . . provide easier access to data from mul- tiple data sources, along with advanced modeling algorithms and easy-to-use visualization approaches and could finally give manufacturers new ways to control and optimize all processes throughout their entire operations." Figure 4 depicts data from multiple sources accessed from a single ad vanced analytics application. The introduction of machine learn- ing and other analytic techniques accel- erate an engineer's efforts when seeking correlations, clustering, or any other needle-within-the- haystack analysis of process data (figure 5). With these fea- tures built on multidimensional mod- els and enabled by assembling data from different sources, engineers gain an order-of-magnitude improvement in analytic capabilities, akin to moving from pen and paper to the spreadsheet 30 years ago. These innovations in advanced ana- lytics are not a black box replacement for the expertise of the engineers but are instead a complement and accelerator to their skills, with transparency to the underlying algorithms supporting a first principles approach to investigations. 3. Analytics moving to the cloud. Com- panies of all types, including process manufacturers, are moving their IT infrastructure and data to public and hybrid clouds to increase agility, speed responsiveness, and reduce complexity. Driving this growth are the burgeoning data volumes and increased demand from compute-intensive workloads. Analytics workloads are particularly suited for migration, because most use cases require the scalability, agility, time to market, and reduced costs provided PROCESS AUTOMATION 20 INTECH JULY/AUGUST 2019 WWW.ISA.ORG Figure 3. Self-service analytics enable engi- neers to work at the application level and gain productivity, empowerment, interac- tion, and ease-of-use benefits using Seeq R21 software. Figure 4. Advanced analytics applications can access data from multiple sources.

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