InTech

JUL-AUG 2019

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PROCESS AUTOMATION insights are broadly useful and may be shared in near real time. l Monitoring analytics track asset, batch, or operations performance and seek to answer the question "what is happening now?" Typi- cally, monitoring solutions answer the ques- tion of current status in dashboards or pro- cess graphics updated in near real time, but experience of plant employees. The spreadsheet, the backbone of the past 30 years of analytics ef forts in manufacturing, will simply not suffice for the next 30 years. There is too much data, too few engineering professionals, and too many de mands for insight from improvements in ana- lytics for spreadsheets to be the primary solution. Analytics defined The increased attention on analytics in process manufacturing has led to a taxonomy for differ- ent types of analytics. It is important to identify how they may be applied in process manufac- turing (figure 2). l Descriptive analytics are by definition back- ward-looking, because they describe what happened in reports, charts, and KPIs based on collected data. This is the most widely used type of analytics across all industries, and the FAST FORWARD l Data analytics has a long and checkered history of overpromising and underdelivering. l The main problem has been inadequate tools for data analytics, particularly the spreadsheet, to keep up with increasing data volumes and demands. l Self-service advanced analytics applications have been created to address this and other issues, and their use is rapidly growing across the process industries. INTECH JULY/AUGUST 2019 19 Data rich... Data in process historians, manufacturing & IT systems ... but information poor Inflexible, static reports Long time to insight Manual analysis in spreadsheets Figure 1. Many process manufacturing companies are drowning in data but thirsting for information. Descriptive What happened? Prescriptive What should happen? Benefits Create and share insights to inform decisions plantwide Benefits Evaluate options to make decisions that optimize outcomes Benefits Advisory real-time and prediction view of process and asset status Benefits Increase asset availability and improve batch outcomes Benefits Root cause investigations on/of historical datasets Predictive What will happen? Monitoring What is happening? Diagnostic Why did it happen? Figure 2. This taxonomy describes five different types of analytics in process manufacturing and their benefits.

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