Automation, IIoT, machine learning, artificial intelligence and augmented reality – the next decade promises to be the digital revolution of the industrial world. Our challenge is integrating legacy systems and assets with new technologies to harness the explosion of data to drive new opportunities without risking existing operations. This keynote will cover the challenges of the digital revolution and best practices for establishing an enterprise data infrastructure to support your digital transformation as you invest in edge, enterprise and cloud technologies.

We are being asked to run equipment longer, faster, and harder with less resources while minimizing failures. Condition based maintenance (CBM) takes a data driven approach to maximizing the performance of your assets and maintenance schedules.  Discover how the PI System can be used for CBM through simple drag and drop configuration. Configure digital representations of your assets, calculate key performance indicators (KPIs), and even send notifications or kick off maintenance work flows based on the real condition of your assets.  Jump-starting your CBM project has never been easier with the modern PI System.

A picture is worth a thousand words.  That's why executives and managers around the world rely on visual reports and dashboards to stay in touch with the health of their businesses.  Generating these reports is often a tedious, time-consuming task that repeats every month, quarter, or year.  In this session, you’ll see how you can quickly access disparate data coming from both legacy and new IoT sources to deliver smart, real-time dashboards that drive visibility across the enterprise.  And that monthly compliance report?  The PI System allows you to easily refresh reports and be alerted to compliance deviations before they become an issue.

A foundation of DCP’s digital transformation is having near real time, accurate, meaningful, and complete data with context. An important part of this capability is provided by a company-wide PI System that was deployed in early 2017 integrating data from SCADA, DCS, FlowCal, and many other disparate systems. Change has been rapid, with DCP moving from no centralized means of collecting and using operational data to having 28 gas plants, 5 Frac Plants, 500 compressor stations (and their associated gathering systems), and the company’s major interstate pipelines connected to the PI System in 10 months. To facilitate enterprise collaboration, an Integrated Collaboration Center (ICC) was installed in the Denver HQ that includes Gas Control. In parallel with deployment, DCP has put the PI System to use, building products that support the improved business processes and organizational changes. The key focus has also included advanced conditioned based maintenance (ACBM) and associated Gas/Frac Plant Views in addition to Advanced Compression Health Monitoring(ACBM). The next phase of the DCP journey with the PI System will be in the area of expanding the layers of advanced analytics into areas of cloud enabled “big data” and “super system optimization”. Since deploying the PI System in 2017, DCP Midstream is reported more than $20 million of cost savings and operational benefits with a projected incremental +$20 million in 2018.

Industrial Analytics is the buzz of the day. Big data, data lakes, machine learning and artificial intelligence are offering the allure of new insights and answers to efficiencies. However sometimes the first big win comes from a small improvement and the data your already own. Learn how you can break down seemingly complex big data problems into a manageable sequence of steps with the PI System. Even without waiting for completion of a full-blown statistical model, your operational experts can often spot anomalous patterns with little more than a few events captured by PI System event frames and compiled in a report. And when you are ready to push data to advanced machine learning (ML) tools, the PI Integrator line is available to jump-start your data science initiative with clean, ready-to-consume, reliable data.