IoT Empowered Predictive Maintenance

Session Abstract:

When milliseconds matter, IoT empowers companies and governments to optimize the performance of their machinery, fleets, infrastructure, and buildings.

Organizations that use IoT for predictive maintenance keep high-capital assets running at maximum efficiency and achieve greater production targets, lower operating costs, and minimize environment, health, and safety risks. Using data mining, advanced analytics, and data visualization, they detect hidden patterns in their data, allowing them to troubleshoot performance issues faster and more effectively – so that corrective action can be taken quickly.

Jennifer Robinson will lead a panel discussion on the Artificial Intelligence of Things (AIoT) for predictive maintenance and reliability. This panel will discuss AIoT technology in energy, manufacturing, and government – experiences, lessons learned, and best practices that can help organizations striving to improve asset performance.

Jennifer will be joined by experts who have worked on energy, manufacturing, and government projects and will discuss:
• how data ingestion, integration, visualization, and analytics tie together improve the reliability and availability of physical assets
• how predictive maintenance is keeping a company’s fleet safely in operation
• how a manufacturing company went from 0 to 10,000 operational models in under a year and how some of those models are running multiple times a second to oversee the health of their assets

This panel will be concluded with advice from these experienced technologists on how to embark on optimizing asset performance and the actions to take to position your organization for success.


SASJennifer Robinson is SAS’ Global Government Strategic Advisor, working to help governments maximize the use of their data through data integration, data management, and analytics. Jennifer has a background in software development and local government. She co-wrote the book A Practical Guide to Analytics for Government and is featured in the book Smart Cities, Smart Future. In addition to writing articles and blogs about data driven governing, she speaks to government leaders about emerging technologies and how to strategically adopt them.

Sam Coyne is the current Director of Artificial Intelligence at Georgia-Pacific’s CSC (Collaboration and Support Center) where he supports all AI/ML initiatives across Georgia-Pacific’s manufacturing sites, leading a team of world-class data scientists to deliver value for its facilities. He has supported both commercial and manufacturing projects, including time series forecasting, predictive maintenance, computer vision and anomaly detection. He holds an MS in data science with a specialization in machine learning from Southern Methodist University and a BS in logistics from the University of Tennessee.


Bryan Saunders is certified Six Sigma Black Belt and Mechanical Engineer with over 20 years of industry experience in the manufacturing, utilities and transportation industries. Currently, Bryan is Head of Industry Consulting for SAS Institute’s IoT Division, where he provides thought leadership and industry expertise to drive advanced analytic solutions across industry in support of the Internet of Things. Bryan helps customers uncover value and drive improved business outcomes from the growing volume of connected devices, with a specialized focus on improved service delivery, asset utilization, equipment reliability and overall product quality. Prior to joining SAS, Bryan worked for the General Electric Company in a variety of roles supporting manufacturing and quality solutions, where he developed deep domain experience in equipment condition monitoring and reliability, supporting remote service delivery for the electric utility market.

SASSteve Burgess – Over 20 years’ experience across Strategy, Architecture, Transformation and Delivery of Business and Technology Solutions and Services. I currently help government organisations evolve effectively into truly data-driven organisations through data and analytics; this is by making sense of data, gaining insights from it and deploying those insights to generate decisions faster, using both Analytics and Artificial Intelligence in a fair, transparent and ethical way.

Mike Isbill is a LM Fellow on the Digital Sustainment Analytics team at Lockheed Martin Aeronautics. He first joined Lockheed Martin in 1992 and worked on initiatives across product lines, including the award-winning Joint Strike Fighter (F-35) proposal. After leaving in 1999 for an 11 years stint with a startup health care software company, Isbill returned to Lockheed Martin in 2010 to lead the data analytics capability development on the C-130J program. His current work involves innovative applications of survival analysis techniques to aircraft LRU failure rates, machine learning techniques to improve failure trouble shooting and anomaly detection to enhance CBM+ capabilities. He holds a bachelor’s degree in education from the University of Tennessee and a master’s degree in applied mathematics from the University of Georgia.

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