From Uncertainty to Action: Data’s Role in Steering Change
Join us for an insightful webinar diving into the transformative power of data analytics and predictive models in manufacturing decision-making. Discover how harnessing data enables the identification of crucial patterns and trends, leading to optimized production processes, maximized uptime, and more consistent product quality. Beyond just data interpretation, we will discuss how AI technology can serve as a valuable tool in prescribing efficient and effective proactive measures – all without having to be a data scientist!
Explore the synergy between data analytics, artificial intelligence, and predictive maintenance to shape a resilient and efficient future for your company.
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Speakers
Alexander Hill
Global Head of Business Development, Senseye Predictive Maintenance
With over eight years of experience in developing and scaling predictive maintenance solutions across various industries, Alex currently serves as the Global Head of Business Development for Senseye Predictive Maintenance at Siemens. Alex joined Siemens after the successful acquisition and integration of Senseye, a company he co-founded and led as the Chief Global Strategist.
In his role, Alex leverages his engineering background, sales and marketing skills, and strategic business acumen to steer global expansion, nurture strong client relationships, collaborate with cross-functional teams, and identify and analyze market trends and opportunities. He delivers cutting-edge, end-to-end asset intelligence solutions that enable clients to optimize their operations, reduce costs, and enhance performance.
John Patanian
Data Analytics Manager, Alcon
John is the Data Analytics Manager at Alcon Laboratories. He leads a team of data scientists focused on optimizing the production of contact lenses at the world's largest contact lens manufacturing facility.
John has extensive experience working in the manufacturing industry, previously serving as Data Science Manager with Rheem Manufacturing and as Principal Data Scientist with GE Power where he worked for over 12 years.
John is skilled in machine learning and AI, developing and analyzing analytics for anomaly detection, predictive maintenance, forecasting, and optimization, and software development.
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