Date: Thu, June 29, 2:00pm EDT
Duration: 1 hour
Event Type: Live Webinar
In today's Smart Factory, data abounds. Connected stations and machines generate terabytes of it every day -- but what next? How do you know you have the right data? Can you access it and use it to resolve issues quickly before they become bigger headaches down the line, or -- even worse -- once your product is in customers' hands?
In this webinar, you will learn how to make practical use of the data you collect to solve the top five problems manufacturers typically experience on their production lines:
- Balancing cycle time and repeatability (Gage R)
- Setting proper test limits
- Increasing first time yield (FTY)
- Narrowing down to selective recalls
- Achieving faster runoff of new stations and production lines
Chief Technology Officer
Richard Brine has been with Sciemetric for more than 25 years. He is currently the company's Chief Technology Officer. In this role, Richard is responsible for Sciemetric's complete product vision, from concept to deployment and ongoing customer support. During his career at Sciemetric, Brine has led most aspects of new technology development and commercialization, working hand-in-hand with customers. He is a graduate of Queen's University and a joint patent holder on the technology behind Sciemetric's 3520 Series Leak Tester.
Dave Mannila has more than 20 years' experience in product development for manufacturing test and quality assurance. As Sciemetric's Product Manager, Mannila has broad responsibility for new product concept, definition and development, as well as maintaining the company's overall product roadmap. Previously, he worked as Principal Product Engineer for new product introduction engineering with medical device maker Abbott Point of Care. Dave holds a Bachelor of Electrical Engineering degree from Lakehead University.
This webinar will be conducted using a slides-and-audio format. After you complete your registration, you will receive a confirmation email with details for joining the webinar.