Smart Factory Vertical - Predictive Maintenance
08/02/2016 19:00 to 08/02/2016 23:00
In collaboration with 3IF - LSec & Sirris
IoT enabled intelligent fleet management.
Moving from reactive to predictive maintenance
- How to improve quality and service by predicting malfunctions before they cause unscheduled downtime and higher costs?
- Which are the key challenges to implement an IoT enabled predictive maintenance?
- How to build a layered architecture for Sensing, Communication, Service and Infrastructure?
- How predictive maintenance requires a two-step data analytics?
- How IoT help us to implement a transformational business models like performance-based or pay-per use billing?
By Kalman Tiboldi - Chief Business Innovation Officer @ TVH
|||Kalman Tiboldi is the Chief Business Innovation Officer of TVH, worldwide market leader in replacement parts for material handling and in-plant industrial vehicles and has over 35 year experience in using information technology for business process innovation.
Kalman merged IT and Business in a new department called Business Innovation through IT (BI²T) and managed to promote the collaboration between IT and Business as driving force behind innovation. With his team he has successfully implemented a flawless IT infrastructure with flexible applications, based on service-oriented architecture, turning TVH into a real-time, extended enterprise.
Providing the leadership and direction towards the development and implementation of information systems, Kalman is taking advantage from Cloud-based Services, Big Data and Internet of Things and strongly support Open Source solutions.
Kalman holds a Civil engineer polytechnician degree from Military Technical Academy of Bucharest and a Master of Mathematics and Computer Science degree from University of Bucharest.
He was named IT Manager of the Year’ – Large Organizations in 2004 and 2011 by Leading European Trade Publication, Data News.
Although Predictive Analytics has been around for a while, it is still a field of research. In this talk, Prof. Dr. Mannens will explain the current state-of-the-art as well as the main challanges & pitfalls when it comes down to setting up a predictive analytics project.
By Prof. Dr. Mannens - Research Manager @ iMinds
|||Erik Mannens is Professor @ MMLab's KNoWS group / Research Manager @ iMinds Media Technologies Dept. / experienced Project Manager @ iMinds - MMLab (formerly known as IBBT) since 2005 where he has successfully managed +30 projects. He received his PhD degree in Computer Science Engineering (2011) at UGent, his Master’s degree in Computer Science (1995) at K.U. Leuven University, and his Master’s degree in Electro-Mechanical Engineering (1992) at KAHO Ghent.
Before joining iMinds-Ghent University-MMLab in 2005 as research manager, he was a software engineering consultant and Java architect for over a decade. His major expertise is centered around metadata modeling, semantic web technologies, broadcasting workflows, iDTV and web development in general. He is involved in several projects as senior researcher and just finished up his PhD on Semantic News Production; he was co-chair of the W3C Media Fragments Working Group and actively participating in other W3C’s semantic web standardization activities (Media Annotations, Provenance, Hydra, Linked Data Platform, and eGovernment).
New Year Drink.