Industry 4.0 – beyond the hype

Industry 4.0 is increasingly becoming a priority for most high-tech industries. It calls for a future of agile, affordable manufacturing fueled by technology enablers such as the Internet of Things (IoT), Cloud computing, Mobile Devices and Big Data. While for most manufacturers it is a lucrative concept with the potential of bringing unforeseen benefits and synergies to their operations, the implementation of Industry 4.0 though remains a challenge.

This presentation will first focus on concrete projects that manufacturers are currently implementing as part of their Industry 4.0 strategic initiatives in industries such as semiconductor, electronics, medical devices and automotive - what is being done and how different it is from the automation and digitalization initiatives performed over the past 10 years. Secondly, will show what the target vision of Industry 4.0 is within such manufacturing environments with decentralized, autonomous networks of smart products and automated equipment collaborating in smart supply chains.

Speaker: Francisco Almada Lobo, Chief Executive Officer and co-founder of Critical Manufacturing

Francisco Almada Lobo holds an MBA and an Electrical Engineering Degree from University of Porto. He started his career in a CIM R&D institute, and joined Siemens Semiconductor in 1997. Throughout Siemens, Infineon and Qimonda, he gained experience in several manufacturing areas having, in 2004, led the first migration of an MES system in a running high-volume facility. Between 2005 and 2009, he managed the Porto Development Center for Infineon and Qimonda, with implementation of automation projects in the group plants worldwide.

Francisco acted as Chief Operating Officer of Critical Manufacturing where, among other areas, he was responsible for the Product business unit. Since 2010 he's the company's CEO.

Making the Case for Safety of Machine Learning in Highly Automated Driving

This talk describes the challenges involved in arguing the safety of automated driving functions which make use of machine learning techniques.

An assurance case structure is used to highlight the systems engineering and validation considerations when applying machine learning methods for highly automated driving. Particular focus is placed on addressing functional insufficiencies in the perception functions based on convolutional neural networks and possible types of evidence that can be used to mitigate against such risks. Recent developments within the industry as well as the application of the techniques in other autonomous domains will also be discussed.

Speaker: Dr. Simon Burton , Chief Expert within the Robert Bosch GmbH Central Research division

Dr. Simon Burton graduated in computer science at the University of York, where he also achieved his Phd on the topic of the verification and validation of safety-critical systems. Dr. Burton has a background in a number of safety-critical industries. He has spent the last 16 years mainly focusing on automotive, working in research and development projects within a major OEM as well as leading consulting, engineering service and product organisations supporting OEM's and their supply chain with solutions for process improvement, embedded software, safety and security. He currently has the role of Chief Expert within the Robert Bosch GmbH Central Research division, where he coordinates research strategy in the area of safety, security, reliability and availability of software intensive systems.