About: Antonio Maffei was born in Benevento, Italy, in 1982. He received the B.E. and the M.E. degree in industrial engineering from the University of Pisa, Tuscany, Italy, in 2004 and 2007 respectively. Antonio received a Ph.D. degree in production engineering from KTH Royal Institute of Technology in Stockholm, Sweden, in 2012.
Dr. Maffei has pioneered the study of Business Models in the domain of production engineering, using tools that belongs to strategic and economic research. He can be a world expert in this relatively new field of studies. He is working in harmonizing the business dimension with some of the most challenging innovation patterns highlighted by the 4th industrial revolution: autonomous automation, circular economy and clouds manufacturing.
In 2016, he started a tenure track with in this topic a assistant professor position at the Department of Production Engineering in KTH Royal Institute of Technology in Stockholm. Dr. Maffei is currently Head of the research group named Digital Smart Production. Since 2008 he has been active in teaching activities at undergraduate and recently also at graduate level; consequently he has built up a strong pedagogical background.
Other research interests include advanced automation technology, assembly technology and engineering education. Dr. Maffei is a Research Affiliate of The International Academy for Production Engineering (CIRP); the Swedish Production Academy, Sweden.
Title of Speech
"Industry 4.0 and Digital Factories: a technological or a business model challenge?
An analysis of the historical context, barriers, catalysts and key question related with the fourth industrial revolution"
Abstract: Industri 4.0 is a new wording for a challenge that academics have been dealing with for long time: the actual push towards a more digital/connected factory dates back to the turn of the century. The initial ideas and concepts on this theme were first formalised within the ”Visionary Manufacturing Challenges” document (USA,2001) and subsequently elabo-rated in the Assembly-Net Roadmap (FP4, 2003). Since then the research community has developed concepts, launched new paradigms and even presented demonstrators on these themes. This is particularly well established in Europe with several high-profile EC projects having led the way to software, hardware and mechatronic innovations (FP5-EUPASS, FP7-IDEAS, FP7-PRIME, H2020-openMOS, FP7-GRACE, etc.). By 2008 the European commu-nity had presented its first physical system demonstrator at the Hannover Messe 2008. The first self-configuring assembly system was demonstrated at the FESTO premises in november 2011, and its pre-industrial variant at MASMEC SpA in 2012. Having achieved commercially avaliable controllers for self-configuring systems (ELREST GmbH), and shown that distrib-uted control was viable, the research community then moved forwards to management exe-cution layer and planning (openMOS, GRACE). At the same time the ideas of exploiting massive sensor information, adopting augmented reality, applying quantifiable business mod-els and other concepts began to take form in industry. Basically, the only thing that appeared to be new to the R&D community was the terminology brought forward by industry. How-ever, even though the push towards a new industrial paradigm is well supported, decades of research and development have highlighted that there are well-entrenched barriers to the achievement of the next generation factories, as well as proven catalysts for it. This article will therefore focus on the lessons learnt and observations made since 2000, including details of some new approaches, in order to clarify that an evolution to the next generation of indus-trial renewal will not succeed if such known barriers are abolished and new transitionary approaches adopted.
About: Evgeny Burnaev obtained his MSc in Applied Physics and Mathematics from the Moscow Institute of Physics and Technology in 2006. After successfully defending his PhD thesis in Foundations of Computer Science at the Institute for Information Transmission Problem RAS (IITP RAS) in 2008, Evgeny stayed with the Institute as a head of IITP Data Analysis and Predictive Modeling Lab. Since 2007 Evgeny Burnaev carried out a number of successful industrial projects with Airbus, SAFT, IHI, and Sahara Force India Formula 1 team among others. The corresponding data analysis algorithms, developed by Evgeny Burnaev and his scientific group, formed a core of the algorithmic software library for metamodeling and optimization. Thanks to the developed functionality, engineers can construct fast mathematical approximations to long running computer codes (realizing physical models) based on available data and perform design space exploration for trade-off studies. The software library passed the final Technology Readiness Level 6 certification in Airbus. According to Airbus experts, application of the library “provides the reduction of up to 10% of lead time and cost in several areas of the aircraft design process”. Nowadays a spin-off company Datadvance develops a Software platform for Design Space Exploration with GUI based on this algorithmic core. Since 2015 Evgeny Burnaev works as Associate Professor of Skoltech Center for Computational Data-Intensive Science and Engineering.