Lecture Topic:Industry 3.5 Mnaufacturing Strategy and Empirical Studies

lecturer: Chen-Fu Chien

Time:October 21, 2019 (Monday) 10:00-11:50

Place:Room 101, Graduate School Building, Zhongguancun Campus

Organizer:Graduate School, School ofMechanical Engineering

【Introduction to the lecturer】

Chen-Fu Chien is a Tsinghua Chair Professor and Micron Chair Professor with NTHU. He is the Convener of Industrial Engineering and Management Program, Ministry of Science and Technology (MOST), the Director of the Artificial Intelligence for Intelligent Manufacturing Systems (AIMS) Research Center of MOST, the NTHU-Taiwan Semiconductor Manufacturing Company (TSMC) Center for Manufacturing Excellence and the Principal Investigator for the MOST Semiconductor Technologies Empowerment Partners (STEP) Consortium. He received the B.S. (Phi Tao Phi Hons.) with double majors in Industrial Engineering and Electrical Engineering from NTHU, Hsinchu, Taiwan, in 1990, M.S. in Industrial Engineering, and Ph.D. in Decision Sciences and Operations Research from the University of Wisconsin-Madison, Madison, WI, USA, in 1994 and 1996, respectively, and the PCMPCL Executive Training from Harvard Business School, Boston, MA, USA, in 2007. From 2002 to 2003, he was a Fulbright Scholar with the University of California-Berkeley, Berkeley, CA, USA. From 2005 to 2008, he had been on-leave as a Deputy Director with Industrial Engineering Division, TSMC. His research efforts center on decision analysis, big data analytics, modeling and analysis for semiconductor manufacturing, and manufacturing intelligence. He has received 10 US invention patents on semiconductor manufacturing and published five books, over 170 journal papers, and 11 case studies in Harvard Business School. His book on Industry 3.5 (ISBN 978-986-398-380-4) that proposes Industry 3.5 as hybrid strategy for emerging countries to migrate for intelligent manufacturing is one of bestselling books in Taiwan. He has been invited to give keynote lectures at international conferences including APIEMS, C&IE, FAIM, IEEM, IEOM, IML, ISMI and leading universities worldwide. He was the recipient of the National Quality Award, the Executive Yuan Award for Outstanding Science and Technology Contribution, the Distinguished Research Awards, and the Tier 1 Principal Investigator (Top 3%) from MOST, the Distinguished University-Industry Collaborative Research Award from the Ministry of Education, the University Industrial Contribution Awards from the Ministry of Economic Affairs, the Distinguished University-Industry Collaborative Research Award and the Distinguished Young Faculty Research Award from NTHU, the Distinguished Young Industrial Engineer Award, the Best IE Paper Award, and the IE Award from Chinese Institute of Industrial Engineering, the Best Engineering Paper Award and the Distinguished Engineering Professor by Chinese Institute of Engineers in Taiwan, the 2011 Best Paper Award of the IEEE Transactions on Automation Science and Engineering, and the 2015 Best Paper Award of the IEEE Transactions on Semiconductor Manufacturing.

【Lecture Information】

Leading industrialized countries with advanced economies have reemphasized the importance of advanced manufacturing via national competitive strategies such as Industry 4.0 of Germany and AMP of USA. The paradigms of global manufacturing networks are shifting, in which the increasing adoption of AI, Internet of Things (IOT), big data analytics, and robotics have empowered an unprecedented level of manufacturing intelligence. However, most of industry structures in emerging countries may not be ready for the migration of advanced cyber-physical manufacturing systems as proposed in Industry 4.0, while also facing other needs to enhance research and practice for industrial engineering and management. This study aims to introduce proposed strategy called “Industry 3.5” as a hybrid strategy between the existing Industry 3.0 and to-be Industry 4.0. Furthermore, the developments of new technologies such as AI, Big Data Analytics also provide opportunities for disruptive innovations to support smart production, while industrial engineering research also need to transform itself from methodologies to technologies and solution providers. Indeed, leading international companies are battling for dominant positions in this newly created arena via providing novel value-proposition solutions and/or employing new technologies to construct “manufacturing platform” to attract and recruit partners and user companies. Thus, little room shall be remaining for small and medium-sized enterprises (SMEs), which will affect healthy sustainability of the whole industry ecosystem. A number of empirical studies in high-tech manufacturing and other industries are used for validation that we have enabled intelligent manufacturing under existing Industry 3.0 to address some of the needs for flexible decisions and smart production in Industry 4.0. Future research directions are discussed to implement the proposed Industry 3.5 to bridge value propositions of industrial engineering research in the restructuring value chains of global manufacturing networks.