講座題目：工業3.5製造戰略與產業實證研究

Industry 3.5 Mnaufacturing Strategy and Empirical Studies

報 告 人：簡禎富

時 　 間：2019年10月21日(周一)10:00-11:50

地 　 點👴🏿：中關村校區研究生教學樓101報告廳

主辦單位⏬⚜️：意昂平台⚱️、機械與車輛學院

報名方式：登錄意昂官网微信企業號---第二課堂---課程報名中選擇“【百家大講堂】第250期🧙🏼‍♀️：工業3.5製造戰略與產業實證研究”

【主講人簡介】

  簡禎富現任新竹清華大學清華講座教授暨美光講座教授，他在新竹清華大學工業工程與工程管理學系以及科技管理學院EMBA/MBA開課🏠，並兼任新竹清華大學 智能製造跨院高階主管碩士專班(AIMS Fellows)主任；他也是科技部工業工程與管理學門召集人，並擔任科技部人工智能製造系統研究中心主任，主持「清華-臺積電卓越製造中心」🧗🏿‍♀️🏄。新竹清華大學工業工程系暨電機工程系雙學位（斐陶斐榮譽會員）；美國威斯康辛大學麥迪遜分校決策科學與作業研究博士；美國加州大學柏克萊分校傅爾布萊特學者。曾任新竹清華大學秘書長、副研發長兼首任產學合作執行長、國科會固本精進計劃推動辦公室總主持人🔃、「竹科2.0」規劃計劃主持人、劍橋大學訪問教授、日本早稻田大學青年訪問學者獎等🧜🏽‍♀️。發表超過170篇學術期刊論文，著有《工業3.5》《大數據分析與數據挖礦》《決策分析與管理》《紫式決策工具全書》及《半導體製造技術與管理》等書；主編《智能製造 AI臺灣》《創業清華》《固本科園 臺灣精進》《產業工程與管理個案》及《清華百人會》等書及《竹科30》有聲書。並撰寫臺積電🪶、聯發科、創意電子等12篇哈佛商業個案。領導研究團隊深耕大數據分析👧🏿、資源優化和數字決策等智能製造技術🙇🏿，已取得23項智能製造發明專利（10項美國；13項中華民國）；並與各個產業龍頭和隱形冠軍建立雙贏的產學合作機製⛹🏼‍♂️🧖🏻‍♂️，創造具體產業效益，因而榮獲行政院傑出科技貢獻獎(2016)🛫、行政院國家質量獎-研究類個人獎(2012)、科技部傑出研究獎(2016、2011、2007)🛡、國科會優秀年輕學者研究計劃、第一級計劃主持人獎、經濟部大學產業經濟貢獻獎 (2009)、教育部產學合作研究獎(2003)、東元科技獎 (2018)、IEEE Trans. on Semiconductor Manufacturing 2015年最佳論文獎、IEEE Trans. on Automation Sciences & Engineering 2011年最佳論文獎🤚、科技管理獎(學研團隊類)(2017)、工業工程學會會士(2018)、APIEMS Fellow (2016)、科技管理學會院士(2012)🧛🏼、傑出工程教授(2010)👧、工業工程獎章：產業貢獻(2010)和學術貢獻(2016)、第一屆東森杯大數據競賽冠軍(2014)、工程論文獎(2003)🪵、呂鳳章獎章(2003)👨🏼‍🎨、工業工程論文獎(2003)等殊榮🦹🏽‍♀️，以及國立清華大學傑出產學合作獎(2019📄、2016、2007)等🦼，也是國科會《學與致用》(2007)的九個典範之一。研究領域包括：決策分析、大數據分析🖨、智能製造、半導體製造、數字決策、工業3.5等。

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. 

【講座信息】

  隨著物聯網、大數據、機器人和人工智能的發展⤴️，產業轉型升級的工業革命已經在進行中🤽🏽‍♀️，越來越多工作機會因為自動化和智能化而消失🧙🏻‍♀️，年輕人和弱勢族群更不容易找到好的工作。世界各國均提出自己的製造戰略，包括：德國工業4.0、美國再工業化🍘🫖、日本工業4.1J📵、韓國產業創新3.0等，先進工業國家基於既有的競爭優勢以拿回先進製造，也為了爭奪第四次工業革命的主導地位。隨著產業價值鏈因為工業革命而即將重構，跨國企業藉助雲網端等資通訊技術的發展🌕，日益強化對上下遊廠商的信息穿透和供應鏈掌控能力🌱，逐步將製造「平臺化」。另一方面，大多數的企業還沒有準備好工業4.0的升級，缺的不是工業4.0的軟硬件設備🔎，所以需要「補課」。根據長期產學合作實證研究發現，應該發展「工業3.5」作為「工業3.0」和「工業4.0」之間的混合策略。因為，工業4.0虛實整合系統就像是「機械公敵」電影裏的機器人和人工智能系統🥬；而工業3.5則像是人和智慧機械混合的鋼鐵人。機器人取代人的工作，鋼鐵人則強化人的機能🤞🏼🥿。更何況我們人口稠密🏆，導入更多無人化的系統只會加速貧富差距和社會不安。因為製造離不開現場，工業工程的機遇是整合軟硬件技術和領域專家的管理優勢🎊，建立大數據分析和智能製造能力🖐，並再造決策流程🖨，提升決策反應的速度和質量，搶先適應工業4.0時代的快速競爭型態，用大數據分析⌛️、資源優化和人工智能做到「工業3.5」的彈性決策和聰明生產，搶先收割工業4.0轉換的利益👩🏿‍🏭。

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.