講座題目：工業(yè)3.5制造戰(zhàn)略與產(chǎn)業(yè)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(guān)村校區(qū)研究生教學樓101報告廳

主辦單位：研究生院,、機械與車輛學院

報名方式：登錄北京理工大學微信企業(yè)號---第二課堂---課程報名中選擇“【百家大講堂】第250期：工業(yè)3.5制造戰(zhàn)略與產(chǎn)業(yè)實證研究”

【主講人簡介】

  簡禎富現(xiàn)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(yè)工程與工程管理學系以及科技管理學院EMBA/MBA開課，并兼任新竹清華大學 智能制造跨院高階主管碩士專班(AIMS Fellows)主任,；他也是科技部工業(yè)工程與管理學門召集人,，并擔任科技部人工智能制造系統(tǒng)研究中心主任,，主持「清華-臺積電卓越制造中心」。新竹清華大學工業(yè)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(yè)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(fā)長兼首任產(chǎn)學合作執(zhí)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(guī)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(fā)表超過170篇學術(shù)期刊論文,，著有《工業(yè)3.5》《大數(shù)據(jù)分析與數(shù)據(jù)挖礦》《決策分析與管理》《紫式?jīng)Q策工具全書》及《半導(dǎo)體制造技術(shù)與管理》等書,；主編《智能制造 AI臺灣》《創(chuàng)業(yè)清華》《固本科園 臺灣精進》《產(chǎn)業(yè)工程與管理個案》及《清華百人會》等書及《竹科30》有聲書。并撰寫臺積電,、聯(lián)發(fā)科,、創(chuàng)意電子等12篇哈佛商業(yè)個案。領(lǐng)導(dǎo)研究團隊深耕大數(shù)據(jù)分析,、資源優(yōu)化和數(shù)字決策等智能制造技術(shù),，已取得23項智能制造發(fā)明專利（10項美國；13項中華民國）,；并與各個產(chǎn)業(yè)龍頭和隱形冠軍建立雙贏的產(chǎn)學合作機制,，創(chuàng)造具體產(chǎn)業(yè)效益，因而榮獲行政院杰出科技貢獻獎(2016),、行政院國家質(zhì)量獎-研究類個人獎(2012),、科技部杰出研究獎(2016、2011,、2007),、國科會優(yōu)秀年輕學者研究計劃、第一級計劃主持人獎,、經(jīng)濟部大學產(chǎn)業(yè)經(jīng)濟貢獻獎 (2009),、教育部產(chǎn)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(yè)工程學會會士(2018),、APIEMS Fellow (2016),、科技管理學會院士(2012)、杰出工程教授(2010),、工業(yè)工程獎?wù)拢寒a(chǎn)業(yè)貢獻(2010)和學術(shù)貢獻(2016),、第一屆東森杯大數(shù)據(jù)競賽冠軍(2014)、工程論文獎(2003),、呂鳳章獎?wù)?2003),、工業(yè)工程論文獎(2003)等殊榮，以及國立清華大學杰出產(chǎn)學合作獎(2019,、2016,、2007)等，也是國科會《學與致用》(2007)的九個典范之一,。研究領(lǐng)域包括：決策分析、大數(shù)據(jù)分析,、智能制造,、半導(dǎo)體制造、數(shù)字決策,、工業(yè)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(lián)網(wǎng)、大數(shù)據(jù),、機器人和人工智能的發(fā)展,，產(chǎn)業(yè)轉(zhuǎn)型升級的工業(yè)革命已經(jīng)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制造戰(zhàn)略，包括：德國工業(yè)4.0,、美國再工業(yè)化,、日本工業(yè)4.1J、韓國產(chǎn)業(yè)創(chuàng)新3.0等,，先進工業(yè)國家基于既有的競爭優(yōu)勢以拿回先進制造,，也為了爭奪第四次工業(yè)革命的主導(dǎo)地位。隨著產(chǎn)業(yè)價值鏈因為工業(yè)革命而即將重構(gòu),，跨國企業(yè)藉助云網(wǎng)端等資通訊技術(shù)的發(fā)展,，日益強化對上下游廠商的信息穿透和供應(yīng)鏈掌控能力，逐步將制造「平臺化」,。另一方面,，大多數(shù)的企業(yè)還沒有準備好工業(yè)4.0的升級,，缺的不是工業(yè)4.0的軟硬件設(shè)備，所以需要「補課」,。根據(jù)長期產(chǎn)學合作實證研究發(fā)現(xiàn),，應(yīng)該發(fā)展「工業(yè)3.5」作為「工業(yè)3.0」和「工業(yè)4.0」之間的混合策略。因為,，工業(yè)4.0虛實整合系統(tǒng)就像是「機械公敵」電影里的機器人和人工智能系統(tǒng),；而工業(yè)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?nèi)丝诔砻埽瑢?dǎo)入更多無人化的系統(tǒng)只會加速貧富差距和社會不安,。因為制造離不開現(xiàn)場,，工業(yè)工程的機遇是整合軟硬件技術(shù)和領(lǐng)域?qū)＜业墓芾韮?yōu)勢，建立大數(shù)據(jù)分析和智能制造能力,，并再造決策流程,，提升決策反應(yīng)的速度和質(zhì)量，搶先適應(yīng)工業(yè)4.0時代的快速競爭型態(tài),，用大數(shù)據(jù)分析,、資源優(yōu)化和人工智能做到「工業(yè)3.5」的彈性決策和聰明生產(chǎn)，搶先收割工業(yè)4.0轉(zhuǎn)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.