讲座题目：工业3.5制造战略与产业实证研究

Industry 3.5 Mnaufacturing Strategy and Empirical Studies

报 告 人：简祯富

时 　 间：2019年10月21日(周一)10:00-11:50

地 　 点：中关村校区研究生教学楼101报告厅

主办单位：研究生院、机械与车辆学院

报名方式：登录外围滚球app：微信企业号---第二课堂---课程报名中选择“【百家大讲堂】第250期：工业3.5制造战略与产业实证研究”

【主讲人简介】

  简祯富现任新竹清华大学清华讲座教授暨美光讲座教授，他在新竹清华大学工业工程与工程管理学系以及科技管理学院EMBA/MBA开课，并兼任新竹清华大学 智能制造跨院高阶主管硕士专班(AIMS Fellows)主任；他也是科技部工业工程与管理学门召集人，并担任科技部人工智能制造系统研究中心主任，主持「清华-台积电卓越制造中心」。新竹清华大学工业工程系暨电机工程系双学位（斐陶斐荣誉会员）；美国威斯康辛大学麦迪逊分校决策科学与作业研究博士；美国加州大学柏克莱分校傅尔布莱特学者。曾任新竹清华大学秘书长、副研发长兼首任产学合作执行长、国科会固本精进计划推动办公室总主持人、「竹科2.0」规划计划主持人、剑桥大学访问教授、日本早稻田大学青年访问学者奖等。发表超过170篇学术期刊论文，着有《工业3.5》《大数据分析与数据挖矿》《决策分析与管理》《紫式决策工具全书》及《半导体制造技术与管理》等书；主编《智能制造 AI台湾》《创业清华》《固本科园 台湾精进》《产业工程与管理个案》及《清华百人会》等书及《竹科30》有声书。并撰写台积电、联发科、创意电子等12篇哈佛商业个案。领导研究团队深耕大数据分析、资源优化和数字决策等智能制造技术，已取得23项智能制造发明专利（10项美国；13项中华民国）；并与各个产业龙头和隐形冠军建立双赢的产学合作机制，创造具体产业效益，因而荣获行政院杰出科技贡献奖(2016)、行政院国家质量奖-研究类个人奖(2012)、科技部杰出研究奖(2016、2011、2007)、国科会优秀年轻学者研究计划、第一级计划主持人奖、经济部大学产业经济贡献奖 (2009)、教育部产学合作研究奖(2003)、东元科技奖 (2018)、IEEE Trans. on Semiconductor Manufacturing 2015年最佳论文奖、IEEE Trans. on Automation Sciences & Engineering 2011年最佳论文奖、科技管理奖(学研团队类)(2017)、工业工程学会会士(2018)、APIEMS Fellow (2016)、科技管理学会院士(2012)、杰出工程教授(2010)、工业工程奖章：产业贡献(2010)和学术贡献(2016)、第一届东森杯大数据竞赛冠军(2014)、工程论文奖(2003)、吕凤章奖章(2003)、工业工程论文奖(2003)等殊荣，以及国立清华大学杰出产学合作奖(2019、2016、2007)等，也是国科会《学与致用》(2007)的九个典范之一。研究领域包括：决策分析、大数据分析、智能制造、半导体制造、数字决策、工业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. 

【讲座信息】

  随着物联网、大数据、机器人和人工智能的发展，产业转型升级的工业革命已经在进行中，越来越多工作机会因为自动化和智能化而消失，年轻人和弱势族群更不容易找到好的工作。世界各国均提出自己的制造战略，包括：德国工业4.0、美国再工业化、日本工业4.1J、韩国产业创新3.0等，先进工业国家基于既有的竞争优势以拿回先进制造，也为了争夺第四次工业革命的主导地位。随着产业价值链因为工业革命而即将重构，跨国企业藉助云网端等资通讯技术的发展，日益强化对上下游厂商的信息穿透和供应链掌控能力，逐步将制造「平台化」。另一方面，大多数的企业还没有准备好工业4.0的升级，缺的不是工业4.0的软硬件设备，所以需要「补课」。根据长期产学合作实证研究发现，应该发展「工业3.5」作为「工业3.0」和「工业4.0」之间的混合策略。因为，工业4.0虚实整合系统就像是「机械公敌」电影里的机器人和人工智能系统；而工业3.5则像是人和智慧机械混合的钢铁人。机器人取代人的工作，钢铁人则强化人的机能。更何况我们人口稠密，导入更多无人化的系统只会加速贫富差距和社会不安。因为制造离不开现场，工业工程的机遇是整合软硬件技术和领域专家的管理优势，建立大数据分析和智能制造能力，并再造决策流程，提升决策反应的速度和质量，抢先适应工业4.0时代的快速竞争型态，用大数据分析、资源优化和人工智能做到「工业3.5」的弹性决策和聪明生产，抢先收割工业4.0转换的利益。

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.