Prof. Jianjun (Jan) Shi
Georgia Institute of Technology, USA
Speech Title: Advancements of Modeling and Analysis of Multistage Manufacturing Processes
Abstract: A multistage manufacturing process (MMP) refers to a manufacturing system consisting of multiple machines, stations, or operations to produce a final product. The final product quality is a result of the complex interactions among these multiple stages. The quality characteristics of one stage are not only influenced by local variations at that stage but also by variations propagated from upstream stages. With the advancement of sensing technologies and data science methodologies, the R&D in modeling, analysis and control of MMPs has been evolving in the past decades. This presentation provides a brief summary of how the co-evolution of sensing data modality and data science propels the advancement in variability modeling and analysis of a MMP, followed by detailed discussions on the challenges and opportunities in applying AI and machine learning to modeling, monitoring, diagnosis and control of MMPs. Examples of ongoing research projects will be used to illustrate and exemplify the frontiers of this research area. All examples involve the development of fundamental methodologies motivated by industrial demands and their implementations in real-life production systems.
Bio: Dr. Jianjun Shi is the Carolyn J. Stewart Chair and Professor in H. Milton Stewart School of Industrial and Systems Engineering, with joint appointment in the George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology. Prior to joining Georgia Tech in 2008, he was the G. Lawton and Louise G. Johnson Professor of Engineering at the University of Michigan.
Dr. Shi is a pioneer in the development and application of data fusion for quality improvements. His In Process Quality Improvements (IPQI) and Stream of Variation (SoV) methodologies integrate system informatics, advanced statistics, and control theory for the design and operational improvements of manufacturing and service systems by fusing engineering systems models with data science methods. He has produced 44 Ph.D. graduates, 30 of which have joined IE department as faculty members. Among them, 7 have received NSF CAREER Awards and one has received the NSF PECASE award. He has published one book and more than 250 papers. He has served as PI and co-PI for projects totaling more than 25 million dollars, which were funded by the National Science Foundation, NIST Advanced Technology Program, Department of Energy, General Motors, Daimler-Chrysler, Ford, Boeing, Lockheed-Martin, Honeywell, Pfizer, Samsung, and various other industrial companies and funding agencies. The technologies developed in Dr. Shi’s research group have been widely implemented in various production systems with significant economic impacts.
Dr. Shi was elected a member of the National Academy of Engineering (2018), and an Academician of the International Academy for Quality (2013). He is a Fellow of American Society of Mechanical Engineering (2007), the Institute of Industrial and Systems Engineering (2007), Institute of Operations Research and the Management Science (2008), Society of Manufacturing Engineering (2021). He received the ASA Deming Lecturer Award (2025), the ASQ Shainin Metal (2025), the ENBIS George Box Medal (2022), the Statistics in Physical and Engineering Sciences (SPES) Award (2022), the ASQ Walter Shewhart Medal (2021), the SME/NAMRI S. M. Wu Research Implementation Award (2021), the ASQ Brumbaugh Award (2019), the Horace Pops Medal Award (2018), IISE David F. Baker Distinguished Research Award (2016), the IIE Albert G. Holzman Distinguished Educator Award (2011), Forging Achievement Award from Forging Industry Educational and Research Foundation (2007), Monroe-Brown Foundation Research Excellence Award (2007), the 1938E Award (1998) at The University of Michigan, and NSF CAREER Award (1996).
Dr. Shi is the founding chair (1998-1999) of the Quality, Statistics and Reliability (QSR) Subdivision at the Institute for Operations Research and Management Science (INFORMS). He served as the Editor-in-Chief of the IISE Transactions (2017-2020), the flagship journal of the Institute of Industrial and Systems Engineers. He also served as the Focus Issue Editor of IISE Transactions on Quality and Reliability Engineering (2007-2017), editor of Journal of System Science and Complexity (2008-2020), and advisory editor of Journal of Quality Technology and Quantitative Management (2016-present).
Dr. Shi received his B.S. and M.S. in Electrical Engineering from the Beijing Institute of Technology in 1984 and 1987, and his Ph.D. in Mechanical Engineering from the University of Michigan in 1992.
Prof. Kwang-Jae Kim
Pohang University of Science and Technology, Korea
Speech Title: From Manufacturing to Valufacturing in Korea:
Perspectives from Industrial Engineering
Abstract: Over the past six decades, Korea has achieved remarkable economic growth driven by its manufacturing sector. Manufacturing has played a critical role in boosting exports, driving value creation, and generating employment. However, the manufacturing sector now faces significant challenges, including a rigid labor market, highly hierarchical SME networks, and an aging industrial ecosystem. The key question is: Where should Korea’s manufacturing sector go from here? While past growth was driven by resource input and market expansion, future success will depend on innovation-driven, high-value strategies.
To address these challenges, the National Academy of Engineering of Korea has proposed K-GIFT (Green and Intelligent Factory for Tomorrow) Valufacturing, a novel paradigm aimed at restructuring Korea’s manufacturing sector with a focus on economic value creation. Valufacturing extends beyond traditional efficiency-driven approaches to incorporate economic sustainability, technological innovation, and social responsibility. This transition is essential for ensuring Korea’s continued competitiveness in the rapidly evolving global economy.
This talk will introduce the concept, strategic directions, and key values of Valufacturing. We will present recent cases from leading Korean firms that have successfully embraced this transformation. Furthermore, we will explore the role of industrial engineering in driving the transition toward Valufacturing. Finally, we will review recent research trends in the Korean industrial engineering community and discuss future directions for industrial engineering in the Valufacturing era.
Bio:
Dr. Kwang-Jae Kim is a Professor in the Department of Industrial
and Management Engineering and the Head of the Graduate Program
in Industrial Data Science at Pohang University of Science and
Technology (POSTECH), Korea. His research focuses on quality
assurance in product and service design and analytics-driven new
service development.
His work has been applied to various fields, including
semiconductor manufacturing, steel manufacturing, automobile
design and manufacturing, healthcare and wellness, smart energy,
smart transport, telematics, and ICT services. His research has
been supported by organizations such as the National Research
Foundation, the Ministry of Science and Technology, the Ministry
of Knowledge Economy, the Ministry of Health and Welfare, the
Ministry of Trade, Industry and Energy, and the Ministry of ICT
of Korea, as well as leading companies including Samsung, LG,
POSCO, Hyundai, SK Hynix, KT, IBM, and Microsoft.
He has received several prestigious awards, including the Proud
POSTECHian Award, the Literati Award for Excellence, the KIIE
Jung-Hun Academic Excellence Award, and the KSQM Academic
Excellence Award. He is a member of the National Academy of
Engineering of Korea and currently serves as the President of
the Korean Institute of Industrial Engineers (2023–2024). More
details can be found at http://quality.postech.ac.kr/.
Prof. Bopaya Bidanda
University of Pittsburgh, USA
Speech Title: AI and Sustainability: Emerging
Roles for Industrial Engineers
Abstract: This talk will share
insights on how industrial engineering is evolving with the help
of AI and how it is helping us balance efficiency and
sustainability. Industrial engineering is at the forefront of
process optimization and system efficiency. But with rising
global challenges such as climate change and resource
limitations, the role of industrial engineers is expanding. AI
has become a crucial tool in tackling these challenges, helping
industries become more sustainable while improving their
operational efficiency.
The talk with begin with discussing how industrial engineering
can evolve to meet today’s challenges. Then it will detail how
innovation is driving new efficiencies and how sustainability is
becoming a major focus. The next part will dive into how AI is
being applied to industrial engineering. We’ll explore specific
ways AI is improving both efficiency and sustainability, using
real- world examples from leading industries. Finally, it will
detail the emerging roles industrial engineers are taking on in
this AI- driven landscape with a focus how industrial engineers
can bridge the gap between technology and operations.
Bio:
Dr. Bopaya Bidanda is currently the Ernest E. Roth Professor at
the Department of Industrial Engineering at the University of
Pittsburgh. He also serves as the Director of the Center for
Industry Studies. He returned to the faculty in 2021 after 21+
years as Department Chair. He also serves as Director of the
Manufacturing Assistance Center Initiative that helps
underserved communities develop manufacturing skills. His most
recent book includes the Maynard Industrial & Systems
Engineering Handbook, Sixth Edition that serves as the
definitive body of knowledge for industrial engineering. His
research focuses on different dimensions of manufacturing from
reverse engineering to manufacturing modernization.
He is also the Past President of the Institute of Industrial &
Systems Engineers (IISE), the largest and oldest professional
society representing the discipline. Dr. Bidanda has also served
on the ABET Board (of Delegates) and is an active accreditation
visitor for the past 20+ years and has led ABET teams to many
countries. Dr. Bidanda has received many international awards -
last year alone, he received the Frank & Lillian Gilbreth Award
by IISE and also the Lillian Gilbreth award by the Indian
Institution of Industrial Engineering.
Bidanda is also a dual Fulbrighter, first appointed in 2004, as
Fulbright Senior Specialist to Uruguay, and more recently as a
Fulbright-Nehru Senior Scholar to India, by the J. William
Fulbright Foreign Scholarship Board and the U.S. Department of
State. Over the past decade, he has delivered keynote and
plenary addresses at conferences in Brazil, Israel, Spain,
Turkey, and India.
Assoc. Prof. Zhisheng Ye
National University of Singapore, Singapore
Speech Title:
Learning Short and Long Term Failure Patterns from Massive Network Failure Data
Abstract: Many lifeline infrastructure systems consist of thousands of components configured in a complex directed network. Disruption of the infrastructure constitutes a recurrent failure process over a directed network. Statistical inference for such network recurrence data is challenging because of the large number of nodes with irregular connections among them. In this talk, we focus on both short term cascading failures and long term ageing failures. Repair of a pipe might generate shocks to neighbouring pipes and cause short term cascading failures. Understanding the short-term cascading failure is important for the utility to allocate additional resources to monitor the neighbouring pipes after a repair. On the other hand, understanding long-term failures is helpful in risk analysis of the whole pipe network and prioritizing replacements of old pipes. Statistical modelling of the two failure modes are extremely challenging because of the large pipe network and the huge failure data set. We develop novel statistical methods that are computationally tractable to fit the data. Applying the methods to a large data set from the Scottish Water network, we demonstrate the usefulness of our models in aiding operation management and risk assessment of the water utility.
Bio: Dr. Ye received a joint B.E. (2008) in Material Science & Engineering, and Economics from Tsinghua University. He received a Ph.D. degree from National University of Singapore. He is currently an Associate Professor and Dean's Chair in the Department of Industrial Systems Engineering & Management at National University of Singapore. His research areas include reliability modelling, estimation and optimization, condition-based maintenance and data-driven decision-making. His work has been published in flagship journals in reliability (Technometrics, JQT, IISE Trans, ITR, RESS), statistics (JRSS-B, JRSS-C, JASA, Biometrika) and operations management (OR, MSOM and POMS).