Keynote Speech

Confirmed keynote speakers are below sorted by last name.

Prof. Jian Pei, Ph.D
Vice President,

Big Data and its Application in Supply Chain


With the most recent advancements in AI and Big Data technologies, companies have started to benefit from these researches in a wide variety of application areas. has accumulated enormous amount of data in its daily operations and a rich collection of business scenarios to interact with customers.

In this talk, we cover how JD invests in the building of Big Data infrastructures and other emerging technologies to support various phases in Supply Chain Management, such as inventory management, logistics, omni-channel retailing, etc.


Jian Pei is a Vice President of and head of Big Data and Smart Supply Chain. He has more than 25 years of academic expertise in data research and application, technology industry leadership and corporate management experience. As a professor of Simon Fraser University (on leave), he is a Canada Research Chair (Tier 1) in Big Data Science. Recognized as an ACM Fellow and an IEEE Fellow, he published over 200 technical publications, which have been cited more than 77,000 times, and over 33,900 in the last 5 years. Dr. Pei’s research has generated substantive impact beyond academia, and he has received several prestigious awards, such as the SIGKDD Innovation Award and Service Award, and the ICDM Research Award. He is a Distinguished Visiting Professor of Tsinghua University.

Prof. J. George Shanthikumar, Ph.D
Richard E. Dauch Chair in Manufacturing and Operations Management
Krannert School of Management, Purdue University
President of Production and Operations Management Society (POMS)

Operational Data Analytics for Supply Chain Management


We are living in an era in which data is generated in huge volume with high velocity and variety. Big Data and technology are reshaping our life and business. Our research inevitably needs to catch up with these changes. In this talk, we focus on two aspects of supply chain management, namely, demand management and manufacturing. We feel that, while rapidly growing research on these two areas is contributed by scholars in computer science and engineering, the developments made by production and operations management society have been insufficient. We believe that our field has the expertise and talent to push for advancements in the theory and practice of demand management and manufacturing (of course, among many other areas) along unique dimensions. We summarize some relevant concepts emerged with Big Data and present several prototype models to demonstrate how these concepts can lead to rethinking of our research. Our intention is to generate interests and guide directions for new research in production and operations management in the era of Big Data.


J. George Shanthikumar joined the Krannert School as the Richard E. Dauch Chair in Manufacturing and Operations Management in 2009. In 2014, he is recognized as the University Distinguished professor of Management. Before joining Purdue, he was a Chancellor’s Professor of Industrial Engineering and Operations Research at the University of California, Berkeley, CA. He received the B. Sc. degree in mechanical engineering from the University of Sri Lanka, Peradeniya, and the M. A. Sc. and Ph. D. degrees in industrial engineering from the University of Toronto, Toronto, Canada.

His research interests are in integrated inter-disciplinary decision making, model uncertainty & learning, production systems modeling and analysis, queueing theory, reliability, scheduling, semiconductor yield management, simulation, stochastic processes, and sustainable supply chain management. He has written or written jointly over 300 papers on these topics. He is a coauthor (with John A. Buzacott) of the book Stochastic Models of Manufacturing Systems and a coauthor (with Moshe Shaked) of the book Stochastic Orders and Their Applications and the book Stochastic Orders.

He is a member of the editorial advisory boards of Asia-Pacific Journal of Operations Research and IEEE Transactions on Automation Sciences and Engineering, is an area editor for Journal of the Production and Operations Management Society and an associate editor of Probability in the Engineering and Informational Sciences. He was a member of the editorial advisory board of Journal of the Production and Operations Management Society, was a co-editor of Flexible Services & Manufacturing Journal, area editor for Operations Research Letters and was an associate editor for IIE Transactions, International Journal of Flexible Manufacturing Systems, Journal of Discrete Event Dynamic Systems, Operations Research, OPSEARCH, and Queueing Systems: Theory and Applications. He is also a fellow of INFORMS and POMS.

Dr. Shanthikumar has extensively consulted for various companies like Applied Materials (AMAT), Bellcore, IBM, KLA-Tencor, NTT (Japan), Intel, Intermolecular, ReelSolar, Safeway, and Southern Pacific Railways and through KLA-Tencor worked on Joint Development Projects for AMD, IBM, Intel, LSI, Motorola, TI, Toshiba, Fujitsu, TSMC and UMC. He is an advisory consultant for Sensor Analytics and a member of the technical advisory board of Inter Molecular Inc. and Reel Solar, Inc.

Dr. Shanthikumar has helped KLA-Tencor to successfully transform itself from an engineering inspection tool company to in-process inline inspection and process control company. He developed sample planning methods and fab-level capacity modeling to allow accurate quantification of return on investment of inspection tools. His research convinced, Intel, a major client of KLA-Tencor, to change its strategic thinking regarding the use of inline inspections. This led to a paradigm shift of monitoring reduction to smart monitoring to achieve lower cost. His work resulted in several major patents for yield prediction and tool matching/qualification. The SK-index, named after Dr. Shanthikumar, is used by fab engineers in matching and qualifying the tools for the customers.

Dr. Shanthikumar developed a data-driven production planning tool for Intel. Through this data-driven model, he discovered several shop-floor level coordination between manufacturing and maintenance, which helped to significantly improve the working process performance. This study also led to a new theory in stochastic models that inspires a new stream of academic research.

Prof. David Simchi-Levi, Ph.D
Professor of Engineering Systems, MIT
Chairman of Opalytics

The New Frontier in Price Optimization


Retail and online sellers are dealing with a competitive environment while at the same time managing their own complex multi-channel environments. These companies are already lean so there is not much room to cut costs. Therefore, the ability to increase revenue and improve margins through a more technologically advanced price optimization process can make a huge difference in their business. Indeed, the combination of accessible data, ability to experiment on line and new analytic technologies enable an innovative approach to pricing.

In practice, however, online sellers are faced with a few business constraints, including the inability to conduct extensive experimentation, limited inventory and high demand uncertainty. In this talk we discuss models and algorithms that combine machine learning and optimization for pricing that significantly improve revenue. We report results from live implementations at companies such as Rue La La, Groupon and B2W, a large South American online retailer.


David Simchi-Levi is a Professor of Engineering Systems at MIT and Chairman of Opalytics, a cloud analytics platform company. He is considered one of the premier thought leaders in supply chain management and business analytics.

His research focuses on developing and implementing robust and efficient techniques for operations management. He has published widely in professional journals on both practical and theoretical aspects of supply chain and revenue management.

His Ph.D. students have accepted faculty positions in leading academic institutes including U. of California Berkeley, Columbia U., Cornell U., Duke U., Georgia Tech, Harvard U., U. of Illinois Urbana-Champaign, U. of Michigan, Purdue U. and Virginia Tech.

Professor Simchi-Levi co-authored the books Managing the Supply Chain (McGraw-Hill, 2004), the award winning Designing and Managing the Supply Chain (McGraw-Hill, 2007) and The Logic of Logistics (3rd edition, Springer 2013). He also published Operations Rules: Delivering Customer Value through Flexible Operations (MIT Press, 2011).

Professor Simchi-Levi is the current Editor-in-Chief of Management Science, one of the two flagship journals of INFORMS. He served as the Editor-in-Chief for Operations Research (2006-2012), the other flagship journal of INFORMS and for Naval Research Logistics (2003-2005). He is an INFORMS Fellow, MSOM Distinguished Fellow and the recipient of the 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; 2009 INFORMS Revenue Management and Pricing Section Prize and Ford 2015 Engineering Excellence Award.

Professor Simchi-Levi has consulted and collaborated extensively with private and public organizations. He was the founder of LogicTools which provided software solutions and professional services for supply chain optimization. LogicTools became part of IBM in 2009. In 2012 he co-founded OPS Rules, an operations analytics consulting company. The company became part of Accenture in 2016.

Prof. Chung-Piaw Teo, Ph.D
Provost Chair, Professor
Department of Decision Sciences, NUS Business School
National Univeristy of Singapore

Data-Driven Approach in Supply Chain and Logistics Innovation


Increased computing power and the explosion of data have created opportunities for the POM community to analyse data (in real time) to identify new models and approaches to drive and adapt decisions and actions. We describe how the ability to adapt decisions and actions can affect some of the fundamental trade-offs in resource allocation, and provide an overview of some of the potential applications of this new approach in the field of logistics and supply chains.


Dr. Chung-Piaw Teo is Provost Chair Professor and Director of the Institute of OR and Analytics in the National University of Singapore. Prior to his current appointments, he was a Head of Department, Acting Deputy Dean, Vice-Dean of the Research & PhD Program as well as Chair of the PhD Committee in the NUS Business School. He was an Eschbach Scholar in Northwestern University (US), Professor in Sungkyunkwan Graduate School of Business (Korea), and a Distinguished Visiting Professor in YuanZe University (Taiwan). He is currently spearheading an effort to develop the Institute on Operations Research and Analytics, as part of the University's strategic initiatives in the Smart Nation Research Program. His research interests lie in service and manufacturing flexibility, discrete optimization, ports container operations, matching and exchange, and healthcare. He is currently an area editor for MS (Optimization), and a former area editor for OR (Operations and Supply Chains). He has also served on several international committees such as the Chair of the Nicholson Paper Competition (INFORMS, US), member of the LANCHESTER and IMPACT Prize Committee (INFORMS, US), Fudan Prize Committee on Outstanding Contribution to Management (China).

Prof. David D. Yao, Ph.D
Professor of IEOR Department, Columbia University

Data, Risk and Analytics --- Rethinking Supply Chain Management


How data and analytics will impact traditional OR/OM approaches to supply chain management? In particular, how integrating certain real-time control and hedging strategies into production planning can better mitigate the risk associated with demand uncertainty? Some general thoughts and ideas will be shared, along with an overview of some technical issues involved.


David D. Yao is the Piyasombatkul Family Professor of Industrial Engineering and Operations Research at Columbia University, where he is the founding chair of the Financial and Business Analytics Center at Columbia Data Science Institute. His research and teaching interests are in applied probability and stochastic systems, focusing on resource control and risk management issues. He is an IEEE Fellow, an INFORMS Fellow, and a member of the National Academy of Engineering. He currently serves on the Board on Mathematical Sciences and Analytics of the National Academies of Science, Engineering and Medicine.



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