IMMS 2026 Invited Speakers
Assoc. Prof. Peng Jiang
Sichuan University, China
Peng Jiang is an Associate Professor at the Department of Industrial Engineering and Management, Business School, Sichuan University. He obtained a Ph.D. from Shanghai Jiao Tong University (SJTU) and participated in a joint Ph.D. program at the National University of Singapore (NUS). Prior to joining his current affiliation, he successively held positions as Postdoctoral Fellow at SJTU, Research Fellow at the NUS, and Research Scientist at A*STAR's Institute of High Performance Computing (IHPC), Singapore. He was a Visiting Professor at A*STAR. His research focuses on systems modeling and intelligent algorithms. He has published papers in journals/proceedings including Engineering, Environmental Science & Technology, Water Research, European Journal of Operational Research, Decision Support Systems, Computers & Operations Research, International Journal of Production Research, Computers & Industrial Engineering, and IFAC World Congress. He was granted 6 national invention patents. He was ranked as a top 2% scientist by Stanford University (2024, 2025). His work has been cited in more than 30 international policy documents, including reports by the United Nations (IPCC Climate Change Reports), the World Bank, and the WHO. He served as conference co-chair/session chair for several international conferences and was invited to give keynote/invited talks for conferences and the Ezra Systems Seminar at Cornell University.
Title: Machine Learning Modeling for Relative Risk Management of Emerging Contaminants
Assoc. Prof. Farzad Sabetzadeh
City University of Macau, China
Farzad is a full-time associate professor of Knowledge and Innovation Management at the faculty of business at the City University of Macau. Currently, he is serving as the program coordinator for the International Business Cohort (IBC) program at the City University of Macau. Over the past two decades, he has developed knowledge-based innovation processes and systems both in academia and industry. He teaches undergraduate and graduate students in Greater China and Southeast Asia in a variety of subjects related to knowledge management, innovation management, change management, emerging technologies, and business research methods. He has a diverse professional and academic background, but his current academic focus is on teaching, research, and consulting for the development of innovation and entrepreneurship in China's Greater Bay Area, South East Asia, and projects associated with China's One Belt One Road Initiative. Furthermore, he is an active technology advisor within the IT industry, as well as participating in academic events, seminars, and talks. In the past decade, he has completed multiple projects in the areas of knowledge management, personalized learning environments, Massive Open Online Courses (MOOCs), Smart Cities, and Internet of Things (IoT).
Title: Beyond Substitution: Managing Human-AI Co-Evolution Through Symbiotic Intelligence
Discussions surrounding artificial intelligence in management typically frame the technology through a lens of substitution and competition. However, empirical evidence indicates that simply combining human and machine capabilities does not automatically yield positive synergy. This keynote examines the transition from isolated tool use to true co-evolution, introducing the framework of symbiotic intelligence. Drawing on organizational design and recent synthesis in human-AI collaboration, the presentation establishes a six-step operational loop—framing, exploration, joint challenge, authorization, action, and cumulative learning—that converts routine interactions into ongoing organizational adaptation. capabilities.
Prof. Jichang Zhao
Beihang University, China
Jichang Zhao is a Full Professor at the School of Economics and Management, Beihang University, China. His research interests focus on Business Intelligence and Big Data Analytics. He has published more than 90 academic papers in leading journals and conferences, including Nature Communications, and has been granted 7 patents and 11 software copyrights. He has led and participated in more than 10 research projects funded by the National Natural Science Foundation of China, the National Key Research and Development Program of China, and other major funding agencies. He received the First Prize of the Natural Science Award from the Ministry of Education of China and the Outstanding Undergraduate Thesis Supervisor Award of Beijing Municipality. He has also supervised students who received numerous honors, including the Grand Prize of the China Youth Science and Technology Innovation Competition, and the First Prize of the Beijing College Students’ “AI+” Innovation Competition.
Title: Artificial Intelligence in Live-Streaming E-Commerce
Multimedia content such as text, images, and videos has increasingly become a key data element in emerging e-commerce formats such as live streaming e-commerce. The rapid advancement of AI technologies provides an important opportunity to perceive and measure new influencing factors from such multimodal, high-dimensional, and information-rich data. By integrating AI technologies with management theories and drawing on traditional research methods such as econometric models and online experiments, we can, on the one hand, verify whether AI-based factor measurement aligns with human cognition, and, on the other hand, more robustly reveal the effects of these factors from multiple perspectives while supplementing and refining their influence mechanisms. Following this approach, in recent years we have employed AI technologies such as topic classification, pose recognition, action detection, and depth estimation to examine, from textual, visual, and auditory dimensions, the effects of multimodal factors on dependent variables such as live streaming sales, as well as the pathways through which these effects occur. These findings not only provide live streaming platform practitioners with guidance for optimizing operations and behaviors, but also offer support for generation optimization in applications such as virtual streamers and automated clip generation.
Dr. Sugoutam Ghosh
Singapore University of Social Sciences, Singapore
He currently serves as the Head of the Graduate Diploma in Logistics and Supply Chain Management at the School of Business, Singapore University of Social Sciences (SUSS). Dr Ghosh holds a PhD in Supply Chain Management from Nanyang Technological University, Singapore, an MTech in Industrial Engineering and Management from the Indian Institute of Technology, and a Bachelor of Engineering in Production Engineering from Jadavpur University, India. His academic and professional career includes appointments as a Senior Lecturer at SUSS, a Sessional Lecturer at the Rotman School of Management, University of Toronto, and an Assistant Professor at Antalya International University in Turkey. He has also held Research Fellow positions at several leading institutions, including Nanyang Business School and the National University of Singapore.
Dr Ghosh’s research focuses on supply chain risk management, supply planning, and inventory management. His work has been published in leading journals such as Production and Operations Management (POM) and the European Journal of Operational Research (EJOR) and presented at numerous international conferences. One of his articles received the Best Track Paper in Logistics award at the 2nd Australian Conference on Industrial Engineering and Operations Management in 2023. He teaches courses in Operations Management, Inventory Management, Optimisation, and Simulation. He has designed and delivered executive training programmes on Supply Chain Planning and Supply Chain Risk Management and delivered keynote speeches at international conferences. He has received the Outstanding Service Award from the IEOM Society.
Title: Inventory Cost Analysis under Supplier Capacity Uncertainty and Emergency Ordering
Supplier production capacity can be disrupted by various risk events, potentially preventing the timely delivery of materials to the manufacturer. Such supply shortages impose an opportunity cost on the manufacturer and may require it to obtain replacement supplies through emergency or spot purchases, which typically involve a higher unit cost. This study establishes the conditions under which an emergency order is economically justified when supplier capacity is uncertain. The analysis focuses exclusively on low-probability, high-impact (LPHI) risk events. The impact of such risks can be characterised using an exponential function, and the resulting decline in supplier capacity is assumed to follow an exponential probability distribution.
We formulate the expected inventory cost under two alternative replenishment strategies: one in which the manufacturer relies solely on regular orders, and another in which emergency replenishment is available when required. A comparison of the expected costs associated with these strategies enables us to establish the maximum unit price that the manufacturer should be willing to pay for emergency procurement. Finally, numerical experiments are conducted to examine the cost implications of emergency ordering and to assess the sensitivity of the expected inventory cost to changes in the model parameters.