IMMS 2025 Keynote Speakers
Prof. Dr. Habil. Dmitry Ivanov
Director of the Digital-AI Supply Chain Lab
Berlin School of Economics and Law, Germany
Biography: Prof. Dr. Dr. habil. Dmitry Ivanov is Professor of Supply Chain and Operations Management, and director of the Digital-AI Supply Chain Lab at the Berlin School of Economics and Law. His research spans supply chain resilience and digital supply chain twins. He has made influential contributions, particularly exploring structural dynamics of supply chains. Author of the Viable Supply Chain Model and founder of the ripple effect and viability research in supply chains. Recipient of several research excellence awards. His research record counts around 470 publications, with more than 170 papers in prestigious academic journals and the leading books “Global Supply Chain and Operations Management” (three editions), “Introduction to Supply Chain Resilience”, “Introduction to Supply Chain Analytics”, „Structural Dynamics and Resilience in Supply Chain Risk Management“, “Scheduling in Industry 4.0 and Cloud Manufacturing”, “Digital Supply Chain” and „Handbook of Ripple Effects in the Supply Chain“. He delivered invited plenary, keynote, panel and guest talks at the conferences of INFORMS, IFPR, IFIP, IFAC, IEEE, DSI and POM, and over 40 universities worldwide. He has been Chairman, IPC Chair, and Advisory Board member for over 80 international conferences in supply chain and operations management, industrial engineering, control and information sciences. Principal investigator in several projects about digital supply chain twins and resilience funded by EU Horizon and DFG. Several Awards for Best Papers (IJPR, IISE Transactions, Omega), Clarivate Highly Cited Researcher Awards. Ranked #1 worldwide in Supply Chain by ScholarGPS and #1 in Operations Research by Standford/Elsevier ranking. Ranked #1 in German-Austrian-Switzerland Ranking of Top Scientists in Business and Management area. Chair of IFAC CC 5 “Cyber-Physical Manufacturing Systems”, Editor-in-Chief of International Journal of Integrated Supply Management, Editor Annals of Operations Research, Associate Editor of International Journal of Production Research and OMEGA, guest editor and Editorial Board member in over 20 leading international journals including IISE Transactions and IJPE, to name a few.
Title: Supply Chain Stress Testing: State-of-the-Art and Future Directions
This talks presents the results of a literature review on stress tests developed and applied in non-food supply chains. The primary focus of the project is on quantitative and qualitative methodologies applied in the literature for stress testing non-food supply chains, their advantages and disadvantages, data used, and identification of stress test typologies applied in different methodologies. We also identify methodological gaps in the literature, especially those related to real-world applications of stress testing. e identify objectives of stress tests, disruptions/shocks used in stress tests, different methodological approaches used for stress tests, design and typologies of disruption scenarios used for stress tests, data used for stress tests, and indicators used to analyse impacts/responses to different stress factors. Likewise, we analyse managerial recommendations resulting from stress tests and evaluate the advantages and disadvantages of different methodologies developed and applied for stress tests.
Prof. Xin Wang
Tianjin University, China
Biography: Prof. Xin Wang is a Distinguished Professor at College of Intelligence and Computing, Tianjin University. He obtained his Ph.D. and Bachelor degrees in Computer Science from Nankai University in 2009 and 2004, respectively, and worked as a visiting scholar at the University of Western Australia and Griffith University. He is a distinguished member of China Computer Federation (CCF), and the secretary-general of CCF Technical Committee on Information Systems. His research interests include knowledge graph data management, large-scale graph databases, and big data processing. He has been the principal investigator of the National Key Research and Development Project of China, and four research projects funded by the National Natural Science Foundation of China (NSFC). He has published more than 100 research papers in various international conferences and journals, including IEEE TKDE, WWW, SIGMOD, ICDE, IJCAI, CIKM, ISWC. He served as a PC co-chair of WISE’25, DASFAA’23, and APWeb-WAIM’20. He is an associate editor of the journal of Knowledge-Based Systems, World Wide Web, and Data Science and Engineering.
Title: Knowledge Graphs in the Era of Large Language Models
In today's intelligent era, on the one hand, knowledge graph technology is becoming a necessary data support for large models to be applied in vertical domains; on the other hand, knowledge graphs hold significant application potential in the fields of management operations and industrial engineering, including supply chain management, production planning and scheduling, quality control, decision support, and enterprise knowledge management. This talk will elaborate on the construction of knowledge graphs and graph-based question-answering systems, semantic representation learning of knowledge graphs based on large language models, and retrieval-augmented techniques for knowledge graphs in vertical domains. It will also introduce the applications of knowledge graph methods and technologies in various vertical fields, and provide an outlook on the development direction of knowledge graph technology in the era of large language models.
Prof. Kecheng Liu
University of Reading, United Kingdom
Biography: Prof. Kecheng Liu, a Fellow of the British Computer Society and Senior Fellow of the UK Higher Education Academy, is a Professor of Applied Informatics at the University of Reading and the Director of the Digital Talent Academy at Henley Business School. In his roles as academic lead, senior advisor, and consultant, he has significantly contributed to academic research, industrial projects and business consulting in areas such as business and IT strategy, information management, and digital leadership across public and private sectors. He has published over 300 journal and conference papers and 25 books on topics including organisational semiotics, business informatics, and intelligent spaces for work and living. Additionally, he has successfully supervised more than 60 PhD students from diverse international backgrounds. His extensive experience in management and leadership spans research projects, academic centres, schools, and universities.
Title: Human-AI Collaboration – semiotics and norms for trustworthy AI
AI has become a key driver of productivity and competitiveness for individuals and businesses, offering transformative strengths and benefits. However, current AI systems often operate under the assumption that common values and moral principles are universally shared, making AI-generated responses seemingly applicable to all contexts. In business settings, especially in disputes involving multiple stakeholders, this assumption presents serious challenges. Different parties may pose the same question to AI, each expecting a response aligned with their own interests and perspectives. This raises critical concerns: Can AI generate answers that are not only legally sound and ethically acceptable but also contextualised and personalised to individual needs? More importantly, how can AI maintain fairness, transparency, and ethical integrity while adapting to diverse perspectives?
This keynote explores the evolving relationship between humans and AI, particularly in the context of Generative AI (GenAI) and its users. It introduces a norm-based framework grounded in organisational semiotics as an approach to overcome current AI limitations, fostering a collaborative, contextualised, and trustworthy human–AI relationship.
Prof. Cong Wang
Peking University, China
Biography: Dr. Cong Wang is an assistant professor at Guanghua School of Management, Peking University. She holds a Ph.D. in Information Systems from Tsinghua University. She got her BA in information systems and economics from Peking University. She worked as a postdoc fellow at Carnegie Mellon University prior to joining Guanghua. Dr. Wang's research interest lies in the intersection of big data analytics, machine learning and management information systems, focusing on decision support with uncertainty and temporal dynamics, pattern recognition and knowledge discovery from big data, as well as their applications in areas of e-commerce, fintech and healthcare etc. Her research work has been published in journals including Information Systems Research, INFORMS Journal on Computing etc.
Title: From Voice to Value: Predicting Long-Horizon Purchases of High-Involvement Products through Conversational Analytics
Accurate early-stage prediction of customers' purchase behavior of high-involvement products is critical for optimizing resource allocation and guiding strategic sales decisions. While voice interactions serve as a rich source of behavioral signals during early customer engagement, their unstructured and multimodal nature presents significant analytical challenges. This study introduces CogniAffect Dual-Involvement Prediction (CADIP), a novel, theory-driven approach for forecasting customer purchase decisions based on voice interaction data. Grounded in involvement theory, CADIP identifies and models two latent behavioral dimensions embedded in voice interactions: cognitive engagement and affective arousal. CADIP leverages the multimodal capabilities of large language models and large speech models to extract both linguistic and acoustic features. It captures cognitive engagement through an attention mechanism that treats proactive inquiries as contextual queries, and it detects arousal expression via a dedicated arousal-gating module that dynamically modulate vocal cues to reflect affective intensity. Together, these mechanisms form a dual-pathway architecture designed to detect nuanced signals of customer involvement from early-stage dialogues. We evaluate CADIP using real-world automotive sales call data and show that it significantly outperforms state-of-the-art benchmarks in identifying high-potential customers at the early decision stage. Ablation analyses further demonstrate the value of combining cognitive and affective signals, as well as the benefits of cross-modal integration. Beyond its methodological contributions to voice analytics, deployment simulations suggest substantial managerial benefits: a 51.8% reduction in sales representatives' workload and an 80% increase in conversion rates. This research contributes to the information systems literature by introducing CADIP, a multimodal, theory-guided IT artifact that operationalizes psychological constructs from customer behavior in a predictive framework. It also advances the design of intelligent sales management systems by extracting long-horizon insights from unstructured early-stage voice data.