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| August 24, 2025 | Conference Registration |
| August 25, 2025 | Opening ceremony + Full-day conference + Welcome ceremony |
| August 26, 2025 | Full-day conference + Gala dinner |
| August 27, 2025 | Full-day conference + Closing ceremony |
| Plenary / Keynote Speakers: | |
|---|---|
Professor Madjid Tavana (Business Analytics, La Salle University, Philadelphia, Pennsylvania, United States)  | Title: The Art and Science of Business Analytics: A Journey from Data to Action Abstract: Business analytics is a balanced fusion of art and science, where data-driven algorithms and methodologies form the backbone, while creativity, context, and imagination breathe life and meaning into the paradigm. The key to success in problem formulation lies in the often-overlooked art of asking the right questions. Successful problem formulation combines curiosity, empathy, and courage to challenge assumptions, pushing beyond surface-level solutions to uncover deeper insights. This transition from inquiry to creation is where science takes place, as clarity of purpose guides every step of model-building. Models are like maps; even the most intricate ones are useless without a clear destination. Just as an artist requires the right tools to bring their vision to life, a model requires high-quality, well-structured data to deliver transformative results. But even the best model will fall short without insight. The art of storytelling transforms raw data into compelling narratives that connect with stakeholders, guiding them to take decisive action. Model-building demands both precision and humility. It’s about knowing the limits of what a model can do and embracing the opportunity to learn from failure. It is a continual process of experimentation, iteration, and refinement—an evolving journey where every failure opens the door to innovation. In this dynamic process, the best models don’t just solve problems; they spark curiosity, question assumptions, and inspire action. Business analytics is a powerful blend of imagination and rigor, where creativity and precision come together to drive change. Models are more than just equations; they are dynamic reflections of real-world complexities. Design models to ignite curiosity, challenge assumptions, and guide the journey from data to insightful actions. |
Professor Apostolos Burnetas (Department of Mathematics, National and Kapodistrian University of Athens) | Title: Managing service provision under strategic customer behavior: Models and data-driven policies Abstract: In many modern service systems—ranging from healthcare and call centers to digital platforms and cloud computing—customers are not passive participants. They make strategic decisions about when to join a queue, whether to abandon service, or which provider to choose, based on their expectations of congestion, pricing, and service quality. These choices, in turn, influence system performance and complicate the design of effective operational policies. This talk explores a framework for managing service provision under strategic customer behavior by integrating queueing theory, game-theoretic models, and data-driven optimization. We begin by presenting models that capture the interactions between service providers and rational customers who respond to system parameters such as prices, wait times, or information disclosures. Using equilibrium analysis, we illustrate how customer decisions can lead to non-intuitive outcomes like self-induced congestion or inefficient routing. Building on this foundation, we introduce mechanisms and control policies—such as admission pricing, priority schemes, and capacity adjustments—that are robust to strategic behavior. A particular emphasis is placed on data-driven approaches: we discuss how empirical data on arrival patterns, service times, and customer decisions can be used to learn key model parameters and optimize service design in real time. Examples from sectors such as ride-hailing, telehealth, and public administration are used to highlight practical implications. The talk concludes with a discussion of ongoing challenges, including fairness considerations, heterogeneous customer populations, and the integration of machine learning for adaptive policy design. By combining theory with analytics and real-world data, we aim to provide actionable insights for improving service efficiency and user experience in strategic environments. |
Professor Kostas Nikolopoulos (Business Information Systems and Analytics at Durham University Business School, UK) | Title: Strategic Analytics: Big Data Business Analytics, in Practice, Now, and for long-term Strategic Decisions Authors: Konstantinos Nikolopoulos & Vasileios Bougioukos Abstract: We do define, and explore the frontiers, of a new and niche area of analytics – Strategic analytics, where big data analytics now, can inform and drive decisions that will affect the long term future. We do present the theoretical framing and the data and methods requirements ot be able to do so, and we go further and illustrate some signifcant illustratory examples ranging from savings, retirement and pension attitudes, perceptions, policies and services up to to long-term economic activity diversification. We do also invite the academic and practitoner analytics community to contribute further to this novel area by contributing in an edited volume for Springer on the subject matter, to be published in 2026. |
Professor Vincent Charles (Queen’s Business School, Queen’s University Belfast, Belfast BT9 5EE, UK) | Title: Unpacking the Layers of Analytics: Unlocking Business Value with Agentic AI Abstract: In today’s data- and AI-driven economy, analytics is evolving into a dynamic socio-technical process that seeks to integrate data, human judgement, and ethical responsibility. Increasingly shaped by organisational context, stakeholder values, and emerging technologies, this process may be supported by Agentic AI (i.e., semi-autonomous, goal-directed systems designed to assist human decision-making through contextual reasoning and adaptive insight generation). Agentic AI has the potential to move beyond traditional predictive analytics by enabling prescriptive decision-making, and may incorporate ethical reasoning and intentionality when explicitly designed to do so. This could shift the focus from simply asking what will happen to also considering what ought to be done, by integrating foresight, stakeholder priorities, and strategic aims. Such a development suggests increasing attention to fairness, accountability, transparency, and long-term societal impact. When aligned effectively with human judgment and organisational objectives, Agentic AI can leverage rich data to help enterprises realise more sustainable value, supporting a shift from passive data use to more ethically informed, responsible intelligent action(s). This evolving paradigm frames analytics as a potentially collaborative endeavour, in which human expertise and Agentic AI work together to navigate complexity, responsibility, and opportunity. |
Professor Markos Koutras (University of Piraeus, Greece) | Title: A Unified Framework for Generating Flexible Distributions: Applications in Business Analytics Abstract: In today’s landscape of data-driven decision-making, traditional probability distributions often fall short in capturing the behavior of real-world business data. These datasets frequently display characteristics such as skewness, heavy tails, and non-linear hazard rates, which limit the applicability and effectiveness of classical models. Business applications such as customer survival analysis (e.g., churn prediction), customer lifetime value (CLV) estimation, and targeted marketing benefit greatly from flexible distribution models that can accommodate such behaviours. Accurate modelling in these contexts supports better segmentation, improves personalization, and enhances return on investment (ROI) by guiding data-informed strategies across marketing, finance, and operations. Several classical approaches have been proposed to enrich distribution families. Special mention worth the Azzalini method for introducing skewness, the probability integral transform method, the formulation of composite models combining two or more distributions, and the addition of shape parameters to existing cumulative distribution functions. In this talk we discuss a recently developed unified framework for constructing new families of continuous univariate distributions. This method is based on transforming cumulative distribution functions using generator and parametric functions, offering high flexibility and adaptability to empirical data. We will demonstrate how this approach captures complex data features such as aging properties and tail behaviour, and show its practical utility in various business analytics contexts—including insurance risk modelling, supply chain forecasting, customer behaviour modelling, and financial data analysis. Emphasis will be placed on how these advanced models outperform classical ones in both predictive accuracy and interpretability, leading to improved operational insights and strategic decision-making. |
Professor Bagos Pantelis (University of Thessaly, Greece) | Title: Methods for post-GWAS analysis using summary statistics Abstract: Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. In this talk I will try to review and summarize the various methods, software tools and databases, developed over the last years for the downstream analysis using GWAS summary data, the so-called “post-GWAS” analysis. I will also present key aspects of our work in the field, including methods for meta-analysis of GWAS, methods for multiple traits, robust methods, and imputation of GWAS summary statistics. |
Professor Demosthenes Panagiotakos (Harokopio University in Athens, Greece) | Title: Using Epidemiology to Shape Health Economics and Reduce NCDs: Insights from Greece, 2025 Abstract: Epidemiology plays a pivotal role in shaping health economics and informing strategies to reduce non-communicable diseases (NCDs). By quantifying disease burden, identifying risk factors, and analyzing trends, epidemiology provides the evidence base needed for effective policymaking and resource allocation. Understanding the distribution of NCDs such as cardiovascular diseases, diabetes, and cancers allows for targeted interventions and prevention programs, optimizing health outcomes while minimizing costs. Epidemiological data, if accurate, representative and updated, can guide economic evaluations, such as cost-effectiveness and cost-benefit analyses, helping health systems prioritize investments in prevention, early diagnosis, and management. This is particularly crucial given the rising global prevalence of NCDs and their significant impact on healthcare budgets and workforce productivity. Ultimately, integrating epidemiological insights into health economics fosters sustainable healthcare models, reduces inequalities, and supports evidence-based decisions that can curb the growing burden of NCDs, improve population health, and ensure the efficient use of limited healthcare resources. In this study, a working example of the 2025-public health action for CVDs, “PROLAMVANO (prevent)”, of the Greek Ministry of Health, will be discussed in terms of expected benefits, in the light of current epidemiologic data. |
Professor Dimitra Lingri (Hellenic Organization for Health Care Services Provision, Greece) | Title: The legal Architecture of AI and Analytics: Global and EU Regulatory Impacts on Identifying Waste in National Healthcare Systems Abstract: This presentation explores the emerging global and European Union (EU) regulatory frameworks for Artificial Intelligence (AI), with a particular focus on their implications for analysing and detecting inefficiencies and waste in national healthcare systems. The study begins by outlining the key features of international AI governance, including OECD principles, UNESCO guidelines, and major national strategies, before turning to the EU’s landmark AI Act, which establishes a risk-based regulatory approach. Special emphasis is placed on how high-risk AI applications, such as those used in health data analytics, must comply with strict transparency, accountability, and human oversight requirements. The presentation examines the practical challenges and opportunities these frameworks create for leveraging AI in healthcare cost reduction and system optimisation—especially in identifying fraud, duplications, misallocated resources, and inefficiencies. The study also considers legal and ethical tensions, such as data protection under the GDPR, algorithmic bias, and institutional resistance. Ultimately, it argues that while regulation may impose constraints on AI deployment, it also provides a necessary foundation for building trust, ensuring equity, and enabling the responsible use of AI in transforming healthcare systems for greater efficiency and sustainability. |
