Keynote Lectures

Accelerating Supply Chain Digital Transformation with Digital Twins and Agentic AI
Oleg Gusikhin, Ford Motor Company, United States, United States

A Path for Quality Software Engineering in the Age of AI
Bertrand Meyer, ETH Zurich, Switzerland, Switzerland

Decision Guidance Systems and their Applications
Alexander Brodsky, George Mason University, United States, United States

Abstract
Digital Twins and Agentic Artificial Intelligence (AI) are driving a fundamental change in how enterprise information systems are designed, deployed, and managed. This new paradigm empowers business users to directly define and manage AI agents using intuitive, no-code tools and large language models emphasizing self-service analytics, enabling rapid, iterative decision-making and driving a shift from reactive to proactive enterprise management. This broader change has particularly profound implications for supply chain management, where conventional systems often struggle to provide the real-time adaptability and predictive insights required by today's dynamic global markets. In this talk, we explore how the convergence of digital twins and agentic AI is accelerating the reinvention of supply chain information systems. Digital twins create data-driven, real-time representations of supply chains, enabling continuous monitoring, scenario simulation, and performance evaluation. Building on this foundation, agentic AI introduces autonomy into decision-making, allowing AI agents to reason over alternatives, negotiate trade-offs, and coordinate actions across organizational silos, often by seamlessly integrating and orchestrating existing software tools and platforms. This presentation overviews the development of Ford's end-to-end supply chain digital twin, illustrating its foundational capabilities and the comprehensive visibility it provides. It demonstrates how agentic AI is incorporated into this digital twin environment to address critical practical challenges.

Abstract
AI tools and vibe coding have upended the way we look at software. It is becoming increasingly clear, however, that the fundamentals of software engineering have not changed and that discipline is needed more than ever together with the power of requirements, quality-focused design and verification. Taking advantage of my long-running work on Design by Contract and more generally on software quality, this talk will describe a path towards building software better and faster by combining the best software engineering techniques with the best of what AI can give us to help.

Abstract
Decision Support Systems (DSS) are widely used to support organizational and personal decision-making. While DSS is broad in scope, Decision Guidance Systems (DGS) are a class of DSS designed to elicit knowledge from domain experts and provide actionable recommendations to human decision-makers, aiming to identify the best possible course of action. In this talk, we overview the Decision Guidance Analytics Language (DGAL) and Management System (DGMS), a powerful and easily extensible platform for rapid development of DG applications, similar to DB application development using DBMS. We then describe how DGMS technology is applied to decision guidance systems in (1) renewable energy investment for cost-effective carbon neutrality at George Mason University; (2) the Market of Virtual Things for on-demand products and services in cloud manufacturing; (3) optimal sensor placement in counter-UAV systems; and (4) non-pharmaceutical pandemic mitigation used by George Mason University to successfully mitigate COVID-19.