Lucic Alan

I am a PhD candidate in Management Engineering at Politecnico di Milano, specialising in the enablement, design, and governance of AI-driven Digital Twins within Cyber-Physical Systems (CPS) for high-stakes industrial environments. My research focuses on how to operationalise AI and Digital Twin architectures as decision infrastructures to addressthe “Wicked Problem” of contamination dynamics in Semi-Controlled Industrial Indoor Environments (SCIIE), with a strategic application focus on giga-factories and advanced technology manufacturing.

My work advances the Systemic Adaptive Governance (SAG) framework, positioning AI-enabled Digital Twins as the core cyber layer that transforms industrial environments from static monitoring systems into 3D Volumetric Observability platforms. Through this lens, I model the Indoor Contamination Ecosystem (ICE) as a Complex Adaptive System (CAS), enabling real-time situational awareness and decision augmentation. The overarching objective is to architect CPS ecosystems that reduce decision latency and mitigate heuristic overload in stochastic, non-linear operational contexts.

Methodologically, I combine Design Science Research and Action Research, leveraging high-fidelity empirical data generated through my role as Director of Innovation at Rain Technologies Ltd., where I lead an Innovation Lab focused on translating AI and Digital Twin concepts into deployable industrial artefacts. I also serve as an external contributor to the Laboratory for Computational Modelling and Optimisation (FER) at the University of Zagreb, focusing on the orchestration and scaling of complex cyber-physical architectures.

I hold an MBA in Innovation & Business Creation from the Technical University of Munich (TUM) and UnternehmerTUM, and I completed an academic module at UC Berkeley focused on Lean Innovation for technology-driven ventures. My research trajectory integrates Control Theory, Systems Engineering, and Management Science to develop scalable frameworks for enabling AI-centric Digital Twin infrastructures in next-generation industrial systems.