Riccardo Patriarca is an associate professor in the Department of Mechanical and Aerospace Engineering at Sapienza University of Rome, Italy. He holds a BSc in Aerospace Engineering, an MSc in Aeronautical Engineering, and a PhD in Industrial and Management Engineering. He currently teaches Operations Management, Aviation Safety Management, and Human Factors for the MSc courses in Mechanical Engineering and Aeronautical Engineering at Sapienza. His research is oriented towards modelling complex socio-technical systems - including their cyber dimension - untangling their inherent complexities and advancing risk and resilience-aware solutions for an ever-evolving world.

List of Contributors. Foreword. Preface. Acknowledgements. PROLOGUE Introducing the FRAM. Chapter 1 The FRAM for Socio-Technical Safety Management and Beyond. Chapter 2 A Brief and Unofficial History of the FRAM. Chapter 3 The FRAM Essentials: Principles and Building Steps. Chapter 4 The Last Five Years (2019-2024) of FRAM Literature. SECTION I Step 1: To Identify and Describe the System's Functions. Chapter 5 The Use and Implications of Small FRAM Models: A Case from Healthcare. Chapter 6 Function Identification and FRAM Modelling of an Aviation Near-Miss Using Convergent Mixed Methods. Chapter 7 Building a FRAM Model Using Large Language Models: A Dream or a Nightmare? Chapter 8 A Semantic Lifting of Language Models to Support FRAM Models Building. SECTION II Step 2: To Identify the Variability. Chapter 9 Navigation Support from Shore: Analyzing Remote Pilotage Using the FRAM. Chapter 10 Fuzzy Assessment of Variability in FRAM Models. Chapter 11 Pathways to Industry 5.0 and Society 5.0: Socio-Technical Systems Modelling through FRAM and Discrete-Event Simulation. SECTION III Step 3: Aggregate Variability, Actual, and Potential. Chapter 12 The Milky Way to FRAM: Comparing Instantiations via Network Metrics in a Dairy Production Plant. Chapter 13 Understanding Patterns in Mixed Road Traffic: Comparing Functional Critical Paths between Drivers and Automated Vehicles. Chapter 14 Machine Learning to Support Human Learning from Variable Performance in FRAM. Chapter 15 The FRAM-IA to Understand and Assess Distributed Cognition and Control. SECTION IV Step 4: Assess the Consequences of the Analysis. Chapter 16 Dynamic Variability Modelling: Capturing Complexities in Variability. Chapter 17 Integrating the FRAM and Lean for Waste Analysis in Production Systems. Chapter 18 Maximizing the Impact of the FRAM: From Analysis to Implementation. SECTION V Implementation and Reflections on the FRAM. Chapter 19 FRAM via Software: The FRAM Model Visualizer. Chapter 20 FRAMing Learning: Learning about FRAM through Experience. SECTION VI Final Thoughts. Chapter 21 To FRAM or not to FRAM? Glossary. Index.