In the world of safety science, where complex socio-technical systems demand innovative approaches, the Functional Resonance Analysis Method (FRAM) stands as a beacon of insight. Rooted in modern systemic management and Resilience Engineering principles, the FRAM offers a unique perspective, viewing systems through the lens of everyday performance variability.
Developed over decades of meticulous research, the FRAM serves as a powerful tool for modelling intricate systems, encompassing various realms: aviation, healthcare, IT software requirements, crisis management, and more. This book represents a culmination of two decades of research and practical applications of the FRAM. It offers an operational perspective and a useful reference for researchers and practitioners alike, empowering them to tackle a myriad of socio-technical modelling challenges. It blends traditional applications with cutting-edge advancements and looks at case studies in which the FRAM was utilized. Delving into the intricacies of the FRAM's structure and functionality, the book equips readers with operational knowledge essential for navigating diverse domains.
Written for researchers and practitioners in Occupational Health and Safety, Risk Engineering, Safety Engineering, Resilience Engineering, Human Factors, and related fields, Navigating the FRAM: Mastering the Functional Resonance Analysis Method for Modelling Complex Socio-Technical Systems resonates with those seeking to deepen their understanding of complex systems.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Für höhere Schule und Studium
Academic
Illustrationen
44 s/w Abbildungen, 3 s/w Photographien bzw. Rasterbilder, 41 s/w Zeichnungen, 57 s/w Tabellen
57 Tables, black and white; 41 Line drawings, black and white; 3 Halftones, black and white; 44 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
ISBN-13
978-1-032-85051-1 (9781032850511)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
