Assessing the reliability and validity of an FRAM model: the case of driving in an overtaking scenario

Grabbe, Niklas; Arifagic, Almin; Bengler, Klaus

doi:10.1007/s10111-022-00701-7

Cited by 9 publications

(6 citation statements)

References 70 publications

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“…Construct validity was assured by the sound system theory basis of FRAM and over a decade of highly credible research 10,11 and by comprehensively following established FRAM approaches 8 with experienced FRAM analysts DS & RM. Content validity was maintained by the face validity of using subjective evaluation through the above iterative interviews 3 , and discussions with colleagues who have a deep knowledge of the events under study.…”

Section: Validation and Veri Cation Of The Fram Modelmentioning

confidence: 99%

A Functional Resonance Analytical Methodology exploration of the essential functions of a paediatric major trauma centre responding to a mass casualty incident

MacKinnon,

Slater,

Jenner

et al. 2024

Preprint

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Background Functional Resonance Analytical Methodology (FRAM) is a systematic approach to model and explore how complex systems adapt to variations and to understand resilient properties in the face of perturbations. Mass Casualty Incidents are rare but can significantly stress healthcare systems. The aim of this study was to use FRAM to create a model of a paediatric trauma system during the initial response to the Manchester Arena Attack to provide resilience-based insights for the management of future Mass Casualty Incidents (MCI). Methods Qualitative interviews in the immediate aftermath of a terrorist bombing, were followed up with further in-depth probing of subject matter experts to create a validated and verified FRAM model. This model was compared with real incident data, then simplified for future studies. Results A Work As Imagined (WAI) model of how a paediatric emergency department provided resilient healthcare for MCI patients from reception and resuscitation to definitive care is presented. A simplified model focussing upon the pathway for the most severely injured patients that will facilitate the simulation of a myriad of potential emergency preparedness resilience response scenarios is also presented. Conclusions The systematic approach undertaken in this study has produced a model of a paediatric trauma system during the initial response to the Manchester Arena Attack, providing key insights on how a resilient performance was sustained. This modelling may provide an important step forward in the preparedness and planning for future MCIs.

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Section: Validation and Veri Cation Of The Fram Modelmentioning

confidence: 99%

A Functional Resonance Analytical Methodology exploration of the essential functions of a paediatric major trauma centre responding to a mass casualty incident

MacKinnon,

Slater,

Jenner

et al. 2024

Preprint

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“…The equation can be set to a pseudo-random number within a given range. For example, to start an instantiation with a random integer between 10 and 30 (inclusive), the equation would be TEMP_Start_C = rndi (10,31). Double precision floating point numbers can also be generated using rnd().…”

Section: Cyclementioning

confidence: 99%

“…3. Self-driving cars (Grabbe) Auto-autos (Grabbe 2023) Just the ability to process sensor data on the physical surroundings to predict and operate in real world environments, is not sufficient to design and program safe (fail safe) behaviours for fully self-driving vehicles. These systems have to have the resilience to adapt to humans in the environments, who operate with a real time, adaptive behaviour, which as we all know is not necessarily commensurate with the highway code.…”

Section: Human -Human Systems (Shiota (2023)mentioning

confidence: 99%

Complex Systems Modelling: A Systematic, Quantitative Systems Engineering Approach

Slater,

Hill

2024

Preprint

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In his Cynefin classification of system behaviours, Snowden (2007), posits that his Simple (Clear ) and even Complicated system categories are sufficiently understandable to be amenable to the traditional methods of system analysis and modelling. Here, relationships between entities in the system are clear and unambiguous and they behave predictably. So, we can build mathematical “models” to describe how the system is expected to function in different situations. Leaving aside his fourth category, Chaotic, as too hard for the moment, this paper focusses on the third category, Complex systems. Yaneer Bar-Yam [1998] defined "complex systems" as systems that "have multiple interacting components, whose collective behaviour cannot be simply inferred from the behaviour of components." So, the traditional system modelling approach of decomposition into components and attempting to build up a picture of system performance from individual pieces, alone, is no longer appropriate for this class. The problem is that most of the methods we currently employ, and particularly the methods that attempt to make the case that these systems are safe, (such as Fault trees, FMEA. and Root Cause Analysis), rely on this decomposition into components and attempt to make the parts more reliable individually. Even the more systemic approaches, such as Reason’s (1990) Swiss Cheese Model, and Leveson’s System Theoretic Analysis (STPA) (2004), encourage adding layers of protection, barriers, strengthened control loops and better safety critical systems, in the hope that they are making the systems safer. But without a valid model to test the effectiveness of these add-ons, we cannot be sure that they will not have an opposite effect, of making the systems even more complex, unreliable and unpredictable. For complex systems, the careful gathering and documentation of system components and their relationships is still a necessary first step, but we need an additional capacity to model these complex systems to test out and prove they work safely and successfully in the real world. (not just draw fixed component relationships as system “models” (wiring diagrams)) In the last 10 years, an original approach developed by Hollnagel, (2012), to model systems as sets of interactive, interdependent “functions”, (abstracted from and independent of component details), has now been developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, such as MBSE( Goldberg (1994)), and can pull it all together in a dynamic system model from which analysts can discern how it really works in practice, and predict the emergent behaviours characteristic of complex systems. The paper describes how this methodology developed and how it is currently used to model real situations, to analyse incidents and anticipate events.

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“…For example, it can arise from the machine or device used, from human activities or from organisations and social settings (Hollnagel, 2017, p. 100-106). Variability also occurs in the connections between functions, such as input, precondition, resource, control and time (Grabbe et al, 2022). For example, the output can be too fast or too slow, it can be too long or short (e.g., a machined product), it can be the wrong output (e.g., wrong information delivered), it can be too much or too little, it can leave too early or too late, and so on.…”

Section: Introductionmentioning

confidence: 99%

Learning From Delays in Daily Design Work – Comparison of Root Cause Analysis and Functional Resonance Analysis

Lappalainen¹,

Hänninen²,

Uusitalo³

et al. 2023

Proceedings of the 31st Annual Conference of the International Group for Lean Construction (IGLC31)

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Procurement and construction work depend on error-free and on-time designs. However, the design process may be erroneous and behind schedule, which often causes cascading delays and problems in the construction process. Hence, when a major delay occurs, practitioners often query the design process, and much time and many resources may be required to find the root cause. However, minor delays and mistakes that occur in everyday work are not usually investigated, even though they can contain information necessary to avoid significant adverse events. This study aimed to determine how three deviations that occurred in a normal, wellprogressing project can be investigated using two different methods, as well as the significance of small errors and events in preventing larger errors and events in the future. Root cause analysis and functional resonance analysis were the research methods. The findings of this study showed that slight variability in trivial design and design management tasks generated a considerable number of unnecessary tasks and delays. Therefore, examining variability in the outputs of tasks could benefit designers and design management.

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Assessing the reliability and validity of an FRAM model: the case of driving in an overtaking scenario

Cited by 9 publications

References 70 publications

A Functional Resonance Analytical Methodology exploration of the essential functions of a paediatric major trauma centre responding to a mass casualty incident

A Functional Resonance Analytical Methodology exploration of the essential functions of a paediatric major trauma centre responding to a mass casualty incident

Complex Systems Modelling: A Systematic, Quantitative Systems Engineering Approach

Learning From Delays in Daily Design Work – Comparison of Root Cause Analysis and Functional Resonance Analysis

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