Brief intro to FRAM

the FUNCTIONAL RESONANCE

  ANALYSIS METHOD


Extending the capabilities of the FMV


There is an increasing interest in utilising the FRAM approach for the analysis of what exactly is going on in complex sociotechnical systems in practical high hazard environments. There are, for example, a number of ongoing projects at the moment in aviation, self-driving vehicles and, of course, on the challenges of the COVID 19 pandemic for healthcare responses. But as well as these practical applications there is an increasing interest from the academic community, in extending and developing further, the underpinning concepts, as shown in the recent review (below)



A brief introduction to the FRAM

The understanding of the human role in accidents has gone through three stages.


  • In the classical view, humans were seen as error prone or as fallible machines. The purpose of an accident investigation was therefore often to find the "human error" that either was the primary (or even "root") cause or the initiating event.
  • When in the 1990s it was realised – gradually at first - that the "human error" view was not tenable, explanations changed to look for how performance shaping factors or performance conditions could “force” people to fail. This did not remove the concept of a "human error", but changed from an inherent human ‘error mechanisms’ to be a product of working conditions and work pressures.
  • Although this change for a while enabled people to understand accidents of a more complex nature, it still fell short in a number of situations. This led to the recognition, strongly supported by resilience engineering, that failures and successes have the same source, and that they metaphorically speaking are two sides of the same coin.


The Functional Resonance Analysis Method or FRAM (Hollnagel, 2004 & 2012) provides a way to describe outcomes using the idea of resonance arising from the variability of everyday performance. To arrive at a description of functional variability and resonance, and to lead to recommendations for damping unwanted variability, a FRAM analysis consists of four steps:


  • Identify and describe essential system functions, and characterise each function using the six basic characteristics (aspects). In the first version, only use describe the aspects that are necessary or relevant. The description can always be modified later.
  • Check the completeness / consistency of the model.
  • Characterise the potential variability of the functions in the FRAM model, a well as the possible actual variability of the functions in one or more instances of the model.
  • Define the functional resonance based on dependencies / couplings among functions and the potential for functional variability.
  • Identify ways to monitor the development of resonance either to dampen variability that may lead to unwanted outcomes or to amplify variability that may lead to wanted outcomes.


The Methodology is being widely utilised by different groups worldwide. Below is shown the November 2020 FMV users


The FRAM Sandbox Facility



© Copyright Erik Hollnagel 2016. All Rights Reserved.