FRAMily 2016



The FRAM Sandbox Facility

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)

These groups are thus interested in taking the modelling power of the FRAM approach to the next level. Their interests include exploring in more detail how interactions occur and how the effects of variability can be addressed and predicted more formally, as well as enabling dynamic visualisation of processes and the quantification of expected outcomes.

What makes this now more attractive, has been the development of a rigorous model checking methodology, the FRAM Model Interpreter, FMI (Hollnagel, E,) ( )

To support these developments the FRAM Model Visualiser has also been given extra facilities, which now include an option to use, what is essentially a laboratory sand box to provide research laboratory experimental facilities for different groups to utilise.

From time to time, it is planned to add additional features that are still under development and not officially released as part of the FMV Pro version, but may be made available to groups for the purposes of experimentation and feedback from the users.


Building on the FMI functionality, the metadata feature in the FMV, now provides the ability to calculate metadata values as Functions are activated during the FMI cycles.

The manual details these here

The calculations are expressed as user defined equations that can reference other metadata keys, standard variables, standard mathematical functions, logic conditions, and mathematical operators. One or two resulting metadata values can then be expressed as a coloured visualisation within each function. The colours and value ranges can be customised by the user.

When you Ctrl-Click a Function, or select and press Ctrl-M, the metadata section will appear above the model in the visualiser window and display the extended features.

Key/Value pairs

The first two text boxes are for entering metadata as a list of key/value pairs, as is already available in the standard FMV Pro versions. A Key is entered in the first box shaded grey, but it will not be saved for the selected Function until a Value or an Equation is also entered for that Key. The corresponding Value is entered in the second text box.

When a Key is saved for a Function, It will be shown along with all saved Key names when any other Function is selected. As such, the Key names become common across all Functions. However, the corresponding Values are unique to each individual Function.

Key names can be used as Variable names and referenced in Equations if they start with a capital letter (this is to differentiate between Variables and mathematical functions).


To calculate the Value of a Key for a Function (when it is activated by the FMI) click the ‘=’ button and another text input box will appear for entering an equation.

Equations can contain Key names to reference other Values that appear above them in the same Function, or Standard Variables can be used to reference Values from upstream Functions, connected by couplings that are activated during the FMI cycles. This is explained in further sections.

To turn the equation off, click the ‘=’ button again (this is a toggle button) and the equation will disappear, it is still saved but will not be used to calculate the Value.

Functions/Variables List

The next section is a list of available mathematical functions and variables available for use in Equations. You can switch between these two types by using the Functions/Variables toggle buttons above the list. The Variables list is initially empty but will be populated as you create Keys and make selections within the model.

Display Results

The top colour range is used to display results in the inside top of each function, the bottom range in the inside bottom of each function. Click on any of the three main colour circles to change the colour. The intermediate colours are blended from the three main colours.

The number boxes below the colour ranges are used to convert the Values to the colour range for dsiplay.

The last two text boxes on the right labeled ‘Key 1’ and ‘Key 2’ are for selecting which of the Keys provide values for displaying the results, Key 1 for the top range and Key 2 for the bottom range.

The 10th FRAMily meeting/workshop, June 1-3 2016

University of Lisbon, Portugal


The programme for the 2016 FRAMily meeting is here.


Alastair Ross, Lecturer, University of Glasgow

Anabela Simões, Professor, Universidade Lusófona

Ângelo Teixeira, Professor, CENTEC

Arie Adriaensen, Safety researcher, Safety consultant

Bart Accou, Head of Methods and safety manag., Infrabel

Benedicte Schou, Risk Manager, Mental Health services - Capital region, Denmark

Carlos Guedes Soares, Professor, CENTEC

Christian Beckert, Captain, German Air Line Pilots' Association

Cristina Martelli, Associate professor, Department of Statistics, UniFi

David Slater, Professor, Cardiff University

Dominic Furniss, Senior research associate, University College London

Doug Smith, PhD Student, Memorial University of Newfoundland

Duncan McNab, Associate adviser in patient safety and quality improvement, NHS education for Scotland

Erik Hollnagel, Professor, Centre for Quality - University of Southern Denmark

Fernando Santos, CENTEC

Flora Salvatori, Research fellow, Department of Statistics, UniFi

François Laporte, Conceiller, Infrabel

Georg Effenberger, Head of Prevention Department, Austrian Workers´ Compensation Board

Gianluca Del pinto, Air traffic controller, ANACNA

Heinrich Kuhn, Professor, Zurich University of Applied Sciences

Ivonne Herrera, Adjunct Associate Professor, Norwegian University of Science and Technology

Jaap Hamming, Prof. Of surgery, Leiden University  - Medical Centre

Jan van Schaik, Vascular Surgeon, Leiden University  - Medical Centre

Jeanette Hounsgaard, Deputy Manager, Centre for Quality - University of Southern Denmark

Joana da Guia, CENTEC

Karen Ørnebjerg, Risk Manager, Mental Health services - Capital region, Denmark

Marcus Arenius, Research Fellow, University of Kassel

Maria André, Técnica Superior, GPIAA – Gabinete de Prevenção e Investigação de Acidentes com Aeronaves

Marit de Vos, PhD researcher, Leiden University  - Medical Centre

Nicolas Wertz, Ingenieur FOH, Infrabel

Nippin Anand, Principal Specialist Safety Man. Sys., DNV-GL

Pedro Ferreira, CENTEC Tecnico

Ray Master, Director Loss prevention/safety consultant, Construction risk partners

Riccardo Patriarca, PhD Stud. (MSc Aeronautical Engineering), University of Rome

Roberto Gnesotto, MD, MPH, MSc ,Doctors with Africa/Cuamm

Romano Luisoni, Risk Manager, PricewaterhouseCoopers Switzerland

Simon Albery, Safety Innovation Leader, THIESS

Vivek Menon, Maritime Health & Safety consultant, SeaHealth



The presentations from the tutorial were: Understanding how things happen, The four principles of FRAM, First steps of FRAM.

Workshop presentations

Jeanette Hounsgaard: FRAM and implementation of Safeward

Nippin Anand: Boxing and dancing - The challenges of enforcement in global shipping

Doug Smith: Experiences using FRAM in engineering and the maritime domain

Gianluca Del Pinto: FRAM model applied to the Aerodrome Air Traffic Control to manage the variability in regard of runway incursion

Simon Albery: The visualisation of FRAM

Dominic Furniss: Using FRAM beyond safety: A case study to explore how sociotechnical systems can flourish or stall

Benedicte Schou: Use of the FRAM as Accident Analysis tool in Risk Management

Marcus Arenius: From quantitative to qualitative: Transforming quantitative data regarding the distribution of visual attention into a representation compatible with FRAM

Riccardo Patriarca: Monte Carlo simulation to assess performance variability in the FRAM

Cristina Martelli: FRAM visualizer and relational databases integrated approach: potentialities and perspectives

David Slater (submitted after FRAMily meeting): FRAM as a "front end" to quantification and dynamic simulation

Erik Hollnagel: The way ahead: FMV and FMI

Group discussions

A proposal from Nicolas Wertz.

A View of the Key (Unique) current Advantages and achievable Advances of the FRAM methodology, highlighted in FRAMILY 2016 by David Slater.

We are looking forward to seeing the results of using this new facility and seeing the range and scope of applications and studies significantly extended. Perhaps we will see some examples at the next FRAMily Workshop in Kyoto next year, COVID willing.

© Copyright Erik Hollnagel 2016. All Rights Reserved.