OVERVIEW OF SYSTEMIC MODELING APPROACHES ROSS APTED

TASK

To give an overview of systemic modeling approaches

Discuss selected systemic accident modeling techniques and the academic literature surrounding them.

To expanded the frame work for comparing accident modeling techniques set out in Comparison of some selected methods for accident investigation

(Sklet, 2004)

To Compare selected techniques using the expanded framework

SYSTEMIC APPROACH

Considers the performance of the system as a whole.

Organization

Environmental

Human

Technical

System is view as many components interacting causing a equilibrium.

Systemic can evolve dynamically

Flawed interactions between components could cause system to be thrown out of balance

Accident

METHODS REVIEWED

Cognitive Reliability Error Analysis Method (CREAM) (Hollnagel E. , Cognitive Reliability and Error Analysis Method., 1998)

The Functional Resonance Analysis Method (FRAM)(Hollnagel E. , FRAM – The Functional Resonance Analysis Method, 2012)

AcciMap(Rasmussen, 1997)

Systems-Theoretic Accident Model and Processes (STAMP) (Leveson, 2004)

CREAM - COGNITIVE RELIABILITY AND ERROR ANALYSIS METHOD  

Background:

Developed by Erik Hollnagel in 1998

Cognitive system engineering approach

design of human-machine systems accounting for factors of the environment in which the system exists.

Key idea:

Cognitive modeling of human performance for accident analysis or performance predictions

(Hollnagel E. , Cognitive Reliability and Error Analysis Method., 1998)

COGNITIVE SYSTEM ENGINEERINGTechnology has changed the way in which humans work

Manual tasks

Knowledge heavy(thinking) tasks.

Change has lead to new problems in human performance causing new types of failures in sociotechnical systems.

Human reliability analysis context-dependent cognitive reliability analysis.

Analysis of the probability of a person performing a system required action in a given time with out an

activity that will be detrimental to the system being performed.

SOLUTION - CREAM

AIM:

1. To identify components of the systems which relies on human cognition

2. To find conditions under which cognition is reduced and thus leading to failure state.

3. To evaluate human performance in the system and there effect on the safety of the system can be used as part of probability risk assessment(PRA).

4. To develop new components or to improve exciting components to increase cognitive reliability and reduce risk.

METHOD

Control modes:

Reliability interval – The probability of action failures

Control mode Reliability interval

Strategic 0.5 E-5 < p < 1.0 E-2

Tactical 1.0 E-3 < p < 1.0 E-1

Opportunistic 1.0 E-2 < p < 0.5 E-0

Scrambled 1.0 E-1 < p < 1.0 E-0

Degree of

control

METHOD

Common Performance Conditions:

The minimum number of factors that are vital in order to describe the context of the system.

State of each CPC is assessed by analyst

(Kim, Seong, & Hollnagel, 2006)

METHODControl mode determination:

CPC Score = (number of reduced, number of improved)

Operators performance is the accessed and improvements are recommended

(Hollnagel E. , Cognitive Reliability and Error Analysis Method., 1998)

FRAM - FUNCTIONAL RESONANCE ANALYSIS METHOD

Background:

Developed by Erik Hollnagel in 2004

Performance variability

Performance in a system whither internal, external dynamically fluctuates. Variability in complex systems is normal.

Key idea:

Models how components of a system resonate and interact with each other causing the system to lose balance leading to accidents.

(Hollnagel E. , FRAM – The Functional Resonance Analysis Method, 2012)

METHOD

1. Identify Vital system functions and categories functions

(Hollnagel E. , Functional Resonance Accident Model Method and examples, 2005)

METHOD

2. Describe potential variability of system.

3. Identify functions that have dependency that may effect the system

4. Identify barriers for variability and specify required performance monitoring

(Hollnagel E. , Functional Resonance Accident Model Method and examples, 2005)

ACCI-MAP

Background:

Developed by J. Rasmussen and I. Svedung in 2000

Utilizes Rasmussen hierarchical model of socio-technical systems

Key idea:

A model that describes an accident in terms of different levels of socio-technical systems

(Rasmussen, 1997)

HIERARCHICAL MODEL OF SOCIO-TECHNICAL SYSTEMS

 (Rasmussen, 1997)

METHODCause-Consequence chart that extends across the hierarchical levels. (Transportation of dangerous goods)

(Svedung & Rasmussen , 2002)

STAMP - SYSTEMS-THEORETICACCIDENT MODEL AND PROCESSES

Background:

Developed by Nancy Leveson in 2004

System theory

Systems are self regulating, this is achieved through feedback loops

Key idea:

Accidents do not occur as a result of individual component failures. Accidents are a results of external forces and dysfunctional interactions of components not being correctly managed .

(Leveson, 2004)

METHOD

1. Development of hierarchical control structure which show the interactions between different system components, safety regulations and constraints.

STAMP Hierarchical Command & ControlStructure of the Black Hawk fratricide

(Qureshi, 2007)

METHOD

Identification of flawed control measures and there causes looking at component interactions.

Can identify constraints at each level

Can see dysfunctional interactions

Chain of events

COMPARISON OF TECHNIQUES

Method Accident sequence

Focus on safety barriers

Levels of analysis

Primary secondary

Analytical approach

Training need

CREAM No No 1-3 Primary Deductive & inductive

Expert

FRAM Yes Yes 1-2 Primary Deductive & inductive

Expert

Acci-Map No Yes 1-6 Primary Deductive & inductive

Expert

STAMP No Yes 1-6 Primary Deductive & inductive

Expert

REFERENCESHollnagel, E. (1998). Cognitive Reliability and Error Analysis Method. Oxford: Elsevier Science Ltd.

Hollnagel, E. (2012). FRAM – The Functional Resonance Analysis Method. Farnham: Ashgate.

Hollnagel, E. (2005). Functional Resonance Accident Model Method and examples. COGNITIVE SYSTEMS ENGINEERING LABORATORY . University of Linköping.

Hollnagel, E. (2002). Understanding accidents-from root causes to performance variability. Human Factors and Power Plants, 2002. Proceedings of the 2002 IEEE 7th Conference on , (pp. 1 - 1-6 ).

Kim, M., Seong, P., & Hollnagel, E. (2006). A probabilistic approach for determining the control mode in CREAM. Reliability Engineering and System Safety , 191-199.

Leveson, N. G. (2004). A new accident model for engineering safer systems. Safety Science , 237-270.

Qureshi, Z. H. (2007). A review of accident modelling approaches for complex socio-technical systems. SCS '07 Proceedings of the twelfth Australian workshop on Safety critical systems and software and safety-related programmable systems (pp. 47-59). Darlinghurst: Australian Computer Society.

Rasmussen, J. (1997). Risk management in a dynamic society: a modelling problem. Safety Sci. , 183–213.

Sklet, S. (2004). Comparison of some selected methods for accident investigation. Journal of hazardous materials , 29-37.

Svedung, I., & Rasmussen , J. (2002). Graphic representation of accident scenarios: mapping system structure and the causation of accident. Safety Science , 397-417.