1. Human reliability analysis – challenges in modelling operational risk
Tim Bedford
Strathclyde Business School
University of Strathclyde

2. Objectives
Discuss modelling issues surrounding human reliability issues in operational risk
Consider how time dynamics can be incorporated, and the potential benefits and difficulties
Work done on safety relevant to other operational risks

3. Example – Lambrigg Derailment
February 2007, Virgin train derails between Preston and Carlisle
1 Fatality, 22 Hospitalised
Primary cause identified as faulty set of points

4. Inquiry Findings
Deficiencies in the inspection and maintenance regime resulted in the points falling into disrepair. These deficiencies included:
A breakdown in the local management structure responsible for inspection and maintenance
The track patrolling regime’s systematic failure to inspect the area adequately
Quality standards not being communicated or executed in the proper manner
A lack of sample checking of the track to test inspection quality and arrangements

5. Inquiry Findings
The patrol scheduled for 18 February 2007 was not done
The QA regime did not identify failures in the reliability of inspection regimes, nor failures in application of best practice.
Emergence of “Them & Us” culture
Management structure based on activity, not location

6. Inquiry Findings High proportion of Staff on Temporary promotion
Culture of “Learned Helplessness”
Insufficient records on staff training and competencies
Staff unsure of their contracted responsibilities
Lapsed engineering qualifications

7. Is risk static? Clearly not
Physical systems change through time, either through degradation or upgrading
Human systems change through time, as a result of operating procedures, staff ability, organisational changes etc

8. Should we be concerned about dynamically changing risks?
Maybe yes, maybe no…!
No – over time it averages out to the same as the “static” risk, so that cumulative risk is same.
Yes – If different risks change dynamically in a coupled way, then this can magnify the overall effect
Yes – If no intervention then the risk at the end may be lower than acceptable (eg often regulate annual risk)
Yes – If understanding the dynamics helps you create new strategies to reduce risks

9. Dynamic versus static statistical
PRA models usually assume rates/probabilities not time dependent r
Worst case
Statistical estimate
with conf bounds
Achievable t

10. Interacting dynamics of productivity and safety pressures
D. L. Cooke and T. R. Rohleder, Learning from incidents: from normal accidents
to high reliability, Sys Dyn Review

11. Feedback from incidents
Examples:
Accident
Precursors;
CIRAS
D. L. Cooke and T. R. Rohleder, Learning from incidents: from normal accidents
to high reliability, Sys Dyn Review

12. Human reliability models
In widespread use as part of Probabilistic Risk Analysis
Aim to “give a number” as well as understanding of source of risk.
Largely based on task analysis, breaking down human behaviour into steps (cognitive, decision, action etc).
Performance shaping factors influence probability of success, and may be common to more than one step
First generation methods
Eg THERP, HCR, HEART, JHEDI
Second generation methods
Eg ATHENA, CREAM
Third generation
Monte Carlo based – linking cognition based models to technical system dynamics

13. { THERP HRA Tree Total HEP [(7.5E-3)*3] +3E-3 = 2.55E-2 EF from Table
Monitor primary system pressure & temp.;
Table
(20-10)1
Start
SP pump 2 {
THERP HRA Tree 1 3 4 5 6 7 8 9 10
Start LH pump 1
Start LH pump 2
Start LH pump 3
FT open PS
valve 2
valve 3
1. Start
confinement spray pumps
2. Start Low pressure pumps
3. Open Pressurizer safety valves
(Depressurization)
7.5E-3
FT open press. safety valve 1
Stress
Mod high, skilled, dynamic (heavy task load)
THERP Table (20-16)5a = 5
Dependency
Action could start as early as 6 minutes, so dependency based on 10 minutes
Operator 2 = complete = 1
Shift Super. = high = 0.5
Assumed all pumps are required
4. Monitor primary system temperature & pressure
Start SP pump 1; Selecting wrong control from functional group
Table (20-12)3
=1E-3
[1E-3 * 5 (stress) * .5 (dependency)]
* 3 branches
= 7.5E-3
3E-3
Total HEP
[(7.5E-3)*3] +3E-3 =
2.55E-2
EF from Table
(20-20)7 = 5 SP
pump 3 2

14. THERP Data Summary Table

15. What drives the main risks?
The standard HRA models, while useful do not appear to capture the main sources of risk
Accidents continue, and many (most?) are not due to random human failures
Models do give insight and guidance about risk reduction including prioritization
Qualitative approaches such as normal accident theory and HRO do not give guidance about prioritization, but may give insights about strategies for risk reduction

16. Organisational failure: Reason’s Swiss Cheese model

17. Modelling for understanding, or for optimization?
Models typically one of
Formative: inform system, organisation and process design, guiding management practice
Summative: used to support decisions on, e.g., adoption, licensing or maintenance, by modelling cost/benefit trades
Qualitative HR modelling tends to be formative.
Quantitative HR modelling should be summative, but if not modelling the most significant system behavior then maybe actually most value in formative sense (risk analysis rather than management)

18. Summative Modelling Model building philosophy
Models appropriate to purpose
Cost-effective
Taking account of uncertainties
Models for DM should be able to include effects of intervention.
Hard and soft interventions possible
Hard example – employ extra staff member to increase capacity
Soft example – give employees performance feedback

19. Some dynamic approaches to HR
Holmberg et al (2000)
Suggested use of marked point process
David L. Cooke Thomas R. Rohleder (2008)
Used systems dynamics
Zahra Mohaghegh, Reza Kazemi, Ali Mosleh (2009)
Used hybrid approaches combining SD, PRA and BBNs
Lots of other dynamic risk modelling approaches, eg petri nets, living psa

20. Mohaghegh, Kazemi, Mosleh

21. Common framework A marked point process requires specification of
Possible marks (event types)
Relevant history for each mark
The likelihood for a mark occuring, given the history
Broadly, all three approaches fit into this framework, with either SD or BBNs driving the likelihood.

22. Main difficulties
Complexity – existing models seem very complex… is this necessary for summative purposes?
Measurement scales – for soft interventions these are often vaguely defined and not sufficient to build a robust model
Elicitation – require ways of robustly assessing rates etc for these models
Dependencies – interventions may impact on many different aspects of the system
Model uncertainties – folding these into analysis of options

23. Possible approaches
Complexity – restrict attention to cost/benefit of “discrete” feedback (major accidents) and “continuous” feedback (eg CIRAS). However, for summative approach also need to account for model uncertainties, which makes more complex again!
Measurement scales – use locally valid subjectively defined scales
Elicitation – assess possible changes in system outcomes and derive parameters implicitly (inversion)
Dependencies – model through impact of intervention on common PSFs (eg workload)
Model uncertainties – simulation

24. Broad brush effects on HR+
Safety first culture
Clear Quality standards -
Quality drift
Productivity focus
Cost cutting

25. Example discrete feedback
System is designed to have exponential time to failure with MTTF 1000 years
However, due to lack of failures the system management becomes lax, and rate increases. When failure happens, system is reset to design standard. Suppose 1 failure per 30 years. +
Hazard
rate -
Failure
event

26. Model for failure rate is +t MTTF is 30=
Solving gives =0.0017
Normal tail with mean –lam/mu and sd=1/sqrt(mu)

27. Local measurement scales example SLIM – based on MCDA
Success Likelihood Index Methodology is an early HRA method
Combines Performance Shaping Factor scores using “multiattribute utility” method to quantify Human Error Probability
Key ideas
Ideal points on PSF scale,
Expert defined scores
Pairwise comparison for attribute weights
Two point calibration to identify scale length
Common PSFs provide dependency across HR elements
No nominal probability involved here!
Theoretically a much better approach because don’t need to worry about the definition of the scale used to measure the PSF’s

28. Conclusions New growth in dynamic human reliability modelling
Approaches more applicable to service operations
Hybrid HR models with feedback loops give the possibility of modelling “soft” interventions
BUT many open problems in implementing robustly

29. Acknowledgements
Work in EPSRC funded project with Simon French, Jerry Busby, Emma Soane, David Tracy and others