Performance Inﬂuencing Factors - · PDF file innovative entrepreneurial global Arshad...

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Arshad Ahmad [email protected]

Performance Influencing Factors

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Performance Influencing factors

(PIF)


Human Reliability

§  How do we measure human performance ? §  How do we measure human reliability ? §  Conditions that influence human performance have been represented

via several ‘context factors’ §  These context factors are referred to by different terms according to

method •  PSF (performance shaping factors) •  PIF (performance influencing factors) •  IF (influencing factors) •  PAF (performance affecting factors) •  EPC (error producing conditions) •  CPC (common performance conditions), and so on.

§  These factors are used as causes or contributors to unsafe, human actions in event analysis

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Performance Influencing Factor (PIF)

§  PIF is a factor that combine with basic human error tendencies to create error-likely situations. So, it can be used to determine the likelihood of error or effective human performance.

§  PIF depends on human conditions (physically / emotionally) and the work environment or conditions

§  External factors are considered to have greater impact.

§  PIFs such as quality of procedures, level of time stress, and effectiveness of training, will vary on a continuum from the best practicable to worst possible

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Example of Internal & External PIFs

Internal PIF §  Emotional state §  Intelligence

§  Motivation/attitude

§  Perceptual abilities

§  Physical condition

§  Sex differences

§  Skill level

§  Social factors

§  Strength / endurance

§  Stress level

§  Task knowledge

§  Training/experience

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§  External PIF §  Inadequate workspace and layout

§  Poor environmental conditions

§  Inadequate design

§  Inadequate training and job aids

§  Poor supervision


More Example of PIFs

Policy & Organizational Culture §  Safety beliefs §  Attitudes towards blame

§  Reporting and Feedback System

§  Reward Systems

§  Third Party

§  Policy for procedures and training

§  Policies for design

§  Policies for systems of work

§  Level of participation

§  Management communications and feedback

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Job & Task Characteristics §  Systems of work §  Maintenance

§  Control room design

§  Control panel design

§  Job aids and procedures

§  Training §  Task allocation

§  Field workplace design


Example of PIFs

Process Environment Demands §  Control room environment §  Field work environment

§  Levels of demands on personnel

§  Complexity of process events

§  Perceived risk

§  Suddenness of onset of events §  Requirements for concurrent

tasks §  Work pattern

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Work Group Issues §  Functional interfaces §  Distribution of workload and

resources §  Clarity of responsibilities

§  Communication – internal & external

§  Authority and leaderships

§  Group planning and orientation Work Group Issues

§  Competence

§  Motivation

§  Interpersonal style §  Learning style

§  Thinking style


Example of PIF Rating

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PIF Eval. Scale (Qualitative and

Quantitative) Procedures Physical Work Environment

WORST 1

•  No written procedures, or standard way of performing tasks

•  Not integrated with training

•  High levels of noise •  Poor lighting •  High or very low temperatures

and high humidity or wind chill factors

AVERAGE 5

•  Written procedures available, but not always used

•  Standardized method for performing task

•  Moderate noise levels •  Temperature and humidity •  range

BEST 9

•  Detailed procedures and checklists available

•  Procedures developed using task analysis

•  Integrated with training

•  Noise levels at ideal levels •  Lighting design based on

analysis of task requirements •  Temperature and humidity at

ideal levels


Example - Fatigue

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Scale Time at Work Amount of Sleep Shift Rotation

1 Work shifts of twelve hours or longer, with few breaks and are unhappy with their working environment

Less than five hours sleep and have no opportunities to reclaim lost sleep during the week

Worker frequently change shift, start earlier than previous shifts and undertake shifts of longer than 12 hours

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3,4,5,6

7 Workers work regular shifts of eight hours or less, have regular breaks and are happy in environment

Have at least eight hours sleep per night with regular and frequent rest days

Workers do the same shift all the time and work shifts of 8 hours or less

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§  Fatigue is caused by (1) Time at Work, (2) Amount of Sleep, (3) Shift Rotation


Example- Fatigue

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Using these scales, the task expert decides that the amount of sleep that the workers achieve and the shift rotation schedules are fairly close to the best case scenarios describes in the scales

Fatigue (3 + 8 + 7) / 3 = 6

Time at Work (3)

Amount of Sleep (8)

Shift Rotation (7)


PIF Example: Operator Behavior

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ways by which the value or state of a given PIF can beassessed directly, or as a function of other PIFs. Support-ing evidence from psychological literature, experiments,actual events, and various HRA methodologies is pro-vided.

An influence diagram, discussed in this paper is used torepresent a set of causal relations among the PIFs. Thisinfluence diagram is supplemented by a number ofmathematical relations for a more explicit representationof such relationships. These sometimes take the form ofcorrelation or stochastic relations rather than deterministiclinks.

2. IDAC performance influencing factors

When an individual encounters an abnormal event, thenatural reaction often includes physical, cognitive, andemotional responses [7]. These three types of response alsoinfluence each other. There is ample evidence suggestingthat they also affect an individual’s problem-solvingbehavior (discussed in details in Paper 4 [2]). In additionto these internal PIFs, there are external PIFs (e.g.,organizational factors) that also affect an individual’sbehavior directly and indirectly.

The PIFs discussed in this paper are those that couldplay a tangible role in altering the course of an eventthrough their effects on the operators’ responses. Thescenarios of interest are relatively dynamic and have a timewindow of up to a few hours. Thus, the PIFs requiring arelatively long time to have effects are not considered (e.g.,learning related factors). The PIFs identified in this paperare mostly ‘‘frontline’’ factors. Those factors that have anindirect influence on operators’ response are implicitlymodeled by their influences on these frontline factors. Forexample, continuously long work hours or hard-to-adjustshift schedule could cause fatigue. In the current discus-sion, fatigue is a PIF affecting the operator’s performance.The inappropriate shift schedule and long shifts are notincluded. Operator training is another example. Trainingaffects the operator’s proficiency in handling systemanomalies; thus the proficiency (i.e., knowledge and skills)but not the training is a IDAC PIF. Of course such factorscan also be added to the list explicitly in another deeperlayer of the causal model.

A key requirement in identifying factors for use in acausal model for human errors is to have a precisedefinition for each factor, and to ensure that they do notoverlap in definition and role in the overall model. This isimportant since IDAC is primarily developed to be appliedin computer simulation. Accordingly all the rules andfactors that guide and affect an operator’s behaviors mustbe explicitly represented as computer instructions. As aresult, there are fifty PIFs (divided into eleven groups) inthe IDAC model compared with ten or even fewer PIFsused in typical HRA methods designed to be usedmanually (e.g., [8,9]). The IDAC PIFs allow a more precisedefinition in state assessment and causal mechanisms, and

enable computer rules to interpret small differences incontext which could result in visible different behaviors. Insimpler models where expert judgment is often used torelate context to behavior, it is not practical to considermore than a handful of PIFs. It is relatively easy to see thatthe larger set of IDAC PIFs can be reduced throughgrouping and/or scope reduction.Developing precise and non-overlapping definitions for

all PIFs is extremely difficult given the current state of theart, the quality, form, and availability of relevantinformation, and complexities of communication acrossdiverse disciplines in which subjects are studied often forentirely different reasons and end objectives. IDAC hasmade an attempt to meet these requirements. The fiftyIDAC PIFs are classified into eleven hierarchicallystructured groups. The PIFs within each group areindependent; however, dependencies may exist betweenPIFs within different groups. Fig. 1 shows the dependen-cies of the IDAC PIF groups.As stated earlier, PIFs are grouped into internal PIFs

and external PIFs. The internal PIFs are further dividedinto three groups: Mental State, Memorized Information,and Physical Factors. Mental State covers the operator’scognitive and emotional states. It consists of five PIF sub-groups representing different facets of an operator’s stateof mind. These five PIF sub-groups are hierarchicallystructured to represent a process of cognitive andemotional responses to stimuli, from top to bottom,including Cognitive Modes and Tendencies, EmotionalArousal, Strains and Feelings, Perception and Appraisal,and Intrinsic Characteristics. Memorized Informationrefers to the system-related information that is either

ARTICLE IN PRESS

Fig. 1. Organization of PIF groups and high-level interdependencies.

Y.H.J. Chang, A. Mosleh / Reliability Engineering and System Safety 92 (2007) 1014–1040 1015


Full Set PIF Taxonomies

§  Detailed set of PIF is developed for human factor analysis •  CSNI taxonomy (Rasmussen, 1981)

•  THERP (Swain and Guttman, 1983)

•  HEART (Williams, 1988)

•  PHECA (Whalley, 1987)

•  PSF taxonomy (Bellamy, 1991) •  Influencing factors (Gerdes, 1997)

•  K-HPES (KEPRI, 1998).

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PIF Taxonomy for Human Reliability Analysis (HRA)

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•  Quantification of HEP: SLIM (Embrey, 1984), PLG- SLIM (Chu et al., 1994), INTENT (Gertman et al., 1992), STAHR (Phillips, Humphreys, Embrey & Selby, 1990), and HRMS (Kirwan, 1997)

•  Analysis of errors of commission: Macwan’s PIF taxonomy for errors of commission (Macwan & Mosleh, 1994), Julius’ PIF taxonomy for errors of commission (Julius, Jorgenson, Parry & Mosleh, 1995), and A THEANA (US NRC, 2000)

•  Overall context assessment and error analysis: HRMS, CREAM (Hollnagel, 1998; Hollnagel, Kaarstad & Lee, 1999), and INCORECT (Kontogiannis, 1997)

•  Database for HRA: Taylor-Adams’ PSF taxonomy for CORE-DATA (Taylor-Adams, 1995), and Rogers’ PSF taxonomy for CORE-DATA (Gibson et al., 1998).


PIF Development for HRA (Example of Accident Modeling)

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Full Set PIF HRA PIF

H U M A N

Cognitive Factors •  Attention •  Intelligence •  Knowledge •  experiences Psychological States •  Stress •  Burden Personal States Self-esteem Self-confidence Sense of responsibility : Morale & motivation

•  Competence •  Adequacy of

training & experience

•  Stress •  Workload

•  motivation

Candidate PIF

Knowledge, experiences, adequacy of training & experience, competence, stress, burden, fear of consequence, Morale & motivation

Grouping

•  Knowledge, experiences, training, competence

•  stress, burden, fear of consequence, •  Morale & motivation

Final Selection & Structuring

•  Training & experiences •  …

Situational characteristics of accident model

Selection Criteria for HRA

J.W. Kim, W. Jung (2003). A taxonomy of performance influencing factors for human reliability analysis of emergency tasks, Journal of Loss Prevention in the Process Industries,16,479–495


PIFs for HRA (Example of Accident Modeling)

PIF Group Representative PIF Sub-items Human 1. Training & Experience Adequacy of training (frequency, recent training, fidelity of simulation

program) Experiences/practices of real operating events Learning of the past events/experiences Career of the operators

Task 2. Availability & quality of procedures 3. Simultaneous goals/tasks 4. Task type/attributes

Availability Format or type Clarity of instruction and terminology Decision-making criterion Logic structure Number of simultaneous goals/tasks Priority between goals/tasks Conflict between goals Type of man–machine interaction Dynamic/step-by-step Task criticality/consequences Degree of discrepancy with familiar tasks

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J.W. Kim, W. Jung (2003). A taxonomy of performance influencing factors for human reliability analysis of emergency tasks, Journal of Loss Prevention in the Process Industries,16,479–495


PIFs for HRA (Example of Accident Modeling)- cont’d

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PIF Group Representative PIF Sub-items

System 5. Availability and quality of information 6. Status and trend of critical parameters 7. Status of safety system/component 8. Time pressure

Information availability (instrumentation fail/stuck) Clearness of meaning (direct indication/interpretation required/ambiguous/unreliable information) Distinguishability of information Control display relationships Value of critical parameters Trend of critical parameters (rate of change of critical parameters Number of dynamic changing variables Degree of alarm avalanche Success/fail state of safety system/component Level of trust on the system/component Number of failed/stuck components Previous operation history and current status of safety system Available time vs. required time


PIFs for HRA (Example of Accident Modeling)- cont’d

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PIF Group Representative PIF Sub-items

Environment 9. Working environmental features 10. Team cooperation and communication 11. Plant policy and safety culture

Task location: (MCR/local CR/local area) Accessibility Clearness in role/responsibility definition Direction, type, method, protocol Standardization in instruction/information delivery Team collaboration/cooperation Adequacy of distributed workload Plant specific prioritized (or preference for/objection to) goals/strategies Attitude toward EOP/AMP training Safety/economy tradeoff Routine violations

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