Human Reliability Analysis: A review of the state of the art

IRACST- International Journal of Research in Management & Technology (IJRMT), ISSN: 2249-9563 

Vol. 2, No. 1, 2012 

Human Reliability Analysis: A review of the state of 

the art 

Fabio De Felice 

Department of Mechanism, Structures and Environment 

University of Cassino, 

Cassino, Italy 

Antonella Petrillo 




Corresponding author: a.petrillo@unicas.it 

Armando Carlomusto 




Antonio Ramondo 

ARPAC 

Naples, Italy 

Abstract— This paper contains a review of Human Reliability 

Analysis (HRA) developments since its inception. The aim of this 

paper is a review on the methodological developments rather 

than reporting its applications that have appeared since its 

introduction. There are many and varied methods available for 

HRA; for this reason our review try to identify the range of 

qualitative and quantitative HRA techniques available in order to 

carry out an assessment of their strengths and weaknesses. 

Keywords- Human Reliability Analysis; Human factor, Safety, 

Risk Management. 

I. INTRODUCTION 

Over the past years, technological developments have led to 

a decrease of accidents due to technical failures through the use 

of redundancy and protection. However, is impossible to talk 

about reliability of a system without consider the failure rate of 

all its components. One of those components is the “man”, 

whose rate of failure/error goes to change the rate of 

breakdowns of components with which it can interact [1]. This 

has highlighted that “human factor” contributes significantly in 

dynamics accidents, both statistically and in terms of severity 

of the consequences. In fact, estimates agree that the errors 

committed by man are causes of 60÷70% of accidents and only 

for the remaining part the causes are due to technical 

deficiencies [2]. Thus, in order to ensure effective prevention 

of dangerous events, during risk assessment process should be 

considered particularly the role of man in accidents dynamics. 

It is abundantly clear how complex are the efforts, made by 

researchers, to propose models of human behavior that favor 

numerical values of error probability in order to predict and 

prevent unsafe conduct. 

The analysis of human factors constitute today a highly 

interdisciplinary field of study not yet well defined, for which 

doesn’t exist a complete and universally accepted taxonomy of 

different types of human error and causes that determine them 

[3]. 

None of techniques in literature can be considered better; 

each has advantages and disadvantages and may be more or 

less appropriate depending on context to be examined, 

resources and available expertise. 

Therefore, the goal of this paper is to introduce an overview 

of some techniques regard Human Reliability Analysis (HRA) 

through methodology comparison, in order to highlight features 

of each technical prescription and their effective 

simultaneously applications in assessment of business risks. 

This comparison is based on evaluation of model, taxonomy, 

data and method that characterize each technique.. 

II. THE HUMAN RELIABILITY ANALYSIS 

In response to ever-changing market needs, there was a 

diffusion of technologically advanced plants that can provide 

flexibility and timeliness in production. The use of those 

advanced technologies, beside managerial advantages, has led 

to reliability issues specifically intended as the probability that 

a system fulfill assigned mission. To this reliability concept are 

closely related risk and workers safety that may be directly and 

indirectly affected by the processes in place. Has been observed 

that system failures due to human intervention are not 

negligible [4]; in particular, some sources report that human 

error is cause of failure systems with, in many cases, disastrous 

consequences. Fortunately, in recent years, technological 

advances have shifted human intervention from a direct 

commitment to the simple manual control of automatic 

processes of machines. 

Generally, in systems reliability studies [5; 6], the 

assessment focuses on industry process and about technologies 

that constitute it, disregarding aspects that depend on human 

factor and its contribution to the same system reliability; but it 

35

should be noted that human error is a major contributor to the 

risks and reliability of many systems: over 90% in nuclear 

industry [7], over 80% in chemical and petro-chemical 

industries [8], over 75% of marine casualties [9], and over 70% 

of aviation accidents [10], [11]. For this reason, starting from 

high risk industrial areas, such as nuclear, aerospace and 

petrochemical, up to individual SMEs, there was the need to 

common techniques of risk analysis with human factor 

evaluation methodologies, collected under the name of Human 

Reliability Analysis (HRA). 

Human Reliability Analysis identifies errors and 

weaknesses in a system by examining methods of work 

including those who work in the system. HRA falls within the 

field of human factors and has been defined as the application 

of relevant information about human characteristics and 

behavior to the design of objects, facilities and environments 

that people use [12]. HRA techniques may be used 

retrospectively, in incidents analysis, or more likely 

prospectively to examine a system. Most approaches are firmly 

grounded in a systemic approach which sees the human 

contribution in context of wider technical and organizational 

context [13]. The purpose is to examine task, process, system 

or organizational structure for where weakness may lie or 

create a vulnerability to errors, not to find fault or apportion 

blame. Any system in which human error can arise, can be 

analyzed with HRA, which in practice, means almost any 

process in which humans are involved [14]. 

III. HRA METHODOLOGIES 

Over the years several methodologies for Human 

Reliability Analysis have been made. This development has led 

researchers to analyze accurately information in order to 

understand what could be the best approach for HRA. 

Information refers to costs, ease of application, ease of 

analysis, availability of data, reliability, and face validity [15]. 

Developed methodologies can be distinguished into two 

macro-categories: First and Second generation methods. 

First generation methods include 35-40 methods for human 

reliability, many of which are variations on a single method. 

Theoretical basis which relates most of first-generation 

methods are: error classification method according to the 

concept “omission-commission”; definition of “performance 

shaping factors” (PFS); cognitive model: skill-based, rulebased, 

knowledge-based. 

The most accredited theory to define and classify wrong 

action is the error classification method according to the 

concept “omission-commission” [16]. This concept contains 

the following meanings: Omission: identifies an action that is 

not done, is done late, or is done in advance; Commission: is 

the implementation of a performance by the operator that is not 

required by the process. 

Starting from these theories, have been developed cognitive 

models of second generation; the most representative 

techniques are: OAT (Operator Action Tree); THERP 

(Technique for Human Error Rate Prediction); TESEO 

(Empirical technique to estimate operator’s error); HCR 

(Human Cognitive Reliability model). 


Vol. 2, No. 1, 2012 

Second generation methods, term coined by Doughty [17] 

try to overcome limitations of traditional methods, in 

particular: 

- provide guidance on possible and probable decision 

paths followed by operator, using mental processes models 

provided by cognitive psychology; 

- extend errors description beyond usual binary 

classification (omission-commission), recognizing importance 

of so-called "cognitive errors"; 

- consider dynamic aspects of human-machine 

interaction and can be used as basis for simulators development 

of operator performance. 

In order to estimate and analyze cognitive reliability, is 

required a suitable model of human information processing. 

The most popular cognitive models are based on the following 

theories: 

- S.O.R. Paradigm (Stimulus-Organism-Response): 

argues that response is a function of stimulus and organism, 

thus a stimulus acts on organism which in turn generates a 

response; 

- Man as a mechanism of information processing: 

according to this vision, mental processes are strictly specified 

procedures and mental states are defined by causal relations 

with other sensory inputs and mental states. It is a recent theory 

that sees man as an information processing system (IPS); 

- Cognitive Viewpoint: in this theory, cognition [18] is 

seen as active rather than reactive; in addition, cognitive 

activity is defined in a cyclical mode rather than sequential 

mode. 

Starting from these theories, have been developed cognitive 

models of second generation; the most representative 

techniques are: ATHEANA (A Technique for Human Error 

ANAlysis); CREAM (Cognitive Reliability and Error Analysis 

Method). 

A. First generation methodologies 

These tools were the first to be developed to help risk 

evaluators predict and quantify the likelihood of human 

error.First generation approaches tend to be atomistic in nature; 

they encourage the evaluator to break a task into component 

parts and then consider the potential impact of modifying 

factors such as time pressure, equipment design and stress. 

First generation methods focus on the skill and rule base level 

of human action and are often criticised for failing to consider 

such things as the impact of context, organisational factors and 

errors of commission. Despite these criticisms they are useful 

and many are in regular use for quantitative risk assessments. 

OAT (Operator Action Tree) 

Operator Action Tree (OAT) was developed by John 

Wreathall in early 1980s. The OAT approach to HRA is based 

on the premise that the response to an event can be described as 

consisting of three stages [19]: observing or noting the event; 

diagnosing or thinking about it; responding to it. 

36

The method is based on a logical tree, which identifies 

possible ways of worker fallibility, after that an accident has 

occurred. It’s contains the following five steps: 

Identify the relevant plant safety functions from 

system even trees; 

Identify event specific actions required to achieve the 

plant safety functions; 

Identify displays that represent relevant alarm 

indications and operators’ available time to take appropriate 

mitigating actions; 

Represent error in fault trees; 

Estimate the probability of errors. Once the thinking 

interval has been established, nominal error probability is 

calculated from the time-reliability relationship. 

Thus, estimate of failure probability is closely related to the 

nominal interval of time required to make a decision when an 

anomaly is detected. This interval can be written formally: 

T=t 1 -t 2 -t 3 (1) 

where: 

T represents required time to make decision; 

t 1 represents interval time between the beginning of incident 

and the end of actions that are related; 

t 2 represents time between start of incident and planning of 

mind intervention; 

t 3 represents required time to implement what is planned 

(t 2 ). 

OAT identifies three types of purely cognitive errors: 

failure to perceive that there was an accident; failure to 

diagnose nature of incident and identify actions required to 

remedy it; error in temporal evaluation of correct behavior 

implementation. 

OAT methodology considers the probability of failure in 

diagnosing an event, i.e., the classical case of response or nonresponse. 

Because of this, OAT cannot be said to provide an 

adequate treatment of human erroneous actions. It however 

differs from the majority of first generation methodology for 

HRA by maintaining a distinction among three phases of 

response: observation, diagnosis and response. This amounts to 

a simple process model and acknowledges that response is 

based on a development that includes various activities by 

operator. 

THERP (Technique for Human Error Rate Prediction) 

The Technique for Human Error Rate Prediction (THERP) 

began already in 1961, but the main work was done during 

1970s [20], resulting in the so-called THERP handbook [21]. 

This is a first generation methodology which means that its 

procedures follow the way conventional reliability analysis 

models a machine. 


Vol. 2, No. 1, 2012 

The aim of this methodology is to calculate the probability 

of successful performance of necessary activities for realization 

of a task. THERP involves performing a task analysis to 

provide a description of performance characteristics of human 

tasks being analysed. Results are represented graphically in an 

HRA event tree, which is a formal representation of required 

actions sequence. 

THERP relies on a large human reliability database 

containing HEPs (Human Error Probabilities) which is based 

upon both plant data and expert judgments. The technique was 

the first approach in HRA to come into broad use and is still 

widely used in a range of applications even beyond its original 

nuclear setting. 

The methodology consists of the following six steps: 

Define the system failures of interest. These failures 

include functions of the system in which human error has a 

greater likelihood of influencing the probability of a fault, and 

those which are of interest to the risk assessor; 

Identify, list and analyze related human operations 

performed and their relationship to system tasks and function 

of interest. This stage of process necessitates a comprehensive 

task and human error analysis. Task analysis lists and 

sequences the discrete elements and information required by 

task operators. For each step of task, possible occurring errors 

which may transpire are considered by analyst and precisely 

defined. An event tree visually displays all events which occur 

within a system. The event tree thus shows a number of 

different paths each of which has an associated end state or 

consequence. 

Estimate relevant human error probabilities. 

Estimate the effects of human error on the system 

failure events. With the completion of the HRA the human 

contribution to failure can then be assessed in comparison with 

the results of the overall reliability analysis 

Recommend changes to the system and recalculate the 

system failure probabilities. Once the human factor 

contribution is known, sensitivity analysis can be used to 

identify how certain risks may be improved in the reduction of 

HEPs. Error recovery paths may be incorporated into the event 

tree as this will aid the assessor when considering the possible 

approaches by which the identified errors can be reduced. 

Review consequences of proposed changes with 

respect to availability, reliability and cost-benefit. 

THERP is probably the best known of first-generation HRA 

methods. This methodology in complete than other because 

describes both how events should be modelled and how they 

should be quantified. Dominance of HRA event tree, however, 

means that classification scheme and model necessarily remain 

limited, since event tree can only account for binary choices 

(success-failure). A final feature of THERP is the use of 

performance shaping factors to complement task analysis. The 

use of this technique to account for non-specific influences is 

found in most first-generation HRA methods [22]. The separate 

use of performance shaping factor is relevant for an evaluation 

37

of operator model, since it suggests that the model by itself is 

context independent. 

TESEO(Empirical technique to estimate operator’s error) 

The empirical technique to estimate operator’s error 

(TESEO) was developed in 1980 [23]. The methodology is 

relatively straightforward and is easy to use but is also limited; 

it is useful for quick overview HRA assessments as opposed to 

those which are highly detailed and in-depth. Within the field 

of HRA, there is a lack of theoretical foundation of the 

technique as is widely acknowledged throughout. 

This technique is used in HRA for evaluating probability of 

a human error occurring throughout the completion of a 

specific task. From such analyses measures can then be taken 

to reduce likelihood of errors occurring within a system and 

therefore lead to an improvement in overall levels of safety. 

This method determines probability of operator error (Pe) 

through the product of five factors, each featuring an aspect of 

system (man, plant, environment, etc.). 

P e =K 1 K 2 K 3 K 4 K 5 (2) 

where: 

K 1 : type of task to be executed; 

K 2 :time available to the operator to complete the task; 

K 3 : operator’s level of experience/characteristics; 

K 4 : operator’s state of mind; 

K 5 : environmental and ergonomic conditions prevalent. 

When putting this technique into practice, it is necessary for 

the designated HRA evaluator to thoroughly consider the task 

requiring assessment and therefore also consider the value for 

K n that applies in the context. Once this value has been decided 

upon, the tables are then consulted from which a related value 

for each of the identified factors is found in order to allow the 

HEP to be calculated. 

TESEO technique is typically quick and straightforward in 

comparison to other HRA tools, not only in producing a final 

result, but also in sensitivity analysis e.g. it is useful in 

identifying the effects improvements in human factors will 

have on the overall human reliability of a task. It is widely 

applicable to various control room designs or with procedures 

with varying characteristics [24]. 

In contrast, it remains to be assumed that these 5 factors are 

suffice for an accurate assessment of human performance; as 

no other factors are considered, this suggests that to solely use 

these 5 factors to adequately describe the full range of error 

producing conditions fails to be highly realistic. Further to this, 

the values of K1-5 are unsubstantiated and the suggested 

multiplicative relationship has no sufficient theoretical or 

empirical evidence for justification purposes. 

HCR (Human Cognitive Reliability model) 

HCR is a cognitive modeling approach to HRA developed 

in 1984 [25]. The method uses Rasmussen’s idea of rule-based, 

skill-based, and knowledge-based decision making to 

determine likelihood of failing a given task [26], as well as 


Vol. 2, No. 1, 2012 

considering the PSFs of operator experience, stress and 

interface quality. The database underpinning this methodology 

was originally developed through the use of nuclear powerplant 

simulations due to a requirement for a method by which 

nuclear operating reliability could be quantified. The basis for 

HCR approach is actually a normalized time-reliability curve, 

where shape is determined by dominant cognitive process 

associated with task being performed. 

HCR method can be described as having the following six 

step: 

Identify actions that must be analyzed by HRA using 

task analysis method; 

Classify types of cognitive processing required by 

actions; 

Determine median response time (T 1/2 ) of a crew to 

perform required tasks; 

Adjust median response time (T* (1/2)) to account for 

performance influencing factors; This is done by means of the 

PSF coefficients K 1 (operator experience), K 2 (stress level) and 

K 3 (quality of operator/plant interface) given in the literature 

and using the following formula: 

T (1/2)= T* (1/2) (1+K 1 )(1+K 2 )(1+K 3 ) (3) 

Determine, for each action, the system time window 

(TSW) in which action must be taken; 

Divide T SW with T 1/2 to obtain a normalised time 

value. Probability of non-response is found using a set of timereliability 

curves. 

HCR is probably the first-generation HRA approach that 

most explicitly refers to a cognitive model. Since it was 

developed, the treatment of human erroneous actions in HCR 

remains on the same level as in many other first-generation 

methods, i.e., a basic distinction between success and failure 

and in this case also no-response. Some advantages of this 

method are: the approach explicitly models the time-dependent 

nature of HRA; it is a fairly quick technique to carry out and 

has a relative ease of use; the three modes of decision-making, 

knowledge-based, skill-based and rule-based are all modeled; 

in contrast, some disadvantages are: the rules for judging 

Knowledge-based, Skill-based and Rule-based behavior are not 

exhaustive. Assigning the wrong behavior to a task can mean 

differences of up to two orders of magnitude in the HEP; the 

method is very sensitive to changes in the estimate of the 

median time. Therefore, this estimate must be very accurate 

otherwise the estimation in the HEP will suffer as a 

consequence; as the HCR correlation was originally developed 

for use within the nuclear industry, it is not immediately 

applicable to situations out-with this domain. 

B. Second generation methodologies 

The development of ‘second generation’ tools began in the 

1990s and is on-going. Benefits of second generation over first 

generation approaches is yet to be established. They have also 

yet to be empirically validated. Kirwan reports that the most 

notable tools of the second generation are ATHEANA and 

CREAM. Literature shows that second generation methods are 

38


Vol. 2, No. 1, 2012 

generally considered to be still under development but that in detail that one could be sure that different teams would produce 

their current form they can provide use fulinsight to human the same results; the quantification method is [29]. 

reliability issues. 

ATHEANA(A Technique for Human Error ANAlysis) 

ATHEANA is both a retrospective and prospective HRA 

methodology developed by the US nuclear industry regulatory 

commission in 2000 [27]. It was developed in the hope that 

certain types of human behavior in nuclear plants and 

industries, which use similar processes, could be represented in 

a way in which they could be more easily understood. 

It seeks to provide a robust psychological framework to 

evaluate and identify PSFs - including 

organizational/environmental factors - which have driven 

incidents involving human factors, primarily with intention of 

suggesting process improvement. Essentially it is a method of 

representing complex accident reports within a standardized 

structure, which may be easier to understand and communicate. 

The basic steps of the ATHEANA methodology are [28]: 

Define and interpret under consideration issue; 

Detail required scope of analysis; 

Describe the base case scenario for a given initiating 

event, including norm of operations within environment, 

considering actions and procedures; 

Define Human Failure Events (HFEs) and/or unsafe 

actions (UAs) which may affect task in question; 

Identify potential vulnerabilities in operators’ 

knowledge base; 

Search for deviations from base case scenario for 

which UAs are likely; 

Identify and evaluate complicating factors and links to 

PSFs; 

Evaluate recovery potential; 

Quantify HFE probability; 

Incorporate results into the PRA. 

The probability of a HFE in ATHEANA, given a particular 

initiator, is determined by summing over different error forcing 

conditions associated to HEF, taking account of likelihood of 

unsafe actions given the EFC, and likelihood of no recovery 

action given the EFC and the UA. 

The most significant advantage of ATHEANA is that it 

provides a much richer and more holistic understanding of the 

context concerning the Human Factors known to be the cause 

of the incident, as compared with most first generation 

methods. Compared to many other HRA quantification 

methods, ATHEANA allows for the consideration of a much 

wider range of performance shaping factors and also does not 

require that these be treated as independent. This is important 

as the method seeks to identify any interactions which affect 

the weighting of the factors of their influence on a situation. In 

contrast some criticisms are: the method is cumbersome and 

requires a large team; the method is not described in sufficient 

CREAM(Cognitive Reliability and Error Analysis Method) 

CREAM methodology was developed by Eric Hollnagel in 

1998 following an analysis of the methods for HRA already in 

place. It is the most widely utilized second generation HRA 

technique and is based on three primary areas of work; task 

analysis, opportunities for reducing errors and possibility to 

consider human performance with regards to overall safety of a 

system. 

This methodology is a technique used in HRA for the 

purposes of evaluating the probability of a human error 

occurring throughout the completion of a specific task. From 

such analyses measures can then be taken to reduce likelihood 

of errors occurring within a system and therefore lead to an 

improvement in the overall levels of safety. HRA techniques 

have been utilized in a range of industries including healthcare, 

engineering, nuclear, transportation and business; each 

technique has varying uses within different disciplines. 

Compared to many other methods, it takes a very different 

approach to modeling human reliability. There are two versions 

of this technique, the basic and the extended version, both of 

which have in common two primary features; ability to identify 

the importance of human performance in a given context and a 

helpful cognitive model and associated framework, usable for 

both prospective and retrospective analysis. Prospective 

analysis allows likely human errors to be identified while 

retrospective analysis quantifies errors that have already 

occurred. 

Cognition concept is included in the model through use of 

four basic ‘control modes’ which identify differing levels of 

control that an operator has in a given context and 

characteristics which highlight occurrence of distinct 

conditions. The control modes which may occur are as follows: 

Scrambled control: choice of forthcoming action is 

unpredictable or haphazard; 

Opportunistic control: next action is determined by 

superficial characteristics of situation, possibly through habit or 

similarity matching. Situation is characterized by lack of 

planning and this may possibly be due to the lack of available 

time; 

Tactical control: performance typically follows 

planned procedures while some ad-hoc deviations are still 

possible; 

Strategic control: plentiful time is available to 

consider actions to be taken in light of wider objectives to be 

fulfilled and within the given context. 

The particular control mode determines level of reliability 

that can be expected in a particular setting and this is in turn 

determined by collective characteristics of relevant Common 

Performance Conditions (CPCs). 

The basic steps of the CREAM methodology are: 

1.Task analysis. The basic method adopted by CREAM 

technique provides an immediate reliability interval based on 

39

an assessment of given control mode, as highlighted by figures 

provided in table below (Tab.1). As can be seen by contents of 

table, each of specified control modes has an individual 

reliability level. In extended CREAM version, control modes 

play the role of a weighting factor which scales a nominal 

failure probability associated to a given cognitive function 

failure. This version of CREAM is intended to be used for 

purposes of a more in depth analysis of human interactions. 

TABLE I. 

Control mode 

RELIABILITY INTERVAL 

Reliability Interval 

strategic 0.5 E-5 

tactical 1.0 E-3 

opportunistic 1.0 E-2 

scrambled 1.0 E-1 

2.Context description. The intention of basic CREAM 

method is to use it as a screening technique with the aim of 

identifying processes which require a deeper level of analysis; 

this analysis may then be carried out by the extended CREAM 

method. 

3.Specification of Initiating Events. When using basic 

CREAM method, a task analysis is conducted prior to further 

assessment. CPCs are assessed according to descriptors, given 

in the table below (Tab.2), in order to judge their expected 

effect on performance. 

TABLE 2. 

COMMON PERFORMANCE CONDITIONS (CPCS) 

CPC Descriptors Expected effect 

Adequacy of Very efficient Improved 

organisation 

Efficient Not significant 

Working 

conditions 

Adequacy of 

MMI and 

operational 

support 

Availability 

of 

procedures/ 

Inefficient 

Deficient 

Advantageous 

Compatible 

Incompatible 

Supportive 

Adequate 

Tolerable 

Inappropriate 

Appropriate 


Vol. 2, No. 1, 2012 

CPC Descriptors Expected effect 

plans 

Reduced 

Reduced 

Improved 

Not significant 

Reduced 

Improved 



Reduced 

Improved 

Number of 

simultaneous 

goals 

Accettable 

Inappropriate 

Fewer than capacity 

Matching current 

capacity 

More than capacity 


Reduced 



Reduced 

Available Adequate 

Improved 

time 

Temporarily Not significant 

inadequate 

Continuously Reduced 

inadequate 

Time of day Day-time (adjusted) Not significant 

Adequacy of 

training and 

expertise 

Crew 

collaboration 

quality 

Night-time 

(unadjusted) 

Adequate, high 

experience 

Adequate, limited 

experience 

Inadequate 

Very efficient 

Efficient 

Inefficient 

Deficient 

Reduced 

Improved 


Reduced 

Improved 



Reduced 

4.Error prediction. Assessments of the CPCs then require to 

be adjusted according to some specified rules in order to take 

account of synergistic effects. Matrix above would be 

considered in the context of the situation under assessment and 

by this means the previously considered initiating events are 

reviewed with respect to how they could potentially lead to 

occurrence of an error. Rows of matrix identify the possible 

outcomes while the columns show the precursors. Analyst then 

has task of identifying the columns for which all the rows have 

been similarly classified into the same group according to 

column headings. Predicting the possible outcomes for each of 

rows should be done until there are no remaining possible 

paths. Each of identified errors requires to be noted along with 

causes and the outcomes. 

5.Determination of control mode. Finally, a simple count is 

performed of the number of CPCs that are causing an 

improvement in reliability and those which are reducing it. On 

the basis of this number the probable control mode is 

determined. 

The main advantages of this methodology are: the 

technique uses the same principles for retrospective and 

40

predictive analyses; the approach is very concise, wellstructured 

and follows a well laid out system of procedure; the 

technique allows for the direct quantification of HEP; it also 

allows evaluator using the CREAM method to specifically 

tailor the use of technique to contextual situation [30]. Instead, 

the main criticism are: this technique requires a high level of 

resource use, including lengthy time periods for completion; 

CREAM also requires an initial expertise in field of human 

factors in order to use technique successfully and may therefore 

appear rather complex for an inexperienced user; CREAM does 

not put forth potential means by which identified errors can be 

reduced; time required for application is very lengthy. 

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