- Email: [email protected]
Reducing Human Error by Improvement of Design and Organization
0957–5820/06/$30.00+0.00 # 2006 Institution of Chemical Engineers Trans IChemE, Part B, May 2006 Process Safety and Environmental Protection, 84(B3): 191– 199
www.icheme.org/psep doi: 10.1205/psep.05182
REDUCING HUMAN ERROR BY IMPROVEMENT OF DESIGN AND ORGANIZATION ¨ WE1 and H.-J. LO ¨ HER2 T. DALIJONO1, J. CASTRO1, K. LO 1
Technische Universita¨t Berlin, Institute of Process and Plant Technology, Berlin, Germany 2 Bayer CropScience GmbH, Frankfurt, Germany
W
ith the goal to reduce the possibilities of operator error, improvements in both plant design and organization in process industry can be made (Lo¨we et al., 2005). However, before any improvement can be made, a systematic examination of operator tasks must be carried out first using task analysis methods (Kirwan and Ainsworth, 2001). Applying task analysis can be divided into several steps. In one of these steps, factors that influence operator error must be determined (Dalijono et al., 2005). These factors are commonly known as Performance Influencing Factors (PIFs). All human factors aspects must be considered to determine the underlying PIFs. This paper explains how to identify PIFs with the help of a man-machine-system model. In this model the aspects of human factors are divided into sub-aspects, like environment, communication between the operators during task performance, and so on. The next step is to consider how significant these aspects are for further examination. The knowledge on PIFs is important for finding a solution that would help avoid human errors. It is widely believed that design changes are more expensive than organizational changes, but if all PIFs that lead to human errors are identified, a logical conclusion can be reached on whether to change the design or the organization. This is what the proposed method aims to achieve. Keywords: task analysis; man-machine-system; operator error.
INTRODUCTION
In one of the task analysis steps, PIFs and their influence on the operator tasks must be determined and can be described in a man-machine-system model. This model includes all factors that influence operators’ task performance. This paper presents a new man-machine-system model and a means for determining PIFs, which primarily influence the operators during their task performance. To find these influences, a mathematical method is used. The result of this evaluation is the comparison between each factor in PIFs that influences the operator’s capability. These comparisons produce values, which can be sorted from the highest to lowest. From these values can be seen which factor has the greatest influence. The man-machine-system model and PIFs determination method have already been validated on an industrial plant. This validation shows that the man-machine-system model and PIFs determination are applicable to find suggestions for design and organisation improvement.
Information from surveys indicates that most accidents in the process industries are caused by operator errors (Lo¨we and Kariuki, 2004). These operator errors are in most cases affected by the failure of design and organization (known as latent errors). Nevertheless the accidents are not directly caused by latent errors. Generally speaking, the accident occurs when latent error and operator error come together. In order to reduce operator errors in a system, which consists of machine and operator, the capability of machines should be conformable with human capabilities. Therefore the first step is to investigate the operator tasks and the factors that influence these operator tasks (PIFs). The next step is to improve the design and organization, which should conform to human abilities. For the examination of operator tasks and PIFs, there are several task analysis methods, which can be generally used (Dalijono et al., 2005). The implementation of task analysis itself is divided into several steps. For each of these steps, combinations of existing task analysis methods can be used.
TASK ANALYSIS Task analysis is defined as a systematic and structured method used to identify and analyse tasks, which are relevant for safe operations (Kirwan and Ainsworth, 2001).
Correspondence to: Dr K. Lo¨we, Technische Universita¨t Berlin, Sekr. TK0-1, Straße des 17 Juni 135, 10623 Berlin, Germany. E-mail: [email protected]
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It can be implemented for two different conditions of operation, normal operation and abnormal operation. Kirwan and Ainsworth (2001) classify task analysis methods into five different groups. These groups are divided according to the usage of the methods. An extensive compilation of the existing task analysis techniques is given in Kirwan and Ainsworth (2001). The implementation of task analysis can be divided into several steps. Below are the steps, which are used in the examination of operator tasks and PIFs. Identification of Place and Point in Time of the Control Actions The identification of the place and the point of time of each necessary operator action is essential in this process. Therefore the available documents in the plant were used, particularly P&ID and control instructions. The results from this identification were kept in the form of tables in a database. Identification of the Particularly Relevant Operator Actions In the following steps, the specific relevant operator actions were identified. Specific relevant operator action is defined as those with special safety relevance that could affect the reliability of the system. For the identification of specific relevant operator action, questions related to Human Factors were defined. Below are some examples of the questions: . Is the work under less favourable conditions (operator needs personal protective equipment) designated? . Have work on limited workstation conditions been made? . Are special physical efforts necessary? . Are particularly complex activities required? If from 50% of the questions were answered with ‘yes’, then the task of the operator has been therefore identified as specific relevant operator action. Hence the task was analysed further. Collection of Task Data In here, specification of tasks was determined, upon which the actual analysis of tasks could then be implemented. It is necessary for the Operator Action Analysis to describe the operator task in detail. To achieve this, an instruction manual was used. However, the information written in the manual needs to be compared with the way the task is performed. Since quite often the operators perform the task contrary to what is written in the manual due to unrealistic instructions.
these possibilities of error, various keywords (e.g., too early, too late, and so on) are used as different types of error (Dalijono et al., 2005). Additionally PIFs such as complexity and time requirement of the task, organizational conditions and efficiency of the user, must be integrated into this analysis. Hence, the man-machine-system model is used here. Based on the existing models, a checklist has been developed. This checklist contains all elements that influence the capability of operators to carry out their task. From this task analysis all detailed information about operator tasks, which are safety relevant and the PIFs can be gained. The next step should be to evaluate the influence of PIFs within the task. Therefore one method was developed (as described later).
MAN-MACHINE-SYSTEM MODEL A system can have several components. A system with human and machine as components is called a manmachine-system (MMS). The safety and reliability of such systems are mainly affected by each component, together with the relationship between these components. As seen as physical ability and ability to gather and process information, the capabilities of a human being during tasks performance is often lower than machines capabilities. Nevertheless, machines cannot replace the human ability to analyse, predict and make decisions in unpredictable situations. Therefore, understanding and recognizing the limitations, capabilities and relationships between each component of a system is essential in order to raise the performance of safety and reliability within MMS. The MMS model can be described generally in Figure 1. The input of this system is a Function, while the output is a Result. Operators can use this result as a feedback during task performance. With this feedback, operators evaluate the result of their actions. The environment influences the interaction between operator and machine. Depending on each individual (training, character, and so on) these environmental influences can lead to stress for the operator. The environment’s influences and also the individual characteristics of operator are called performance influencing factors (PIFs). Table 1 describes the PIFs. Generally, PIFs are divided into two areas, external PIFs and internal PIFs. The external PIFs come from outside of the human environment while the internal PIFs come from inside of human environment itself. The internal PIFs depend on the characteristics of each operator. Therefore it
Identification of Possible Operator Error It is of special interest in the safety relevant analysis of a process engineering system to identify possible operator errors and their consequences. To identify possible operator errors and the resulting consequences a method similar to HAZOP analysis is used. All safety relevant operator actions are examined for error possibilities. To define
Figure 1. Man-machine-system model.
Trans IChemE, Part B, Process Safety and Environmental Protection, 2006, 84(B3): 191– 199
REDUCING HUMAN ERROR BY IMPROVEMENT OF DESIGN AND ORGANIZATION Table 1. Performance influencing factors. External performance influencing factors Technical conditions E.g.: † time span to realize the task † temperature † vibration † noise † etc.
Organizational conditions
Internal performance influencing factors
E.g.: † frequency of task † instructions (who gives the instructions, form of the instructions)
E.g.: † skills † communication with other operators (verbal, via radio, and so on) † stress level † etc.
is impossible to measure these factors (VDI, 2002). External PIFs consist of technical and organizational conditions. The technical conditions can often be measured by physical scales (e.g., temperature, noise, time and so on) while the organizational conditions are described verbally. Various sciences examine the physical and mental capabilities of human beings during their actions (Umweltbundesamt, 2001). Some results of these examinations can be utilized as standard methods to evaluate external PIFs. By making external PIFs conform to human necessities, the operator reliability can be influenced positively (VDI, 2002). Based on the above model, PIFs can be summarized in more detail in a checklist. Therefore, this checklist can be used to perform task analysis on gathering PIF information. INFORMATION ON OPERATOR TASKS The identification of the operator task is one of the essential aspects of reducing the possibility of operator error. Task analysis gathers the information which is required for further examination. This includes information about operator actions during task performance and information about PIFs. This chapter shows the practical steps that are taken to identify this information.
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elements of PIFs was created. This checklist is divided into three elements. The first element is Facility. Here all parts, which directly influence the operator conditions (e.g., vibration, temperature, feedback, and so on) must be notable. The second element is Human. All internal influenced factors (e.g., training, communication, and so on) are classified in this element. The last element is Organization. An example of this element is control (feedback) from the operator’s task performance. With the use of this checklist, all information from PIFs from one task will be collected. The completion of this checklist is done by conducting an interview with the operators and through direct observation of the operator’s work situation. After collecting information on the task and operator’s situations during the performance of the task, the dominant cause of operator errors can be found using multi-criteria analysis methods. This method is described next. METHOD TO FIND HOW PIFS INFLUENCE TASK PERFORMANCE In order to be able to further examine task performance, a new method was created. It was developed to give a hint at which direction should one look to find a solution that would help to avoid operator errors. The aim of the method is to find out which PIFs aspects have the greatest influence on the task under study. Due to the importance of evaluating PIFs to reach this goal, it was necessary to develop this method. The PIFs themselves are obtained through the task analysis. However, more observations are necessary in order to determine which factors dominantly affect the task. To find out how PIFs influence the task performance, it is meaningful to know their degree of influence. This is a decision process, therefore the evaluation of the PIFs is based on a multicriteria analysis, which is a decision-making tool developed for complex multi-criteria problems that include qualitative and/or quantitative aspects of the problem in the decision-making process (Mendoza and Macoun, 1999).
Information of Operator Action Information on operator action is collected in several documents such as P&ID and operating manual documentation. However, the operator manual can explain the task inappropriately so that in practice it cannot be followed (Kletz, 1991). For this reason, the information from the above documents can be only used as starting information and needs further examination. The combination of several task analysis methods such as interviews, walk-through, talk-through, simulation, and so on, are essential to obtain data of operator actions. These are useful to identify more advanced operations. Better knowledge of operator actions in practice can be helpful in reducing the possibilities of operator errors. Performance Influencing Factors Beside the information of operator actions, information of factors that influence operators during task performance (PIFs) is also necessary. The model for PIFs is described earlier. From this model, a checklist that contains all
Description of the New Method In the checklist (see Figure 2), the human factor aspects are divided into sub-aspects (facility, human and organization). For each human factor aspect four groups are formed—e.g., facility is divided into situation, technical system, environment and response, to be able to compare them and reach a logical conclusion about which aspect most influences the task performance. The method is carried out as follows: Step 1 (give a ranking of the groups) The first step is to assign a rank to each group that reflects its perceived degree of importance relative to the influence on the task. A ranking system is used that is to give a value to each group based on a scale (1 ¼ no influence, 10 ¼ highest influence). This evaluation is subjective, made by the investigators. The perceived degree of importance will vary according to the person judging it, therefore it is important to have, if possible, more than one investigator
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Figure 2. Distribution of points.
ranking the task and the average will be taken as acceptable rank. Additionally, it is possible to accentuate the influence of the best ranked groups and reduce a little the subjectivity using analytic hierarchy process (AHP), a mathematical
technique for multi-criteria decision making (Saaty, 1990). In this technique, first every value (rank) is compared with every other with the help of a pair wise comparison using Table 2. The comparative importance value is calculated
Trans IChemE, Part B, Process Safety and Environmental Protection, 2006, 84(B3): 191– 199
REDUCING HUMAN ERROR BY IMPROVEMENT OF DESIGN AND ORGANIZATION
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Table 2. Pairwise comparisons based on Mendoza and Macoun (1999). Comparative importance 1 3 5 7 9 2, 4, 6, 8 Reciprocals
Definition
Explanation
Equally important Moderately more important Strongly more important Very strongly more important Extremely more important Intermediate judgement values
Two groups equally influence the task One group is moderately more influential than the other One group has a considerably stronger influence than the other One group has a significantly larger influence over the other The difference between influences of the two groups is extremely significant Judgement values between equally, moderately, strongly, very strongly, and extremely If v is the judgement value when i is compared to j, then 1/v is the judgement value when j is compared to i
through formula (a). (a)
CIi, j ¼ 8
The points were given based on: Ranki Rankj þ1 Rankmax Rankmin
where CIi,j is comparative importance of group i in relation to group j; Ranki is value of the perceived degree of importance (subjective scale) for group i; Rankmax is maximum given value; Rankmin is minimum given value. These values are organized in a matrix (Table 3), where Cj,i ¼ 1/Ci,j. This means, if Group 3 and Group 1 are compared and obtain seven as a number (Group 3 ‘is very strongly more important’ as Group 1, see Table 2), the comparison from Group 1 and Group 3 will acquire the number 1/7 (Group 1 is ‘very strongly less important’ as Group 3). According to Saaty (1990), the best method to derive priorities is to use the eigenvector of the matrix. Therefore to evaluate the degree of influence of a group in relation with the others, the eigenvector of the created matrix is found. The definition of eigenvector and how it is created is explained in detail in Furlan (2000). The eigenvector from the matrix provide a vector with degree of influence of each group as components. Step 2 (give points in the groups) The next step is to develop a system that uses points to evaluate every group. The distribution of the points is arranged by the investigators based on the requirement of human beings. Figure 2 shows our proposition for the distribution of the points. The values were decided in a similar way to the following examples. For ‘Form of instruction’: Form of instruction Points
Routine
Written
Oral
8
1
4
. A routine task is delegated to the lower levels of the brain and is not continually monitored by the conscious mind (Kletz, 1991), therefore it is possible to make an error cause by carelessness. Founded in this information it was decided to give ‘routine’ the highest value (8). . Additionally, knowing that visual perception is the most influencing of all senses for most of the people, ‘written’ was given the smallest significance (1) and ‘oral’ obtains a middle value (4 points). Another example to show how the system was developed is the distribution of point for ‘Environment’:
Environment
Points
Outside Dirty Lack of space Wet Unpleasant smell Cold Other Temperature 30 –40 40 –50 .50 Strong wind Unsafe work Strong vibration Illumination Too much Too little Noise Normal Loud Very loud
5 1 1 1 1 1 1 1 2 3 1 1 1 1 1 2
Here the points were given using different sources that include: Table 3. Pairwise matrix.
Group 1 Group 2 Group 3
Group 1
Group 2
Group 3
1 1/3 1/7
3 1 1/5
7 5 1
. Physical environmental factors like illumination, noise, dirt, wetness, and so on, can influence directly the ability of an operator (VDI, 2002), these influences are not totally understood and therefore it was decided to evaluate each item with one point. . According to ISO 11399 (1995) when the temperature increases, the operator reliability decreases. According
Trans IChemE, Part B, Process Safety and Environmental Protection, 2006, 84(B3): 191 –199
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DALIJONO et al. Table 4. Errors and actions to prevent them.
Error type (Kletz)
Error type (Senders and Green)
Action to prevent
Mistakes: operator does not know what to do
Mistake: the input data are correctly perceived, an incorrect intention is formed and the wrong action is performed
Violations: operator decides not to do it Mismatches: operator is unable to do it Slips and lapses of attention
Omission: leaving out of an appropriate step in the task Misperception: the input data are incorrectly perceived and the wrong action is performed Slip: the input data are correctly perceived, the correct intention is formed and the wrong action is performed
Design: remove opportunities for error by changing design Organization: better training and instructions, if possible first simplify the task Organization: before trying to persuade people, see if possible to remove opportunities for error Design: avoid opportunities of wrong decisions, change hardware and/or software It is not possible to prevent these errors. Design and organization: remove opportunities for error
Figure 3. Results of the task analysis case A.
Trans IChemE, Part B, Process Safety and Environmental Protection, 2006, 84(B3): 191– 199
REDUCING HUMAN ERROR BY IMPROVEMENT OF DESIGN AND ORGANIZATION to OSHA standard 1926.52 the permissible noise exposure in hours per day decreases when the noise level increases. Based on these standards the distribution of points for temperature and noise were decided. . Outside receives a value of 5, because it substitutes some of the other items: it is not logical to have wet and cold for a task performed outside; additionally the category outside is highly influenced by the weather. It is important to have the same maximal reachable amount of points for every group, if not it is necessary to normalised the values (e.g., for Group 3, Environment, the total points was normalized by dividing it by 1.5). The task analysis is evaluated with this system and each group obtains a score.
Step 3 (obtain a result) The final step is to calculate the influence of each HF aspect on the performance of the task. The degree of influence of every group, determined in step 1, is multiplied by the score from step 2. By representing this value as a percentile and adding them for each HF aspect, it is possible to see which PIF most influences the task under analysis. Based on this method it is possible to make a logical conclusion whether to change the design or the organization.
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EXAMPLES This method was validated on an industrial plant from Bayer CropScience, Germany. Two cases will be explained next. Case A The task consists of feeding material (labels, bottles, package, and so on) in an automatic machine that fills up bottles with a chemical product. Figure 3 shows the result of the task analysis. Table 5 contains the outcome of the method. According to this data it is possible to conclude that the organization is the most influential HF aspect for this task, and especially the frequency is to be taken into account to give the recommendations to increase the reliability of the process. The high frequency of the task implies that an error of the type ‘slip’ is probable. We should accept that these errors will occur. A recommendation to reduce the error rate is to change the design or method of working, to remove opportunities for error. An approach to design change could be task automation, avoiding a reduction of the operator’s concentration. Additionally, if achievable, making the time span two times larger than the necessary time to realize the task is desirable (by changing software and/or hardware).
HUMAN ERRORS AND MEASURES TO BE TAKEN
Case B
The best way to decide how to change design or organization is to recognize the different kinds of errors operators can make and to find out what to do to prevent or reduce them. One approach in this direction is given by Kletz (1991), who divides errors into four types. A similar taxonomy is given by Senders and Green (‘Human Error in Medicine’). On this basis, it is possible to develop a plan of countermeasures. Table 4 explains the types of errors and gives some possibilities to prevent them. Additionally it is of great importance to consider some of the following facts (VDI, 2002):
The task is to connect a hose between a tanker and a tank to receive a product. Figure 4 shows the result of the task analysis. Table 6 contains the outcome of the method. In both cases the same ranking values were given as shown in Tables 5 and 6. By analysing this information it is possible to see that Facility has the highest percentile, even though Organization is a close second. Here a definitive answer is not possible to make. If we compare all the values, environment is the highest. In this case it is possible to deduce that a
. The more complex the task, the easier to make an error. The designer should optimise the machine according to the capabilities of the operator. . To reduce the error probability, the time span should be two times larger than the necessary time to perform the task. . Automation should generally be recommended, if problems with human restrictions occur, that is with accuracy, speed and reliability. . If automation is possible, it should be taken into account to include the human somehow actively into the system, to thus keep up his/her concentration and state of training and therefore keep the overall reliability of the system. . Human reliability increases if the same information can be perceived with more than two senses. . Devices should be provided for the operator, so that he/ she can correctly operate the machine.
Table 5. Outcome of the method case A. Ranking
Points
Result
Facility Situation Technical system Environment Response
4 5 7 4
2 2 1 4
1.8% 3.4% 5.5% 3.5% 14.1%
Human Behaviour Clothing Skills Stress level
3 7 5 5
4 1 3 1
1.8% 5.5% 5.1% 1.7% 14.0%
Organization Task Frequency Instructions Realization
3 7 3 3
3 10 1 1
16.4% 54.6% 0.4% 0.4% 71.9%
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Figure 4. Results of the task analysis case B.
change in the design would be the better approach to reduce or eliminate the probability of operator errors. In conclusion, with the help of this method it was possible to obtain a better understanding of the task. This was
acquired to get a reference on how strong the different PIFs influence the performance of the analysed task. This information gives a better approach to propose recommendations in order to reduce the possibilities of operator error.
Trans IChemE, Part B, Process Safety and Environmental Protection, 2006, 84(B3): 191– 199
REDUCING HUMAN ERROR BY IMPROVEMENT OF DESIGN AND ORGANIZATION Table 6. Outcome of the method case B. Ranking
Points
Result
Facility Situation Technical system Environment Response
4 5 7 4
4 8 5 4
3.3% 12.6% 25.4% 3.3% 44.5%
Human Behaviour Clothing Skills Stress level
3 7 5 5
4 2 1 1
1.6% 10.2% 1.6% 1.6% 15.0%
Organization Task Frequency Instructions Realization
3 7 3 3
3 4 10 2
15.2% 20.3% 4.1% 0.8% 40.5%
SUMMARY This paper lays out a systematic method that aims to reduce the outcome of operator errors, by the performance of a task, through an improvement of the design and/or organization. In both cases, to make any improvement, an identification of operator tasks and PIFs must be carried out. This is achievable with the help of a man-machinesystem model. This information is processed with a new method to determine which PIF most influences the performance of the task under study. This method is based on a multi-criteria analysis where the evaluation of the perceived degree of importance is made by a pairwise comparison and a points system to help valuate the total influence. With this knowledge it is possible to make a logical conclusion on whether to change the design or the organization.
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This method was validated on an industrial plant and the results give a pattern to propose recommendations to increase the reliability of the whole process. REFERENCES Dalijono, T., Lo¨we, K. and Lo¨her, H.-J., 2005, Development and verification of a new approach for operator action analysis, Process Safety and Environmental Protection, Part B, IChemE, 83(B4): 331– 337. Furlan, P., 2000, Das Gelbe Rechenbuch 1 fu¨r Ingenieure, Naturwissenschaftler und Mathematiker, 101 –106 (Dortmund, Verlag Martina Furlan). ISO, norm number 11399, 1995, Ergonomics of the thermal environment. Kirwan, B. and Ainsworth, L.K., 2001, A Guide to Task Analysis (Taylor and Francis Ltd, London, UK). Kletz, T., 1991, An Engineer’s View of Human Error, 2nd edition (Institution of Chemical Engineers, Rugby, UK and VCH Publishers, Inc., New York, USA). (A later edition is now available from the Institution of Chemical Engineers.) Lo¨we, K. and Kariuki, G., 2004, Methods for incorporating human factors during design phase, Loss Prevention and Safety Promotion in the Process Industries, 5205– 5215. Lo¨we, K., Kariuki, S.G., Porcsalmy, L. and Fro¨hlich, B., 2005, Development and validation of a human factors engineering guideline for process industries, Loss Prevention Bulletin (LPB), IChemE, 182: 9 –14. Mendoza, G. and Macoun, P., 1999, Guidelines for applying multi-criteria analysis to the assessment of criteria and indicators (online), The Criteria & Indicators Toolbox Series, part 9, available from www.cifor.cgiar.org. OSHA, standard number 1926.52, 2001, Occupational health and environmental controls (online), available from www.osha.gov. Saaty, T.L., 1990, Multicriteria Decision Making: The Analytic Hierarchy Process (RWS Publications, Pittsburgh, USA). Senders, J. and Green, M., Human error in medicine (online), available from www.visualexpert.com/Resources/mederror.html. Umweltbundesamt, 2001, Strategien zur Verhinderung von Fehlhandlungen in verfahrenstechischen Anlagen, Forschungsbericht. VDI-Richtlinie, 2002, Human reliability—Ergonomic requirements and methods of assessment, VDI-Gesellschaft Systementwicklung und Projektgestaltung (VDI 4006 Blatt 1). The manuscript was received 3 August 2005 and accepted for publication after revision 15 February 2006.
Trans IChemE, Part B, Process Safety and Environmental Protection, 2006, 84(B3): 191 –199
www.icheme.org/psep doi: 10.1205/psep.05182
REDUCING HUMAN ERROR BY IMPROVEMENT OF DESIGN AND ORGANIZATION ¨ WE1 and H.-J. LO ¨ HER2 T. DALIJONO1, J. CASTRO1, K. LO 1
Technische Universita¨t Berlin, Institute of Process and Plant Technology, Berlin, Germany 2 Bayer CropScience GmbH, Frankfurt, Germany
W
ith the goal to reduce the possibilities of operator error, improvements in both plant design and organization in process industry can be made (Lo¨we et al., 2005). However, before any improvement can be made, a systematic examination of operator tasks must be carried out first using task analysis methods (Kirwan and Ainsworth, 2001). Applying task analysis can be divided into several steps. In one of these steps, factors that influence operator error must be determined (Dalijono et al., 2005). These factors are commonly known as Performance Influencing Factors (PIFs). All human factors aspects must be considered to determine the underlying PIFs. This paper explains how to identify PIFs with the help of a man-machine-system model. In this model the aspects of human factors are divided into sub-aspects, like environment, communication between the operators during task performance, and so on. The next step is to consider how significant these aspects are for further examination. The knowledge on PIFs is important for finding a solution that would help avoid human errors. It is widely believed that design changes are more expensive than organizational changes, but if all PIFs that lead to human errors are identified, a logical conclusion can be reached on whether to change the design or the organization. This is what the proposed method aims to achieve. Keywords: task analysis; man-machine-system; operator error.
INTRODUCTION
In one of the task analysis steps, PIFs and their influence on the operator tasks must be determined and can be described in a man-machine-system model. This model includes all factors that influence operators’ task performance. This paper presents a new man-machine-system model and a means for determining PIFs, which primarily influence the operators during their task performance. To find these influences, a mathematical method is used. The result of this evaluation is the comparison between each factor in PIFs that influences the operator’s capability. These comparisons produce values, which can be sorted from the highest to lowest. From these values can be seen which factor has the greatest influence. The man-machine-system model and PIFs determination method have already been validated on an industrial plant. This validation shows that the man-machine-system model and PIFs determination are applicable to find suggestions for design and organisation improvement.
Information from surveys indicates that most accidents in the process industries are caused by operator errors (Lo¨we and Kariuki, 2004). These operator errors are in most cases affected by the failure of design and organization (known as latent errors). Nevertheless the accidents are not directly caused by latent errors. Generally speaking, the accident occurs when latent error and operator error come together. In order to reduce operator errors in a system, which consists of machine and operator, the capability of machines should be conformable with human capabilities. Therefore the first step is to investigate the operator tasks and the factors that influence these operator tasks (PIFs). The next step is to improve the design and organization, which should conform to human abilities. For the examination of operator tasks and PIFs, there are several task analysis methods, which can be generally used (Dalijono et al., 2005). The implementation of task analysis itself is divided into several steps. For each of these steps, combinations of existing task analysis methods can be used.
TASK ANALYSIS Task analysis is defined as a systematic and structured method used to identify and analyse tasks, which are relevant for safe operations (Kirwan and Ainsworth, 2001).
Correspondence to: Dr K. Lo¨we, Technische Universita¨t Berlin, Sekr. TK0-1, Straße des 17 Juni 135, 10623 Berlin, Germany. E-mail: [email protected]
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It can be implemented for two different conditions of operation, normal operation and abnormal operation. Kirwan and Ainsworth (2001) classify task analysis methods into five different groups. These groups are divided according to the usage of the methods. An extensive compilation of the existing task analysis techniques is given in Kirwan and Ainsworth (2001). The implementation of task analysis can be divided into several steps. Below are the steps, which are used in the examination of operator tasks and PIFs. Identification of Place and Point in Time of the Control Actions The identification of the place and the point of time of each necessary operator action is essential in this process. Therefore the available documents in the plant were used, particularly P&ID and control instructions. The results from this identification were kept in the form of tables in a database. Identification of the Particularly Relevant Operator Actions In the following steps, the specific relevant operator actions were identified. Specific relevant operator action is defined as those with special safety relevance that could affect the reliability of the system. For the identification of specific relevant operator action, questions related to Human Factors were defined. Below are some examples of the questions: . Is the work under less favourable conditions (operator needs personal protective equipment) designated? . Have work on limited workstation conditions been made? . Are special physical efforts necessary? . Are particularly complex activities required? If from 50% of the questions were answered with ‘yes’, then the task of the operator has been therefore identified as specific relevant operator action. Hence the task was analysed further. Collection of Task Data In here, specification of tasks was determined, upon which the actual analysis of tasks could then be implemented. It is necessary for the Operator Action Analysis to describe the operator task in detail. To achieve this, an instruction manual was used. However, the information written in the manual needs to be compared with the way the task is performed. Since quite often the operators perform the task contrary to what is written in the manual due to unrealistic instructions.
these possibilities of error, various keywords (e.g., too early, too late, and so on) are used as different types of error (Dalijono et al., 2005). Additionally PIFs such as complexity and time requirement of the task, organizational conditions and efficiency of the user, must be integrated into this analysis. Hence, the man-machine-system model is used here. Based on the existing models, a checklist has been developed. This checklist contains all elements that influence the capability of operators to carry out their task. From this task analysis all detailed information about operator tasks, which are safety relevant and the PIFs can be gained. The next step should be to evaluate the influence of PIFs within the task. Therefore one method was developed (as described later).
MAN-MACHINE-SYSTEM MODEL A system can have several components. A system with human and machine as components is called a manmachine-system (MMS). The safety and reliability of such systems are mainly affected by each component, together with the relationship between these components. As seen as physical ability and ability to gather and process information, the capabilities of a human being during tasks performance is often lower than machines capabilities. Nevertheless, machines cannot replace the human ability to analyse, predict and make decisions in unpredictable situations. Therefore, understanding and recognizing the limitations, capabilities and relationships between each component of a system is essential in order to raise the performance of safety and reliability within MMS. The MMS model can be described generally in Figure 1. The input of this system is a Function, while the output is a Result. Operators can use this result as a feedback during task performance. With this feedback, operators evaluate the result of their actions. The environment influences the interaction between operator and machine. Depending on each individual (training, character, and so on) these environmental influences can lead to stress for the operator. The environment’s influences and also the individual characteristics of operator are called performance influencing factors (PIFs). Table 1 describes the PIFs. Generally, PIFs are divided into two areas, external PIFs and internal PIFs. The external PIFs come from outside of the human environment while the internal PIFs come from inside of human environment itself. The internal PIFs depend on the characteristics of each operator. Therefore it
Identification of Possible Operator Error It is of special interest in the safety relevant analysis of a process engineering system to identify possible operator errors and their consequences. To identify possible operator errors and the resulting consequences a method similar to HAZOP analysis is used. All safety relevant operator actions are examined for error possibilities. To define
Figure 1. Man-machine-system model.
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REDUCING HUMAN ERROR BY IMPROVEMENT OF DESIGN AND ORGANIZATION Table 1. Performance influencing factors. External performance influencing factors Technical conditions E.g.: † time span to realize the task † temperature † vibration † noise † etc.
Organizational conditions
Internal performance influencing factors
E.g.: † frequency of task † instructions (who gives the instructions, form of the instructions)
E.g.: † skills † communication with other operators (verbal, via radio, and so on) † stress level † etc.
is impossible to measure these factors (VDI, 2002). External PIFs consist of technical and organizational conditions. The technical conditions can often be measured by physical scales (e.g., temperature, noise, time and so on) while the organizational conditions are described verbally. Various sciences examine the physical and mental capabilities of human beings during their actions (Umweltbundesamt, 2001). Some results of these examinations can be utilized as standard methods to evaluate external PIFs. By making external PIFs conform to human necessities, the operator reliability can be influenced positively (VDI, 2002). Based on the above model, PIFs can be summarized in more detail in a checklist. Therefore, this checklist can be used to perform task analysis on gathering PIF information. INFORMATION ON OPERATOR TASKS The identification of the operator task is one of the essential aspects of reducing the possibility of operator error. Task analysis gathers the information which is required for further examination. This includes information about operator actions during task performance and information about PIFs. This chapter shows the practical steps that are taken to identify this information.
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elements of PIFs was created. This checklist is divided into three elements. The first element is Facility. Here all parts, which directly influence the operator conditions (e.g., vibration, temperature, feedback, and so on) must be notable. The second element is Human. All internal influenced factors (e.g., training, communication, and so on) are classified in this element. The last element is Organization. An example of this element is control (feedback) from the operator’s task performance. With the use of this checklist, all information from PIFs from one task will be collected. The completion of this checklist is done by conducting an interview with the operators and through direct observation of the operator’s work situation. After collecting information on the task and operator’s situations during the performance of the task, the dominant cause of operator errors can be found using multi-criteria analysis methods. This method is described next. METHOD TO FIND HOW PIFS INFLUENCE TASK PERFORMANCE In order to be able to further examine task performance, a new method was created. It was developed to give a hint at which direction should one look to find a solution that would help to avoid operator errors. The aim of the method is to find out which PIFs aspects have the greatest influence on the task under study. Due to the importance of evaluating PIFs to reach this goal, it was necessary to develop this method. The PIFs themselves are obtained through the task analysis. However, more observations are necessary in order to determine which factors dominantly affect the task. To find out how PIFs influence the task performance, it is meaningful to know their degree of influence. This is a decision process, therefore the evaluation of the PIFs is based on a multicriteria analysis, which is a decision-making tool developed for complex multi-criteria problems that include qualitative and/or quantitative aspects of the problem in the decision-making process (Mendoza and Macoun, 1999).
Information of Operator Action Information on operator action is collected in several documents such as P&ID and operating manual documentation. However, the operator manual can explain the task inappropriately so that in practice it cannot be followed (Kletz, 1991). For this reason, the information from the above documents can be only used as starting information and needs further examination. The combination of several task analysis methods such as interviews, walk-through, talk-through, simulation, and so on, are essential to obtain data of operator actions. These are useful to identify more advanced operations. Better knowledge of operator actions in practice can be helpful in reducing the possibilities of operator errors. Performance Influencing Factors Beside the information of operator actions, information of factors that influence operators during task performance (PIFs) is also necessary. The model for PIFs is described earlier. From this model, a checklist that contains all
Description of the New Method In the checklist (see Figure 2), the human factor aspects are divided into sub-aspects (facility, human and organization). For each human factor aspect four groups are formed—e.g., facility is divided into situation, technical system, environment and response, to be able to compare them and reach a logical conclusion about which aspect most influences the task performance. The method is carried out as follows: Step 1 (give a ranking of the groups) The first step is to assign a rank to each group that reflects its perceived degree of importance relative to the influence on the task. A ranking system is used that is to give a value to each group based on a scale (1 ¼ no influence, 10 ¼ highest influence). This evaluation is subjective, made by the investigators. The perceived degree of importance will vary according to the person judging it, therefore it is important to have, if possible, more than one investigator
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Figure 2. Distribution of points.
ranking the task and the average will be taken as acceptable rank. Additionally, it is possible to accentuate the influence of the best ranked groups and reduce a little the subjectivity using analytic hierarchy process (AHP), a mathematical
technique for multi-criteria decision making (Saaty, 1990). In this technique, first every value (rank) is compared with every other with the help of a pair wise comparison using Table 2. The comparative importance value is calculated
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Table 2. Pairwise comparisons based on Mendoza and Macoun (1999). Comparative importance 1 3 5 7 9 2, 4, 6, 8 Reciprocals
Definition
Explanation
Equally important Moderately more important Strongly more important Very strongly more important Extremely more important Intermediate judgement values
Two groups equally influence the task One group is moderately more influential than the other One group has a considerably stronger influence than the other One group has a significantly larger influence over the other The difference between influences of the two groups is extremely significant Judgement values between equally, moderately, strongly, very strongly, and extremely If v is the judgement value when i is compared to j, then 1/v is the judgement value when j is compared to i
through formula (a). (a)
CIi, j ¼ 8
The points were given based on: Ranki Rankj þ1 Rankmax Rankmin
where CIi,j is comparative importance of group i in relation to group j; Ranki is value of the perceived degree of importance (subjective scale) for group i; Rankmax is maximum given value; Rankmin is minimum given value. These values are organized in a matrix (Table 3), where Cj,i ¼ 1/Ci,j. This means, if Group 3 and Group 1 are compared and obtain seven as a number (Group 3 ‘is very strongly more important’ as Group 1, see Table 2), the comparison from Group 1 and Group 3 will acquire the number 1/7 (Group 1 is ‘very strongly less important’ as Group 3). According to Saaty (1990), the best method to derive priorities is to use the eigenvector of the matrix. Therefore to evaluate the degree of influence of a group in relation with the others, the eigenvector of the created matrix is found. The definition of eigenvector and how it is created is explained in detail in Furlan (2000). The eigenvector from the matrix provide a vector with degree of influence of each group as components. Step 2 (give points in the groups) The next step is to develop a system that uses points to evaluate every group. The distribution of the points is arranged by the investigators based on the requirement of human beings. Figure 2 shows our proposition for the distribution of the points. The values were decided in a similar way to the following examples. For ‘Form of instruction’: Form of instruction Points
Routine
Written
Oral
8
1
4
. A routine task is delegated to the lower levels of the brain and is not continually monitored by the conscious mind (Kletz, 1991), therefore it is possible to make an error cause by carelessness. Founded in this information it was decided to give ‘routine’ the highest value (8). . Additionally, knowing that visual perception is the most influencing of all senses for most of the people, ‘written’ was given the smallest significance (1) and ‘oral’ obtains a middle value (4 points). Another example to show how the system was developed is the distribution of point for ‘Environment’:
Environment
Points
Outside Dirty Lack of space Wet Unpleasant smell Cold Other Temperature 30 –40 40 –50 .50 Strong wind Unsafe work Strong vibration Illumination Too much Too little Noise Normal Loud Very loud
5 1 1 1 1 1 1 1 2 3 1 1 1 1 1 2
Here the points were given using different sources that include: Table 3. Pairwise matrix.
Group 1 Group 2 Group 3
Group 1
Group 2
Group 3
1 1/3 1/7
3 1 1/5
7 5 1
. Physical environmental factors like illumination, noise, dirt, wetness, and so on, can influence directly the ability of an operator (VDI, 2002), these influences are not totally understood and therefore it was decided to evaluate each item with one point. . According to ISO 11399 (1995) when the temperature increases, the operator reliability decreases. According
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DALIJONO et al. Table 4. Errors and actions to prevent them.
Error type (Kletz)
Error type (Senders and Green)
Action to prevent
Mistakes: operator does not know what to do
Mistake: the input data are correctly perceived, an incorrect intention is formed and the wrong action is performed
Violations: operator decides not to do it Mismatches: operator is unable to do it Slips and lapses of attention
Omission: leaving out of an appropriate step in the task Misperception: the input data are incorrectly perceived and the wrong action is performed Slip: the input data are correctly perceived, the correct intention is formed and the wrong action is performed
Design: remove opportunities for error by changing design Organization: better training and instructions, if possible first simplify the task Organization: before trying to persuade people, see if possible to remove opportunities for error Design: avoid opportunities of wrong decisions, change hardware and/or software It is not possible to prevent these errors. Design and organization: remove opportunities for error
Figure 3. Results of the task analysis case A.
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REDUCING HUMAN ERROR BY IMPROVEMENT OF DESIGN AND ORGANIZATION to OSHA standard 1926.52 the permissible noise exposure in hours per day decreases when the noise level increases. Based on these standards the distribution of points for temperature and noise were decided. . Outside receives a value of 5, because it substitutes some of the other items: it is not logical to have wet and cold for a task performed outside; additionally the category outside is highly influenced by the weather. It is important to have the same maximal reachable amount of points for every group, if not it is necessary to normalised the values (e.g., for Group 3, Environment, the total points was normalized by dividing it by 1.5). The task analysis is evaluated with this system and each group obtains a score.
Step 3 (obtain a result) The final step is to calculate the influence of each HF aspect on the performance of the task. The degree of influence of every group, determined in step 1, is multiplied by the score from step 2. By representing this value as a percentile and adding them for each HF aspect, it is possible to see which PIF most influences the task under analysis. Based on this method it is possible to make a logical conclusion whether to change the design or the organization.
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EXAMPLES This method was validated on an industrial plant from Bayer CropScience, Germany. Two cases will be explained next. Case A The task consists of feeding material (labels, bottles, package, and so on) in an automatic machine that fills up bottles with a chemical product. Figure 3 shows the result of the task analysis. Table 5 contains the outcome of the method. According to this data it is possible to conclude that the organization is the most influential HF aspect for this task, and especially the frequency is to be taken into account to give the recommendations to increase the reliability of the process. The high frequency of the task implies that an error of the type ‘slip’ is probable. We should accept that these errors will occur. A recommendation to reduce the error rate is to change the design or method of working, to remove opportunities for error. An approach to design change could be task automation, avoiding a reduction of the operator’s concentration. Additionally, if achievable, making the time span two times larger than the necessary time to realize the task is desirable (by changing software and/or hardware).
HUMAN ERRORS AND MEASURES TO BE TAKEN
Case B
The best way to decide how to change design or organization is to recognize the different kinds of errors operators can make and to find out what to do to prevent or reduce them. One approach in this direction is given by Kletz (1991), who divides errors into four types. A similar taxonomy is given by Senders and Green (‘Human Error in Medicine’). On this basis, it is possible to develop a plan of countermeasures. Table 4 explains the types of errors and gives some possibilities to prevent them. Additionally it is of great importance to consider some of the following facts (VDI, 2002):
The task is to connect a hose between a tanker and a tank to receive a product. Figure 4 shows the result of the task analysis. Table 6 contains the outcome of the method. In both cases the same ranking values were given as shown in Tables 5 and 6. By analysing this information it is possible to see that Facility has the highest percentile, even though Organization is a close second. Here a definitive answer is not possible to make. If we compare all the values, environment is the highest. In this case it is possible to deduce that a
. The more complex the task, the easier to make an error. The designer should optimise the machine according to the capabilities of the operator. . To reduce the error probability, the time span should be two times larger than the necessary time to perform the task. . Automation should generally be recommended, if problems with human restrictions occur, that is with accuracy, speed and reliability. . If automation is possible, it should be taken into account to include the human somehow actively into the system, to thus keep up his/her concentration and state of training and therefore keep the overall reliability of the system. . Human reliability increases if the same information can be perceived with more than two senses. . Devices should be provided for the operator, so that he/ she can correctly operate the machine.
Table 5. Outcome of the method case A. Ranking
Points
Result
Facility Situation Technical system Environment Response
4 5 7 4
2 2 1 4
1.8% 3.4% 5.5% 3.5% 14.1%
Human Behaviour Clothing Skills Stress level
3 7 5 5
4 1 3 1
1.8% 5.5% 5.1% 1.7% 14.0%
Organization Task Frequency Instructions Realization
3 7 3 3
3 10 1 1
16.4% 54.6% 0.4% 0.4% 71.9%
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Figure 4. Results of the task analysis case B.
change in the design would be the better approach to reduce or eliminate the probability of operator errors. In conclusion, with the help of this method it was possible to obtain a better understanding of the task. This was
acquired to get a reference on how strong the different PIFs influence the performance of the analysed task. This information gives a better approach to propose recommendations in order to reduce the possibilities of operator error.
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REDUCING HUMAN ERROR BY IMPROVEMENT OF DESIGN AND ORGANIZATION Table 6. Outcome of the method case B. Ranking
Points
Result
Facility Situation Technical system Environment Response
4 5 7 4
4 8 5 4
3.3% 12.6% 25.4% 3.3% 44.5%
Human Behaviour Clothing Skills Stress level
3 7 5 5
4 2 1 1
1.6% 10.2% 1.6% 1.6% 15.0%
Organization Task Frequency Instructions Realization
3 7 3 3
3 4 10 2
15.2% 20.3% 4.1% 0.8% 40.5%
SUMMARY This paper lays out a systematic method that aims to reduce the outcome of operator errors, by the performance of a task, through an improvement of the design and/or organization. In both cases, to make any improvement, an identification of operator tasks and PIFs must be carried out. This is achievable with the help of a man-machinesystem model. This information is processed with a new method to determine which PIF most influences the performance of the task under study. This method is based on a multi-criteria analysis where the evaluation of the perceived degree of importance is made by a pairwise comparison and a points system to help valuate the total influence. With this knowledge it is possible to make a logical conclusion on whether to change the design or the organization.
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This method was validated on an industrial plant and the results give a pattern to propose recommendations to increase the reliability of the whole process. REFERENCES Dalijono, T., Lo¨we, K. and Lo¨her, H.-J., 2005, Development and verification of a new approach for operator action analysis, Process Safety and Environmental Protection, Part B, IChemE, 83(B4): 331– 337. Furlan, P., 2000, Das Gelbe Rechenbuch 1 fu¨r Ingenieure, Naturwissenschaftler und Mathematiker, 101 –106 (Dortmund, Verlag Martina Furlan). ISO, norm number 11399, 1995, Ergonomics of the thermal environment. Kirwan, B. and Ainsworth, L.K., 2001, A Guide to Task Analysis (Taylor and Francis Ltd, London, UK). Kletz, T., 1991, An Engineer’s View of Human Error, 2nd edition (Institution of Chemical Engineers, Rugby, UK and VCH Publishers, Inc., New York, USA). (A later edition is now available from the Institution of Chemical Engineers.) Lo¨we, K. and Kariuki, G., 2004, Methods for incorporating human factors during design phase, Loss Prevention and Safety Promotion in the Process Industries, 5205– 5215. Lo¨we, K., Kariuki, S.G., Porcsalmy, L. and Fro¨hlich, B., 2005, Development and validation of a human factors engineering guideline for process industries, Loss Prevention Bulletin (LPB), IChemE, 182: 9 –14. Mendoza, G. and Macoun, P., 1999, Guidelines for applying multi-criteria analysis to the assessment of criteria and indicators (online), The Criteria & Indicators Toolbox Series, part 9, available from www.cifor.cgiar.org. OSHA, standard number 1926.52, 2001, Occupational health and environmental controls (online), available from www.osha.gov. Saaty, T.L., 1990, Multicriteria Decision Making: The Analytic Hierarchy Process (RWS Publications, Pittsburgh, USA). Senders, J. and Green, M., Human error in medicine (online), available from www.visualexpert.com/Resources/mederror.html. Umweltbundesamt, 2001, Strategien zur Verhinderung von Fehlhandlungen in verfahrenstechischen Anlagen, Forschungsbericht. VDI-Richtlinie, 2002, Human reliability—Ergonomic requirements and methods of assessment, VDI-Gesellschaft Systementwicklung und Projektgestaltung (VDI 4006 Blatt 1). The manuscript was received 3 August 2005 and accepted for publication after revision 15 February 2006.
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