Latent human error analysis and efficient improvement strategies by fuzzy TOPSIS in aviation maintenance tasks

Applied Ergonomics 54 (2016) 136e147

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Latent human error analysis and efficient improvement strategies by fuzzy TOPSIS in aviation maintenance tasks Ming-Chuan Chiu*, Min-Chih Hsieh Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 December 2014 Received in revised form 17 November 2015 Accepted 29 November 2015 Available online xxx

The purposes of this study were to develop a latent human error analysis process, to explore the factors of latent human error in aviation maintenance tasks, and to provide an efficient improvement strategy for addressing those errors. First, we used HFACS and RCA to define the error factors related to aviation maintenance tasks. Fuzzy TOPSIS with four criteria was applied to evaluate the error factors. Results show that 1) adverse physiological states, 2) physical/mental limitations, and 3) coordination, communication, and planning are the factors related to airline maintenance tasks that could be addressed easily and efficiently. This research establishes a new analytic process for investigating latent human error and provides a strategy for analyzing human error using fuzzy TOPSIS. Our analysis process complements shortages in existing methodologies by incorporating improvement efficiency, and it enhances the depth and broadness of human error analysis methodology. © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Keywords: Aviation Human error MCDM TOPSIS Fuzzy set theory

1. Introduction Human error is a major common factor in aviation accidents, it has been described as a wrong action made by a human in a wrong time and a wrong place (Wickens and Hollands, 2002). Fortunately, human error analysis has seen significant advancement with studies on the classification of operational errors (Swain and Guttmann, 1983), human information processing error types (Norman, 1988; Reason, 1990, 1997), analysis and classification of human errors (Rouse and Rouse, 1983; Wu and Hwang, 1989), human error prediction (Hammer, 1999), and even the publication of books on human errors (Dhillon, 2009; Modarre, 2009). Human errors that occur in the aviation system may be divided into two types: active human errors and latent human errors. Active human errors result in accidents directly and their influence is immediate, it also recorded in the general accident report. Latent human errors cause accidents indirectly; their adverse consequences may lie dormant within the system for a long time, only becoming evident when they combine with other factors to breach the system's defenses. In general, active errors are associated with the performance of

* Corresponding author. No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan E-mail addresses: [email protected], [email protected] (M.-C. Chiu). http://dx.doi.org/10.1016/j.apergo.2015.11.017 0003-6870/© 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

the ‘front-line’ operators of a complex system, identifiable in an accident report and patently obvious. Latent errors lie hidden below the obvious surface, constituting an unforeseeable threat to safety in a complex system. Typically, there are one or more root problems in the system well before an operator's active error takes place, often expressed in terms of a system within which prior weaknesses have been created, such as poor design. During emergency situations, human operators are under significant stress and are prone to make mistakes. While active errors are hard to prevent, findings show that latent errors can be reduced effectively to lower the probability of emergency disaster (Reason, 1990). Therefore, this work takes up the investigation of latent error. Previous researches indicate that 70e80% of all aviation accidents are caused by human error (McFadden and Towell, 1999; Chen et al., 2009), which involve 15e20% maintenance errors (Drury, 2000). Thus, latent human error is a critical matter in aviation safety (Latorella and Prabhu, 2000; Drury, 2000). Our investigation addresses latent human error directly related to maintenance, specifically focusing on an aviation accident adopted from the Aviation Safety Council (ASC) of Taiwan. The description of the accident follows. On May 25 2002, a Boeing 747-200 (CI611) crashed into the Taiwan Strait approximately 23 nautical miles northeast of Makung, in the Penghu Islands of Taiwan. Radar data indicated that the aircraft experienced an in-flight breakup at an altitude of 34,900 feet, before reaching its cruising altitude of 35,000 feet. The

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aircraft was on a scheduled passenger flight from Taiwan to Hong Kong. One hundred seventy-five of the 225 occupants aboard the CI611 flight, which included 206 passengers and 19 crew members, sustained fatal injuries; the remainder were missing and presumed killed (Aviation Safety Council, 2005). According to the accident report, the main reason for the occurrence of this 2002 accident was an event that had occurred in 1980: the aircraft had experienced a significant tail strike event in Hong Kong. (A tail strike occurs when an aircraft's tail touches the ground.) The 2002 accident report indicates that a permanent repair to the 1980 tail strike was not accomplished in accordance with Boeing's Structural Repair Manual (SRM), which subsequently resulted in the 2002 crash. The ASC investigated the failure to follow the SRM and identified a series of communication and cognitive problems between Boeing Commercial Airlines and China Airlines (CAL) regarding the 1980 tail strike repair. Of note was that the Boeing Field Service Representative (FSR) would have seen the tail strike scratches on the underside of the aircraft. However, the opportunity to provide expert advice on a critical repair appears to have been lost, as there are no records to show the FSR had a role in providing advice about a permanent repair (Aviation Safety Council, 2005). Moreover, records indicate the CAL operators noted the repair manner did not follow the SRM, and that the Boeing FSR acquiesced to the decisions from CAL. Thus, both communication and cognition errors formulated the latent human error in the maintenance tasks. Many human errors occurred in the events surrounding this accident, and the accident occurrence was not dependent on one single latent human error. This event is well illustrated by Reason's Swiss cheese theory (1990), which purports that an alignment of opportunity in a series of unmatched conditions can lead to disaster. Therefore, one can speculate that to reduce the most important latent error in such an accident might produce the most efficient way to decrease the influence of human error. To analyze and help prevent human error, Rankin et al. (2000) developed a maintenance error decision aid (MEDA) for use among system operators to regularize their checking tasks. However, latent human error is not always easy to detect during these tasks. Subsequently, Stanton et al. (2005) developed a human error identification method (HEI) to identify latent human and operation errors that may arise as a result of humanemachine interactions in complex and dynamic systems, and to identify the causal factors, consequences and recovery strategies associated with those errors. The concept of HEI methodology emphasizes the analysis and prediction of operation errors in humanemachine interactions, using an understanding of task characteristics and action details. The analysis results of HEI methodology usually describe potential (latent) errors, their consequences, recovery potential and probability, and then offers associated design remedies or error reduction strategies. Space research investigation has been focused on latent error analysis; hence the main focus of this study was to develop an effective procedure related to the analysis of the factors in latent human error. HEI methodology can be used along with other processes to analyze the important factors of human error accident such as the Human Factors Analysis and Classification System (HFACS) (Wiegmann and Shappell, 2001, 2003), root cause analysis (RCA), the Human Error Assessment and Reduction Technique (HEART) (Williams, 1988), the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr) (Shorrock and Kirwan, 2002), and the Systematic Human Error Reduction and Prediction Approach (SHERPA) (Embrey, 1986). These methods have been widely applied to analyze the factors that have caused accidents in different fields. For example, Daramola (2014) used HFACS to analyze air accidents in Nigeria; Cacciabue and Vella (2010) used

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RCA in a human error analysis within the healthcare system; Noroozi et al. (2014) utilized HEART to analyze engineering maintenance procedures for pumps. Wenner and Drury (2000) applied SHELL model (Software, Hardware, Environment, and liveware) to analyze 130 ground incidents and categorized the error factors into active errors and latent errors. However, an improvement strategy based on different criteria in terms of the error-reducing time and cost is absent from the traditional HEI methodology. Failure mode and effects analysis (FMEA) can be used to rank the importance of error factors based on their risk priority number (RPN) value, although some studies have suggested that the evaluation process of RPN value is not appropriate for decision making (Gilchrist, 1993; Ben-Daya and Raouf, 1993). RPN is determined as the product of severity, occurrence, and detection. However, there has been no theory or evidence to validate its use. Therefore, this present research applied a multi-criteria decision-making (MCDM) method to generate an improvement strategy for reducing human errors. The advantage of using MCDM is that the decision maker can analyze an accident based on different criterion via proven equations, making it more objective than the use of RPN with FMEA. Various methodologies can be used in MCDM. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a wellknown MCDM method. It has been commonly used in solving decision-making problems. TOPSIS was proposed by Hwang and Yoon (1981), and the basic idea is to consider how alternatives perform when multiple criteria are taken into account simultaneously (Bai et al., 2014). It is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution and the longest geometric distance from the negative ideal solution. The limitation of TOPSIS lies in its inability to solve the vagueness or ambiguity problem during decision-making process (Yu, 2002; Kannan et al., 2014). The limitation of TOPSIS lies in its inability to solve the vagueness or ambiguity problem during decision-making process (Yu, 2002; Kannan et al., 2014). To overcome this limitation of TOPSIS, it was merged with fuzzy set theory, which allows decision makers to incorporate unquantifiable information, incomplete information, non-obtainable information, and partially uncertain facts into the decision model (Kulak et al., 2005; Deviren et al., 2009; Kannan et al., 2014). Prior research has utilized fuzzy TOPSIS to solve decision-making problem in various fields such as shipping company selection (Ding, 2011), manager selection (Kelemenis et al., 2011), ranking airline carriers (Torlak et al., 2011), and weapon selection (Deviren et al., 2009). While fuzzy TOPSIS has gained popularity in decision-making analysis, scant research has used it to investigate improvement strategies for human error analysis. Previous relevant research has focused primarily on the influence of accident error factors, but ignored the improvement efficiency potential for reducing these human error factors. A disparity exists between the concepts of influence and improvement efficiency of human error factors. Therefore, to reduce this disparity, latent human error analysis and MCDM were integrated in this study. The goal of this study is to develop a latent human error analysis process which can be used to evaluate the human error factors in maintenance tasks using fuzzy TOPSIS to help generate an efficient improvement strategy for latent human error that can be used by decision makers to reduce accidents. It is expected that this research may enhance depth and broadness of methodology of human error analysis and improve system reliability. 2. Human error identification and task assessment methods 2.1. Human factors analysis and classification system Previous studies in aviation accident analysis have typically

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used HFACS (Wiegmann and Shappell, 2001a, 2001b, 2003, 2004; Li et al., 2008; Daramola, 2014). HFACS was developed from Reason (1990, 1997) organizationally based model of human error and provides an organizational framework for accident analysis (Daramola, 2014). HFACS has been used as the factors database of human error for aviation maintenance tasks. It divides human error accidents into four categories based on the structure and levels of organization, including organizational influences, unsafe supervision, preconditions for unsafe acts, and unsafe acts of operators. The category “Unsafe acts” represents the majority of accident research investigations. These address the operator behaviors that result directly in accidents during maintenance procedures and form the active error in accidents. Failures at this category are further classified into two sub-categories: errors and violations (instances of willful disregard for rules that subsequently result in an accident). “Preconditions for unsafe acts” involves the psychological precursors of the active failures within the category “Unsafe acts” and form the latent error in accidents. Failures at this category are further classified into three sub-categories: environmental factors, condition of the operators, and personnel factors. The category “Unsafe supervision” is also a latent error, which traces the causal chain of events producing the unsafe acts up to the level of the front-line supervisors. The category “Organizational influences” is a latent error category in accident analysis, involving the cause of faulty decisions at the management level that directly affect supervisory practices.

2.2. Root cause analysis (RCA) RCA is a process designed for use in investigating and categorizing the root causes of events in many different fields. It helps identify what, how and why accidents happened, thus preventing their recurrence (Rooney and Heuvel, 2004). For investigating human errors, RCA presents a useful method for establishing the model. The steps of RCA may be identified as follows: Step 1, data collection: without complete information and an understanding of the event, the causal factors and root causes associated with the event cannot be identified. The majority of time required to analyze the event is spent in data collection. Step 2, causal factor charting: this element provides a design structure used to organize and analyze the information; the causal factor chart is simply a sequence diagram with logic tests that describes the events leading up to an occurrence. Step 3, root cause identification: this step involves the use of a decision diagram (the root cause map) to identify the underlying reason or reasons for each causal factor. Step 4, recommendation generation and implementation: following the identification of the root causes for a particular causal factor, achievable recommendations for preventing its recurrence are generated. Here we use the Boeing air crash of 2002 (described in Section 1) as an example. First, the investigation group for assessing human error was constituted with three experts prior ro the formal analysis. Second, the failures modes of the accident were classified based on the error factors of HFACS, and the causal factors of this accident were divided into Errors and Violations. Third, a reality chart was used to show the relationship of each error factor in this accident. From the reality chart, the root cause of this accident could then be identified (Fig. 1). After the analysis of 24 incidents that relate to maintenance problems, the error factors were collected and further analysis was executed. Suggestions for improvement strategies were generated, based on the systematic analysis of this research.

3. Fuzzy TOPSIS This study adopted fuzzy TOPSIS for conducting the MCDM analysis of human error improvement strategy selection for airline maintenance tasks. Fuzzy set theory and TOPSIS are described in this section.

3.1. Fuzzy set theory Due to the vagueness, imprecision, and subjective nature of human thinking, judgment, and preferences, prior research has indicated that crisp numbered data are insufficient to represent the real world system (Kannan et al., 2014). Thus fuzzy set theory (fuzzy multi-criteria decision making [FMCDM]) was developed and has been used to model the uncertainty of human judgments (Bellman and Zadeh, 1970). The fuzzy set theory applies linguistic value to represent the selection of the decision makers, and then converts the selection to fuzzy numbers to solve the FMCDM problem. In addition, previous studies have indicated that triangular fuzzy numbers (TFN) are an effective way of formulating decision issues related to subjective and imprecise information (Chang and Yeh, 2002; Chang et al., 2007; Torlak et al., 2011; Kannan et al., 2014). Hence, the fuzzy set theory was applied to model the improvement strategy selection for reducing errors during airline maintenance tasks and TFN were used to assess the selection of the decision makers. A TFN can be represented as a triplet (l, m, u). The membership functions of a fuzzy number meðxÞ are illustrated and defined as A shown (Rouyendegh and Saputro, 2014):  l to m is increasing function  m to u is decreasing function  lmu

8 0 > > > > > < xl meðxÞ ¼ ml A > > > > > : ux um

for x < l; x > u for l  x  m

(A)

for m  x  u

The linguistic variable is one in which the values are words or sentences of a natural or artificial language that is expressed in linguistic terms represented by a TFN. Using TFN, Chen and Hwang (1992) developed a set of eight different scales with up to eleven fuzzy linguistic variables. Their Scale Three with five (5) linguistic variables was used to assess the level of each TOPSIS criterion in this research (Table 1).

3.2. TOPSIS TOPSIS was developed by Hwang and Yoon (1981). This technique is one of the classical methodologies for solving MCDM problems. The major objective of TOPSIS is to identify the positive ideal solution (PIS) and the negative ideal solution (NIS), and then to measure the distance of each alternative from the ideal solution to generate a ranking of the alternatives (Kannan et al., 2014). The PIS is that which maximizes the benefit criteria and minimizes the cost criteria, while the NIS maximizes the cost criteria and minimizes the benefit criteria. The selected alternative should be the one closest to the PIS and farthest away from the NIS. The analytic steps of fuzzy TOPSIS are summarized in Appendix A.

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Fig. 1. The reality chart of the accident.

Table 1 The relationship of linguistic variables and fuzzy numbers (Chen and Hwang, 1992). Linguistic Values

Fuzzy numbers

Exact Value

Very Low (VL) Low (L) Medium (M) High (H) Very High (VH)

(0, 0.1, (0.1, (0.3, (0.6, (0.8,

0.091 0.283 0.500 0.717 0.909

0.2) 0.25, 0.4) 0.5, 0.7) 0.75, 0.9) 0.9, 1)

4. Methodology This research focuses on constructing a latent human error analysis process developed from historical data for an aircraft maintenance program (AMP). According to each airplane model's checklist, the AMP covers five different checking operations including Daily check, Extended-range Twin-engine Operational Performance Standards (ETOPS) pre-departure service check, PreFlight check, Transit check and Weekly check. The operation “Daily check” was selected as the example for use in this research. Both HFACS and RCA were applied to distinguish the latent human error in the “Daily check” tasks and to construct the analysis process. 4.1. Human error factors in maintenance tasks In the beginning, the initial error factors related to maintenance tasks were selected from HFACS and divided into active (A) and latent (L) error factors. Second, based on the initial factors this research, interviewed three experts who had worked in the airline business with a relevant history of more than 15 years per person and a total among them of 50 years; these experts helped to identify and analyze 24 maintenance accidents that had occurred in Taiwan using the RCA method, and helped combine the error factors with the same meaning in maintenance tasks into a new one. For instance, this research found that the active factors “Fail to apply support or assistance” and “Fail to take appropriate workaround” have a relatively similar meaning with regard to maintenance tasks; specifically, operators who have less experience with the relevant task or who lack expertise are more likely to influence

the occurrence of an accident. Hence, these two factors were combined into one category titled “A1: Poor adaptability”. This factor approximated the rule-based mistakes, and it occurs in the planning stage of the work process. The new factor “A2: Task execution error” was summarized from six different HFACS factors representing the actions of the operators which are Fail to follow the work procedures, Fail to follow the operating procedures of device, Fail to find out the defect, Excessive dependence on visual, Not authorized to execute the work, and Fail to return important information. Prior to the maintenance work the operators had already interpreted and planned the work process well, but errors occurred in the execution stage; this type of error did not involve the judgment ability of the operators, which means that the right intention was incorrectly carried out. The “A2: Task execution error” approximated the slips error, and it occurs in the action execution period of the work process. The new factor “A3: Information cognitive errors” was summarized from four HFACS factors and represents the comprehension ability of the operators in the tasks, which are Information misunderstand, Turn off/Ignore the alarm system, Fail to remove known risks, and Underestimate the severity of hazards. These approximated the knowledge-based mistakes, errors occurring in the unknown or a new situation, in which the operators failed to understand the meaning of the information. Next, the error factors related to maintenance capability that do not lead directly to an accident were classified into new latent error factor category “L1: Maintenance capability”, which includes four HFACS factors: Poor maintenance capability, Lack of experience, Poor professionalism, and Unfamiliar technology and tasks. Error factors related to light, temperature and device usability were classified into new latent error factors “L2: Environment”, which composes of three HFACS factors: Poor regulation of temperature/ light, Interface complex, and Lack of mark. Error factors related to the irrelevancy of a written workbook or instruction manual were classified into new latent error factors category “L3: Workbook/ Instruction/Provision outdated”, which is Workbook, instruction, and provision outdated. The error factors related to the interaction of operators were classified into new latent error factors category “L4: Coordination, Communication, and Planning”, which include

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two HFACS factors: Fail to comprehensive handover and Fail to double-check each other. The error factors related to various indisposed physiological states (such as sickness) were classified into new latent error factors category “L5: Adverse physiological states”, which is Adverse physiological states as HFACS factor. Finally, the error factors related to the psychological pressure (such as time and work) were classified into new latent error factors category “L6: Physical/Mental limitations”, which composes of four HFACS factors: Inattention, Working time pressure, Afraid to ask question, and Wrong estimation of operating space. Thus there are six new latent error categories developed in this research. 4.2. Questionnaire design The purpose of the questionnaire was to investigate the importance of active errors and latent errors and to identify a potential improvement strategy for aviation maintenance performance, focused specifically on the most common mistakes in operational procedures. There is a growing awareness within the human reliability community to discover and neutralize latent errors, and this investigative attempt was anticipated to have a greater beneficial effect upon system safety than simply localizing efforts to minimize active errors (Reason, 1990). Thus, the survey was completed by following the eight execution procedures of the “Daily check” from the AMP, incorporating active error into the real situations of the operations. The theoretical background of the real situation was addressed using RCA to identify what, how and why the error happened. A total of 115 operators from Taiwan Airline's maintenance staff (seniority 1e31 years, avg ¼ 15.05) participated in this study. After being informed of the aims of the research, all operators were trained for 5 min, which served to familiarize them with the logic of RCA and with an understanding of how to see the descriptive active error set into real-life situations in operations. The questionnaire was divided into two phases. In phase 1, each operation had three questions that described a real situation involving an active error. The three questions in each operation were directly related to three active error types. Thus, given the eight execution procedures, there were 24 questions in phase 1. In phase 2, the operators were asked to evaluate the six latent errors for maintenance tasks under each active error type. During the entire questionnaire survey process, subjects were allowed to ask questions and were given feedback to ensure accurate scoring. A portion of questionnaire is provided in Appendix B. 5. Results

multiplying the score with the weight from the historical maintenance data of Taiwan Airlines. According to the test of normality, the weighted scores were approximately normally distributed (A1: P ¼ 0.2, A2: P ¼ 0.2, A3: P ¼ 0.2). Thus, we conducted a statistical test using paired T-test with an alpha level of 0.05. As a result, no significant interaction effects were found between A1 and A2, which means the importance of A1 and A2 were the same. There were significant difference between A1 and A3 (T ¼ 3.174, P ¼ 0.002), and A2 and A3 (T ¼ 3.706, P < 0.001), which represented that A1 and A2 are more important than A3. 5.1.2. Latent error factors The importance of latent error factors under each active error is analyzed, and Willcoxon Signed Rank Test is applied to analyze the importance of latent errors in this section. The results indicated that the importance of L1 was significantly different from L2(Z ¼ 5.527, P < 0.001), L3(Z ¼ 4.424, P < 0.001), L4(Z ¼ 2.883, P < 0.004), and L5(Z ¼ 4.998, P < 0.001), the importance of L2 was significantly different from L4(Z ¼ 2.919, P ¼ 0.004) and L6(Z ¼ 3.207, P ¼ 0.001), the importance of L3 was also significantly different from L4(Z ¼ 2.651, P ¼ 0.008) and L6(Z ¼ 2.828, P ¼ 0.005), the importance of L4 was significantly different from L5(Z ¼ 3.624, P < 0.001), and the importance of L5 was significantly different from L6(Z ¼ 4.824, P < 0.001). With regard to the latent error factors under active error “A2: Task execution error”, Willcoxon Signed Rank Test results showed that the importance of L1 was significantly different from other five latent error factors(L2: Z ¼ 5.874, P < 0.001; L3: Z ¼ 2.605, P ¼ 0.009; L4: Z ¼ 3.325, P ¼ 0.001; L5: Z ¼ 4.814; P < 0.001; L6: Z ¼ 2.522, P ¼ 0.012), the importance of L2 was significantly different from L3(Z ¼ 4.011, P < 0.001), L4(Z ¼ 4.297, P < 0.001), and L6(Z ¼ 5.247, P < 0.001), the importance of L3 was significantly different from L5(Z ¼ 2.557, P ¼ 0.011), the importance of L4 was significantly different from L5(Z ¼ 2.716, P ¼ 0.007), and the importance of L5 was significantly different from L6(Z ¼ 4.442, P < 0.001). As for active error “A3: Information cognitive errors”, Willcoxon Signed Rank Test results indicated that the importance of L1 was significantly different from L2(Z ¼ 6.063, P < 0.001), L3(Z ¼ 4.182, P < 0.001), and L5(Z ¼ 5.566, P < 0.001), the importance of L2 was significantly different from L4(Z ¼ 4.78, P < 0.001) and L6(Z ¼ 3.506, P < 0.001), the importance of L3 was significantly different from L4(Z ¼ 2.471, P ¼ 0.013) and L5(Z ¼ 2.728, P ¼ 0.006), the importance of L4 was significantly different from L5(Z ¼ 5.068, P < 0.001), and the importance of L5 was significantly different from L6(Z ¼ 5.047, P < 0.001).

5.1. Questionnaire The validity and reliability of this questionnaire was tested before formal experiment. The result shows that the value of Cronbach's alpha related to active error questionnaire is 0.7900, and the value of Cronbach's alpha related to latent error questionnaire is 0.9441. Both of these two values have exceeded 0.7. Therefore, the questionnaire has internal consistency and reliability, and the analysis results can be trusted. The questionnaire was delivered to 115 operators and 109 responses were returned. After removing invalid samples for missing a tick or choosing only extreme values, the total number of valid samples was 100 in phase 1 and 87 in phase 2.

5.1.3. Ranking for the importance of active error factors and latent error factors After comparing the mean scores of active error factors and latent error factors, the importance ranking of active errors and latent errors (Fig. 2) is given as follows:  Active error factors ranking: A2 / A1 / A3.  Latent error factors ranking under “A2: Task execution error”: L1 / L6 / L4 / L3 / L2 / L5.

5.1.1. Active error factors In the questionnaire, each maintenance procedure contains three error-related events, and each event corresponds to an active error factor. The weighted score as analysis data was obtained by

 Latent error factors ranking under “A1: Poor adaptability”: L1 / L6 / L3 / L4 / L5 / L2.

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Table 2 The fuzzy decision matrix for A1: poor adaptability. Poor adaptability

Influence Time Cost Benefit

Maintenance Capability (L1) H Environment (L2) VH Workbook/Instruction/Provision Outdated (L3) H Coordination/Communication/Planning L Factors (L4) Adverse Physiological States (L5) L Physical/Mental Limitations (L6) H

Fig. 2. Mean score of latent error factors under three active error factors.

 Latent error factors ranking under “A3: Information cognitive errors”: L1 / L4 / L6 / L3 / L2 / L5. 5.2. Application of fuzzy TOPSIS In this section, a five-step process was utilized to select the improvement strategy for human errors based on the six identified latent human errors related to airline maintenance tasks. The process was as follows: Step 1: The criteria used to determine the improvement strategy for human error selection were identified with the assistance of the experts and relevant literature. There were four criteria in this study (influence, time, cost, and benefit), and the relevant definition of each criterion is shown as bellows. The experts determined the weight preference for each criterion and rated the alternatives based on the criteria.    

Influence: The severity of the accident in the maintenance tasks. Time: The time of reducing the human error. Cost: The cost of reducing human error. Benefit: Expected benefits in the maintenance tasks

Step 2: The same questionnaire was used to evaluate the human error factors based on the four criteria. Step 3: Three experts (the decision makers, identified in Section 4.1) were invited to evaluate the questionnaire. In order to determine the weight of the criteria used in this study, the experts were asked to compare the importance of the criteria using a two-bytwo, and then the weights were calculated using formula (2) and formula (3) (see Appendix A). The inter-reliability was tested by Pearson correlation coefficient, and the results showed that the uniformity was significant among the weights provided by three experts. After determining the weight of each criterion, the experts evaluated the alternatives (latent human errors) using the linguistic terms given in Table 1. As noted previously, a scale adapted from Chen and Hwang (1992) with five linguistic variables was used in this research. The calculated weight of influence is 0.5, Time is 0.1667, Cost is 0.0833, and Benefit is 0.25. All active human errors are analyzed, and the active human error of “A1: poor adaptability” was used as an example to illustrate the fuzzy decision matrix (Table 2). Step 4: The fuzzy TOPSIS methodology was used to rank the human error improvement strategy. The fuzzy decision matrix was normalized using formula (4) (Appendix), and the weight of each criterion was multiplied with the normalized matrix to form a

VL H L VL

VL M M L

H H H H

VL L

L M

VH H

weighted normalized fuzzy decision matrix. After generating the matrix, the positive idea solutions (PIS) and negative idea solutions (NIS) were determined by following formula (6) and formula (7) (Appendix), respectively. In the next step, the distance of the criteria from the PIS and NIS was calculated using the formula (8) and (9) (Appendix). In order to rank the human error based on their closeness to the PIS and remoteness to the NIS, the closeness coefficient (CC) was calculated using formula (10) (Appendix). All active human errors were analyzed, and the active human error of “A1: poor adaptability” was utilized as an example to illustrate normalized decision matrix. The weighted normalized decision matrix, the distance of each criterion to PIS and NIS, closeness coefficient, and ranks obtained by each criterion calculated in this study (Tables 3e5), respectively. The results obtained from the traditional questionnaire analysis and fuzzy TOPSIS approach for the improvement strategy selection of human error for the airline are summarized and shown in Table 5. Based on the results of the questionnaire and the closeness coefficients, the ranks (improvement strategies) obtained for the six latent human error factors under three active human error factors are summarized as follows:  By fuzzy TOPSIS method and under “A1: Poor adaptability” situation: L5 / L4 / L1 / L3 ¼ L6 / L2.  By fuzzy TOPSIS method and under “A2: Task execution error” situation: L6 / L4 / L5 / L1 ¼ L2 / L3.  By fuzzy TOPSIS method and under “A3: Information cognitive error” situation: L4 / L5 / L6 / L2 / L1 / L3.

6. Sensitivity analysis To analyze the improvement strategy under different criteria

Table 3 The normalized decision matrix for A1: Poor adaptability. Poor adaptability

Influence

Time

Cost

Benefit

Maintenance Capability (L1) Environment (L2) Workbook/Instruction/Provision Outdated (L3) Coordination/Communication/ Planning Factors (L4) Adverse Physiological States (L5) Physical/Mental Limitations (L6)

0.4509 0.5716 0.4509

0.1088 0.8575 0.3385

0.0950 0.5217 0.5217

0.3890 0.3890 0.3890

0.1780

0.1088

0.2953

0.3890

0.1780 0.4509

0.1088 0.3385

0.2953 0.5217

0.4932 0.3890

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Table 4 The weighted normalized decision matrix for A1: Poor adaptability. Poor adaptability

Influence

Time

Cost

Benefit

Maintenance Capability (L1) Environment (L2) Workbook/Instruction/Provision Outdated (L3) Coordination/Communication/Planning Factors (L4) Adverse Physiological States (L5) Physical/Mental Limitations (L6) PIS NIS

0.2254 0.2858 0.2254

0.0181 0.1429 0.0564

0.0079 0.0435 0.0435

0.0973 0.0973 0.0973

0.0890

0.0181

0.0246

0.0973

0.0890 0.2254 0.0890 0.2858

0.0181 0.0564 0.0181 0.1429

0.0246 0.0435 0.0079 0.0435

0.1233 0.0973 0.1233 0.0973

Table 5 The distance, closeness coefficient, and ranks for A1: Poor adaptability. þ

-

7. Discussion

Poor adaptability

D

CCi

Rank

Maintenance capability (L1) Environment (L2) Workbook/Instruction/Provision Outdated (L3) Coordination/Communication/ Planning Factors (L4) Adverse Physiological States (L5) Physical/Mental Limitations (L6)

0.1389 0.2372 0.1484

0.1431 0.0000 0.1055

0.5074 0.0000 0.4155

3 6 4

0.0309

0.2338

0.8832

2

0.0167 0.1484

0.2353 0.1055

0.9337 0.4155

1 4

D

task might be a routine task, and the routine task might not occupy most of the attention resource of employees. Thus, the latent error factor “L6: Physical/Mental limitations” is not an effective solution to reduce human error related to task execution while the task is easy to be executed, the latent error “L5: Adverse physiological states” becomes an effective solution to reduce the related human error. Accordingly, the latent human error factor “L4: Coordination, Communication, and Planning” remains first order to be solved for the human error improvement strategies under “A3: Information cognitive errors” situation since it has robust trend of the closeness coefficient value. Furthermore, the results could be inferred that have a culture of well coordination, communication, and planning are the foundations of a successful organization. In addition, the latent error “L3: Workbook/Instruction/Provision Outdated” has poor efficiency to solve the information cognitive problem.

Dþ: The distance of the criteria from the PIS. D: The distance of the criteria from the NIS.

weights, a sensitivity analysis was conducted (Ount and Soner, 2008). This study presented “A1: poor adaptability” as an example of sensitivity analysis in Table 6 and Fig. 3. The intent of sensitivity analysis is to exchange each weight of criterion with another weight of criterion. Thus, six combinations of the four criteria are analyzed. For each combination, the closeness coefficient of the latent error factors is calculated. Table 6 summarize the numerical results of the calculations, and Fig. 3 present the graphical representation of these results based on different active error factors. According to Table 6 and Fig. 3, L1 has the highest closeness coefficient value of 0.8774 from 0.5074 when the first and the third criteria weights are exchanged in combination 2. L5 will have the highest closeness coefficient value in combination 1, 3, 4, 5 and 6. Based on the results of sensitivity analysis of fuzzy TOPSIS, this study can conclude strongly that the most efficient way of latent human error “L5: Adverse physiological states” remains first order to be solved for the human error improvement strategies under “A1: Poor adaptability” situation since it has robust trend of the closeness coefficient value in Fig. 3. The critical criterion under “A1: Poor adaptability” is “Cost” since the order of latent human error “L5: Adverse physiological states” declines from first to second based on the weight of “Cost” increasing in the combination 2 of Fig. 3. From the perspective of reducing human error, the company might not spend enough money to solve the situation that caused by employees with adverse physiological states, which usually occur in Taiwan. In factor, the employer wouldn't like to build an accessible environment for the employees who injure their body during the work. In addition, the latent error factor “L2: Environment” is not an option to reduce the active human error “A1: Poor adaptability” in accordance with the result of sensitivity analysis. In the same manner, the sensitivity results of “A2: Task execution error” indicate that an employee with good mental model is necessary to solve or prevent the human error related to task execution. Moreover, the weight of the “Influence” represents the

7.1. The importance ranking of error factors Based on the importance ranking of error factors, the most important active error factor is “A2: Task execution error”. A2 subfactors address failure to follow the work procedures and operating procedures of devices, failure to identify defects, excessive dependence on visuals, unauthorized work, and failure to transmit information to decision makers. The most important latent error factor under each active error is “L1: Maintenance capability”. L1 sub-factors address poor maintenance capabilities, a lack of experience, poor professionalism, unfamiliarity with technology and tasks. Therefore, in order to reduce the errors related to maintenance capability, training and briefing before the task is necessary. The two lowest-ranked latent error factors in each active error were “L2: Environment” and “L5: Adverse physiological states”. “Environment” did not appear to be an important factor in the questionnaire results, which is not consistent with general knowledge. The sub-factors of environment such as temperature, light, and interface are important factors in ergonomics (Hsieh et al., 2014). One reason suggested by the experts might explain this anomaly. The maintenance problems related to control interface, temperature, and light do not directly threaten aviation safety; moreover, to reduce the problems may cost money. Thus, the airline doesn't place any emphasis on these environmental challenges. Latent error “L5: Adverse physiological states” also ranked as not an important issue on each active error factor. According to the experts, the airline resolves this problem through job rotation, which reduces the adverse physiological states error. Notably, the three first-ranked latent errors under each active error are related to operator management. First, to reduce the active error “A1: Poor Adaptability”, the most efficient way is to decrease the influence of latent error “L5: Adverse Physiological States”. Operators with negative physiological states that might influence their professional competence and judgment could fail to provide support and assistance. The most efficient way to reduce the problem may involve job rotation activity. Second, to reduce the influence of active error “A2: Task execution error”, the most efficient way is to decrease the influence of the latent error “L6: Physical/Mental limitations”. Operators who cannot concentrate their attention on a task are likely to make errors; the most efficient way to reduce the problem may also be through job rotation activity. This effort does not require a lot of time or money, but it may have a major benefit. As Wickens and Hollands (2002) observed, the problem of coordination or communication is the main factor that leads to errors during information cognitive process. Thus, with regard to the third top-ranked category, there are no doubts that reducing the influence of latent error “L4: Coordination,

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143

Table 6 Sensitivity analysis under “A1: Poor adaptability” situation. Weight of criterion

Original 1 2 3 4 5 6

CCi of latent error factor

W1

W2

W3

W4

L1

L2

L3

L4

L5

L6

0.5000 0.1667 0.0833 0.2500 0.5000 0.5000 0.5000

0.1667 0.5000 0.1667 0.1667 0.0833 0.2500 0.1667

0.0883 0.0833 0.5000 0.0833 0.1667 0.0833 0.2500

0.2500 0.2500 0.2500 0.5000 0.2500 0.1667 0.0833

0.5074 0.8778 0.8774 0.6081 0.4469 0.5923 0.5613

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.4155 0.6650 0.2840 0.4769 0.3208 0.4829 0.3726

0.8832 0.9248 0.6238 0.7453 0.8321 0.9187 0.8251

0.9337 0.9581 0.6341 0.9098 0.8637 0.9423 0.8273

0.4155 0.6650 0.2840 0.4769 0.3208 0.4829 0.3726

Fig. 3. Sensitivity analysis under “A1: Poor adaptability” situation.

Communication, and Planning” is the most efficient way to reduce the active error “A3: Information Cognitive Error”. A strong management approach is critical for an airline company, under which good leadership skills could help prevent or avoid the occurrence of human error and result in better improvement efficiency with respect to airline maintenance tasks. Curiously, the inefficient latent error factors in each active error are “L2: Environment” and “L3: Workbook/Instruction/Provision Outdated”. To build a facility, redesign a human machine interface, or install alternate illumination devices in the facility would cost money and spent a time. Workbooks and instruction manual must follow the rules of the Federal Aviation Administration or the International Civil Aviation Organization which means that if content is to be modified, responses and clearances are required. Thus, addressing the latent error factors “L2: Environment” and “L3: Workbook/Instruction/Provision Outdated” are not efficient ways to reduce human errors in maintenance tasks.

The traditional method was utilized to analyze the importance of error factors; fuzzy TOPSIS was utilized to provide an efficiency improvement strategy based on four different criteria. With regard to the analysis process of human error in accidents, Daramola (2014) applied HFACS to analyze 45 air accidents in Nigeria and tested the associations between HFACS categories. Daramola analyzed the accidents based on the accident reports and the relevant information of the flight, which was an advantage for that study; however, latent human errors were not investigated in that research. Kim et al. (2014) established a method to analyze the human hazards related to nuclear power plants. The method was based on the HFACS framework and tested the correlations between human error hazards. The analysis could provide the most related causes for an accident, but could not identify the latent errors hidden in the accidents. The present study also applied HFACS as a human error factors database in maintenance tasks, and used RCA to identify the latent human errors that occur in accidents. The analysis procedure of RCA traces the results of an accident to the cause of the accident, investigating deeply, clearly, and completely. Therefore, the analysis procedure used in the present study was expected to provide, clearly and completely, the latent human error of maintenance tasks. From the perspective of reducing human error, both Noroozi et al. (2014) and Daramola (2014) provided the most probable human error factors in their studies. However, they did not identify the most efficient way to reduce relevant human error. The present study applied fuzzy TOPSIS after human error analysis to assess the latent error based on different criteria, and provided an improvement strategy for reducing latent human error in maintenance tasks. The analysis process of this study can be used as a reference for analyzing latent human error and for identifying the most efficient way to reduce latent human error.

7.2. Comparison of the improvement strategies 7.3. Limitations and future works To analyze an improvement strategy is a decision-making problem, and more decision criteria can potentially make the results more objective. In this study, fuzzy TOPSIS allowed the use of four different criteria to assess improvement efficiency of each latent error factor, while the traditional questionnaire uses only one criterion. The difference between these two methods is reflected in the analysis results. According to the results of the traditional method, the most important latent error factor under each active error is “Maintenance capability”. As an operator, it is necessary to have a good maintenance knowledge and capability, which might not influence the flight of the airplane. However, the maintenance capability problem is not seen as an efficient way to reduce error, based on fuzzy TOPSIS analysis. From the perspective of decision makers, maintenance knowledge is a basic capability of the operators. The decision maker only can increase the relevant knowledge rather than complement the basic capability of operators. Thus, the different results may be seen to depend on different perspectives.

Three limitations exist in this study. First, due to that the Aviation Safety Council (ASC) of Taiwan did not discuss and describe organizational causes clearly in the 24 aviation accident reports, and only two latent errors related to organizational issue can be identified, it could not be considered as a latent error term to represent the situation of maintenance tasks. As a result, the six latent errors in this study didn't include organizational causes. Second, because this is a pilot study that integrates the ideas of human error analysis and MCDM, the human error factors must be limited in pure situation to reduce other influence factors which could affect the results of this study. It means the human error factors in this study do not involve management causes, resource causes, and etc. It involves the front end of human error factors in HFACS. Hence, this study does not proceed with further investigation to explore the deeper relationship of latent human errors. Third, the efficiency improvement strategies found in this study

144

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depend on the maintenance tasks, so the results might not be applicable to other situations. Based on the above, a human error network of aviation maintenance tasks will be constructed in the next step. The network contains the four-level error factors of HFACS, and the occurrence rate of each error factors will be calculated. The most important part in the human errors network is the correlations between each error factor, which could achieve the goal of predicting occurrence of human errors through Fuzzy TOPSIS.

A1 A D¼ 2 « AM

2 C1 C2 … Cn 3 r11 r12 … r1n 6 r12 r22 / r2n 7 6 7 4 « « 1 « 5 rm1 rm2 / rmn

(1)

where rmn be the rating of alternative Am with respect to criterion Cn. Step 2. Aggregate the evaluation of decision makers

8. Conclusions The purpose of this study was to develop an analytic procedure for identifying latent human error and to provide a human error improvement strategy. HFACS, RCA, and fuzzy TOPSIS were used in this study, guided by the suggestions of expert decision makers. Findings suggest that to reduce errors related to poor adaptability, the most efficient way is to solve the issue of adverse physiological states. Findings also suggest that to reduce errors related to task execution, the most efficient way is to solve the physical/mental limitations of operators. Finally, findings suggest that to reduce errors related to information cognitive skills, the most efficient way is to solve the coordination, communication, and planning problems. This research complements the shortages of the traditional human error identification methodology. Hence, this study suggests that fuzzy TOPSIS should be used to analyze additional results based on different criteria, which could ameliorate the human error accident rate and increase the safety of the operators. Furthermore, this method could be applied to improve safety in other areas such as healthcare, nuclear power plant, military, and other industries in the future. Finally, this is a pilot study for developing an improvement strategy regarding human error analysis, and the reliability of this method must be improved and validated in the future. Additional techniques for assessing the MCDM challenge will be incorporated into the researchdamong them, AHP will be applied in an effort to generate the most efficient analysis procedure for resolving the decision problem in the future.

In this step, the decision makers’ aggregate evaluations for determining the criteria weights is performed. Let Wj be the weight that evaluated by the decision maker Dk to criterion Cj. There were three steps to determine the weight of each criterion in this research. First, the decision makers have to compare the importance of each criterion for reducing the human error two by tow. Then, this research calculate the importance of each criterion as the weight using following formula:

Wkj ¼

The times of the Cj which have been selected by Dk Number of the criteria

(2)

where j ¼ 1, 2, …, n; and k ¼ 1, 2, …, k. Finally, the weight of each criterion was determined using following formula:

Pk Wj ¼

k¼1

Pn

j¼1 Wkj

Numbers of the decision makers

(3)

where j ¼ 1, 2, …, n; and k ¼ 1, 2, …, k. Step 3. Construct the normalized decision matrix Assume that the decision matrix be X ¼ ½xij mn . The decision matrix for m alternatives and n criteria can be normalized as:

  S ¼ sij mn where

Acknowledgement The authors are most appreciative of Dr. and Professor Emeritus Sheue-Ling Hwang for her valuable comments on the preliminary organization of this research. We express our gratitude to Mr. HuiHuang Shih, Jiong-Yu Tu, and Jen-Loong Yong for their participation in this study. The authors would like to thank the Ministry of Science and Technology, Taiwan, for partially financially supporting this research under contract number MOST 103-2221-E-007-051MY3. This paper was also supported by the Advanced Manufacturing and Service Management Research Center (AMSMRC), National Tsing Hua University, Taiwan.

Appendix A. The analysis step of Fuzzy TOPSIS Step 1: Constructing the decision matrix A group with k decision makers (D1, D2, …,Dk), m alternatives (A1, A2, …, Am) and n criteria (C1, C2, …, Cn) for a MCDM problem which is clearly expressed in a matrix format as:

rij sij ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pm 2 i¼1 r ij

(4)

Step 4. Construct the weighted normalized decision matrix In order to consider the different importance of each criterion, this study constructed the weighted normalized fuzzy decision matrix. Let the weighted normalized decision matrix be V ¼ ðvij Þmn .

vij ¼ sij  W where i ¼ 1, 2, …, m and j ¼ 1, 2, …, n. Step 5. Determine the PIS and NIS The PIS and the NIS of this study can be defined as:

(5)

M.-C. Chiu, M.-C. Hsieh / Applied Ergonomics 54 (2016) 136e147

    maxvij jj2J ; minvij jj2J 0 ji ¼ 1; 2…; m   ¼ v*1 ; v*2 ; …; v*n

Aþ ¼

Step 8. Rank the order of alternatives according to the closeness coefficient



(6)



    minvij jj2J ; maxvij jj2J 0 ji ¼ 1; 2…; m n o   ¼ v 1 ; v2 ; …; vn

A ¼

145

According to the closeness coefficient of each alternative, this study determines the ranking order of all alternatives from the highest closeness coefficient to the lowest. The ranking order also represents the efficient strategy for the MCDM problem.

(7) Appendix B. The sample of questionnaire

Step 6. Calculate the distance of each alternative from Aþ and A respectively

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX

2 u n vij  v*j ; i ¼ 1; 2…m; j ¼ 1; 2; …; n d ¼t þ

(8)

j¼1

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX

2 u n ; i ¼ 1; 2…m; j ¼ 1; 2; …; n vij  v d ¼t j 

(9)

j¼1

Step 7. Calculate the closeness coefficient After step 6, this study calculate the closeness coefficient of each alternative using the formula bellow.

CCi ¼

dþ

d þ d

Table A The sample of the questionnaire.

(10)

The following content refers to the description of the questionnaire that used to inform the operators of the purpose of this questionnaire. Table A is the sample of the questionnaire and the sample of questionnaire for Fuzzy TOPSIS. “This research focuses on the human error analysis. The active and latent human error is also discusses in this study. Simply, the active human error could be observed and caused the accident directly. The latent human error hide in the system, it couldn't produce the accident directly. Generally, the active human error is hard to be reduced and avoided, because human usually make the mistake under the highly pressure and complex situation. However, the latent human error could be reduced easily and efficiently by tanning the operators or improving the organization management, which also can decrease the probability of accidents. Thus, latent human error is an important issue in human error analysis. The purpose of this questionnaire tries to understand the situation that the operators meet during maintenance tasks, and discusses the human error that might occur in the human machine interaction. This research also expects that the results of this questionnaire could be a reference on improving the aviation maintenance planning and work process.”

146

Table A (Continued)

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147

Table A (Continued)

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