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This article discusses the importance of identifying and preventing human error in industrial environments, specifically in the sugar production process. The article emphasizes the importance of choosing the right technique for risk assessment studies resulting from human errors. A cross-sectional study was conducted using a multi-stage approach - Hierarchical Task Analysis (HTA), Human Error Calculator (HEC), and Predictive Human Error Analysis (PHEA) - to identify potential human errors in the sugar production process. The HTA, HEC, and PHEA techniques were employed to evaluate each stage of the process for potential human errors. The results of the HTA technique identified 35 tasks and 83 sub-tasks in 14 units of the sugar production process. According to HEC technique 4 tasks with 80 % probability of human error and 2 tasks with 50 % probability of human error had the highest calculated error probabilities. The factors of individual skill, task repetition and importance were the most important factors of human error in the present study. The analysis of PHEA worksheets showed that the number of human errors identified in the tasks with highest probability were 8 errors, of which 50 % were action errors, 25 % checking errors, 13 % selection errors, and 12 % retrieval errors. To mitigate the consequences of human error, it was recommended training courses, raising operator awareness of error consequences, and installing instructions in the sugar production process. Based on the findings, the article concludes that the HEC and PHEA techniques are applicable and effective in identifying and analyzing human errors in process and food industries.

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In the manufacturing environments of today, human-machine systems are constituted with complex and advanced technology, which demands workers' considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back tasks for further analysis of human performance. This study's contribution lies in developing proper cognitive analyses of the graphical user interface, identifying human error when the Dual N-Back tasks are presented in an interface, and seeking better user-system interaction. Hierarchical task analysis and the Task Analysis Method for Error Identification were used for the cognitive analysis. Ten subjects participated voluntarily in the study, answering the NASA-TLX questionnaire at the end of the task. The NASA-TLX results determined the subjective participants' mental workload proving that the subjects were induced to different levels of mental workload (Low, Medium, and High) based on the ANOVA statistical results using the mean scores obtained and cognitive analysis identified redesign opportunities for graphical user interface improvement.

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@#Rail maintenance routines are necessary to enable the all rail operations to achieve its aim in maintaining a safe and efficient operation. The maintenance tasks expose the workers to vibration and noise, as they handle specialized machineries and heavy self-propelled vehicles. Exposure of noise above the permissible exposure limit and over the daily allowable duration may cause noise-induce hearing loss (NIHL). Investigation on the type of task that has high noise level exposure on the maintenance workers was conducted to justify the needs to further detailed of this study. This paper will describe the task analysis on maintenance workers and to identify possible tasks with exposure to high level of noise. It scopes down to grinding crew of the maintenance department of light rail transit. Methods used were real-time sound measurement using a sound level meter, observation and interviews. Then, data were analysed to understand the situation of exposure of noise during rail maintenance. An ergonomic risk assessment was also conducted by adoption of the latest guideline on Ergonomic Risk Assessment (ERA) at Workplace Guidelines recommended by Department of Occupational Safety and Health (DOSH), Malaysia. A hierarchical task analysis (HTA) was generated on the task of the maintenance worker, focusing on rail grinding crew. The newly generated HTA had allowed better understanding about the nature of work and the task conducted by a rail grinder during the work shifts. Tasks involving high noise level was identified. Data recorded shows that the noise level for the blowing activity was relatively high and exceeded the permissible exposure limit of 90dBA. The exposure level was currently controlled by practicing the usage of hearing protection equipment (HPE) and by controlling exposure time in accordance to recommendations of the Factory and Machinery Act (FMA). Thus, it had confirmed that maintenance workers were exposed to high noise levels when performing their daily task. Further studies are needed to investigate the relationship between the duration of exposure and noise-induce hearing loss (NIHL) with consideration of the lifestyles of the maintenance workers.