An Approach to Analyze Human-caused Work Errors

Tropschuh, Barbara; Brunner, Stefan; Dillinger, Fabian; Hagemann, Florian

doi:10.1016/j.procir.2022.02.147

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…One of them is the NASA -TLX developed by Hart [23] . NASA -TLX has been used in various fields including health services [25,26] , public services [5] , aviation [27] , mining industry [28] , and manufacturing industries [3,29] . Even though perceived workload is prone to subject's bias on judging the workload they've experienced [3] , NASA -TLX has showed a reliable result for analyzing perceived workload because of its sensitivity to humans' perception in explaining the workload they're experiencing [1,27] .…”

Section: Nasa -Tlx For Perceived Workload Evaluationmentioning

confidence: 99%

“…Human factors affect the quality of whole production process [1] . Studies found relationship between human factors and human error [2,3] and occupational incidents [4,5] . Thus, monitoring human workload to ensure their working quality is important to maintain the quality of overall process.…”

Section: Introductionmentioning

confidence: 99%

“…Perceived workload measurements are the most common tools to evaluate cognitive workload. Despite of its sensitivity to capture workload felt by the subject [1] , subjective measurements are still prone to bias [3] . Thus, objective methods which uses physiological indicators are much preferred and need to be studied.…”

Section: Introductionmentioning

confidence: 99%

“…A study found that product design could affect the quality of how the product would be manufactured and assembled [20] . Other than the nature of the tasks, personal individual factors like age, gender, lifestyle impacts and personal issues should also be taken into considerations during measurement [3] . To focus on finding the value of workload as an effect of the tasks given, the effect size method principle could be used.…”

Section: Introductionmentioning

confidence: 99%

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Cardiac Biometrics and Perceived Workload Regression Analysis Using Random Forest Regressor in Cognitive Manufacturing Tasks

Harmayanti,

Tama,

Gapsari

et al. 2024

MECHTA

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Workload is crucial in managing and maintaining good performance of human resources and allocations. In an advanced manufacturing industry, human job functions had shifted to cognitive tasks. Thus, cognitive workload evaluation should be used to monitor worker’s workload in optimal condition. Most common tool of cognitive workload tools are perceived measurement, like NASA – TLX questionnaire. Despite of its sensitivity to capture workload felt by the workers, this subjective measurement was prone to bias. Objective measurement utilizing biometrics data of the human body during working state was useful to eliminate bias. Cardiac biometrics were one of the many that were closely related to mental activity changes. The objective of this study was to understand the relationship of cardiac biometrics to perceived workload as an indicator of cognitive workload analysis. The study utilized four biometrics, heart rate, HRV low frequency power, total frequency power and ratio of low and high frequency power, were used to analyzed a one hour long cognitive based study case. The study case was designed in a manufacturing planning context referring to manufacturing aptitude tests, to induce cognition process on 30 participants. The biometrics and NASA – TLX score result of all the participants, were then calculated as effect size standardization before input into random forest regressor model to analyze relationship between cardiac biometrics and perceived workload. The result found a moderate relationship between the two (r2 = 0.576). Features importance also showed the most impactful feature to the model is the effect size of frequency power ratio. However, it is recommended to always consider evaluating multiple cardiac biometrics in workload analysis to ensure good model performance.

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Section: Nasa -Tlx For Perceived Workload Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cardiac Biometrics and Perceived Workload Regression Analysis Using Random Forest Regressor in Cognitive Manufacturing Tasks

Harmayanti,

Tama,

Gapsari

et al. 2024

MECHTA

View full text Add to dashboard Cite

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Lean Ergonomics—are relevant synergies of digital human models and digital twins defining a new emerging subdiscipline?

2022

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Manufacturing companies are facing new kinds of challenges. High cost and time pressure as well as the variety of product variants have added to supply chain issues due to unvorseeable major political, societal or natural events. This requires more than ever load optimization for all agents of a socio-technical system, to enable them to operate competitively and sustainably. Digital tools offer the possibility to proactively plan and analyze diverse processes. Recent developments indicat that human-centric (DHM) and process-centric (DT) tools are diverging rather than converging. The objective of this position paper is to analyze whether the vacuum created by human-centric and process-centric tools can be filled by the new subdiscipline of Lean Ergonomics (LE). LE is defined by synergies of production ergonomics and production management.Practical Relevance: By elaborating a methodological separation of Digital Twin (DT) and Digital Human Modeling (DHM), science and the economy shall be motivated to close the gap. Lean Ergonomics as an intersection of human-centered and system-centered engineering has the potential to incentivize companies to move towards sustainable ergonomics, as the company will directly benefit from this. Ergonomic methods and Key Performance Indicators (KPIs) at the micro level of the individual workstation enable the ergonomic and psychophysiological assessment of the human being in digital and analogue form. This is mirrored on production-organizational methods and KPIs and thereby tested for dependencies and interactions, which enable a holistic optimization of the socio-technical system.

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Lean Ergonomics—an empirical combination of Management Science and Ergonomics

Brunner,

Kühnel,

Bengler

2023

Z. Arb. Wiss.

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This paper presents Lean Ergonomics (LE) as an employee- and process-related method in a practice-oriented way. Stagnations in economic improvements of the processes, combined with known, high stresses of the basic workers on the shopfloor with ambiguous starting points of ergonomic measures hold broad analysis and optimization potential. This can be shown exploratively based on LE. 12 representative work processes at large reactors in the chemical industry form the study area. Each individual work process receives a Lean Ergonomics data set consisting of time data [standard deviation in min & %], objective ergonomics [EAWS] and subjective ergonomics [Borg, NASA-TLX; both pseudonymized]. Two process-identical production halls are provided, which is why KFzA (short questionnaire for work analysis) is also collected anonymously for additional general analysis of work system design. Consequently resulting in a diverse data structure of quantifiable person-related methods, objective, process-related loads and economically relevant, likewise process-related KPIs.

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An Approach to Analyze Human-caused Work Errors

Cited by 6 publications

References 3 publications

Cardiac Biometrics and Perceived Workload Regression Analysis Using Random Forest Regressor in Cognitive Manufacturing Tasks

Cardiac Biometrics and Perceived Workload Regression Analysis Using Random Forest Regressor in Cognitive Manufacturing Tasks

Lean Ergonomics—are relevant synergies of digital human models and digital twins defining a new emerging subdiscipline?

Lean Ergonomics—an empirical combination of Management Science and Ergonomics

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