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Effects of Stress, Repetition, Fatigue and Work Environment on Human Error in Manufacturing Industries
Abstract and Figures
Safety and health at work are extremely important yet they still appear to be one of the most neglected factors in the industry. Almost 90% of accidents that occur in the workplace are due to human errors. While studies suggest that the lack of skills and experience among workers can significantly increase the prevalence of human errors, few studies actually investigate how stress, repetition, fatigue and environment can affect human error. Hence, this study aims to explore the significances of the effects of stress, repetition, fatigue and work environment on human error in manufacturing industries. Questionnaires were constructed and distributed to several manufacturing firms across Peninsular Malaysia. A total of 200 questionnaire responses were collected back. The responses were analysed using descriptive, reliability, correlations and multiple linear regression analyses. It was found that human error is significantly affected by the 4 major factors explored in this study. A total of 48.8% of the variance in human error can be explained by stress, repetition, fatigue and work environment. The results of this study can act as useful protocols for manufacturing managers and policymakers in identifying critical factors to iron out problems such as human error and accidents at the workplace.
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... The subject is now better translated into language that businesspeople in all sectors can understand. One such supporting study [11] carried out in Malaysia in 2014, reported that almost 90% of accidents in the workplace are a result of human error and, that 4 major influencers, namely stress, repetition, fatigue and environment can explain 48.8% of all such human error. This is an interesting view as repetition, fatigue and environment can all be considered 'stress triggers' so far as the 'human brain surviving its environment' is concerned at a neurological level. ...
Editor’s Notes
This issue includes three sections; Featured Articles, Announcements and QOR Newsletter Team.
In the Featured Articles section, David Bovis investigates the intricate relationship between stress and cognitive function within the manufacturing environment and Dr. Velasquez presents HAI’s comprehensive vision of mathematical modelling
in Industry 4.0
There are a number of interesting Announcements including:
webinars at Organizational Excellence Specialists, call for papers at the Journal of Optimization and Supply Chain Management (JOSCM) and Management Science and Information Technology (MSIT), Resources for developing and development countries at IFORS-DC and an International Summit on Industrial &
Manufacturing Engineering 2025 with a collaborating partner offering a real-time assessment on the current state of excellence.
In closing, I would like to thank the contributors to this issue, along with the entire QOR Newsletter Team, and invite readers to share content for future issues. Before submitting content, be sure to review the webpage for guidelines and then contact
me to discuss!
Regards,
Mohammad Hossein Zavvar Sabegh
Editor of QOR NEWSLETTER
Email: mohammad@organizationalexcellencespecialists.ca
https://organizationalexcellencespecialists.ca/newsletter/
... First of all, it affects the workers' well-being, which on the contrary should be a target of the society as a whole. Secondly, increased stress levels among workers are correlated with increased human error, absenteeism, and loss of productivity [3], [4], which thwart the purpose of human-robot collaboration itself. Over the past few years, these considerations elicited a new wave of interest in improving the operators' well-being in industry [5]. ...
... Furthermore, employees are easily distracted by a subpar working environment, resulting in less productivity (Yeow et al., 2014). Poor working conditions not only affect productivity but also increase the risk of occupational injuries. ...
Employee performance is a critical determinant of growth and success within manufacturing organizations, making it essential to understand the factors influencing it. This study delves into the effects of work stress and the work environment on job performance, specifically within the electronics manufacturing sector in Penang, Malaysia. The research addresses how role ambiguity and conflict can act as stressors and also explores how intrinsic and extrinsic motivation factors related to the work environment affect job performance. The study sample comprised 116 participants from five electronics manufacturing firms located in Bayan Lepas, Penang. Data collection was conducted through a structured questionnaire, and the analysis was performed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results confirmed that the measurement model was robust, providing a solid foundation for validating the structural model. The PLS-SEM analysis demonstrated that the model had significant predictive capability, revealing that both work stress and the work environment exert substantial effects on job performance within the electronics manufacturing industry in Penang. Specifically, role stressors such as ambiguity and conflict were found to negatively impact job performance, while a supportive and motivating work environment positively influenced it. The study's results align with the theoretical frameworks of role stressors and self-determination, suggesting that effective management of stressors and the cultivation of a motivating work environment are essential for optimizing employee performance in the manufacturing sector.
... Factors including incomplete information, incorrect data entry, and insufficient coder expertise can lead to inaccurate coding [16]. In addition, errors may also arise from incorrect human perception [17], the complex technical nature of the work [18], and human fatigue from heavy workloads [19]. While natural language processing (NLP)-driven autocoding systems have the potential to enhance the quality of the manual coding results and expedite code assignment, it is imperative to assess their accuracy to ensure cost savings for the hospitals [20]. ...
BACKGROUND
International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision , to the International Classification of Diseases, Tenth Revision ( ICD-10 ), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)–driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs).
OBJECTIVE
This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification ( ICD-10-CM ), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs).
METHODS
By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)–assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023.
RESULTS
Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F 1-score, 0.667 ( F 1-score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F 1-score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs.
CONCLUSIONS
An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.
... Factors including incomplete information, incorrect data entry, and insufficient coder expertise can lead to inaccurate coding [16]. In addition, errors may also arise from incorrect human perception [17], complex technical nature of the work [18], and human fatigue from heavy workloads [19]. While natural language processing (NLP)-driven autocoding systems have the potential to enhance the quality of the manual coding results and expedite code assignment, it is imperative to assess their accuracy to ensure cost savings for the hospitals [20]. ...
Background
International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)–driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs).
Objective
This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs).
Methods
By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)–assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023.
Results
Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F1-score, 0.667 (F1-score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F1-score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs.
Conclusions
An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.
... Second, automation reduces the input of human operators. Since human activities are significantly affected by stress, tediousness, repetition and fatigue [55,109], mechanisation is expected to lead to lower error rates for these operations-especially for higher throughputs. Mechanisation is also expected to yield more homogeneous outputs. ...
Applications in engineering biology increasingly share the need to run operations on very large numbers of biological samples. This is a direct consequence of the application of good engineering practices, the limited predictive power of current computational models and the desire to investigate very large design spaces in order to solve the hard, important problems the discipline promises to solve. Automation has been proposed as a key component for running large numbers of operations on biological samples. This is because it is strongly associated with higher throughput, and with higher replicability (thanks to the reduction of human input). The authors focus on replicability and make the point that, far from being an additional burden for automation efforts, replicability should be considered central to the design of the automated pipelines processing biological samples at scale—as trialled in biofoundries. There cannot be successful automation without effective error control. Design principles for an IT infrastructure that supports replicability are presented. Finally, the authors conclude with some perspectives regarding the evolution of automation in engineering biology. In particular, they speculate that the integration of hardware and software will show rapid progress, and offer users a degree of control and abstraction of the robotic infrastructure on a level significantly greater than experienced today.
Liquid-handling is a fundamental operation in synthetic biology—all protocols involve one or more liquid-handling operations. It is, therefore, crucial that this step be carefully automated in order to unlock the benefits of automation (e.g., higher throughput, higher replicability). In the paper, we present a study, conducted at the London Biofoundry at SynbiCITE, that approaches liquid-handling and its reliable automation from the standpoint of the construction of the calibration curve for lycopene in dimethyl sulfoxide (DMSO). The study has important practical industrial applications (e.g., lycopene is a carotenoid of industrial interest, DMSO is a popular extractant). The study was also an effective testbed for the automation of liquid-handling. It necessitated the development of flexible liquid-handling methods, which can be generalizable to other automated applications. In addition, because lycopene/DMSO is a difficult mix, it was capable of revealing issues with automated liquid-handling protocols and stress-testing them. An important component of the study is the constraint that, due to the omnipresence of liquid-handling steps, errors should be controlled to a high standard. It is important to avoid such errors propagating to other parts of the protocol. To achieve this, a practical framework based on regression was developed and utilized throughout the study to identify, assess, and monitor transfer errors. The paper concludes with recommendations regarding automation of liquid-handling, which are applicable to a large set of applications (not just to complex liquids such as lycopene in DMSO or calibration curves).
Although the advancement of technology can provide a better and safer working environment to people, sometimes, overconfidence in technology can lead to unavoidable accidents and human errors in the workplace. This study aims to determine the effects of familiarity with office equipment on human errors and accidents in the workplace. A total of 150 surveys were collected and analysed with correlations analysis usingSPSS software version 19.0. The results show that although familiarity can reduce the occurrence of human errors, it cannot reduce the occurrence of accidents in the workplace. While it is now understood that improving one's familiarity with office equipment can potentially reduce their mistakes at work, companies should still be observant of other factors that can potentially reduce accidents in the workplace. This study sheds light on potential possibilities to improve the occupational safety, health and environment of organizations.
Several case studies from developing countries such as Malaysia have condoned that a firm's knowledge creation competencies can significantly affect its new product development (NPD) and engineering performance. The generality of these findings however have yet to be tested among a number of firms. Therefore, this study aims to explore the effects of knowledge creation on NPD by surveying several Malaysian manufacturing firms. Using the SECI model, a survey was developed. A total of 150 responses were collected and analysed using reliability, correlations and multiple linear regression analysis. It was found that every variable had a significant correlation with NPD. Also, 44.8% of the variance in NPD was explained by knowledge creation. This study contributes to the understanding of knowledge creation and NPD of most Malaysian manufacturing firms. It contends that knowledge creation acts as a reinforcement and catalyst for NPD success. The outcome of reinforced NPD in Malaysian manufacturing firms may lead to the eventual improvement of the country's overall economy and knowledge, towards the primary goal to achieve the status of a high-income nation.
Engineering improvements have had a significant influence on the industry's safety record. As collieries become fully mechanised further improvements lie with reducing the potential for human error - now the biggest single cause of accidents in most industries. British Coal Corporation (BCC) had become concerned that despite strident efforts to improve safety, small numbers of electricians were being electrocuted by failing to correctly isolate equipment. Traditional approaches to safety were felt to have had only a limited effect, and therefore new approaches were needed. It was only by understanding the problem that management could be sure they were providing the correct solutions. This paper describes an attitude survey which was developed and applied to all electricians in the Nottinghamshire coalfield. Key findings are summarised and initiatives developed by the group are described. A later survey confirmed the success of these initiatives.
Daya Bay nuclear power plant is put into commercial operating in 1994 until 1996, there are 368 human error events in operation and maintenance, occupying 39% of total events. The root causes for human error events were analyzed. The protective measures and treatment principle for human error events were discussed and several applying examples were given. It is put forward that the key to prevent the human error event lies in coordination and management, selection of person in charge of work, and good work habits of staffs.
Finding the right successor to a well-loved founder or president is often the most difficult task an organization can face--and the challenge can be even greater for family-run businesses. Succeeding Generations explores leadership transitions in family businesses, offering a clear-eyed assessment of the different options, from direct succession to building partnerships between siblings and cousins. Family-owned companies may dominate the worldwide business landscape, yet surprisingly few are successfully passed down from one generation to the next, and fewer still reach the third generation intact. Author Ivan Lansberg, an organizational psychologist who grew up in a family business, examines the reasons behind this high failure rate and reveals the factors that contribute to long-term success. He offers practical advice on how to mentor successors, how to set up a systematic selection process, and how to make the best use of the board of directors during times of transition. With a wealth of examples from companies in the United States, Europe, and Latin America, Succeeding Generations provides a thoughtful and comprehensive look at the sensitive dynamics of leadership succession in family businesses.