{"title":"Evaluating human error probability in maintenance task: An integrated system dynamics and machine learning approach","authors":"Vahideh Bafandegan Emroozi, Mostafa Kazemi, Alireza Pooya, Mahdi Doostparast","doi":"10.1002/hfm.21057","DOIUrl":null,"url":null,"abstract":"<p>Human error is often implicated in industrial accidents and is frequently found to be a symptom of broader issues within the sociotechnical system. Therefore, research exploring human error during maintenance activities is important. This article aims to assess the probability of human error in maintenance tasks at a cement factory using the Cognitive Reliability and Error Analysis Method and System Dynamics modeling. Given that human error probability (HEP) is influenced by various common performance conditions (CPCs) and their sub-factors, and changes dynamically in response to other variables, the SD method offers a practical approach for estimating and predicting human error behavior over time. This study identifies and quantifies the variables affecting HEP, explores their interactions and feedback in maintenance tasks, and assesses the associated costs. The machine learning technique is then used to estimate the relationship between HEP and these costs. The optimal value of the HEP function, 0.000772, is determined by identifying the minimum point of a cubic function, thereby minimizing associated costs and occupational accidents. Determining the optimal HEP is crucial for minimizing excessive costs and investing in improved ergonomics and CPCs for better performance. This addresses a significant gap in existing research where the impact of human error on maintenance tasks has not been estimated as a function. Furthermore, three scenarios are presented to help managers allocate the organization's budget more effectively.</p>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Factors and Ergonomics in Manufacturing & Service Industries","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/hfm.21057","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Human error is often implicated in industrial accidents and is frequently found to be a symptom of broader issues within the sociotechnical system. Therefore, research exploring human error during maintenance activities is important. This article aims to assess the probability of human error in maintenance tasks at a cement factory using the Cognitive Reliability and Error Analysis Method and System Dynamics modeling. Given that human error probability (HEP) is influenced by various common performance conditions (CPCs) and their sub-factors, and changes dynamically in response to other variables, the SD method offers a practical approach for estimating and predicting human error behavior over time. This study identifies and quantifies the variables affecting HEP, explores their interactions and feedback in maintenance tasks, and assesses the associated costs. The machine learning technique is then used to estimate the relationship between HEP and these costs. The optimal value of the HEP function, 0.000772, is determined by identifying the minimum point of a cubic function, thereby minimizing associated costs and occupational accidents. Determining the optimal HEP is crucial for minimizing excessive costs and investing in improved ergonomics and CPCs for better performance. This addresses a significant gap in existing research where the impact of human error on maintenance tasks has not been estimated as a function. Furthermore, three scenarios are presented to help managers allocate the organization's budget more effectively.
人为错误往往与工业事故有关,而且经常被认为是社会技术系统中更广泛问题的一种表现。因此,研究维护活动中的人为错误非常重要。本文旨在利用认知可靠性和错误分析方法以及系统动力学建模,评估水泥厂维护任务中的人为错误概率。鉴于人为错误概率(HEP)受各种常见性能条件(CPC)及其子因素的影响,并随着其他变量的变化而动态变化,因此 SD 方法为估计和预测随时间变化的人为错误行为提供了一种实用的方法。本研究确定并量化了影响 HEP 的变量,探讨了它们在维护任务中的相互作用和反馈,并评估了相关成本。然后使用机器学习技术来估算 HEP 与这些成本之间的关系。通过确定立方函数的最小点,确定了 HEP 函数的最佳值 0.000772,从而将相关成本和职业事故降至最低。确定最佳 HEP 对于最大限度地降低过高成本以及投资于改进人体工程学和 CPC 以提高绩效至关重要。这弥补了现有研究中的一个重大缺陷,即没有将人为失误对维护任务的影响作为一个函数进行估算。此外,本文还提出了三种方案,以帮助管理人员更有效地分配组织预算。
期刊介绍:
The purpose of Human Factors and Ergonomics in Manufacturing & Service Industries is to facilitate discovery, integration, and application of scientific knowledge about human aspects of manufacturing, and to provide a forum for worldwide dissemination of such knowledge for its application and benefit to manufacturing industries. The journal covers a broad spectrum of ergonomics and human factors issues with a focus on the design, operation and management of contemporary manufacturing systems, both in the shop floor and office environments, in the quest for manufacturing agility, i.e. enhancement and integration of human skills with hardware performance for improved market competitiveness, management of change, product and process quality, and human-system reliability. The inter- and cross-disciplinary nature of the journal allows for a wide scope of issues relevant to manufacturing system design and engineering, human resource management, social, organizational, safety, and health issues. Examples of specific subject areas of interest include: implementation of advanced manufacturing technology, human aspects of computer-aided design and engineering, work design, compensation and appraisal, selection training and education, labor-management relations, agile manufacturing and virtual companies, human factors in total quality management, prevention of work-related musculoskeletal disorders, ergonomics of workplace, equipment and tool design, ergonomics programs, guides and standards for industry, automation safety and robot systems, human skills development and knowledge enhancing technologies, reliability, and safety and worker health issues.