Task complexity, and operators’ capabilities as predictor of human error: Modeling framework and an example of application

M. Leva, A. Caimo, R. Duane, M. Demichela, Lorenzo Comberti
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引用次数: 4

Abstract

This paper presents the initial framework adopted to assess human error in assembly tasks at a large manufacturing company in Ireland. The model to characterize and predict human error presented in this paper is linked conceptually to the model introduced by Rasch (1980), where the probability of a specified outcome is modelled as a logistic function of the difference between the person capacity and item difficulty. The model needs to be modified to take into account an outcome that is not dichotomous and feed into the interaction between two macro factors: (a) Task complexity: that summarises all factors contributing to physical and mental workload requirements for execution of a given operative task & (b) Human capability: that considered the skills, training and experience of the people facing the tasks, representing a synthesis of their physical and cognitive abilities to verify whether or not they are matching the task requirements. Task complexity can be evaluated as a mathematical construct considering the compound effects of Mental Workload Demands and Physical Workload Demands associated to an operator task. Similarly, operator capability can be estimated on the basis of the operators’ set of cognitive capabilities and physical conditions. A linear regression model was used to fit a dataset collected in R. The estimation of task complexity and operator skills was used to estimate human performance in a Poisson regression model. The preliminary results suggest that both elements are significant in predicting error occurrence. human nature (characteristics, feelings, and behavioural traits) and the impact of the features of the workstation on human nature (typology of activities, working load, anxiety induced, environmental factors etc.) was required to holistically determine the performance shaping factors for the workstations under examination. The focus is on the role of operator’s capability to complete tasks and the means to reduce human errors whilst retraining product quality. Changes were proposed for the assembly lines at the dispatching stations, including changes in the procedures and training to employ an understanding of human performance and improvements to safety, with an overall beneficial impact on both productivity and quality. The researcher conducted a task analysis of the critical activities completed by operators when packing out the variety of product units at two primary workstations. Questionnaires were prepared examining the skills requirements, skills rating of operators, mental workload requirements, physical workload requirements, perceived task complexity and motivation. Finally, the implementation of an applied model Task Execution Reliability Model (TERM) was used to identify the main fac
任务复杂性和操作员作为人为错误预测器的能力:建模框架和应用程序示例
本文提出了初步的框架,采用评估人为错误的组装任务在爱尔兰的一家大型制造公司。本文提出的描述和预测人为错误的模型在概念上与Rasch(1980)引入的模型相关联,其中指定结果的概率被建模为个人能力和项目难度之间差异的逻辑函数。需要对模型进行修改,以考虑到不是二分法的结果,并考虑到两个宏观因素之间的相互作用:(a)任务复杂性:总结了执行给定操作任务所需的体力和脑力工作量的所有因素;(b)人的能力:它考虑了面对任务的人的技能、训练和经验,代表了他们的身体和认知能力的综合,以验证他们是否符合任务要求。任务复杂性可以作为一个数学结构来评估,考虑与操作员任务相关的精神工作量需求和物理工作量需求的复合效应。同样,操作员的能力也可以根据操作员的认知能力和身体状况来估计。采用线性回归模型拟合r采集的数据集,并采用泊松回归模型对任务复杂度和操作员技能进行估计。初步结果表明,这两个因素在预测误差发生方面都很重要。人性(特征、情感和行为特征)和工作站特征对人性的影响(活动类型、工作负荷、焦虑诱发、环境因素等)需要从整体上确定受检查工作站的性能塑造因素。重点是操作员完成任务的能力的作用,以及在重新培训产品质量的同时减少人为错误的方法。建议对调度站的装配线进行改革,包括改变程序和培训,以便了解人员的工作情况和改进安全,从而对生产力和质量产生总的有利影响。研究人员对操作员在两个主要工作站包装各种产品单元时完成的关键活动进行了任务分析。问卷调查包括技能要求、操作员技能等级、心理工作量要求、身体工作量要求、感知任务复杂性和动机。最后,利用应用模型任务执行可靠性模型(Task Execution Reliability model, TERM)对主要问题进行识别
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