Abstract Nowadays, workers, individually or in groups, are continually learning new tasks. The speed at which they learn directly contributes to the success of their firms in competitive markets. Learning curve research has been either on the individual or organizational level. A few papers have developed learning curve models for a group of workers, even fewer that used empirical data for that purpose. However, none of the existing models comprises measurable elements from real industrial tasks. This paper aims to fill this gap in the literature by proposing a bivariate group learning curve model, an aggregation of three learning curves where the number of workers in a group, and the number of repetitions are the independent variables. The dependent variable is the unit assembly time. The three learning curves represent motor, cognitive, and waste per unit assembled. The aggregated learning curve was fitted to experimental data consisting of different group sizes (1 to 4 students/workers), each performing four repetitions, and later compared to two log-linear learning curves, with and without plateauing. The results showed that the aggregated model represented the data the best and that segmenting waste into sub-elements (job familiarization, errors, and group coordination) improved the performance of the model. The parameter values affected by group sizes and repetitions for each task element provided insights that managers could use to improve the performance of their workforce.

中文翻译：

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具有运动、认知和浪费元素的小组学习曲线模型

摘要 如今，工人，无论是个人还是群体，都在不断地学习新任务。他们学习的速度直接有助于他们的公司在竞争激烈的市场中取得成功。学习曲线研究要么是在个人层面，要么是在组织层面。一些论文为一组工人开发了学习曲线模型，为此目的使用经验数据的论文更少。然而，现有模型中没有一个包含来自真实工业任务的可衡量元素。本文旨在通过提出一个双变量组学习曲线模型来填补文献中的这一空白，该模型是三个学习曲线的聚合，其中一组中的工人数量和重复次数是自变量。因变量是单位装配时间。三个学习曲线分别代表运动、认知、和每单位组装的废物。汇总的学习曲线被拟合到由不同组大小（1 到 4 名学生/工人）组成的实验数据，每个组执行四次重复，然后与两条对数线性学习曲线进行比较，有和没有平台期。结果表明，聚合模型最能代表数据，将浪费细分为子元素（工作熟悉、错误和团队协调）提高了模型的性能。受每个任务元素的组大小和重复次数影响的参数值提供了管理人员可以用来提高员工绩效的见解。后来比较了两个对数线性学习曲线，有和没有平台期。结果表明，聚合模型最能代表数据，将浪费细分为子元素（工作熟悉、错误和团队协调）提高了模型的性能。受每个任务元素的组大小和重复次数影响的参数值提供了管理人员可以用来提高员工绩效的见解。后来比较了两个对数线性学习曲线，有和没有平台期。结果表明，聚合模型最能代表数据，将浪费细分为子元素（工作熟悉、错误和团队协调）提高了模型的性能。受每个任务元素的组大小和重复次数影响的参数值提供了管理人员可以用来提高员工绩效的见解。