A group learning curve model with motor, cognitive and waste elements. (August 2020)

• Record Type: Journal Article
• Authors: Peltokorpi, J.; Jaber, M.Y.
• Is Part Of: Computers & industrial engineering. Volume 146(2020)
• Publication Date: 2020-08
• Subjects: Learning curves -- Group size -- Motor/cognitive/waste elements -- Experimental data; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106621
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: An aggregate bivariate group learning curve model was developed. The model describes motor, cognitive and waste elements from real assembly work. For each element, unit time is dependent on the number of workers and repetitions. The aggregate model outperformed a non-aggregate model and its plateau version. Segmenting waste into sub-elements further improved the performance of the model. 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.