Apparel manufacturing is a labour intensive industry which is characterized by rapid product changes. Each time such changeover of product type occurs, it demands operator learning for the process to reach a steady-state. Higher the number of product type changes, higher the adverse impact of learning on the production performance. Purpose of this paper is to analyse and model this learning process in batch assembly (sewing) lines to improve the accuracy of forecasting, production planning and inventory control. For this purpose, empirical data of daily production efficiency is collected from a large scale apparel manufacturer in Sri Lanka over a period of 8 months. Product types or styles are categorized as “Repeat Styles” or “New Styles” based on whether the operators have prior experience in assembling the product type or not. Modelling of learning curves for above two categories of styles was carried out separately, using nonlinear regression analysis. This analysis indicates that the hyperbolic learning curve models are the best fitted learning curve for both style categories with different parameter estimates. Models were validated for both style categories using a separate set of daily efficiency data from same sewing lines.    

Keywords— Manufacturing / Production; operator learning; learning curve; batch assembly; non-linear regression