Modeling human performance using learning curves

In this paper, we investigated a semi-automated automotive engine assembly line in which the traditional strategy of using fixed workers in each manual assembly section is replaced by a new strategy of using walking workers. With this approach, both worker and engine travel simultaneously down the line; each worker is previously trained to accomplish a series of assembly tasks independently from start to finish in each manual assembly section. The main problem of this design is that each worker needs to be cross-trained to acquire a satisfactory level of skills associated with the assignment of assembly tasks in order to retain a relatively even working speed between two adjacent workstations. In theory, the familiar degree of completing the assigned tasks by each worker through training can be measured and expressed as a learning curve. In this case study, the learning curve has been used to determine a trade-off decision between the complexity of assigned tasks and the duration of completing these tasks by a walking worker at a stabilized level. The paper describes a framework of assessing the human performance by modeling a learning curve for each walking worker based on an integrated model. Thus, a possible and realistic assignment of assembly tasks for a walking worker can be quantified.