Modelling human performance within an automotive engine assembly line

Abstract

In this paper, we first investigate 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 study has shown great improvement of the overall system performance in terms of flexibility, efficiency, responsiveness and re-configurability using dynamic, flexible and skilled walking workers. Nevertheless, 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. This is crucial for achieving a relatively even working speed at which each worker assembles a product down the line without major interruption between two adjacent workstations. In theory, the familiar degree of completing assigned tasks by each worker through training can be measured and expressed as a learning curve. In this case, 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 stabilised level. It has also been used to investigate the impact of the system variation that may affect the performance of individual walking workers through a learning process. Thus, the paper also describes a framework to assess the human performance by modelling the learning curve for each walking worker based in an integrated model. This model was created using a simulation tool Witness with its key input/output data manipulated externally by a series of Microsoft Excel worksheets incorporating the effect of a number of human factors in terms of cognitive and physical elements. With this method, the possible and realistic assignment of selected assembly tasks for each walking worker can be quantified.