Consideration of the learning effect in the comparison of Fixed Worker and Walking Worker assembly systems

Abstract

Market demand is always more characterized by products variability and life cycle reduction; consequently, manufacturing systems need to be always more flexible and adaptable, in order to well react to this fluctuating demand. The most common assembly systems configuration is the Fixed Worker (FW) assembly line, where the workers always remain in the same workstation, to perform a single and often repetitive set of assembling tasks characterized by manual operations. On the other side, in the Walking Worker (WW) assembly line each operator travels along the workstations of the line performing all assembly tasks. In this context, the introduction of a new product, or a new worker, has an effect on the performance of the assembly system, caused by the learning of the new tasks. This paper aims to investigate the learning effect on the FW and WW systems and to compare them in order to define the best configuration in terms of performance. In particular, it proposes a mathematical model to represent the learning effect, also applying it to a numerical example. Results show that FW configuration is preferable in standard condition, while when the throughput is fluctuating, WW system is better in terms of performance. Future research will extend this study to more case studies in order to better understand the trend of this correlation, also considering particular phenomena like workers’ absenteeism and turnover.