Statistical Modeling of Human Performance in Seek-And-Place Assembly Tasks

Abstract

Many manufacturing assembly tasks are targeted for automation to improve operational efficiency. However, certain tasks remain unsuitable for automation due to their complexity and the limitations of current technologies, requiring reliance on human-led processes where operator performance is critical. While prior research has extensively explored optimizing assembly tasks through work design and assistive technologies, less attention has been given to understanding how task complexity influences human decision-making during manual assembly. This study addresses this gap by presenting findings from a controlled laboratory experiment in which participants completed a jigsaw puzzle as a representative assembly task under varying levels of complexity. The results examine the impact of task complexity on operator performance, including task completion time and accuracy. Analysis reveals that human performance, both in novel and repeated assembly tasks, follows log-normal distributions, with expected inter-placement times and uncertainty determined by the number of remaining pieces. These insights contribute to understanding human decision-making dynamics in complex assembly processes.