Understanding and Exploring Operator Needs in Mixed Model Assembly

Abstract

Assembly operators are experiencing ever-increasing cognitive loads due to increasing production complexity. Higher quality assembly instructions, with input from operators by sharing their knowledge, can reduce errors during the assembly process. This article describes two studies that investigate the human needs for a digital process that streamlines the input of operators in the creation or adaptation of work instructions in a Mixed-Model Assembly Systems. The first study consisted of contextual inquiries and semi-structured interviews and aimed to discover the high-level needs of the different roles involved in the creation process. The second study used the Wizard of Oz method to investigate which interaction methods could be suitable to provide feedback about erroneous assemblies. We found that any systems should take into consideration, among other things, current operator mobility, presence of multiple operators at a workstation and different technological skill levels as important factors to consider when developing new systems to capture operator knowledge. With respect to interaction methods, participants preferred manual input devices over gestural feedback methods.