Eunji Park
KAIST, Daejeon, Korea, Republic of
A semi-automated manufacturing system that entails human intervention in the middle of the process is a representative collaborative system that requires active interaction between humans and machines. User behavior induced by the operator's decision-making process greatly impacts system operation and performance in such an environment that requires human-machine collaboration. There has been room for utilizing machine-generated data for a fine-grained understanding of the relationship between the behavior and performance of operators in the industrial domain, while multiple streams of data have been collected from manufacturing machines. In this study, we propose a large-scale data-analysis methodology that comprises data contextualization and performance modeling to understand the relationship between operator behavior and performance. For a case study, we collected machine-generated data over 6-months periods from a highly automated machine in a large tire manufacturing facility. We devised a set of metrics consisting of six human-machine interaction factors and four work environment factors as independent variables, and three performance factors as dependent variables. Our modeling results reveal that the performance variations can be explained by the interaction and work environment factors ($R^2$ = 0.502, 0.356, and 0.500 for the three performance factors, respectively). Finally, we discuss future research directions for the realization of context-aware computing in semi-automated systems by leveraging machine-generated data as a new modality in human-machine collaboration.
https://doi.org/10.1145/3544548.3581457
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)