Implicit knowledge and fault diagnosis in the control of advanced manufacturing technology

Abstract

Abstract Field studies have shown that increasing operator responsibility for running advanced manufacturing technology can substantially enhance system performance. Improved fault diagnosis is central to such performance gains, and observations suggest this depends on implicit as well as explicit knowledge. However, the question of whether this is the case has not been systematically investigated. Evidence from field settings is circumstantial, and laboratory investigations of implicit knowledge have been based on other types of task. In this paper we described a study of implicit knowledge in fault diagnosis based on laboratory simulation of a robotics line. This confirms the existence of implicit knowledge in fault diagnosis, as well as raising both conceptual and methodological issues relevant to experimental approaches. We discuss the implications of the study for organizational practice and for the interaction between cognitive and organizational psychology.