Abstract

Human visual inspection skills remain superior for ensuring product quality and conformance to standards in the manufacturing industry. However, at present these skills cannot be formally shared with other workers or used to develop and implement new solutions or assistive technologies because they involve a high level of tacit knowledge which only exists in skilled operators’ internal cognitions. Industry needs reliable methods for the capture and analysis of this tacit knowledge so that it can be shared and not lost but also so that it can be best utilised in the transfer of manual work to automated systems and introduction of new technologies and processes. This paper describes two UK manufacturing case studies that applied systematic task analysis methods to capture and scrutinise the tacit knowledge and skills being applied in the visual inspection of aerospace components. Results reveal that the method was effective in eliciting tacit knowledge, and showed that tacit skills are particularly needed when visual inspection standards lack specification or the task requires greater subjective interpretation. The implications of these findings for future research and for developments in the manufacturing industry are discussed.

Introduction

Visual inspection (VI) is a traditional manual activity that involves careful and critical assessment of an object with reference to a predefined standard (Drury and Watson, 2002; Drury and Dempsey, 2012; See, 2012). In manufacturing, VI is used to identify and diagnose defects, which is essential for ensuring products meet satisfactory quality standards (Garrett et al., 2001). Despite typical error rates of between 20% and 30% (Drury and Fox, 1975), human VI has remained essential in manufacturing because the accuracy and efficiency of human visual acuity has remained superior to the visual inspection capabilities offered by available automated alternatives. Thus, although highly labour-intensive, VI continues to be particularly important in safety critical and high value manufacturing (HVM) processes where the consequences of missed defects are of a higher cost for both human and commercial reasons, e.g. “injury, fatality, loss of expensive equipment, scrapped items, rework, or failure to procure repeat business” (See et al., 2017, p. 262). As visual inspection is highly skilled, best practice knowledge and techniques are valuable, and as it is labour-intensive it is a prime candidate for process improvement/automation to enhance process efficiency in the future.