Purpose: – This “research note” sets out to fuel the debate around the practices and technologies within operations that are critical to success with servitization. It presents a study of four companies which are delivering advanced services and reports on the organisation and skill-sets of people within these. Design/methodology/approach: – This has been case-based research at four manufacturers leading in their delivery of services. Findings: – It describes the desirable behaviour of people in the front-line of service delivery, identifies the supporting skill-sets, how these people are organised, and explains why all these factors are so important. Originality/value: – This paper contributes to the understanding of the servitization process and, in particular, the implications to broader operations of the firm.
3 June 2015 in The International Journal of Advanced Manufacturing Technology
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.
1 January 2020 in Computers & Industrial Engineering