Machine learning applied to quality management—A study in ship repair domain

Abstract

The awareness about the importance of knowledge within the quality management community is increasing. For example, the Malcolm Baldrige Criteria for Performance Excellence recently included knowledge management into one of its categories. However, the emphasis in research related to knowledge management is mostly on knowledge creation and dissemination, and not knowledge formalisation process. On the other hand, identifying the expert knowledge and experience as crucial for the output quality, especially in dynamic industries with high share of incomplete and unreliable information such as ship repair, this paper argues how important it is to have such knowledge formalised. The paper demonstrates by example of delivery time estimate how for that purpose the deep quality concept (DQC)—a novel knowledge-focused quality management framework, and machine learning methodology could be effectively used. In the concluding part of the paper, the accuracy of the obtained prediction models is analysed, and the chosen model is discussed. The research indicates that standardisation of problem domain notions and expertly designed databases with possible interface to machine learning algorithms need to be considered as an integral part of any quality management system in the future, in addition to conventional quality management concepts.