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Machine learning applied to quality management - a study in ship repair domain
Srdoč, Alira (Author), Bratko, Ivan (Author), Sluga, Alojzij (Author)

URLURL - Presentation file, Visit http://dx.doi.org/10.1016/j.compind.2006.09.013 This link opens in a new window

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 andexperience 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. Theresearch 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.

Language:English
Keywords:management kakovosti, metode umetne inteligence, strojno učenje, induktivno učenje, osnova znanje, algoritmi, zajemanje znanja, analize podatkov, modeli kakovosti, popravilo ladij, ekspertno znanje, quality management, knowledge acquisition, deep quality concept, delivery time estimate, dock works
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Year:2007
Number of pages:str. 464-473
Numbering:Letn. 58, št. 5
UDC:658.562:004.8
ISSN on article:0166-3615
COBISS.SI-ID:9965851 Link is opened in a new window
Views:658
Downloads:244
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Record is a part of a journal

Title:Computers in industry
Shortened title:Comput. ind.
Publisher:North-Holland
ISSN:0166-3615
COBISS.SI-ID:528402 This link opens in a new window

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