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A Quality Management Model based on Databases and Knowledge

Alira SRDOČ ; Brodogradilište 3.MAJ, Rijeka, Hrvatska
Ivan BRATKO ; Fakulteta za računalništvo in informatiko, Univerza v Ljubljanii, Ljubljana, Slovenija
Alojzij SLUGA ; Fakulteta za strojništvo, Univerza v Ljubljani, Ljubljana, Slovenija

Puni tekst: engleski pdf 723 Kb

str. 137-145

preuzimanja: 479



In the paper the results of the doctoral research in which a new knowledge-
focused approach to quality management called Deep Quality Concept (DQC)
is conceptualised, are presented in short. The main features of the new quality
management model developed on that approach also are presented. Particular
attention is paid to expert knowledge – especially tacit and deep domain
knowledge, i.e. on knowledge that is not only decisive for quality, but also
for the competitive advantage of an organisation. Given that such knowledge
is hard or even impossible to be formalised by traditional methods, computer
concepts – including artificial intelligence (AI) concepts, also are included in the
model. The DQC model contains both, i.e. (1) the part concerning development
of quality standard i.e. quality award criteria based on the developed approach;
and (2) the part concerning implementation of the so obtained standard i.e.
award criteria. The main points and potential of the model and approach are
validated in the case study by example of delivery time estimate in ship-repair,
aimed to get a more transparent assessment and decision structure through the
use of machine learning – one of the AI’s best known and efficient knowledge
acquisition and representing techniques. The application results showed that
the proposed approach can contribute significantly to the more reliable quality,
particularly in complex and highly dynamic and stochastic domains. That
confirmed that computer and AI concepts need to be considered as an integral
part of quality management systems, as it is anticipated in the DQC model.

Ključne riječi

DQC model, Knowledge management, Knowledge synthesis, Machine learning, Quality management, Ship-repair

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Podaci na drugim jezicima: hrvatski

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