Optimizing reaction and processing times in automotive industry's quality management: A data mining approach

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)

Abstract

Manufacturing industry has come to recognize the potential of the data it generates as an information source for quality management departments to detect potential problems in the production as early and as accurately as possible. This is essential for reducing warranty costs and ensuring customer satisfaction. One of the greatest challenges in quality management is that the amount of data produced during the development and manufacturing process and in the after sales market grows rapidly. Thus, the need for automated detection of meaningful information arises. This work focuses on enhancing quality management by applying data mining approaches and introduces: (i) a meta model for data integration; (ii) a novel company internal analysis method which uses statistics and data mining to process the data in its entirety to find interesting, concealed information; and (iii) the application Q-AURA (quality - abnormality and cause analysis), an implementation of the concepts for an industrial partner in the automotive industry.

Original languageEnglish
Title of host publicationData Warehousing and Knowledge Discovery - 16th International Conference, DaWaK 2014, Proceedings
PublisherSpringer-Verlag Italia Srl
Pages266-273
Number of pages8
ISBN (Print)9783319101590
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014 - Munich, Germany
Duration: 2 Sept 20144 Sept 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8646 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014
Country/TerritoryGermany
CityMunich
Period02.09.201404.09.2014

Keywords

  • Apriori Algorithm
  • Automotive Industry
  • Data Mining
  • Quality Management

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