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10008745
Evaluating 8D Reports Using Text-Mining
Abstract:
Increasing quality requirements make reliable and effective quality management indispensable. This includes the complaint handling in which the 8D method is widely used. The 8D report as a written documentation of the 8D method is one of the key quality documents as it internally secures the quality standards and acts as a communication medium to the customer. In practice, however, the 8D report is mostly faulty and of poor quality. There is no quality control of 8D reports today. This paper describes the use of natural language processing for the automated evaluation of 8D reports. Based on semantic analysis and text-mining algorithms the presented system is able to uncover content and formal quality deficiencies and thus increases the quality of the complaint processing in the long term.
Digital Object Identifier (DOI):

References:

[1] Behrens, B.-A.; Wilde, I.; Hoffmann, M.: Complaint management using the extended 8D-method along the automotive supply chain. In: Production Engineering, vol. 1 (2007), no. 1, pp. 91-95.
[2] Appelfeller, W.; Buchholz, W.: Supplier Relationship Management. 2. ed., Gabler Verlag, Wiesbaden 2011.
[3] Küster, B.; Eilert, B.; Overmeyer, L.: Automated Quality Evaluation of 8D Reports in Context of Complaint Processing. In: Proceedings of Symposium on Automated Systems and Technologies, vol. 3. (2016), pp. 77-80.
[4] Carstensen, K.-U.; Ebert, C.; Ebert, C., Jekat, S.; Langer, H.; Klabunde, R.: Computerlinguistik und Sprachtechnologie. 3. ed., Spektrum Akademischer Verlag, Heidelberg 2010.
[5] Pellegrini, T.; Blumenauer, A.: Semantic Web. Springer Verlag, Berlin 2006.
[6] Pirinen, T. A.; Lindén K.: Creating and Weighting Hunspell Dictionaries as Finite-State Automata. In: Investigationes Linguisticae, vol. 21 (2010), pp. 1-16.
[7] Yujian, L.; Bo, L.: A Normalized Levenshtein Distance Metric. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29 (2007), no. 6, pp. 1091-1095.
[8] Németh, L.; Halácsy, P.; Kornai, A.; Trón, V.: Open source morphological analyzer. https://catalog.ldc.upenn.edu/docs/LDC2008T01/acta04.pdf. Last access: 2017-05-30.
[9] Cutting, D.; Kupiec, J.; Pederson, J.; Sibun, P.: A Practical Part-of-Speech Tagger. In: Proceedings of the third conference on applied natural language processing, vol. 3. (1992) pp. 133-140.
[10] Brill, E.: A simple rule-based part of speech tagger. In: Proceedings of the Third Conference on Applied Computational Linguistics, vol. 3 (1992), pp. 112-116.
[11] Brill, E.: A report of recent progress in transformation-based error-driven learning. In: Proceedings of the Workshop on Human Language Technology, 1992, pp. 256-261.
[12] Brants, T.: TnT- A Statistical Part-of-Speech Tagger. In: Proceedings of the Sixth Applied Natural Language Processing Conference, vol. 6 (2000), pp. 224-231.
[13] Toutanova, K.; Klein, D.; Manning, C. D.; Singer, Y. Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, (2003), pp. 173-180.
[14] Gießbrecht, E.; Evert, S.: Is Part-of-Speech Tagging a Solved Task? An Evaluation of POS Taggers for the German Web as Corpus. In: Proceedings of the 5th Web as Corpus Workshop, vol. 5 (2009).
[15] Robacker, F. J.: A Comparison of Five Readability Indexes. Pennsylvania State University, 1970.
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