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DERIVATION OF DESCRIPTION FEATURES FOR ENGINEERING CHANGE REQUEST BY AID OF LATENT DIRICHLET ALLOCATION

Part of: Design Organisation and Management

Published online by Cambridge University Press:  11 June 2020

M. Riesener ,
C. Dölle ,
M. Mendl-Heinisch ,
G. Schuh  and
A. Keuper
M. Riesener
Affiliation:
RWTH Aachen University, Germany
C. Dölle
Affiliation:
RWTH Aachen University, Germany
M. Mendl-Heinisch
Affiliation:
RWTH Aachen University, Germany
G. Schuh
Affiliation:
RWTH Aachen University, Germany
A. Keuper*
Affiliation:
RWTH Aachen University, Germany
*
*a.keuper@wzl.rwth-aachen.de

Abstract

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Complex products and shorter development cycles lead to an increasing number of engineering changes. In order to be able to process these changes more effectively and efficiently, this paper develops a description model as a first step towards a data driven approach of processing engineering change requests. The description model is systematically derived from literature using text mining and natural language processing techniques. An example of the application is given by an automated classification based on similarity calculations between new and historic engineering change requests.

Keywords

engineering changeproduct developmentdata mininglatent dirichlet allocationautomated classification

Information

Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 697 - 706
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Aßmann, A. (2000), Gestaltung von Änderungsprozessen in der Produktentwicklung, Produktentwicklung, Vol. 38 No. 2. unveränderte Auflage, Utz, Herbert, München.Google Scholar
Belener, P.M. (2008), Technisches Änderungsmanagement modularer Produkte und Prozesse, Shaker Verlag, Aachen.Google Scholar
Bird, S., Klein, E. and Loper, E. (2009), Natural Language Processing with Python, Safari Books Online, O'Reilly Media Inc, Sebastopol.Google Scholar
Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003), “Latent Dirichlet Allocation”, J. Mach. Learn. Res., Vol. 3, pp. 993–1022.Google Scholar
Borrego, M., Foster, M.J. and Froyd, J.E. (2014), “Systematic Literature Reviews in Engineering Education and Other Developing Interdisciplinary Fields”, Journal of Engineering Education, Vol. 103 No. 1, pp. 45–76. https://doi.org/10.1002/jee.20038CrossRefGoogle Scholar
Eversheim, W., Warnke, L. and Schröder, T. (1997), “Änderungsmanagement in Entwicklungskooperationen”, VDI-Z, Vol. 139 No. 3, pp. 60–63.Google Scholar
Fan, J., Han, F. and Liu, H. (2014), “Challenges of Big Data Analysis”, National science review, Vol. 1 No. 2, pp. 293–314, https://doi.org/10.1093/nsr/nwt032CrossRefGoogle ScholarPubMed
Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996), “From Data Mining to Knowledge Discovery in Databases”, AI Magazine, Vol. 17 No. 3, p. 37. https://doi.org/10.1609/aimag.v17i3.1230Google Scholar
Feldhusen, J. and Grote, K.-H. (2013), Pahl/Beitz Konstruktionslehre: Methoden und Anwendung erfolgreicher Produktentwicklung, 8., vollständig überarbeitete Auflage, Springer Vieweg, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-29569-0CrossRefGoogle Scholar
Grieco, A., Pacella, M. and Blaco, M. (2017), “On the Application of Text Clustering in Engineering Change Process”, Procedia CIRP, Vol. 62, pp. 187–192. https://doi.org/10.1016/j.procir.2016.06.019CrossRefGoogle Scholar
Hamraz, B. (2013), Engineering change modelling using a function-behaviour-structure scheme, Apollo - University of Cambridge Repository, 2013. https://doi.org/10.17863/CAM.14061Google Scholar
Huang, A.H. et al. (2018), “Analyst Information Discovery and Interpretation Roles. A Topic Modeling Approach”, Management Science, Vol. 64 No. 6, pp. 2833–2855. https://doi.org/10.1287/mnsc.2017.2751CrossRefGoogle Scholar
Jarratt, T.A.W. et al. (2011), “Engineering change. An overview and perspective on the literature”, Research in Engineering Design, Vol. 22 No. 2, pp. 103–124. https://doi.org/10.1007/s00163-010-0097-yCrossRefGoogle Scholar
Langer, S. et al. (2012), Änderungsmanagement-Report 2012: Studienergebnisse zu Ursachen und Auswirkungen, aktuellen Praktiken, Herausforderungen und Strategien in Deutschland, Technische Universität München.Google Scholar
Liddy, E.D. (2001), “Natural language processing”, In: Encyclopedia of Library and Information Science, 2nd Ed., Marcel Decker, Inc., NY.Google Scholar
Mehta, C. (2010), Knowledge-based Methods for Evaluation of Engineering Changes, Dissertation, University of Michigan, Michigan, 2010.Google Scholar
Morik, K., Jung, A., Weckwerth, J., Rötner, S., Hess, S., Buschjäger, S. and Pfahler, L. (2015), “Untersuchungen zur Analyse von deutschsprachigen Textdaten”, Technische Universität Dortmund, Tech. Rep. 02/2015. https://doi.org/10.17877/DE290R-19161CrossRefGoogle Scholar
Müller, R.M. and Lenz, H.-J. (2013), Business Intelligence, Springer Berlin Heidelberg, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-35560-8.CrossRefGoogle Scholar
Mustafi, J. (2016), “Natural Language Processing and Machine Learning for Big Data”, In: Mishra, B.S.P., Dehuri, S., Kim, E. and Wang, G.-N. (Eds.), Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing, Springer International Publishing, Cham, pp. 53–74. https://doi.org/10.1007/978-3-319-27520-8_4CrossRefGoogle Scholar
Petersen, M. (2017), Considering sustainability in the development of consumer goods, DoctoralThesis Technische Universität Hamburg, 2017. https://doi.org/10.15480/882.1357CrossRefGoogle Scholar
Potdar, P. and Jonnalagedda, V. (2018), “Design and development of a framework for effective engineering change management in manufacturing industries”, International Journal of Product Lifecycle Management, Vol. 11 No. 4, p. 368. https://doi.org/10.1504/IJPLM.2018.097880CrossRefGoogle Scholar
Rebentisch, E. et al. (2018), “Defining Agile Culture Using Topic Modeling”, Faculty of Mechanical Engineering and Naval Architecture, The Design Society, Glasgow, UK, May 21-24, 2018, University of Zagreb, Croatia, pp. 2111–2120. https://doi.org/10.21278/idc.2018.0394CrossRefGoogle Scholar
Riesener, M. et al. (2019), “Literature Based Derivation of a Framework to Evaluate Engineering Change Requests”, PICMET 2019 Conference, Portland, Oregon, USA, August 25-29, 2019.10.23919/PICMET.2019.8893906CrossRefGoogle Scholar
Röder, M., Both, A. and Hinneburg, A. (2015), “Exploring the Space of Topic Coherence Measures”, In: Cheng, X., Li, H., Gabrilovich, E. and Tang, J. (Eds.), WSDM’15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, January 31-February 6, 2015, Shanghai, China, Shanghai, China, 2/2/2015 - 6/2/2015 ACM Association for Computing Machinery, New York, NY, pp. 399–408. https://doi.org/10.1145/2684822.2685324CrossRefGoogle Scholar
Roelleke, T. and Wang, J. (2008), “TF-IDF uncovered”, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, Singapore, Singapore, ACM, New York, NY, p. 435. https://doi.org/10.1145/1390334.1390409CrossRefGoogle Scholar
Runkler, T.A. (2015), Data Mining: Modelle und Algorithmen intelligenter Datenanalyse, Computational intelligence, 2. Auflage, Springer Vieweg, Wiesbaden, https://doi.org/10.1007/978-3-8348-2171-3CrossRefGoogle Scholar
Salton, G. and Buckley, C. (1988), “Term-weighting approaches in automatic text retrieval”, Information Processing & Management, Vol. 24 No. 5, pp. 513–523. https://doi.org/10.1016/0306-4573(88)90021-0CrossRefGoogle Scholar
Schuh, G. et al. (2019), “Data Mining Definitions and Applications for the Management of Production Complexity”, Procedia CIRP, Vol. 81, pp. 874–879. https://doi.org/10.1016/j.procir.2019.03.217CrossRefGoogle Scholar
Schuh, G. and Riesener, M. (2018), Produktkomplexität managen: Strategien - Methoden - Tools, 3, vollständig überarbeitete Auflage, Hanser, München. https://doi.org/10.3139/9783446443549Google Scholar
Schuh, G., Riesener, M. and Dölle, C. (2016a), “Concept for development project management by aid of predictive analytics”, 2016 Portland International Conference on Management of Engineering and Technology (PICMET), Honolulu, HI, USA, pp. 2040–2047. https://doi.org/10.1109/PICMET.2016.7806640CrossRefGoogle Scholar
Schuh, G. et al. (2016b), “Application of highly-iterative product development in automotive and manufacturing industry”, ISPIM Innovation Symposium, The International Society for Professional Innovation Management (ISPIM), p. 1.Google Scholar
Sharafi, A. (2013), Knowledge Discovery in Databases: Eine Analyse des Änderungsmanagements in der Produktentwicklung, Zugl.: München, Techn. Univ., Diss., 2012, Springer Gabler Research, Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-02002-6CrossRefGoogle Scholar
Shearer, C. (2000), “The CRISP-DM Model. The New Blueprint for Data Mining”, Journal of Data Warehousing, Vol. 5 No. 4.Google Scholar
Wänström, C. and Jonsson, P. (2006), “The impact of engineering changes on materials planning”, Journal of Manufacturing Technology Management, Vol. 17 No. 5, pp. 561–584. https://doi.org/10.1108/17410380610668522CrossRefGoogle Scholar
Wei, X. and Croft, W.B. (2006), “LDA-based document models for ad-hoc retrieval”, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, Washington, USA, August 6-11, 2006, New York, NY, p. 178. https://doi.org/10.1145/1148170.1148204CrossRefGoogle Scholar
Wickel, M. et al. (2015), “Comparison of Seven Company-Specific Engineering Change Processes”, Modelling and Management of Engineering Processes: Proceedings of the 3rd International Conference 2013, Berlin, Heidelberg, s.l., Springer Berlin Heidelberg, pp. 125–136. https://doi.org/10.1007/978-3-662-44009-4_11CrossRefGoogle Scholar
Wickel, M.C. (2017), Änderungen besser managen. Eine datenbasierte Methodik zur Analyse technischer Änderungen, Dissertation, Technische Universität München, München, 2017.Google Scholar
Wilberg, J. et al. (2015), “Using a Systemic Perspective to Support Engineering Change Management”, Procedia Computer Science, Vol. 61, pp. 287–292. https://doi.org/10.1016/j.procs.2015.09.217CrossRefGoogle Scholar