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Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning pp 51–67Cite as

Current Approaches with a Focus on Holistic Information Management in Manufacturing

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Abstract

In this section, the focus is laid on existing approaches and concepts that try to address some of the identified challenges of MS when it comes to transparent and product specific information and data management. The main focal methods and concepts are PDM, PLM and quality monitoring in manufacturing. The presented domain specific knowledge is discussed within this section as it has strong relations with the later concept development. In order to allow the reader to easily identify the relation of the individual method to the product state concept, a short conclusion after each section highlights the relevancy and connection to the topic. The final sub-section of this third section will furthermore briefly summarize the complete section and help the reader with the transition towards the next section where the product state concept is presented.

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Notes

  1. 1.

    The content of this section has been partly published in accordance with (Universität Bremen 2007) in (Wuest et al. 2014a).

  2. 2.

    The content of this section has been partly published in accordance with Universität Bremen (2007) in Wuest et al. (2011, 2013b, 2014a, b).

  3. 3.

    The content of this section has been partly published in accordance with Universität Bremen (2007) in Wuest et al. (2013a).

References

  • Abramovici, M. (2007). Future trends in product lifecycle management (PLM). In: F.-L. Krause (Ed.), The Future of Product Development—Proceedings o the 17th CIRP Design Conference (pp. 665–674). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Abramovici, M., & Sieg, O. (2001). PDM—Technologie im Wandel—Stand und Entwicklungsperspektiven. Orientierung für die Praxis. Industrie Management, 5, 71–75.

    Google Scholar 

  • Brecher, C., Müller, S., Breitbach, T., & Lohse, W. (2013). Viable system model for manufacturing execution systems. Procedia CIRP, 7, 461–466.

    Article  Google Scholar 

  • Brinkheinrich, M. (2008). Transparenz durch Traceability. Rückverfolgbarkeit von Produkten und Produktionsprozessen - warum und wie. Der Betriebsleiter, 6(2008):18–19.

    Google Scholar 

  • Cao, H., & Folan, P. (2012). Product life cycle: the evolution of a paradigm and literature review from 1950–2009. Production Planning & Control, 23(8), 641–662.

    Article  Google Scholar 

  • Chryssolouris, G., Mavrikios, D., Papakostas, N., Mourtzis, D., Michalos, G., & Georgoulias, K. (2009). Digital manufacturing: history, perspectives, and outlook. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(5), 451–462. doi:10.1243/09544054JEM1241.

    Article  Google Scholar 

  • Corsten, H. & Gössinger, R. (2008). Lexikon der Betriebswirtschaftslehre (5 Aufl.). Oldenburg.

    Google Scholar 

  • Ding, Y., Shi, J., & Ceglarek, D. (2002). Diagnosability analysis of multi-station manufacturing processes. Journal of Dynamic Systems, Measurement, and Control, 124, 1–13.

    Article  Google Scholar 

  • Fasser, Y., & Brettner, D. (2002). Management for quality in high-technology enterprises. New York: Wiley-Interscience.

    Google Scholar 

  • Fasoli, T., Terzi, S., Jantunen, E., Kortelainen, J., Sääski, J. & Salonen, T. (2011). Challenges in data management in product life cycle engineering. In J. Hesselbach & C. Herrmann (Ed.), Glocalized solutions for sustainability in manufacturing (pp. 525–530). Heidelberg, Berlin: Springer.

    Google Scholar 

  • Forza, C., & Filippini, R. (1998). TQM impact on quality conformance and customer satisfaction: A causal model. International Journal of Production Economics, 55, 1–20.

    Article  Google Scholar 

  • Ganesan, R., Das, T. K., & Venkataraman, V. (2004). Wavelet-based multiscale statistical process monitoring: A literature review. IIE Transactions, 36(9), 787–806.

    Article  Google Scholar 

  • Gimenez, D. M., Vegetti, M., Leone, H. P. & Henning, G. (2008). Product ontology: defining product-related concepts for logistics planning activities. Computers in Industry, 2/3(59), 232–240.

    Google Scholar 

  • Golovatchev, J. D., & Budde, O. (2007). A holistic product lifecycle management framework facing the challenges of 21st century. In 3rd International Conference on Lifecycle Management, Zurich (pp. 577–590).

    Google Scholar 

  • Helo, P., Suorsa, M., Hao, Y., & Anussornnitisarn, P. (2014). Toward a cloud-based manufacturing execution system for distributed manufacturing. Computers in Industry, 65(4), 646–656. doi:10.1016/j.compind.2014.01.015.

    Article  Google Scholar 

  • Hoyer, R. W., & Hoyer, B. B. Y. (2001). What is quality. Quality Progress, 34, 53–62.

    Google Scholar 

  • Hribernik, K. A., Pille, C., Jeken, O., Thoben, K.-D., Windt, K. & Busse, M. (2010). Autonomous control of intelligent products in beginning of life processes. In International Conference on Product Lifecycle Management.

    Google Scholar 

  • Hussain, M. A. (1999). Review of the applications of neural networks in chemical process control—Simulation and online implementation. Artificial Intelligence in Engineering, 13(1), 55–68.

    Article  Google Scholar 

  • Jansen-Vullers, M. H., van Drop, C. A., & Beulens, A. J. M. (2003). Managing traceability information in manufacture. International Journal of Information Management, 23(2003), 395–413.

    Article  Google Scholar 

  • Jenab, K., & Ahi, P. (2010). Fuzzy quality feature monitoring model. International Journal of Production Research, 48(17), 5021–5030. doi:10.1080/00207540903117907.

    Article  MATH  Google Scholar 

  • Jiang, P., Jia, F., Wang, Y., & Zheng, M. (2012). Real-time quality monitoring and predicting model based on error propagation networks for multistage machining processes. Journal of Intelligent Manufacturing, (2012). doi:10.1007/s10845-012-0703-0.

  • Jun, H.-B., Kiritsis, D., & Xirouchakis, P. (2007). Research issues on closed-loop PLM. Computers in Industry, 58(8–9), 855–868. doi:10.1016/j.compind.2007.04.001.

    Article  Google Scholar 

  • Kano, M., & Nakagawa, Y. (2008). Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry. Computers & Chemical Engineering, 32(1–2), 12–24. doi:10.1016/j.compchemeng.2007.07.005.

    Article  Google Scholar 

  • Kaynak, H. (2003). The relationship between total quality management practices and their effects on firm performance. Journal of Operations Management, 21, 405–435.

    Article  Google Scholar 

  • Kiritsis, D., Bufardi, A., & Xirouchakis, P. (2003). Research issues on product lifecycle management and information tracking using smart embedded systems. Advanced Engineering Informatics, 17(3–4), 189–202. doi:10.1016/j.aei.2004.09.005.

    Article  Google Scholar 

  • Köksal, G., Batmaz, İ., & Testik, M. C. (2011). A review of data mining applications for quality improvement in manufacturing industry. Expert Systems with Applications, 38(10), 13448–13467. doi:10.1016/j.eswa.2011.04.063.

    Article  Google Scholar 

  • Leong, K. K., Yu, K. M., & Lee, W. B. (2002). Product data allocation for distributed product data management systems. Computers in Industry, 47(2002), 289–298.

    Article  Google Scholar 

  • Liu, T. D., & Xu, W. (2001). A review of web-based product data management systems. Computers in Industry, 44(2001), 251–262.

    Google Scholar 

  • Meffert, H., Burmann, C., & Kirchgeorg, M. (2008). Marketing - Grundlagen marktorientierter Unternehmensführung (p. 924). Gabler.

    Google Scholar 

  • Montgomery, D. (2005). Introduction to statistical quality control. Hoboken, NJ: Wiley.

    MATH  Google Scholar 

  • Moorthy, A., & Vivekanand, S. (2007). Integration of PLM with other concepts for empowering business environments. In M. Garetti, S. Terzi, P. D. Ball, & S. Han (Eds.), Product lifecycle management, Special Publication 3 (p. 93). Bergamo/Milano.

    Google Scholar 

  • Musa, A., Gunasekaran, A., & Yusuf, Y. (2013). Supply chain product visibility: Methods, systems and impacts. Expert Systems with Applications. doi:10.1016/j.eswa.2013.07.020.

  • Nebl, T. (2007). Produktionswirtschaft (6th edn.). München: Oldenbourg Verlag.

    Book  Google Scholar 

  • Paul, G., & Paul, R. (2008). Engineering data management and product data management: Roles and prospects. In International Scientific Conference Computer Science (pp. 614–619).

    Google Scholar 

  • Peltonen, H., Pitkänen, O., & Sulonen, R. (1996). Process-based view of product data management. Computers in Industry, 31(3), 195–203. doi:10.1016/S0166-3615(96)00051-6.

    Article  Google Scholar 

  • Pokharel, S., & Mutha, A. (2009). Perspectives in reverse logistics: A review. Resources, Conservation and Recycling, 53(4), 175–182. doi:10.1016/j.resconrec.2008.11.006.

    Article  Google Scholar 

  • Robinson, C. J., & Malhotra, M. K. (2005). Defining the concept of supply chain quality management and its relevance to academic and industrial practice. International Journal of Production Economics, 96, 315–337.

    Article  Google Scholar 

  • Samson, D., & Terziovski, M. (1999). The relationship between total quality management practices and operational performance. Journal of Operations Management, 17, 393–409.

    Article  Google Scholar 

  • Saaksvuori, A., & Immonen, A. (2004). Product lifecycle management. Berlin, Heidelberg, NY: Springer.

    Book  Google Scholar 

  • Seifert, M. (2007). Unterstützung der Konsortialbildung in Virtuellen Organisationen durch prospektives Performance Measurement. Dissertation. Universität Bremen, Bremen, Germany.

    Google Scholar 

  • Sendler, U. (2009). Das PLM-Kompendium. Referenzbuch des Produkt-Lebenszyklus- Managements. Heidelberg: Springer.

    Book  Google Scholar 

  • Silva, R. G. (2009). Condition monitoring of the cutting process using a self-organizing spiking neural network map. Journal of Intelligent Manufacturing, 21(6), 823–829. doi:10.1007/s10845-009-0258-x.

    Article  Google Scholar 

  • Simão, J. M., Stadzisz, P. C., & Morel, G. (2006). Manufacturing execution systems for customized production. Journal of Materials Processing Technology, 179(1–3), 268–275. doi:10.1016/j.jmatprotec.2006.03.064.

    Article  Google Scholar 

  • Sitek, P. (2012). Quality management to support single companies in colloborative enterprise networks. Dissertation. University of Bremen, Bremen, Germany.

    Google Scholar 

  • Stark, J. (2011). Product lifecycle management, 21st century paradigm for product realization. Heidelberg: Springer. doi:10.1007/978-0-85729-546-0.

    Google Scholar 

  • Steffelbauer-Meuche, G. (2004). Qualitätsmanagement in der Internen Revision (1. Aufl.). Sternenfels: Wissenschaft und Praxis.

    Google Scholar 

  • Sukchotrat, T., Kim, S. B., & Tsung, F. (2009). One-class classification-based control charts for multivariate process monitoring. IIE Transactions, 42(2), 107–120. doi:10.1080/07408170903019150.

    Article  Google Scholar 

  • Sundin, E. (2009). Life-cycle perspectives of product/service-wSystems: In design theory. In M. L. Sakao (Ed.), Introduction to product/service-system design (pp. 31–49). London: Springer.

    Chapter  Google Scholar 

  • Terzi, S., Panetto, H., Morel, G., & Garetti, M. (2007). A holonic metamodel for product traceabily in PLM. International Journal of Product Lifecycle Management, 2(3), 253–289. doi:10.1504/IJPLM.2007.016292.

    Article  Google Scholar 

  • Tietjen, T., Decker, A., & Müller, D. H. (2011). FMEA Praxis - Das Komplettpaket für Training und Anwendung. München: Carl Hanser Verlag.

    Google Scholar 

  • Universität Bremen. (2007). Promotionsordnung der Universität Bremen für die mathematischen, natur- und ingenieurwissenschaftlichen Fachbereiche vom 14. März 2007. Retrieved August 13, 2013, from http://www.math.uni-bremen.de/cms/media.php/59/PromO%20FB%202-5%20_14%203%2007_.6442.pdf.

  • Van Dorp, K.-J. (2002). Tracking and tracing: A structure for development and contemporary practices. Logistics Information Management, 15(1), 24–33. doi:10.1108/09576050210412648.

    Article  Google Scholar 

  • Wannenwetsch, H. (2010). Integrierte Materialwirtschaft und Logistik Beschaffung, Logistik, Materialwirtschaft und Produktion. Berlin: Springer.

    Google Scholar 

  • Wiers, V. C. S. (2002). A case study on the integration of APS and ERP in a steel processing plant. Production Planning & Control, 13(6), 552–560.

    Article  Google Scholar 

  • Wuest, T., Klein D., Seifert, M. & Thoben, K.-D. (2011). Challenges for grown engineering SMEs with a diverse product portfolio on information management and product tracking and tracing. In: Frick, J. (Eds.), Value Networks: Innovation, Technologies and Management. Proceedings of the APMS 2011 International Conference of Advances in Production Management Systems, September 26–28, 2011, Stavanger, Norway. ISBN 978-82-7644-461-2.

    Google Scholar 

  • Wuest, T., Irgens, C., & Thoben, K.-D. (2013a). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25(5), 1167–1180.

    Google Scholar 

  • Wuest, T., Werthmann, D. & Thoben, K. (2013b). Towards an approach to identify the optimal instant of time for information capturing in supply chains. In V. Prabhu, M. Taisch & D. Kiritsis (Eds.), APMS 2013, Part I, IFIP AICT 414, IFIP International Federation for Information Processing (pp. 3–12).

    Google Scholar 

  • Wuest, T., Hribernik, K., & Thoben, K.-D. (2014a). Accessing servitization potential of PLM data by applying the product avatar concept. Production Planning and Control, XX(XX), XX–XX (accepted).

    Google Scholar 

  • Wuest, T., Liu, A., Lu, S. C.-Y., & Thoben, K.-D. (2014b). Application of the stage gate model in production supporting quality management. Procedia CIRP, 17, 32–37. doi:10.1016/j.procir.2014.01.071.

    Article  Google Scholar 

  • Zantek, P. F., Wright, G. P., & Plante, R. D. (2006). A self-starting procedure for monitoring process quality in multistage manufacturing systems. IIE Transactions, 38(4), 293–308. doi:10.1080/07408170500208354.

    Article  Google Scholar 

  • Zhang, Y., Jiang, P., Huang, G., Qu, T., Zhou, G., & Hong, J. (2010). RFID-enabled real-time manufacturing information tracking infrastructure for extended enterprises. Journal of Intelligent Manufacturing, 23(6), 2357–2366. doi:10.1007/s10845-010-0475-3.

    Article  Google Scholar 

  • Zorriassatine, F., & Tannock, J. D. T. (1998). A review of neural networks for statistical process control. Journal of Intelligent Manufacturing, 9(3), 209–224.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of ICT Applications for Production, BIBA—Bremer Institut für Produktion und Logistik GmbH, Bremen, Germany

    Thorsten Wuest

  2. Department of Production Engineering, University of Bremen, Bremen, Germany

    Thorsten Wuest

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  1. Thorsten Wuest
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Correspondence to Thorsten Wuest .

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Wuest, T. (2015). Current Approaches with a Focus on Holistic Information Management in Manufacturing. In: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-17611-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-17611-6_3

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