Hostname: page-component-5c6d5d7d68-sv6ng Total loading time: 0 Render date: 2024-08-29T12:33:41.672Z Has data issue: false hasContentIssue false
  • English
  • Français

DETERMINATION OF ENGINEERING DIGITALIZATION MATURITY

Published online by Cambridge University Press:  27 July 2021

Mona Tafvizi Zavareh  and
Martin Eigner
Mona Tafvizi Zavareh*
Affiliation:
Institute of Virtual Product Engineering; University of Kaiserslautern
Martin Eigner
Affiliation:
Institute of Virtual Product Engineering; University of Kaiserslautern
*
Tafvizi Zavareh, Mona, TU Kaiserslautern, MV, Germany, tafvizi@mv.uni-kl.de

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Engineering Digitalization enables development of new high intelligent products containing mechanical, electrical, software and communication components. As these complex products are result of multidisciplinary engineering processes, digitalization also enforces companies to raise, adapt and revise their engineering competencies and process capabilities to increase agility and maintain competitiveness. Also, the growing amount of data related to product and processes requires a well-structured management concept. In order to encounter all these changes and new requirements companies should identify their specific strengths and weaknesses and derive needs for action. This paper presents a novel maturity model for evaluation of capabilities of Engineering Digitalization in areas of processes, products, services, data, human and organization. The maturity model enables the detection of enhancement potentials and conception of individual digitalization plans for production companies. It has been composed based on a proven multidisciplinary engineering methodology along the product lifecycle process, which includes Model Based Systems Engineering Methods, and a multilevel IT architecture integration concept.

Keywords

Systems Engineering (SE)Digital / Digitised engineering value chainsProduct Lifecycle Management (PLM)Maturity ModelSystem Lifecycle Management
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1193 - 1202
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), 2021. Published by Cambridge University Press

References

Abramovici, M. (2018), “Engineering smarter Produkte und Services Plattform Industrie 4.0 STUDIE”.Google Scholar
Ahern, D.M., Clouse, A. and Turner, R. (2003), CMMI distilled: A practical introduction to integrated process improvement, 2. ed., Addison-Wesley, Boston.Google Scholar
Ashton, K. (2009), “That ‘internet of things’ thing”, RFID journal, Vol. 22 No. 7, pp. 97–114.Google Scholar
Aurich, J.C., Koch, W. and Kölsch, P. (2019), Entwicklung datenbasierter Produkt-Service Systeme: Ein Ansatz zur Realisierung verfügbarkeitsorientierter Geschäftsmodelle, [1. Auflage].CrossRefGoogle Scholar
Batenburg, R., Helms, R.W. and Versendaal, J. (2006), “PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment”, International Journal of Product Lifecycle Management, Vol. 1 No. 4, p. 333, https://dx.doi.org/10.1504/IJPLM.2006.011053.CrossRefGoogle Scholar
Bauer, W., Stowasser, S., Mütze-Niewöhner, S., Zanker, C. and Brandl, K.-H. (Eds.) (2019), TransWork - Arbeit in der digitalisierten Welt: Stand der Forschung und Anwendung im BMBF-Förderschwerpunkt, Fraunhofer IAO, Stuttgart.Google Scholar
Becker, J., Knackstedt, R. and Pöppelbuß, J. (2009), “Developing Maturity Models for IT Management”, Business & Information Systems Engineering, Vol. 1 No. 3, pp. 213–222, https://dx.doi.org/10.1007/s12599-009-0044-5.CrossRefGoogle Scholar
Berghaus, Sabine and Back, Andrea (2016), “Stages in Digital Business Transformation: Results of an Empirical Maturity Study”, in MCIS 2016 Proceedings. 22.Google Scholar
Bruin, T., Freeze, R., Kaulkarni, U. and Rosemann, M. (Eds.) (2005), Understanding the main phases of developing a maturity assessment model.Google Scholar
Bundschuh, M., Dumke, R., Schmietendorf, A. and Ebert, C. (2005), Best practices in software measurement: How to use metrics to improve project and process performance ; with 37 tables, Springer, Berlin, Heidelberg, New York.Google Scholar
Carolis, A. de, Macchi, M., Negri, E. and Terzi, S. (2017), “A Maturity Model for Assessing the Digital Readiness of Manufacturing Companies”, in Lödding, H., Riedel, R., Thoben, K.-D., Cieminski, G. von and Kiritsis, D. (Eds.), Advances in production management systems: The path to intelligent, collaborative and sustainable manufacturing ; IFIP WG 5.7 International Conference, APMS 2017, Hamburg, Germany, September 3-7, 2017 ; proceedings, IFIP Advances in Information and Communication Technology, Vol. 513, Springer, Cham, pp. 13–20.Google Scholar
Crosby, P.B. (1979), Quality is free: The art of making quality certain, McGraw-Hill, New York.Google Scholar
Dickopf, T. (2020), A Holistic Methodology for the Development of Cybertronic Systems in the Context of the Internet of Things, Dissertation, Schriftenreihe VPE, Band 23, Martin Eigner (publ.), Kaiserslautern.Google Scholar
Eigner, M. (2014), “Modellbasierte Virtuelle Produktentwicklung auf einer Plattform für System Lifecycle Management”, in Sendler, U. (Ed.), Industrie 4.0: Beherrschung der industriellen Komplexität mit SysLM, Xpert.press, Springer Vieweg, Berlin, Heidelberg, pp. 91–110.Google Scholar
Eigner, M. (2021), System Lifecycle Management: Digitalisierung des Engineering, Springer Vieweg, soon be published in English, Berlin, https://dx.doi.org/10.1007/978-3-662-62183-7.Google Scholar
Eigner, M., Dickopf, T. and Apostolov, H. (2019), “Interdisziplinäre Konstruktionsmethoden und -prozesse zur Entwicklung cybertronischer Produkte. Teil 2”, Konstruktion.Google Scholar
Eigner, M., Koch, W. and Muggeo, C. (Eds.) (2017), Modellbasierter Entwicklungsprozess cybertronischer Systeme: Der PLM-unterstützte Referenzentwicklungsprozess für Produkte und Produktionssysteme, Springer Vieweg, Berlin.CrossRefGoogle Scholar
Estefan, J.A. (2008), “Survey of Model-Based Systems Engineering (MBSE) Methodologies”, available at: http://www.omgsysml.org/MBSE_Methodology_Survey_RevB.pdf.Google Scholar
Gausemeier, J., Bätzel, D. and Orlik, L. (2002), “Potenzialfindung im Rahmen der strategischen Produkt- und Prozessplanung”, ZWF, Vol. 97 No. 9, pp. 453–458, https://dx.doi.org/10.3139/104.100568.CrossRefGoogle Scholar
Gausemeier, J., Wiesecke, J., Echterhoff, B., Isenberg, L., Koldewey, C., Mittag, T. and Schneider, M. (Eds.) (2017), Mit Industrie 4.0 zum Unternehmenserfolg - Integrative Planung von Geschäftsmodellen und Wertschöpfungssystemen: Cooperate succes with industry 4.0 - Integrative planning of business models and value creation systems, Heinz Nixdorf Institut Universität Paderborn, Paderborn.Google Scholar
Höhn, H., Sechser, B., Dussa-Zieger, K., Messnarz, R. and Hindel, B. (2015), Software Engineering nach Automotive SPICE: Entwicklungsprozesse in der Praxis ; Ein Continental-Projekt auf dem Weg zu Level 3, 1. Aufl., dpunkt, s.l.Google Scholar
Humphrey, W.S. (1988), “Characterizing the software process: a maturity framework”, IEEE Software, Vol. 5 No. 2, pp. 73–79, https://dx.doi.org/10.1109/52.2014.CrossRefGoogle Scholar
ISO (2015), Information Technology – Process Assessment – Requirements for process measurement frameworks: ISO/IEC JTC 1/SC 7 No. ISO/IEC 33003, 1st ed.Google Scholar
Issa, A., Lucke, D. and Bauernhansl, T. (2017), “Mobilizing SMEs Towards Industrie 4.0-enabled Smart Products”, Procedia CIRP, Vol. 63, pp. 670–674, https://dx.doi.org/10.1016/j.procir.2017.03.346.CrossRefGoogle Scholar
Jodlbauer, H. and Schagerl, M. (2016), “Reifegradmodell Industrie 4.0 - Ein Vorgehensmodell zur Identifikation von Industrie 4.0 Potentialen”, in Mayr, H.C. and Pinzger, M. (Eds.), Informatik 2016: Tagung vom 26.-30. September 2016 in Klagenfurt, GI-Edition Lecture Notes in Informatics Proceedings, Gesellschaft für Informatik, Bonn, pp. 1473–1487.Google Scholar
Klötzer, C. and Pflaum, A. (2017), “Toward the Development of a Maturity Model for Digitalization within the Manufacturing Industry's Supply Chain“, in Hawaii International Conference on System Sciences– HICSS- 50.CrossRefGoogle Scholar
Leyh, C., Schäffer, T., Bley, K. and Forstenhäusler, S. (2016), “SIMMI 4.0 – A Maturity Model for Classifying the Enterprise-wide IT and Software Landscape Focusing on Industry 4.0”, in Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, 11.09.2016 - 14.09.2016, IEEE, pp. 1297–1302.CrossRefGoogle Scholar
Lichtblau, K., Stich, V., Bertenrath, R., Blum, M. and Bleider, M. (2015), Industrie 4.0-Readiness, Frankfurt IMPULS-Stiftung VDMA.Google Scholar
Martin, J.N. (1997), Systems engineering guidebook: A process for developing systems and products, Systems engineering series, CRC Press, Boca Raton.Google Scholar
Nolan, R.L. (1973), “Managing the computer resource: a stage hypothesis”, Communications of the ACM, Vol. 16 No. 7, pp. 399–405, https://dx.doi.org/10.1145/362280.362284.CrossRefGoogle Scholar
Nyffenegger, F., Ríos, J., Rivest, L. and Bouras, A. (Eds.) (2020), Product Lifecycle Management Enabling Smart X, IFIP Advances in Information and Communication Technology, Springer International Publishing, Cham.Google Scholar
Pfenning, P., Eibinger, H.C., Rohleder, C. and Eigner, M. (2020), “A Comprehensive Maturity Model for Assessing the Product Lifecycle”, in Nyffenegger, F., Ríos, J., Rivest, L. and Bouras, A. (Eds.), Product Lifecycle Management Enabling Smart X, IFIP Advances in Information and Communication Technology, Vol. 594, Springer International Publishing, Cham, pp. 514–526.Google Scholar
Reichert, A. (2020), “Industrie 4.0 – Ansätze zur Strategieentwicklung in der Produktentwicklung”, Studienprojekt, Lehrstuhl für Virtuelle Produktentwicklung, TUK, Kaiserslautern, 2020.Google Scholar
Schuh, G., Anderl, R., Dumitrescu, R., Krüger, A. and Michael, t.H. (Eds.) (2020), Industrie 4.0 Maturity Index: Die digitale Transformation von Unternehmen gestalten – UPDATE 2020, acatech STUDIE, acatech, Deutsche Akademie der Technikwissenschaften e.V, München.Google Scholar
Schumacher, A., Nemeth, T. and Sihn, W. (2019), “Roadmapping towards industrial digitalization based on an Industry 4.0 maturity model for manufacturing enterprises”, Procedia CIRP, Vol. 79, pp. 409–414, https://dx.doi.org/10.1016/j.procir.2019.02.110.CrossRefGoogle Scholar
Siedler, C., Dupont, S., Tafvizi Zavareh, M., Zeihsel, F. and Aurich, J.C. (2020), “Reifegradmodell zur Bestimmung des Digitalisieurngsgrads”, in Aurich, J.C., Pier, M., Siedler, C. and Sinnwell, C. (Eds.), Bedarfsgerechte Digitalisierung von Produktionsunternehmen: Ein modulares Transformationskonzept als praxisorientierter Ansatz., Synnovating, Kaiserslautern, 21-36.Google Scholar
Stich, V., Schumann, J.H. and Beverungen, D. (2019), Digitale Dienstleistungsinnovationen: Smart Services agil und kundenorientiert entwickeln, 1st ed.CrossRefGoogle Scholar
Zavareh, Tafvizi, Sadaune, M., Siedler, S., Aurich, C., Zink, J.C., and Eigner, K.J., M. (2018), “A Study on the socio-technical Potentials of industrial Product Development Technologies for future digitized integrated Work Systems”, in Proceedings of Norddesign 2018, Linköpig, Sweden.Google Scholar
VDA QMC Working Group 13 (2017), “Automotive SPICE® Process Reference Model Process Assessment Model. Version 3.1”.Google Scholar
Vial, G. (2019), “Understanding digital transformation: A review and a research agenda”, J. Strateg. Inf. Syst., https://dx.doi.org/10.1016/j.jsis.2019.01.003.CrossRefGoogle Scholar
Vogelsang, A. (2018), “Reif für MBSE? Ein Reifegradmodell für modellbasiertes RE”.Google Scholar
Wagner, T., Herrmann, C. and Thiede, S. (2017), “Industry 4.0 Impacts on Lean Production Systems”, Procedia CIRP, Vol. 63, pp. 125–131, https://dx.doi.org/10.1016/j.procir.2017.02.041.CrossRefGoogle Scholar