Skip to main content
SpringerLink
Log in

Future Virtual Product Creation Solutions with New Engineering Capabilities

  • Chapter
  • First Online:
Virtual Product Creation in Industry

Abstract

This chapter deals with the future capabilities and solutions of Virtual Product Creation. Based on the introduced new technical system requirements and opportunities in the previous chapter (Industrie 4.0 and IoT Technologies) this chapter provides answers how future digital engineering elements will look like as part of future Virtual Product Creation. The chapter starts with Model-based Systems Engineering (MBSE) and how it is connected by also different to Systems Engineering (SE). The theory and principles of MBSE are explained first before introducing the disciplines and the core elements of MBSE. Furthermore, the chapter explains the co-existence and interaction with the (classical) major technologies of Virtual Product Creation and describes examples of MBSE method and tools. It also adresses the challenge of introducing and integrating MBSE into industry. The second sub-chapter is devoted to the upcoming new key discipline of Virtual Product Creation, Data Engineering and Analytics (DEA). Here, the chapter introduces the eight disciplines of DEA and explains the connection to MBSE and AI. The third sub-chapter puts the focus on the new VPC capability called Digital Twin Engineering (DTE). The eight dimensions' model of Digital Twins and the design elements of Digital Twins are described with respect to the necessary engineering capabilities. The fourth sub-chapter deals with the fast-growing key VPC capability called Digital Platform Engineering (DPE) which includes new ways of distributed engineering as well as the new core technology streaming engineering. The fifth sub-chapter provides an insight how human skills for future Virtual Product Creation needs to be shaped and trained. The closing sub-chapter explains the Engineering System of the Future: new Engineering Intelligence levels and new/modified Engineering Principles are explained.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The Systems Modeling Language (SysML) is standardized graphical modeling language to describe systems of interest. It aims to be an interdisciplinary, general purpose modeling language with focus on Systems Engineering. It uses nine different diagrams in the current standard v1.6, depicting the views on the system: requirement diagram (req), activity diagram (act), sequence diagram (sd), state machine diagram (stm), use case diagram (uc), block definition diagram (bdd), package diagram (pkg), internal block diagram (ind) and parametric diagram (par).

  2. 2.

    The Unified Modeling language (UML) is another standard of the Object Management Group (OMG). It is agraphical modeling with focus on Software Engineering. In the current version of SysML (v1.6), UML forms the foundation for SysML. In the currently developed standard v2 of SysML, a new foundation will be used and UML might become a domain specific language (DSL) for the Software domain again.

  3. 3.

    The Object-Process Methodology (OPM) is combination of modeling languages and a methodology for modeling different systems, mainly automation system. It is standardized as ISO/PAS 19,450. It comprises a graphical modeling language, which uses Object-Process Diagrams (OPD) and a textual expression in form of the Object-Process Language (OPL). It is mainly used to describe objects and their transformation or use by processes.

  4. 4.

    The initiative Open Services for Lifecycle Collaboration (OSLC) aims at providing standardized interfaces between different applications to connect application data. It is based on the Representational State Transfer (REST) software paradigm used in web applications. Specifications are defined for the core of OSLC and different domains like PLM, ALM or Requirements Management. It misses, however, semantic and parametric interactions with domain specific engineering models such as CAD, CAE and mathematical models.

  5. 5.

    The Specification Integration Facility (SpecIF) aims at a more artifact-centered exchange instead of a document-centered exchange. Its core is the extraction of semantic information of each model and thus the combination of different forms of models on semantic level.

  6. 6.

    www.modelica.org.

  7. 7.

    Object Process Methodology (OPM) is a conception modeling language and methodology for capturing knowledge and designing systems, specified as ISO/PAS 19,450.

  8. 8.

    Rankings available on https://db-engines.com/de/ranking. NoSQL meanwhile stands for “not only SQL (Standard Query Langugae)”, originally referring to “non-SQL” or “non-relational”, and designates databases to provide a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases.

  9. 9.

    In 2020 the Industrial Digtal Twin Association (IDTA) has been founded in order to standardize digital standards for Digital Twins: https://idtwin.org/en/.

  10. 10.

    Please compare Chap. 20 for the IoT protocol explanations.

  11. 11.

    Meanwhil NoSQL (“No” or “not only” Structured Query Language) data bases are designed for rapid data acquisition and storage capabilities which make them preferrable within edge networks. They are non-tabular, and store data differently than relational tables. NoSQL databases come in a variety of types based on their data model. The main types are document, key-value, wide-column, and graphs. They provide flexible schemas and scale easily with large amounts of data and high user loads and need special mechanisms to ensure database integrity.

  12. 12.

    https://internationaldataspaces.org/.

  13. 13.

    https://www.clous.io/.

  14. 14.

    The technology of transmitting audio and video files in a continuous flow over a wired or wireless internet connection. Streaming services, therefore, have a high need on network bandwith and latency requirements for dynamic interactions.

  15. 15.

    Engineering Intelligence describes the ability of the Engineering System to reach its goals and target deliveries even under conditions of uncertainty.

References

  1. Klaus G, Liebscher H (1979) Wörterbuch der Kybernetik. Fischer-Taschenbuch-Verlag, Frankfurt am Main

    Google Scholar 

  2. Tomiyama T, Lutters E, Stark R, Abramovici M (2019) Development capabilities for smart products. CIRP Ann 68(2):727–750. https://doi.org/10.1016/j.cirp.2019.05.010

    Article  Google Scholar 

  3. Walden DD, Roedler GJ, Forsberg K, Hamelin RD, Shortell TM (eds) (2015) Systems engineering handbook: a guide for system life cycle processes and activities; INCOSE-TP-2003-002-04, 2015, 4th edn. Wiley, Hoboken, NJ

    Google Scholar 

  4. I. T. Operations (2007) “Systems engineering vision 2020”. INCOSE-TP-2004-004-02

    Google Scholar 

  5. Estefan JA (2008) “Survey of model-based systems engineering (MBSE) methodologies”. International counsil on systems engineering (INCOSE), Seattle, WA, USA

    Google Scholar 

  6. Auricht M (2018) Entwicklung eines Validierungsframeworks zur erlebbaren Absicherung von Fahrerassistenzsystemen. Fraunhofer Verlag, Stuttgart

    Google Scholar 

  7. Saunders S (2011) “Does a model based systems engineering approach provide real program savings?—Lessons learnt

    Google Scholar 

  8. Pagnanelli CAG, Carson RS, Palmer JR, Crow ME, Sheeley BJ (2012) 4.5.3 model-based systems engineering in an integrated environment. INCOSE Int Symp 22(1):633–649. https://doi.org/10.1002/j.2334-5837.2012.tb01361.x

    Article  Google Scholar 

  9. Scott Z (2011) “Model-based systems engineering”. In: APCOSE—Asia Pacific counsil of systems engineering conference, Seoul, South Korea

    Google Scholar 

  10. Haberfellner R, (ed) (2015) Systems engineering: grundlagen und anwendung, 13th ed. Zürich: Orell Füssli

    Google Scholar 

  11. Ropohl G (2009) Allgemeine technologie: eine systemtheorie der technik. s.l.: KIT Scientific Publishing. [Online]. Available: http://www.doabooks.org/doab?func=fulltext&rid=15084

  12. van Bertalanffy L (1968) General system theory: foundations, development, applications. Braziller, New York

    Google Scholar 

  13. Stachowiak H (1973) Allgemeine modelltheorie. Springer, Wien

    Book  Google Scholar 

  14. Hick H, Bajzek M, Faustmann C (2019) Definition of a system model for model-based development. SN Appl Sci 1:1074. https://doi.org/10.1007/s42452-019-1069-0

  15. Brusa E, Calà A, Ferretto D (2018) Systems engineering and its application to industrial product development. Springer International Publishing, Cham

    Book  Google Scholar 

  16. Vaneman WK, Carlson R (Apr 2019) “Model-based systems engineering implementation considerations”. In: 2019 IEEE international systems conference (SysCon), Orlando, FL, USA. pp 1–6

    Google Scholar 

  17. Stark R, Schulze E-E (2010) “Need and potentials to improve systems engineering by future PLM solutions”; prostep ivip symposium Berlin. Available through the prostep ivip association office: https://www.prostep.org/ueber-uns/geschaeftsstelle/

  18. Stark R, Auricht M (2017) “Beschleunigung der Validierung innerhalb der Entwicklung von mechatronischen Systemen mit einer integrierten Plattform (Acceleration of the validation a spart of the development of mechatronic systems with the help of an integrated platform)”. prostep ivip symposium essen; available through the prostep ivip association office: https://www.prostep.org/ueber-uns/geschaeftsstelle/

  19. Schmidt MM, Schmidt S, Zimmermann TC, Stark R (2021) “Conceptual introduction of required disciplines in model based systems engineering”. In: Drive train technology conference ATK2021

    Google Scholar 

  20. Königs SF (2014) Konzeption und Realisierung einer Methode zur templategestützten Systementwicklung. Stuttgart: Fraunhofer Verl. [Online]. Available: http://publica.fraunhofer.de/dokumente/N-292677.html

  21. Eigner M, Dickopf T, Apostolov H, Schaefer P, Faißt K-G, Keßler A (2014) “System lifecycle management: initial approach for a sustainable product development process based on methods of model based systems engineering”. In: Fukuda S, Bernard A, Gurumoorthy B, Bouras A (eds) IFIP advances in information and communication technology, vol 442, product lifecycle management for a global market: 11th IFIP WG 5.1 international conference, PLM 2014, Yokohama, Japan, July 7–9, 2014, Revised Selected Papers. Berlin, Heidelberg, s.l.: Springer Berlin Heidelberg, pp 287–300

    Google Scholar 

  22. Zimmermann TC, Masuhr C, Stark R (2020) “MBSE-Entwicklungsfähigkeit für Digitale Zwillinge”. ZWF 115(special):51–54. https://doi.org/10.3139/104.112312

  23. Huldt T, Stenius I (2019) State-of-practice survey of model-based systems engineering. Syst Eng 22(2):134–145. https://doi.org/10.1002/sys.21466

    Article  Google Scholar 

  24. Wymore AW (2018) Model-based systems engineering. Boca Raton: Chapman and Hall/CRC. [Online]. Available: https://ebookcentral.proquest.com/lib/gbv/detail.action?docID=5389603

  25. Schmidt MM, Zimmermann TC, Stark R (2021) “Systematic literature review of system models for technical system development”. Appl Sci 11(7). https://doi.org/10.3390/app11073014

  26. Sünnetcioglu A, Stark R (2018) Using transitive relations for automatic creation of consistent traceability in model-based systems engineering. Procedia Manuf 24:311–318. https://doi.org/10.1016/j.promfg.2018.06.022

    Article  Google Scholar 

  27. Königs SF, Beier G, Figge A, Stark R (2012) Traceability in systems engineering—review of industrial practices, state-of-the-art technologies and new research solutions. Adv Eng Inform 26(4):924–940. https://doi.org/10.1016/j.aei.2012.08.002

    Article  Google Scholar 

  28. Beier G (2013) Verwendung von traceability-modellen zur unterstützung der entwicklung technischer systeme. Zugl.: Berlin, Techn. Univ., Diss. Stuttgart: Fraunhofer-Verl., 2014. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-51488

  29. Schmidt MM, Stark R (2020) “Model-based systems engineering (MBSE) as computer-supported approach for cooperative systems development”. In: Proceedings of the 18th European conference on computer-supported cooperative work: the international venue on practice-centered computing on the design of cooperation technologies—exploratory papers, reports of the European society for socially embedded technologies, Siegen

    Google Scholar 

  30. Mahboob A, Husung S, Weber C, Liebal A, Krömker H (2019) The reuse of SysML behavior models for creating product use cases in virtual reality. Proc Int Conf Eng Des 1(1):2021–2030. https://doi.org/10.1017/dsi.2019.208

    Article  Google Scholar 

  31. Auricht M, Beckmann-Dobrev B, Stark R (2012) An interdisciplinary approach to validate mechatronic systems in early product development stages. In: NordDesign 2012. Aalborg, Denmark

    Google Scholar 

  32. Chami M, Bruel J-M (2015) Towards an integrated conceptual design evaluation of mechatronic systems: the SysDICE approach. Procedia Comp Sci 51:650–659. https://doi.org/10.1016/j.procs.2015.05.180

    Article  Google Scholar 

  33. Folds DJ, McDermott TA (2019) “The digital (mission) twin: an integrating concept for future adaptive cyber-physical-human systems”. In 2019 IEEE international conference on systems, man and cybernetics (SMC): Bari, Italy. October 6–9, 2019, Bari, Italy, pp 748–754

    Google Scholar 

  34. Holt J, Perry S, Brownsword M (2016) Foundations for model-based systems engineering: from patterns to models. Stevenage: IET

    Google Scholar 

  35. Martin JN (1997) Systems engineering guidebook: a process for developing systems and products. CRC Press, Boca Raton

    Google Scholar 

  36. Buchholz C, Tiemann M, Stark R (2018) “Durchgängiges prototyping mechatronischer systeme im MBSE entwicklungsprozess”. In: Symposium design fox X, pp 128–140

    Google Scholar 

  37. Voirin J-L, Bonnet S, Normand V, Exertier D (2015) “From initial investigations up to large-scale rollout of an MBSE method and its supporting workbench: the Thales experience” In: 25th annual INCOSE international symposium (IS2015), Seattle, WA, July 13–July 16

    Google Scholar 

  38. Eclipse “Capella MBSE tool-ARCADIA”. https://www.eclipse.org/capella/arcadia.html. last accessed 1 July 2021

  39. Zafirov R, Kiefer J, Eigner M (2010) Functional modelling for efficient generation of mechatronic design and validation models of automated production installations. In: International design conference—design 2010, Dubrovnik–Croatia, May 17–20

    Google Scholar 

  40. Pahl G, Beitz W (2007) Engineering design: a systematic approach, 3rd edn. Springer, London

    Book  Google Scholar 

  41. Kroll P, Kruchten P (2003) The rational unified process made easy. A practitioner’s guide to the RUP, Addison Wesley Longman Inc., Amsterdam. ISBN: 0-321-16609-4

    Google Scholar 

  42. Dumitrescu R, Albers A, Riedel O, Stark R, Gausemeier J (2021) Engineering in Deutschland—status quo in wirtschaft und Wissenschaft, Ein Beitrag zum advanced systems engineering. Paderborn

    Google Scholar 

  43. Cameron B, Adsit DM (2020) Model-based systems engineering uptake in engineering practice. IEEE Trans Eng Manage 67(1):152–162. https://doi.org/10.1109/TEM.2018.2863041

    Article  Google Scholar 

  44. White paper prostep ivip association: collaborative systems engineering on the basis of engineering IT standards. ISBN 978-3-9817958-6-8; Version 1.0, Februar 2019 https://www.prostep.org/fileadmin/downloads/WhitePaper_SSB_final_EN.pdf

  45. Ludwig T, Thiemann H (2020) Datenkompetenz—data literacy. Inform Spektrum 43(6):436–439. https://doi.org/10.1007/s00287-020-01320-0

    Article  Google Scholar 

  46. Lueth KL, Patsioura C, Williams ZD, Kermani ZZ (Dec 2016) “Industrial analytics 2016/2017: the current state of data analytics usage in industrial companies”. IoT Anal 58

    Google Scholar 

  47. Jasiulewicz-kaczmarek M, Legutko S, Kluk P (2020) Maintenance 4.0 Technologies—new oppor-tunities for sustainability driven maintenance. Manag Prod Eng Rev 11(2):74–87. https://doi.org/10.24425/mper.2020.133730

    Article  Google Scholar 

  48. Batini C, Cappiello C, Francalanci C, Maurino A (2009) Methodologies for data quality assessment and improvement. ACM Comput Surv 41(3):1–52. https://doi.org/10.1145/1541880.1541883

    Article  Google Scholar 

  49. Liu J, Li T, Xie P, Du S, Teng F, Yang X (Feb 2020) Urban big data fusion based on deep learning: an overview. Inf Fusion 53:123–133. https://doi.org/10.1016/j.inffus.2019.06.016.

  50. Diez-Olivan A, Del Ser J, Galar D, Sierra B (2019) Data fusion and machine learning for industrial prognosis: trends and perspectives towards Industry 4.0. Inform Fusion 50:92–111

    Google Scholar 

  51. Schroeder GN, Steinmetz C, Pereira CE, Espindola DB (2016) Digital twin data modeling with automationML and a communication methodology for data exchange. IFAC-Pap. 49(30):12–17. https://doi.org/10.1016/j.ifacol.2016.11.115

    Article  Google Scholar 

  52. Peckham J, Maryanski F (1988) Semantic data models. ACM Comput Surv 20(3):153–189. https://doi.org/10.1145/62061.62062

    Article  MATH  Google Scholar 

  53. Fürber C (2016) Data quality management with semantic technologies. Springer Fachmedien Wiesbaden, Wiesbaden

    Book  Google Scholar 

  54. Hildebrand K, Gebauer M, Hinrichs H, Mielke M (eds) (2018) Daten- und Informationsqualität. Wiesbaden, Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-21994-9.

  55. Tao F, Zhang H, Liu A, Nee AYC (2019) Digital twin in industry: state-of-the-art. IEEE Trans Ind Inform 15(4):2405–2415. https://doi.org/10.1109/TII.2018.2873186

    Article  Google Scholar 

  56. Carlsson G (2020) Topological methods for data modelling. Nat Rev Phys 2(12):697–708. https://doi.org/10.1038/s42254-020-00249-3

    Article  Google Scholar 

  57. Gadatsch A (2019) Datenmodellierung: Einführung in die Entity-Relationship-Modellierung und das Relationenmodell. Wiesbaden, Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-25730-9

  58. Scheer A-W (1992) Embedding data modelling in a general architecture for integrated information systems. In: Pernul G, Tjoa A (eds) Entity-relationship approach—ER ’92, vol 645. Springer, Berlin Heidelberg, pp 139–161. https://doi.org/10.1007/3-540-56023-8_10

  59. Open data for development network and international development research centre (Canada) (2019) The state of open data: histories and horizons. Cape Town, South Africa; Ottawa, Ontario: African Minds: international development research centre: open data for development OD4D Network

    Google Scholar 

  60. Bokrantz J, Skoogh A, Lämkull D, Hanna A, Perera T (2018) Data quality problems in discrete event simulation of manufacturing operations. SIMULATION 94(11):1009–1025. https://doi.org/10.1177/0037549717742954

    Article  Google Scholar 

  61. Das H, Barik RK, Dubey H, Roy DS (eds) (2019) Cloud computing for geospatial big data analytics: intelligent edge, fog and mist computing, vol 49. Cham, Springer International Publishing. https://doi.org/10.1007/978-3-030-03359-0

  62. Lynn T, Mooney JG, Lee B, Endo PT (eds) (2020) The cloud-to-thing continuum: opportunities and challenges in cloud, fog and edge computing. Cham, Springer International Publishing. https://doi.org/10.1007/978-3-030-41110-7

  63. International Standards Organization (2016) ISO standard 8000 part (61) version (1)—data quality management: process reference model. vol ISO 8000-6

    Google Scholar 

  64. Bühler P, Schlaich P, Sinner D (2019) Datenmanagement: Daten—Datenbanken—Datensicherheit. Berlin, Heidelberg, Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-55507-1

  65. García Márquez FP, Lev B (eds) (2017) Big data management. Cham, Springer International Publishing. https://doi.org/10.1007/978-3-319-45498-6

  66. Meier A, Kaufmann M (2019) SQL & NoSQL databases: models, languages, consistency options and architectures for big data management. Wiesbaden, Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-24549-8

  67. Wingerath W, Ritter N, Gessert F (2019) Real-time & stream data management: push-based data in research & practice. Cham, Springer International Publishing. https://doi.org/10.1007/978-3-030-10555-6

  68. Chavez Z, Hauge JB, Bellgran M (2020) Advances in production management systems. Towards smart and digital manufacturing, vol 592. Cham, Springer International Publishing. https://doi.org/10.1007/978-3-030-57997-5

  69. Elshawi R, Maher M, Sakr S (Jun 2019) Automated machine learning: state-of-the-art and open challenges”. Accessed 15 Jun 2020. [Online]. Available: http://arxiv.org/abs/1906.02287

  70. Gogineni S, Lindow K, Nickel J, Stark R (2020) Applying contextualization for data-driven transformation in manufacturing. Springer, Cham, pp 154–161. https://doi.org/10.1007/978-3-030-57997-5_19

  71. Zaveri A, Färber M, Bartscherer F, Menne C, Rettinger A. Semantic web 0(0)1–53 1 IOS press linked data quality of DBpedia, freebase, OpenCyc, Wikidata, and YAGO.” Accessed: 19 Jun 2020. [Online]. Available: https://www.cia.gov/library/

  72. Woll R (2017) “Optimized data integration for tracelinking in product development through the application of semantic web technologies”. Dissertation, Technische Universität Berlin

    Google Scholar 

  73. Prudhomme C, Homburg T, Ponciano J-J, Boochs F, Cruz C, Roxin A-M (2020) Interpretation and automatic integration of geospatial data into the Semantic web: towards a process of automatic geospatial data interpretation, classification and integration using semantic technologies. Computing 102(2):365–391. https://doi.org/10.1007/s00607-019-00701-y

    Article  MathSciNet  Google Scholar 

  74. Colace F, Lombardi M, Pascale F, Santaniello D (Nov 2018) A multilevel graph representation for big data interpretation in real scenarios. In: 2018 3rd international conference on system reliability and safety (ICSRS). pp 40–47. https://doi.org/10.1109/ICSRS.2018.8688834

  75. Akerkar R, Sajja PS (2016) Intelligent techniques for data science. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-29206-9

  76. Shearer C (2000) The CRISP-DM model: the new blueprint for data mining. J Data Warehous 5(4):13–22

    Google Scholar 

  77. Friendly M (2008) A brief history of data visualization. In: Handbook of data visualization. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 15–56. https://doi.org/10.1007/978-3-540-33037-0_2

  78. Chen M, Hauser H, Rheingans P, Scheuermann G (eds) (2020) Foundations of data visualization. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-34444-3

  79. Waskom M (2021) Seaborn: statistical data visualization. J Open Source Softw 6(60):3021. https://doi.org/10.21105/joss.03021

    Article  Google Scholar 

  80. Stark R, Damerau T (2019) Digital twin. In: Chatti S, Laperrière L, Reinhart G, Tolio T (Hg.) The international academy for production engineering, CIRP encyclopedia of production engineering. 2nd ed. 2018. Springer Berlin, Berlin, Heidelberg. ISBN: 978-3-662-53119-8. https://doi.org/10.1007/978-3-642-35950-7_16870-1

  81. Stark R, Fresemann C, Lindow K (2019) Development and operation of digital twins for technical systems and services. 69th CIRP general assembly–Birmingham–UK–18–24 Aug 2019. CIRP Ann Manuf Technol 68(1):129–132. https://doi.org/10.1016/j.cirp.2019.04.024

  82. GAIA-X: Technical architecture. Federal ministry for economic affairs and energy (BMWi) public relations division 11019 Berlin www.bmwi.d; release, June 2020. June 4th of 2020. https://www.data-infrastructure.eu/GAIAX/Redaktion/EN/Publications/gaia-x-technical-architecture.html

  83. Position paper (Feb 2021) “Stimulating the digital economy by introducing the principle of “Data sovereignty”. Position paper: stimulating the digital economy by introducing the principle of “Data sovereignty”—data sovereignty now

    Google Scholar 

  84. FIWARE Foundation e.V (June 2021) “Fiware for data spaces”. FIWARE foundation e.V. Franklinstraße 13A, 10587 Berlin, Germany

    Google Scholar 

  85. Wolter L, Hayka H, Stark R (2013) Improving the usability of collaboration methods and technologies in engineering. In: Kovács GL, Kochan D (eds) Digital product and process development systems. NEW PROLAMAT 2013. IFIP advances in information and communication technology, vol 411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41329-2_9

  86. VDI/VDE (2020) Entwicklung cyber-physischer mechatronischer systeme (CPMS). Development of cyber-physical mechatronic systems (CPMS). VDI Guideline 2206, Germany

    Google Scholar 

  87. Porter ME, Heppelmann JE (2014) How smart, connected products are transforming competition. Harvard Bus Rev: HBR 92(11):64–88

    Google Scholar 

  88. Abramovici M (2015) Smart products. In: CIRP encyclopedia of production engineering. p 1–5. https://doi.org/10.1007/978-3-642-35950-7_16785-1

  89. Valencia A, Mugge R, Schoormans J, Schifferstein H (2015) The design of smart product-service systems (PSSs): an exploration of design characteristics. Int J Design 9(1)

    Google Scholar 

  90. Lacheiner H, Ramler R (2011) Application lifecycle management as infrastructure for software process improvement and evolution: experience and insights from industry. In: 2011 37th EUROMICRO conference on software engineering and advanced applications. pp 286–293. https://doi.org/10.1109/SEAA.2011.51

  91. Rossberg J (2014) Beginning application lifecycle management: Apress. CA, US, Apress, Berkeley. 978-1-4302-5812-4. https://doi.org/10.1007/978-1-4302-5813-1

  92. The European Comission (2016) The general data protection regulation. Online available under: https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1465452422595&uri=CELEX:32016R0679

  93. Kotter JP (2012) Leading change: Harvard business press. ISBN: 9781422186442

    Google Scholar 

  94. Stark R, Brandenburg E, Lindow K (2021) Characterization and application of assistance systems in digital engineering. CIRP Ann. https://doi.org/10.1016/j.cirp.2021.04.061

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Information Technology, Berlin Institute of Technology, Berlin, Berlin, Germany

    Rainer Stark

Authors
  1. Rainer Stark
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Stark, R. (2022). Future Virtual Product Creation Solutions with New Engineering Capabilities. In: Virtual Product Creation in Industry. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-64301-3_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-64301-3_21

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-64299-3

  • Online ISBN: 978-3-662-64301-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation