Skip to main content
SpringerLink
Log in
Book cover

International Baltic Conference on Digital Business and Intelligent Systems

Baltic DB&IS 2022: Digital Business and Intelligent Systems pp 152–163Cite as

Features of Quantified Products and Their Design Implications

  • Conference paper
  • First Online:

  • 341 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1598))

Abstract

Digital transformation of industrial areas resulted in new products and services that build upon innovative technologies and enable new kinds of business models. Quantified products (QP) are such a kind of new product category that exploits data of on-board sensors. A quantified product is a product whose instances collect data about themselves that can be measured, or, by design, leave traces of data. This paper aims at contributing to a better understanding what design dependencies exist between product, service and ecosystem. For this purpose, we combine the analysis of features of QP potentially affecting design with an analysis of QP case studies for validating the suitability and pertinence of the features. Main contributions of this paper are (1) two case studies showing QP development, (2) a set of features of QPs derived from the cases and (3) a feature model showing design dependencies of these feature.

This is a preview of subscription content, 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   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   89.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

  1. Blixt Hansen, E., Bøgh, S.: Artificial intelligence and internet of things in small and medium-sized. J. Manuf. Syst. 58, 362–372 (2021)

    Article  Google Scholar 

  2. Kaiser, C., Stocker, A., Viscusi, G., Fellmann, M., Richter, A.: Conceptualizing value creation in data-driven services: the case of vehicle data. Int. J. Inf. Manag. 59 (2021). https://doi.org/10.1016/j.ijinfo-mgt.2021.102335

  3. Bichler, M.: Design science in information systems research. Wirtschaftsinformatik 48(2), 133–135 (2006). https://doi.org/10.1007/s11576-006-0028-8

    Article  Google Scholar 

  4. Sandkuhl, K., Shilov, N., Seigerroth, U., Smirnov, A.: Towards the quantified product – product lifecycle support by multi-aspect ontologies. In: IFAC 14th Intelligent Manufacturing Systems (IMS) Conference. Tel Aviv, March 2022 (2022). Accepted for publication in the proceedings

    Google Scholar 

  5. Yin, R.K.: The abridged version of case study research. Handb. Appl. Soc. Res. Methods 2, 229–259 (1998)

    Google Scholar 

  6. Mayer-Schönberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt, Boston (2013). ISBN: 0544002695 9780544002692

    Google Scholar 

  7. Porter, M., Heppelmann, J.: How smart, connected products are transforming competition”. Harv. Bus. Rev. 92(11), 64–88 (2014)

    Google Scholar 

  8. Swan, M.: The quantified self: fundamental disruption in big data science and biological discovery. Big Data. 1(2), 85–99 (2013)

    Google Scholar 

  9. Kaiser, C.: Quantified vehicles: data, services, ecosystems. Ph.D. dissertation. Rostock University (2021)

    Google Scholar 

  10. Farahani, P., Meier, C., Wilke, J.: Digital supply chain management agenda for the automotive supplier industry. In: Oswald, G., Kleinemeier, M. (eds.) Shaping the Digital Enterprise, pp. 157–172. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-40967-2_8

    Chapter  Google Scholar 

  11. Tselentis, D.I., Yannis, G., Vlahogianni, E.I.: Innovative insurance schemes: pay as/how you drive. Transp. Res. Procedia 14, 362–371 (2016)

    Article  Google Scholar 

  12. Kong, X., Song, X., Xia, F., Guo, H., Wang, J., Tolba, A.: LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data. World Wide Web 21(3), 825–847 (2018)

    Article  Google Scholar 

  13. Kang, K.C., Cohen, S.G., Hess, J.A., Novak, W.E., Peterson, A.S.: Feature-oriented domain analysis (FODA) feasibility study (No. CMU/SEI-90-TR-21). Carnegie-Mellon University Pittsburgh, Software Engineering Institute (1990)

    Google Scholar 

  14. Thörn, C., Sandkuhl, K.: Feature modeling: managing variability in complex systems. In: Tolk, A., Jain, L.C. (eds.) Complex Systems in Knowledge-Based Environments. Theory, Models And Applications, vol. 168, pp 129–162. Springer, Berlin (2009). https://doi.org/10.1007/978-3-540-88075-2_6

  15. Davenport, T., Harris, J.: Competing on Analytics: Updated, with a New Introduction: The New Science of Winning. Harvard Business Press, Boston (2017)

    Google Scholar 

  16. Manyika, J., et al.: Big data: the next frontier for innovation, competition (Vol. 5, No. 6). and productivity. Technical report, McKinsey Global Institute (2011)

    Google Scholar 

  17. Hartmann, P.M., Zaki, M., Feldmann, N., Neely, A.: Capturing value from big data – a taxonomy of data-driven business models used by start-up firms. Int. J. Oper. Prod. Manag. 36(10), 1382–1406 (2016). https://doi.org/10.1108/IJOPM-02-2014-0098

    Article  Google Scholar 

  18. Spiekermann, M.: Data marketplaces: trends and monetisation of data goods. Intereconomics 54(4), 208–216 (2019). https://doi.org/10.1007/s10272-019-0826-z

    Article  Google Scholar 

  19. Czarnecki, K., Eisenecker, U.: Generative Programming. Addison-Wesley, Reading (2000)

    Google Scholar 

  20. Gartner Group: Seize the Digital Ecosystem Opportunity. Insights From the 2017 CIO Agenda Report (2017). https://www.gartner.com/imagesrv/cio/pdf/Gartner_CIO_Agenda_2017.pdf. Accessed 7 May 2022

  21. Li, L., et al.: A survey of feature modeling methods: historical evolution and new development. Robot. Comput. Integr. Manuf. 61, 101851 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Rostock, Rostock, Germany

    Kurt Sandkuhl

  2. Jönköping University, Jönköping, Sweden

    Kurt Sandkuhl

Authors
  1. Kurt Sandkuhl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kurt Sandkuhl .

Editor information

Editors and Affiliations

  1. University of Novi Sad, Novi Sad, Serbia

    Mirjana Ivanovic

  2. Riga Technical University, Riga, Latvia

    Marite Kirikova

  3. University of Latvia, Riga, Latvia

    Laila Niedrite

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sandkuhl, K. (2022). Features of Quantified Products and Their Design Implications. In: Ivanovic, M., Kirikova, M., Niedrite, L. (eds) Digital Business and Intelligent Systems. Baltic DB&IS 2022. Communications in Computer and Information Science, vol 1598. Springer, Cham. https://doi.org/10.1007/978-3-031-09850-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-09850-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09849-9

  • Online ISBN: 978-3-031-09850-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation