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Quantified Products: Case Studies, Features and their Design Implications
Volume 34, Issue 4 (2023), pp. 825–845
Kurt Sandkuhl ORCID icon link to view author Kurt Sandkuhl details  

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https://doi.org/10.15388/23-INFOR532
Pub. online: 12 October 2023      Type: Research Article      Open accessOpen Access

Received
1 December 2022
Accepted
1 October 2023
Published
12 October 2023

Abstract

In many industrial sectors, the current digitalization trend resulted in new products and services that exploit the potential of built-in sensors, actuators, and control systems. The business models related to these products and services usually are data-driven and integrated into digital ecosystems. Quantified products (QP) are a new product category that exploits data of individual product instances and fleets of instances. A quantified product is a product whose instances collect data about themselves that can be measured or, by design, leave traces of data. The QP design has to consider what dependencies exist between the actual product, services related to the product, and the digital ecosystem of the services. By investigating three industrial case studies, the paper contributes to a better understanding of typical features of QP and the implications of these features for the design of products and services. For this purpose, we combine the analysis of features of QP potentially affecting design with an analysis of dependencies between features. The main contributions of the work are (1) three case studies describing QP design and development, (2) a set of recurring features of QPs derived from the cases, and (3) a feature model capturing design dependencies of these features.

References

 
Alaluss, M., Drechsler, C., Kurth, R., Mauersberger, A., Ihlenfeldt, S., Marré, M., Labs, R. (2022). Usage-based leasing of complex manufacturing systems: a method to transform current ownership-based into pay-per-use business models. Procedia CIRP, 107, 1238–1244.
 
Baisch, E., Bleile, T., Belschner, R. (1995). A neural fuzzy system to evaluate software development productivity. In: 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century, Vol. 5, pp. 4603–46085. https://doi.org/10.1109/ICSMC.1995.538521.
 
Campbell, R.M. (1998). Analysis-When and When Not. SAE Technical Papers 982011. https://doi.org/10.4271/982011. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072452142&doi=10.4271%2f982011&partnerID=40&md5=54bb7eb76ed9cb46d0f27e3c60a4681b.
 
Czarnecki, K., Østerbye, K., Völter, M. (2002). Generative programming. In: Object-Oriented Technology, ECOOP 2002 Workshops and Posters, pp. 15–29.
 
Dadvandipour, S., Oliaei, S.N.B. (2018). On the digital manufacturing development applying engineering informatics as a discipline of field study. In: 2018 19th International Carpathian Control Conference (ICCC), pp. 492–497. https://doi.org/10.1109/CarpathianCC.2018.8399680. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050220712&doi=10.1109%2fCarpathianCC.2018.8399680&partnerID=40&md5=00550b260c8311cc568f6c7186aefec1.
 
Davenport, T., Harris, J. (2017). Competing on Analytics: The New Science of Winning. Harvard Business Press, Massachusetts.
 
Ebert, C., Riegg, A. (1991). A framework for selecting system design metrics. In: Proceedings. 1991 International Symposium on Software Reliability Engineering, pp. 12–19. https://doi.org/10.1109/ISSRE.1991.145347.
 
Farahani, P., Meier, C., Wilke, J. (2017). Digital supply chain management agenda for the automotive supplier industry. In: Shaping the Digital Enterprise. Springer, Cham, pp. 157–172. https://doi.org/10.1007/978-3-319-40967-2_8.
 
Fortino, G., Guerrieri, A., Russo, W., Savaglio, C. (2015). Towards a development methodology for smart object-oriented IoT systems: a metamodel approach. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1297–1302. https://doi.org/10.1109/SMC.2015.231.
 
Gartner Group (2017). Seize the Digital Ecosystem Opportunity. Insights From the 2017 CIO Agenda Report. https://www.gartner.com/imagesrv/cio/pdf/Gartner_CIO_Agenda_2017.pdf.
 
Hansen, E.B., Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: a survey. Journal of Manufacturing Systems, 58, 362–372.
 
Hartmann, P.M., Zaki, M., Feldmann, N., Neely, A. (2016). Capturing value from big data–a taxonomy of data-driven business models used by start-up firms. International Journal of Operations & Production Management, 3610, 1382–1406.
 
Hevner, A.R., March, S.T., Park, J., Ram, S. (2004). Design science in information systems research. MIS Quarterly, 281, 75–105.
 
Horvath, I., Gerritsen, B.H. (2012). Cyber-physical systems: concepts, technologies and implementation principles. In: Proceedings of TMCE 2012, Vol. 1, pp. 19–36.
 
John, I., Knodel, J., Lehner, T., Muthig, D. (2006). A practical guide to product line scoping. In: 10th International Software Product Line Conference (SPLC’06), pp. 3–12. https://doi.org/10.1109/SPLINE.2006.1691572.
 
Kaiser, C. (2022). Quantified Vehicles: Data, Services, Ecosystems. Dissertation.
 
Kaiser, C., Stocker, A., Viscusi, G., Fellmann, M., Richter, A. (2021). Conceptualising value creation in data-driven services: the case of vehicle data. International Journal of Information Management, 59, 102335.
 
Kang, K.C., Cohen, S.G., Hess, J.A., Novak, W.E., Peterson, A.S. (1990). Feature-Oriented Domain Analysis (FODA) Feasibility Study. Technical Report, CMU/SEI-90-TR-21, ESD-90-TR-222.
 
Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H. (2008). Visual analytics: scope and challenges. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (Eds.), Lecture Notes in Computer Science, Vol. 4404, Visual Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71080-6_6.
 
Kong, X., Song, X., Xia, F., Guo, H., Wang, J., Tolba, A. (2018). LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data. World Wide Web, 21(3), 825–847.
 
Lennartsson, D., Raudberget, D., Sandkuhl, K., Seigerroth, U. (2022). Modularisation metrics – contrasting industrial practice and state-of-research. Proceedings of the Design Society, 2, 2483–2492. https://doi.org/10.1017/pds.2022.251. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131380037&doi=10.1017%2fpds.2022.251&partnerID=40&md5=d47d56aaa7874fad11fcf96d92bcde76.
 
Li, L., Zheng, Y., Yang, M., Leng, J., Cheng, Z., Xie, Y., Jiang, P., Ma, Y. (2020). A survey of feature modeling methods: historical evolution and new development. Robotics and Computer-Integrated Manufacturing, 61, 101851.
 
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Hung Byers, A. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.
 
Mayer-Schönberger, V., Cukier, K. (2013). Big Data: A Revolution that will Transform how We Live, Work, and Think. Houghton Mifflin Harcourt.
 
Noy, N.F., McGuinness, D.L. (2001). Ontology Development 101: A Guide to Creating your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05.
 
Persad, U., Langdon, P., Clarkson, P.J. (2011). Investigating the relationships between user capabilities and product demands for older and disabled users. In: Stephanidis, C. (Ed.), Universal Access in Human-Computer Interaction. Design for All and eInclusion. UAHCI 2011, Lecture Notes in Computer Science, Vol. 6765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21672-5_13. https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960303462&doi=10.1007%2f978-3-642-21672-5_13&partnerID=40&md5=e6bd58d581dca1cbdf0ee606815efaaa.
 
Porter, M.E., Heppelmann, J.E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64–88.
 
Russell, S.J. (2010). Artificial Intelligence a Modern Approach. Pearson Education, Inc..
 
Sandkuhl, K. (2022). Features of quantified products and their design implications. In: International Baltic Conference on Digital Business and Intelligent Systems, pp. 152–163.
 
Sandkuhl, K., Shilov, N., Seigerroth, U., Smirnov, A. (2022). Towards the quantified product-product lifecycle support by multi-aspect ontologies. IFAC-PapersOnLine, 55(2), 187–192.
 
Spiekermann, M. (2019). Data marketplaces: trends and monetisation of data goods. Intereconomics, 54(4), 208–216.
 
Swan, M. (2013). The quantified self: fundamental disruption in big data science and biological discovery. Big Data, 1(2), 85–99.
 
Thörn, C., Sandkuhl, K. (2009). Feature modeling: managing variability in complex systems. In: Complex Systems in Knowledge-based Environments: Theory, Models and Applications. Springer, Berlin, Heidelberg, pp. 129–162.
 
Yin, R.K. (1998). The abridged version of case study research. In: Handbook of Applied Social Research Methods, pp. 229–259.

Biographies

Sandkuhl Kurt
https://orcid.org/0000-0002-7431-8412
kurt.sandkuhl@uni-rostock.de

K. Sandkuhl is full professor of business information systems at the University of Rostock (Germany). He received a diploma (Dipl.-Inform.) and a PhD (Dr.-Ing.) in computer science from Berlin University of Technology in 1988 and 1994, respectively. Furthermore, he received a Swedish degree as “Docent” (postdoctoral lecturing qualification) from Linköping University in 2005. In 1988–1994, Sandkuhl was associated with the Computer Science Faculty of Berlin, University of Technology, Germany. In 1994–2002, Sandkuhl was associated with Fraunhofer-Institute for Software Engineering and Systems Engineering ISST, Berlin and Dortmund, Germany. In 2002, Sandkuhl joined the School of Engineering at Jönköping University and was responsible for the research group in information engineering from 2002–2010. In 2003–2010, Sandkuhl was the head of Fraunhofer ISST’s project group in information engineering at Jönköping University. In 2010, Sandkuhl was appointed professor of business information systems at the University of Rostock (Germany). Sandkuhl is responsible for the BSc and MSc programs in Business Information Systems at Rostock University. Sandkuhl’s current research interests include fields of enterprise modelling, ontology engineering, digital enterprise architectures, and model-based engineering.


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Keywords
quantified product feature modelling product design feature dependencies

Funding
Part of the research was funded by the State of Mecklenburg-Vorpommern with funds of the European Program for Regional Development, project KIDIRA.

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