Hostname: page-component-5db58dd55d-bthnr Total loading time: 0 Render date: 2026-06-16T03:17:54.646Z Has data issue: false hasContentIssue false
  • English
  • Français

FOUR PATTERNS OF DATA-DRIVEN DESIGN ACTIVITIES IN NEW PRODUCT DEVELOPMENT

Published online by Cambridge University Press:  19 June 2023

Boyeun Lee  and
Saeema Ahmed-Kristensen
Boyeun Lee*
Affiliation:
University of Exeter Business School
Saeema Ahmed-Kristensen
Affiliation:
University of Exeter Business School
*
Lee, Boyeun, University of Exeter Business School, United Kingdom, b.l.lee@exeter.ac.uk

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.

In the midst of Industry 4.0 where digitalisation is stimulated through the Internet of Things (IoT), Big Data, and machine learning technologies, an increasing volume of valuable data has been acquired from sensors and interconnected devices. This data-driven paradigm can enable organisations to create new or improved products and services, build long-term customer relationships in a value co-creation manner, adapt to continuous business reconfiguration or address societal challenges such as sustainability. Scientific research addressing Data-driven design has increased steadily in the last few years. However, despite this, there is still a need for a comprehensive understanding of data-driven design processes. Thus, through a systematic literature review, we review the data-driven design activities observed in the new product and service development and types of data utilised in New Product Development (NPD) process. This paper contributes to design research and through reviewing the current landscape of Data-driven design identifies ten data-driven design activities and four-dimensional aspects in NPD process.

Keywords

Big dataNew product developmentDesign practiceData-driven design

Information

Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1925 - 1934
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), 2023. Published by Cambridge University Press

References

Bertoni, A., Larsson, T., Larsson, J., and Elfsberg, J. (2017) Mining Data to Design Value: A Demonstrator In Early Design. International Conference on Engineering Design. Vancouver, Canada.Google Scholar
Bogers, S., Deckers, E., Janne, van K., Hummels, C., and Rutjes, H. (2018) A Showcase of Data-enabled Design Explorations. In CHI- Computer Human Interaction. Montreal. https://dx.doi.org/10.1145/3170427.3186543.CrossRefGoogle Scholar
Bogers, S., Frens, J., van Kollenburg, J., Deckers, E., and Hummels, C. (2016) Connected Baby Bottle: A Design Case Study Towards A Framework for Data-Enabled Design. In DIS (Designing Interactive Systems). Brisbane, Australia. https://dx.doi.org/10.1145/2901790.2901855.CrossRefGoogle Scholar
Bogers, S., van Kollenburg, J., Deckers, E., Frens, J., and Hummels, C. (2018) A Situated Exploration of Designing for Personal Health Ecosystems through Data-enabled Design. In DIS (Designing Interactive Systems). https://dx.doi.org/10.1145/3196709.3196769.CrossRefGoogle Scholar
Briard, T., Jean, C.;, Aoussat, A.;, Véron, P.;, le Cardinal, J.;, and Wartzack, S.; (2021) Data-Driven Design Challenges in the Early Stages of the Product Development Process. In International Conference on Engineering Design. Gothenburg, Sweden. https://dx.doi.org/10.1017/pds.2021.85.CrossRefGoogle Scholar
Cantamessa, M., Montagna, F., Altavilla, S., and Casagrande-Seretti, A. (2020) Data-driven design: the new challenges of digitalization on product design and development. Design Science 6(27): 1–33.CrossRefGoogle Scholar
Carlos Quiñones-Gómez, J. (2021) Creativity Forward: A Framework That Integrates Data Analysis Techniques To Foster Creativity Within The Creative Process In User Experience Contexts. Creativity Studies 14(1): 51–73.CrossRefGoogle Scholar
de longueville, S., Jézegou, J., Bénard, E., and Gourinat, Y. (2021) Enhancing preliminary aircraft design through operational considerations: a data-driven approach. IOP Conference. https://dx.doi.org/10.1088/1757-899X/1024/1/012057.CrossRefGoogle Scholar
de Roeck, D., Moons, I., Slegers, K., Tanghe, J., and Jacoby, A. (2019) Ideas of Things: The IOT Design Kit. In Design Interaction Systems. https://dx.doi.org/10.1145/3301019.3323888.CrossRefGoogle Scholar
Dooley, K. (2021) Direct Passive Participation: Aiming for Accuracy and Citizen Safety in the Era of Big Data and the Smart City. Smart Cities 4: 336–348.CrossRefGoogle Scholar
Du, X., and Zhu, F. (2018) A new data-driven design methodology for mechanical systems with high dimensional design variables. Advances in Engineering Software 117: 18–28.CrossRefGoogle Scholar
Engel, C., and Ebel, P. (2019) Data-Driven Service Innovation: A Systematic Literature Review and Development of a Research Agenda. The 27th European Conference on Information Systems (ECIS). Stockholm, Sweden.Google Scholar
Geiger, C., and Sarakakis, G. (2016) Data driven design for reliability. In 2016 Annual Reliability and Maintainability Symposium (RAMS). https://dx.doi.org/10.1109/RAMS.2016.7448023.CrossRefGoogle Scholar
Ghosh, D., Olewnik, A., Lewis, K., Kim, J., Lakshaman, A., Cyber-, A., Lewis, K. Fellow ASME Professor, and Lakshmanan, A. (2017) Cyber-Empathic Design: A data-driven framework for product. Journal of Mechanical Design 139(9): 1–12.CrossRefGoogle Scholar
Hollauer, C., Shalumov, B., Wilberg, J., and Omer, M. (2018) Graph Databases for Exploiting Use Phase Data In Product-Service -System Development: A Methodology To Support Implementation. In International Design Conference. https://dx.doi.org/10.21278/idc.2018.0399.CrossRefGoogle Scholar
Holler, M., Stoeckli, E., Uebernickel, F., and Brenner, W. (2016) Towards Understanding closed-loop PLM: The Role of Product Usage Data for Product Development enabled by intelligent Properties.Google Scholar
Hou, L., and Jiao, R. J. (2020) Data-informed inverse design by product usage information: a review, framework and outlook. Journal of Intelligent Manufacturing 31: 529–552.CrossRefGoogle Scholar
Jansen, J.-M., Niemantsverdriet, K., Burghoorn, A. W., Lovei, P., Neutelings, I., Deckers, E., and Nienhuijs, S. (2020) Design for Co-responsibility: Connecting Patients, Partners, and Professionals in Bariatric Lifestyle Changes. In DIS (Designing Interactive Systems). https://dx.doi.org/10.1145/3357236.3395469.CrossRefGoogle Scholar
Khoshkangini, R., Mashhadi, P. S., Berck, P., Shahbandi, S. G., Pashami, S., Nowaczyk, S., and Niklasson, T. (2020) Early Prediction of Quality Issues in Automotive Modern Industry. Information 11(7): 354.CrossRefGoogle Scholar
Kim, M.-J., Lim, C.-H., Lee, C.-H., Kim, K.-J., Choi, S., and Park, Y. (2016) Data-driven Approach to New Service Concept Design. International Conference on Exploring Services Science. https://dx.doi.org/10.1007/978-3-319-32689-4_37.CrossRefGoogle Scholar
Kushiro, N., Matsuda, S., Torikai, R., and Takahara, K. (2014) A system Design Method based on Interaction between Logic and Data Sets. In IEEE International Conference on Data Mining Workshop.CrossRefGoogle Scholar
Lakoju, M., Ajienka, N., Khanesar, M. A., Burnap, P., and Branson, D. T. (2021) Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a ‘Chatty Device’. Sensors 21: 1–23.CrossRefGoogle ScholarPubMed
Lee, B.(2022) Understanding New Product Development and Value Creation for the Internet of Things, Doctoral Thesis, Lancaster University. https://doi.org/10.17635/lancaster/thesis/1646CrossRefGoogle Scholar
Lee, B., Cooper, R., Hands, D., and Coulton, P. (2022) Continuous cycles of data-enabled design: reimagining the IoT development process. AIEDAM 36(11): 1–15.CrossRefGoogle Scholar
Lim, C., Kim, M., Heo, J., and Kim, K. (2018) Design of informatics-based services in manufacturing industries: case studies using large vehicle-related databases. Journal of Intelligent Manufacturing 29: 497–508.CrossRefGoogle Scholar
Lim, D. Y. M., Yap, C. E. L., and Lee, J.-J. (2021) Datastorming: Crafting Data into Design Materials for Design Students’ Creative Data Literacy. In Creativity and Cognition. Virtual, Italy https://dx.doi.org/10.1145/3450741.3465246.CrossRefGoogle Scholar
Lovei, P., Funk, M., Deckers, E., and Wensveen, S. (2020) The Marios and Luigis of Design: Design Plumbers Wanted! In DIS (Designing Interactive Systems). Eindhoven https://dx.doi.org/10.1145/3393914.3395898.CrossRefGoogle Scholar
Lovei, P., Niemantsverdriet, K., van Dijk, R., Burghoorn, A. W., Jansen, J.-M., Neutelings, I., Deckers, E., and Nienhuijs, S. (2020) Together in Shape: A Co-responsibility System to Support Bariatric Lifestyle Changes. In DIS (Designing Interactive Systems). https://dx.doi.org/10.1145/3393914.3397094.CrossRefGoogle Scholar
Lukačević, F., Škec, S., Martinec, T., and Štorga, M. (2022) Challenges of Utilizing Sensor Data Acquired by Smart Products in Product Development Activities. Acta Polytechnica Hungarica 19(4): 2022–165.CrossRefGoogle Scholar
Ma, H., Chul, X., Lyu, G., and Xue, D. (2017) An Integrated Approach for Design Improvement Based on Analysis of Time-Dependent Product Usage Data. Journal of Mechanical Design, Transactions of the ASME 139(11).CrossRefGoogle Scholar
Machchhar, R. J., and Bertoni, A. (2021) Data-Driven Design Automation for Product-Service Systems Design: Framework and Lessons Learned from Empirical Studies. In International Conference on Engineering Design. https://dx.doi.org/10.1017/pds.2021.84.CrossRefGoogle Scholar
Maleki, E., Belkadi, F., Boli, N., van der Zwaag, Jan, Alexopoylos, B., Koukas, K., Marin-Perianu, S., Bernard, M., Mourtzis, A., D. (2018) Ontology-Based Framework Enabling Smart Product-Service Systems: Application of Sensing Systems for Machine Health Monitoring. Internet of Things Journal 5(6): 4496–4505.CrossRefGoogle Scholar
Marti, P., Megens, C., and Hummels, C. (2016) Data-Enabled Design for Social Change: Two Case Studies. Future Internet 8(46): 1–16.CrossRefGoogle Scholar
Meyer, M., Fichtler, T., Koldewey, C., and Dumitrescu, R. (2022) Potentials and challenges of analyzing use phase data in product planning of manufacturing companies. AIEDAM 36(14): 1–13.CrossRefGoogle Scholar
Meyer, M., Wiederkehr, I., Koldewey, C., and Dumitrescu, R. (2021) Understanding usage data-driven product planning: A systematic literature review. In Proceedings of the Design Society (Vol. 1). Cambridge University Press.Google Scholar
Montecchi, T., and Becattini, N.; (2021) ‘A Modelling Framework for Data-Driven Design for Sustainable Behaviour in Human-Machine Interactions’, in A Modelling Framework for Data-Driven Design for Sustainable Behaviour In Human-Machine Interactions. : 16–20. https://dx.doi.org/10.1017/pds.2021.16.CrossRefGoogle Scholar
Noortman, R., Lovei, P., Funk, M., Deckers, E., Wensveen, S., and Eggen, B. (2022) Breaking up data-enabled design: expanding and scaling up for the clinical context. AIEDAM 36(19): 1–13.CrossRefGoogle Scholar
Orlovska, J., Wickman, C., and Soderberg, R. (2020) The Use of Vehicle Data In ADAS Development, Verification and Follow-up on The System. In International Design Conference. https://dx.doi.org/10.1017/dsd.2020.322.CrossRefGoogle Scholar
Porter, M., and Heppelmann, J. (2014) How Smart, Connected Products Are Transforming Competition. Harvard Business Review : 23.Google Scholar
Riesener, M., Dölle, C., Becker, A., and Schuh, G. (2019) Framework for the Continuous Increase of Product Performance by Analyzing Product Usage Data. In IEEE International Conference on Industrial Engineering and Engineering Management.CrossRefGoogle Scholar
Shin, J., Kiritsis, D., and Xirouchakis, P. (2015) Design modification supporting method based on product usage data in closed-loop PLM. International Journal of Computer Integrated Manufacturing 28(6): 551–568.CrossRefGoogle Scholar
Singh, V., and Willcox, K. E. (2021) Decision Making Under Uncertainty for a Digital Thread Enabled Design Process. Journal of Mechanical Design 143(9): 1–12.CrossRefGoogle Scholar
Stavrakos, SK., and Ahmed-Kristensen, S. (2016) Methods of 3D data applications to inform design decisions for physical comfort. Work 55(2). pp. 321–334.CrossRefGoogle ScholarPubMed
Stavrakos, SK., Ahmed-Kristensen, S., and Goldman, T. (2016) Using archetypes to create user panels for usability studies: Streamlining focus groups and user studies. Appl Ergon. 56. pp. 108–116.CrossRefGoogle ScholarPubMed
Tan, X., Chen, W., Cao, J., and Ahmed-Kristensen, S. (2020) Identify Critical Data During Product Customisation-A Case Study of Orthoses Fabrication. In International Design Conference. https://dx.doi.org/10.1017/dsd.2020.105.CrossRefGoogle Scholar
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., and Sui, F. (2018) Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology 94: 3563–3576.CrossRefGoogle Scholar
Tranfield, D., Denyer, D., and Smart, P. (2003) Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review *. British Journal of Management 14: 207–222.CrossRefGoogle Scholar
van den Heuvel, R., Driesse, E., Dekker, M., and Calota mscalota, M. (2020) Understanding Routines Around Medicine Intake through a Data-Enabled Design approach. In UbiComp 2020. ACMGoogle Scholar
van den Heuvel, R., Lévy, P., Vos, S., and Hummels, C. (2020) Exploring Public Playgrounds through A Data-Enabled Design Approach. In DIS (Designing Interactive Systems). https://dx.doi.org/10.1145/3393914.3395865.CrossRefGoogle Scholar
van den Heuvel, R., van Bussel, T., and Lallemand, C. (2022) Habilyzer: A User-Driven Open-Ended Sensor Kit for Office Workers. In CHI- Computer Human Interaction.CrossRefGoogle Scholar
van Eck, M., Markslag, E., and Sidorova, N. (2019) Data-Driven Usability Test Scenario Creation. In International Federation for Information.CrossRefGoogle Scholar
Versteegden, D., van Himbeeck, M., Burghoorn, A. W., Lovei, P., Deckers, E., Jansen, J.-M., and Nienhuijs, S. (2022) The Value of Tracking Data on the Behavior of Patients Who Have Undergone Bariatric Surgery: Explorative Study. JMIR 6(5): 1–8.Google ScholarPubMed
Wang, Z., Chen, H., Zheng, P., Li, X., and Khoo, P. (2019) A novel data-driven graph-based requirement elicitation framework in the smart product-service system context. Advanced Engineering Informatics 42: 1–11.CrossRefGoogle Scholar
Wang, Z., Chen, C. H., Zheng, P., Li, X., and Khoo, L. P. (2021) A graph-based context-aware requirement elicitation approach in smart product-service systems. International Journal of Production Research 59(2): 635–651.CrossRefGoogle Scholar
Watanabe, K., Okuma, T., and Takenaka, T. (2020) Evolutionary design framework for Smart PSS: Service engineering approach. Advanced Engineering Informatics 45: 1–11.CrossRefGoogle Scholar
Wilberg, J., Triep, I., Hollauer, C., and Omer, M. (2017) Big Data in Product Development: Need for a Data Strategy. In Proceedings of PICMET.CrossRefGoogle Scholar
Yang, B., and Xiao, R.-B. (2021) Data-Driven Product Design and Axiomatic Design. In 2021 IEEE International Conference on Progress in Informatics and Computing (PIC). https://dx.doi.org/10.1109/PIC53636.2021.9687021.CrossRefGoogle Scholar
Zhang, L., Chu, X., Chen, H., and Xue, D. (2017) Identification of Performance Requirements for Design of Smartphones Based on Analysis of the Collected Operating Data. Journal of Mechanical Design, Transactions of the ASME 139(11).CrossRefGoogle Scholar
Zhang, L., Chu, X., Chen, H., and Yan, B. (2019) A data-driven approach for the optimisation of product specifications. International Journal of Production Research 57(3): 703–721.CrossRefGoogle Scholar
Zheng, P., Chen, H., and Shang, S. (2019) Towards an automatic engineering change management in smart product-service systems – A DSM-based learning approach. Advanced Engineering Informatics 39: 203–213.CrossRefGoogle Scholar
Zheng, P., Lin, T. J., Chen, C. H., and Xu, X. (2018) A systematic design approach for service innovation of smart product-service systems. Journal of Cleaner Production 201: 657–667.CrossRefGoogle Scholar
Zheng, P., Liu, Y., Tao, F., Wang, Z., and Chen, C.-H. (2019) Smart Product-Service Systems Solution Design via Hybrid Crowd Sensing Approach. IEEE Access 7: 128463–128473.CrossRefGoogle Scholar
Zheng, P., Wang, Z., Chen, C. H., and Pheng Khoo, L. (2019) A survey of smart product-service systems: Key aspects, challenges and future perspectives. Advanced Engineering Informatics 42: 1–19.CrossRefGoogle Scholar
Zheng, P., Xu, X., and Chen, H. (2020) A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment. Journal of Intelligent Manufacturing 31: 3–18.CrossRefGoogle Scholar
Zhou, X., Ahmed, B., Aylor, J. H., Asare, P., and Alemzadeh, H. (2021) Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems. In International Conference on Dependable Systems and Networks.CrossRefGoogle Scholar