Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-06-01T16:57:37.512Z Has data issue: false hasContentIssue false
  • English
  • Français

D³IKIT: data-driven design innovation kit

Published online by Cambridge University Press:  16 May 2024

Boyeun Lee  and
Saeema Ahmed-Kristensen
Boyeun Lee*
Affiliation:
University of Exeter Business School, United Kingdom
Saeema Ahmed-Kristensen
Affiliation:
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.

The utilization of data in design is a crucial aspect of shaping the product and service development. Despite the lack of extensive research on this subject, this study aims to bridge the gap by introducing the ‘D³IKIT’, a data-driven design process and toolkit. Through workshops, this process and toolkit offer a practical method for creating innovative product and service concepts using data and machine learning. Developed and tested with the participation of 42 individuals, the ‘D³IKIT’ provides valuable insights for both practitioners and academics.

Keywords

data-driven designinternet of things (IoT)data-driven innovationservice design
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2109 - 2118
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), 2024.

References

King, R., Churchill, E. F., and Tan, C. (2017) Designing with Data: Improving the User Experience with A/B Testing. (1st ed.). O'Reilly.Google Scholar
Osterwalder, A., and Pigneur, Y. (2010) Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. A handbook for visionaries, game changers, and challengers : 288. https://dx.doi.org/10.1523/JNEUROSCI.0307-10.2010.CrossRefGoogle Scholar
Speed, C., Lee, B., and Hands, D. (2019) The Little Book of Creating Value through Design in the IoT. (Coulton, C ed), Lancaster UniversityGoogle 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. https://doi.org/10.3846/cs.2021.12933CrossRefGoogle Scholar
Chattopadhyay, P., Mondal, S., Bhattacharya, C., Mukhopadhyay, A., and Ray, A. (2017) Dynamic Data-Driven Design of Lean Premixed Combustors for Thermoacoustically Stable Operations. Journal of Mechanical Design, Transactions of the ASME 139(11). https://doi.org/10.1115/1.4037307CrossRefGoogle Scholar
Feng, Y., Zhao, Y., Zheng, H., Li, Z., and Tan, J. (2020) Data-driven product design toward intelligent manufacturing: A review. International Journal of Advanced Robotic Systems : 1–18. https://dx.doi.org/10.1177/1729881420911257.CrossRefGoogle Scholar
Kim, H. H. M., Liu, Y., Wang, C. C., and Wang, Y. (2017) Special Issue: Data-Driven Design. Journal of Mechanical Design, Transactions of the ASME 139(11). https://doi.org/10.1115/1.4037943CrossRefGoogle Scholar
Lee, B., Cooper, R., Hands, D., and Coulton, P. (2022a) Continuous cycles of data-enabled design: reimgining the IoT development process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26, e11, 1-15. https://doi.org/10.1017/S0890060421000299Google Scholar
Lee, B., Hands, D., Cooper, R., and Coulton, P. (2022b) Emergent NPD process and development risks for IoT: An exploratory case study in agri-tech. Int. Journal of Business and Systems Research 16(2): 183–198. https://doi.org/10.1504/IJBSR.2022.121141CrossRefGoogle Scholar
Mathis, K. (2015) Data-driven business models for service innovation in small and medium-sized businesses. Journal of Facilities Management 19:2.Google Scholar
Papalambros, P. (2015) Design Science: Why, What and How. Design Science 1(1): 1–38. https://doi.org/10.1017/dsj.2015.1CrossRefGoogle Scholar
Tao, F., Qi, Q., Liu, A., and Kusiak, A. (2018) Data-driven smart manufacturing. Journal of Manufacturing Systems 48: 157–169. https://doi.org/10.1016/j.jmsy.2018.01.006CrossRefGoogle 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, Nl Janne, Frens, S. J. A. B., and Djajadiningrat, J., T. (2016) Data-Enabled-Design: A Situated Exploration of Rich Interactions. In DIS (Designing Interactive Systems). Brisbane, Australia https://dx.doi.org/10.1145/2908805.2913015.CrossRefGoogle Scholar
Diamond, S., Szigeti, S., and Jofre, A. (2017) Building Tools for Creative Data Exploration: A Comparative Overview of Data-Driven Design and User-Centered Design. In International Conference on Distributed, Ambient and Pervasive Interactions. https://dx.doi.org/10.1007/978-3-319-58697-7_39.CrossRefGoogle Scholar
Dove, G., Halskov, K., Forlizzi, J., and Zimmerman, J. (2017) UX Design Innovation: Challenges for Working with Machine Learning as a Design Material. Proceedings of the 2017 CHI conference on Human Factors in Computing Systems. https://dx.doi.org/10.1145/3025453.3025739.Google Scholar
Kim, M., Lim, C., Lee, C., Kim, K., Choi, S., and Park, Y. (2016) Data-driven Approach to New Service Concept Design. In International Conference on Exploring Services Science. https://dx.doi.org/10.1007/978-3-319-32689-4_37.CrossRefGoogle Scholar
Kronsbein, T., and Mueller, R. (2019) Data Thinking: A Canvas for Data-Driven Ideation Workshops. In 52nd Hawaii International Conference on System Sciences.CrossRefGoogle Scholar
Kühne, B., and Böhmann, T. (2020) Formative Evaluation of Data-Driven Business Models-The Data Insight Generator. In Hawaii International Conference on System Sciences. https://dx.doi.org/10.24251/HICSS.2020.053CrossRefGoogle Scholar
Lee, B., Cooper, R., Hands, D., and Coulton, P. (2018) [Re]-imagining vision and values: Design as a driver for value creation in the internet of things. In IET Conference Publications. https://dx.doi.org/10.1049/cp.2018.0023CrossRefGoogle Scholar
Lee, B., Cooper, R., Hands, D., and Coulton, P. (2019) Value creation for IoT: challenges and opportunities within the design and development process. In IET Conference Publications. https://dx.doi.org/10.1049/cp.2019.0127CrossRefGoogle Scholar
Lee, B., and Ahmed-Kristensen, S. (2023) Four Patterns of Data-Driven Design Activities in New Product Development. Proceedings of the Design Society 3: 1925–1934. https://doi.org/10.1017/pds.2023.193CrossRefGoogle Scholar
Gann, D., Parmer, R., and Cohn, D. (2014) The New Patterns of Innovation. Harvard Business Review : 1–10.Google Scholar
Porter, M., and Heppelmann, J. (2014) How Smart, Connected Products Are Transforming Competition. Harvard Business Review : 23.Google Scholar
Lee, B. (2022) Understanding New Product Development and Value Creation for the Internet of Things. Lancaster UniversityGoogle Scholar
Van Kollenburg, J., and Bogers, S. (2019) Data-enabled design : a situated design approach that uses data as creative material when designing for intelligent ecosystems. Eindhoven University of Technology.Google Scholar