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Innovation: A data-driven approach


  • Kusiak, Andrew


A newly introduced product or service becomes an innovation after it has been proven in the market. No one likes the fact that market failures of products and services are much more common than commercial successes. A data-driven approach to innovation is proposed. It is a natural extension of the system of customer requirements in terms of their number and type and the ways of collecting and processing them. The ideas introduced in this paper are applicable to the evaluation of the innovativeness of planned introductions of design changes and design of new products and services. In fact, blends of products and services could be the most promising way of bringing innovations to the market. The most important toll gates of innovation are the generation of new ideas and their evaluation. People have limited ability to generate and evaluate a large number of potential innovation alternatives. The proposed approach is intended to evaluate many alternatives from a market perspective.

Suggested Citation

  • Kusiak, Andrew, 2009. "Innovation: A data-driven approach," International Journal of Production Economics, Elsevier, vol. 122(1), pages 440-448, November.
  • Handle: RePEc:eee:proeco:v:122:y:2009:i:1:p:440-448

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    References listed on IDEAS

    1. Albino, Vito & Carbonara, Nunzia & Giannoccaro, Ilaria, 2006. "Innovation in industrial districts: An agent-based simulation model," International Journal of Production Economics, Elsevier, vol. 104(1), pages 30-45, November.
    2. Gilbert, Nigel & Ahrweiler, Petra & Pyka, Andreas, 2007. "Learning in innovation networks: Some simulation experiments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 100-109.
    3. Eric von Hippel, 1986. "Lead Users: A Source of Novel Product Concepts," Management Science, INFORMS, vol. 32(7), pages 791-805, July.
    4. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    5. Gallouj, Faiz & Weinstein, Olivier, 1997. "Innovation in services," Research Policy, Elsevier, vol. 26(4-5), pages 537-556, December.
    6. Koskinen, Kaj U. & Vanharanta, Hannu, 2002. "The role of tacit knowledge in innovation processes of small technology companies," International Journal of Production Economics, Elsevier, vol. 80(1), pages 57-64, November.
    7. Bordoloi, Sanjeev & Guerrero, Hector H., 2008. "Design for control: A new perspective on process and product innovation," International Journal of Production Economics, Elsevier, vol. 113(1), pages 346-358, May.
    8. Hacklin, Fredrik & Marxt, Christian & Fahrni, Fritz, 2006. "Strategic venture partner selection for collaborative innovation in production systems: A decision support system-based approach," International Journal of Production Economics, Elsevier, vol. 104(1), pages 100-112, November.
    9. de Vries, Erik J., 2006. "Innovation in services in networks of organizations and in the distribution of services," Research Policy, Elsevier, vol. 35(7), pages 1037-1051, September.
    10. Henkel, Joachim, 2006. "Selective revealing in open innovation processes: The case of embedded Linux," Research Policy, Elsevier, vol. 35(7), pages 953-969, September.
    11. Philippe Aghion & Peter Howitt, 1997. "Endogenous Growth Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262011662, September.
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    Cited by:

    1. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.
    2. Adriana Andrea Amaya & Ying-Kai Liao & Sixto Chang, 2019. "The Effects Of Innovation Implementation And Speed To Market On The Relationship Between Team Sense-Making, Trust, And Npd Success," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 23(04), pages 1-29, May.
    3. Hong-Sen Yan & Wen-Chao Li, 2017. "A multi-objective scheduling algorithm with self-evolutionary feature for job-shop-like knowledgeable manufacturing cell," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 337-351, February.
    4. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    5. Prajogo, Daniel I., 2016. "The strategic fit between innovation strategies and business environment in delivering business performance," International Journal of Production Economics, Elsevier, vol. 171(P2), pages 241-249.


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