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Innovation: A data-driven approach

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  • Kusiak, Andrew

Abstract

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

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    Cited by:

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    2. Aksoy, Hasan, 2017. "How do innovation culture, marketing innovation and product innovation affect the market performance of small and medium-sized enterprises (SMEs)?," Technology in Society, Elsevier, vol. 51(C), pages 133-141.
    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. H. Kava & K. Spanaki & T. Papadopoulos & S. Despoudi & O. Rodriguez Espindola & M. Fakhimi, 2024. "Data analytics diffusion in the UK renewable energy sector: an innovation perspective," Post-Print hal-04478933, HAL.
    5. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    6. Wei, Zelong & Huang, Wengao & Wang, Yanping & Sun, Lulu, 2022. "When does servitization promote product innovation? The moderating roles of product modularization and organization formalization," Technovation, Elsevier, vol. 117(C).
    7. Mortati, Marzia & Magistretti, Stefano & Cautela, Cabirio & Dell’Era, Claudio, 2023. "Data in design: How big data and thick data inform design thinking projects," Technovation, Elsevier, vol. 122(C).
    8. 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.
    9. Marija Mosurović Ružičić & Mirjana Miletić & Marina Dobrota, 2021. "Does a National Innovation System Encourage Sustainability? Lessons from the Construction Industry in Serbia," Sustainability, MDPI, vol. 13(7), pages 1-19, March.
    10. 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.
    11. 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.

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