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A data-driven framework for new product development: Integrating QFD and FCM based on online customer reviews

Author

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  • Raafat, Romina
  • Dahooie, Jalil Heidary
  • Garces, Edwin
  • Daim, Tugrul

Abstract

Today, companies increasingly utilize customer-driven approaches like Quality Function Deployment (QFD) in new product development (NPD) to differentiate their products from competitors and satisfy customer needs and demands. Despite QFD's many capabilities in product design based on customer opinions, its practical implementation faces numerous challenges, such as heavy reliance on personal opinions for identifying and assessing the importance of customer requirements (CRs) and engineering characteristics (ECs), as well as the difficulty of understanding interactions between them. This problem worsens when there are connections and correlations between the identified requirements and characteristics. Given the benefits of extracting CRs from product reviews over traditional methods such as questionnaires and interviews, this study proposes a data-driven framework that combines data mining techniques and multi-attribute decision-making to tackle these issues. In this framework, online customer reviews (OCRs) are considered at all stages of NPD to maximize customer involvement, and association rule mining (ARM) is employed to discover causal relationships and weights among CRs, ECs, and their interactions. Additionally, by applying the Fuzzy Cognitive Map (FCM) method and integrating it with QFD, the relationships between CRs and ECs are analyzed, and CRs are prioritized accordingly. To demonstrate the practical application of this data-driven development framework, it is applied to the development of a mobile phone product using OCRs from Amazon.

Suggested Citation

  • Raafat, Romina & Dahooie, Jalil Heidary & Garces, Edwin & Daim, Tugrul, 2026. "A data-driven framework for new product development: Integrating QFD and FCM based on online customer reviews," Technological Forecasting and Social Change, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:tefoso:v:225:y:2026:i:c:s0040162525005542
    DOI: 10.1016/j.techfore.2025.124523
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