A product conceptualization strategy based on crowd-innovation

• Main Author: Chang, Danni
• Other Authors: Lee Ka Man
• Format: Theses and Dissertations
• Language: English
• Published: 2016
• Subjects: DRNTU::Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/65928
• Institution: Nanyang Technological University

Product conceptualization plays a critical role in new product development. Research has shown that 75–80% of a product’s lifecycle cost is determined during the conceptual design phase. Thus, achieving a competitive edge at the front end could lead to more opportunities for successful final products. However, to realize product innovation in the conceptual design stage is challenging. The incomplete and fuzzy nature of this phase causes difficulties in creating a reliable innovation environment. To tackle this problem, a possible solution from the resource-based view (RBV) is to involve external resources and obtain contributions from multiple facets. In this regard, various design strategies, such as open innovation and value co-creation, have been proposed, through which the potential of utilizing the dispersed knowledge distributed among the crowd for creativity is deeply recognized. Hence, crowd wisdom could be a heterogeneous organizational knowledge resource that deserves further research. Therefore, this study seeks to contribute to the utilization of crowdsourcing in product conceptualization for fostering innovative designs and for advancing the research in this field. The integration of crowdsourcing in product conceptualization is a complex task. Firstly, crowdsourcing currently offers only a basic conceptual scheme to connect assigners and contributors and lacks systematic management to obtain effective entries with guaranteed quality. Secondly, product conceptualization consists of a series of consecutive activities, i.e., concept generation, concept evaluation, and selection. Therefore, the integration of crowdsourcing should simultaneously consider each design process. Thirdly, crowdsourcing responses are often in large numbers and in various formats, which leads to further difficulties in dealing with crowdsourced conceptual designs. Specific issues, such as how to incorporate crowdsourcing to facilitate concept generation for the creation of innovative concepts, how to implement concept evaluation and selection in a crowdsourcing environment, and how to verify crowdsourced conceptual designs further in terms of innovativeness, have to be addressed well. In this work, a product conceptualization strategy based on crowd-innovation (PCSCI) is proposed consisting of three sub-systems: web-based knowledge acquisition platform (WKAP), artificial intelligence-based innovative concept discovery platform (AI-ICDP), and concept learning and retention platform (CLRP). Specifically, the WKAP stimulates concept generation and consolidates the collected concepts into a well-organized knowledge base. For this purpose, a crowdsourcing platform development approach is established with careful consideration regarding target analysis, task design, and cheating control. The AI-ICDP selectively extracts concept knowledge from the knowledge base and identifies promising design concepts. In this sub-system, a text mining process is deployed for knowledge extraction, a concept reconstruction strategy is proposed for unifying concept formats, and a concept clustering process is employed for simplifying concept comparison and selection. The third sub-system, i.e., CLRP, examines the competitive edges of the concept candidates selected by the AI-ICDP in terms of innovativeness and retains the concepts with better performance. An enhanced innovation evaluation method based on grey and fuzzy theories is developed. For each sub-system, a numerical illustration based on a future personal computer (PC) design project is presented. The results demonstrate that the proposed PCSCI has advantages in gaining more effective contributions (by WKAP), identifying promising concept candidates more efficiently (by AI-ICDP), and achieving a more scientific and rational measurement of innovativeness (by CLRP). In conclusion, this research creates a collective intelligence environment for product conceptualization, advances the applications of computational intelligence in the conceptual design stage, and lays the foundation for further exploration in related areas.