A conceptual framework of generative customization as an approach to product innovation and fulfillment

Jack Buffington and Donald McCubbrey

European Journal of Innovation Management, 2011, vol. 14, issue 3, 388-403

Abstract: Purpose - The purpose of this paper is to address the problem in research of a lack of a holistic conceptual framework related to incremental and discontinuous innovation. Too often, the term “innovation” is used without an understanding of how it can be applied in a product design and supply chain fulfillment system. In this study, the goal is to develop a holistic “innovation continuum” to treat innovation as a complex adaptive system (CAS). Design/methodology/approach - A comprehensive literature review was conducted, and from it, four propositions and a conceptual framework were developed. Findings - The authors determined that an “innovation continuum” can be established through the use of a complex adaptive system. Two, incremental innovation is determined to possess adaptive qualities, and can be enhanced through the use of collaboration and evolutionary algorithms. Three, discontinuous innovation is established as a creative process enabled through expert designers, and improved through the use of generative design. Next, a supply chain system for incremental innovation can be assisted by the use of adaptive systems, but it is not proven that a generative customization system (for discontinuous innovation) can be improved through the use of a CAS. Originality/value - The paper has established an “innovation continuum” linking incremental and discontinuous innovation within one holistic system. It establishes the use of evolutionary algorithms to improve the viability of incremental innovation, and generative design for discontinuous innovation. Finally, the concept of generative customization as an end to end product design and supply chain fulfillment solution for discontinuous innovation is established.

Keywords: Generative customization; Generative design; Product design; Innovation; Genetic algorithms (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ejimpp:14601061111148852

DOI: 10.1108/14601061111148852

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