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Gravitational intelligent decision-making model at the fuzzy front end with extrinsic idea integration by the K-Means algorithm
(MDPI, 2022) Kurt, Onur Emre; Vayvay, Özalp
If the dynamic fuzziness of the Front End (FE) part of New Product Development (NPD) cannot be treated in a timely manner, fuzziness accumulates over other parts of NPD hence NPD can result in costly mistakes. The authors tried to remedy this strategically critical problem by implementing mainstream theoretical/methodological approaches, but they found inherent weaknesses of each. The purpose of this study is to bring an objective and intelligent decision-making model to FE so as to lessen fuzziness of it. Model quantizes ideas based on pillars, and re-clusters them with every new idea addition thanks to combining non-mainstream approaches like K-means, distance-based algorithm with gravitational theory inspiration, an accumulation of idea and an exponential function. Study showed that fuzziness of FE can be lessened by quantizing, and objectively managing. The founded core reasons of fuzziness can guide practitioners and authors for better understanding and coping with fuzziness of FE; moreover, the model can be used by companies. Introducing an objective and intelligent decision-making model working like a human brain to FE is a unique idea that has not been tried ever before.