A Predictive Method for Weak Signal Evolution During New Product Development Based on an Improved Matter-Element Extension Model
提出一种弱信号演化预测方法，通过改进物元可拓模型丰富弱信号信息并排除不具代表性的信号，以钙钛矿光伏电池为例验证，该方法准确性和适用性更高，有助于减少新产品开发中的认知偏差。
New product development process is complex, involving many decisions that based on incomplete and unrepresentative weak signal information. In this case, cognitive bias will affect decision-making significantly, leading to development failure. Therefore, we propose a weak signal evolution prediction method to enrich weak signal information and exclude unrepresentative weak signal information. Specifically, the weak signal evolution prediction method contains three parts. Firstly, on the basis of matter-element extension model, the system diagram approach is used to construct evolutionary three-process prediction index system. Secondly, evolutionary matter elements, classical and joint domains, correlation functions, and weight coefficients are determined. Finally, the discriminant criterion is improved to divide the evolution evaluation stage, and then weak signal evolution prediction method is constructed on the foundation of improved matter-element extension model. We take perovskite photovoltaic cells as an example to verify the performance of the weak signal evolution prediction method proposed in this paper. Results showed that the method proposed in this paper has higher accuracy and applicability, which is more in line with practical situations. The research results further expand the matter element extension model, providing a new approach to reduce cognitive bias in the process of new product development for enterprises, and offering new insights for new product development. Meanwhile, this study also has shortcomings such as a limited research perspective and a single case selection.