A Data-Driven Fuzzy Front End Model for Contextual Performance and Concurrent Collaboration
针对新产品开发模糊前端阶段参数处理的不确定性和模糊性，提出一个数据驱动模型，通过分析真实场景构建代表性绩效结构，帮助用户理解参数关系以减少歧义。
A data-driven model for the fuzzy front end (FFE) stage in new product development (NPD) programs, with a series of toolkits to decrease uncertainty and ambiguity of parameter processing, has been developed. Parameters produced in toolkits provided in previous models tend to exist independently, without any interrelationship in the contextual performance relationship of a single functional domain nor concurrent collaboration relationship across multiple functional domains. This results in uncertainty and ambiguity triggered by an incorrect interpretation of parameters. The new model involved inferring a single representative FFE scenario wherein diverse FFE performance structures interlock from the contextual performance and concurrent collaboration perspectives by analyzing various real-world FFE scenarios gathered from NPD expert interviews. This representative scenario was embodied into the model with a performative structure, through deployment of toolkits. Users are informed of the purpose, roles, and meanings of parameters and their relationships and thus can infer each parameter from other parameters. This contributes to reduction in uncertainty and ambiguity in processing parameters. This article proposes an FFE execution concept, giving mathematical reasoning behind the performance structure of the model.