Abstract: Aiming at the problem that the fuzziness and randomness of evaluation information were not considered comprehensively in traditional Quality Function Deployment (QFD),an analysis method for engineering characteristics importance degree of Product Service System (PSS) by combining cloud model and QFD was proposed.As cloud model had the advantage that could reflect both the fuzziness and randomness of information,cloud model was employed to process the evaluation information of QFD.A cloud relative preference relation analysis approach was put forward to calculate the importance degree of customer requirements and engineering characteristics.Based on the analysis of technical competitive evaluation matrix,the cloud relative preference relation was used to deal with the matrix,and then the information entropy was employed to compute the modifying factors of engineering characteristics importance degree.According to the modifying factors,the final importance degree of engineering characteristics could be determined.An example of analyzing the importance degree of engineering characteristics of a loader PSS was given to demonstrate the effectiveness of the proposed approach.

Key words: product service system, engineering characteristics, quality function deployment, cloud model, relative preference relation

摘要： 针对传统质量功能展开( QFD)未能综合考虑评价信息中的模糊性和随机性问题,结合云模型能同时反映信息的模糊性和随机性的优势,提出一种基于云模型QFD的产品服务系统工程特性重要度分析方法。采用云模型处理QFD中的评价信息,并提出云相对偏好关系分析方法计算顾客需求及工程特性的重要度。结合对技术竞争性评估矩阵的分析,采用所提云相对偏好关系分析对技术竞争性评估矩阵进行处理,再采用信息熵方法计算工程特性重要度修正因子,进而确定最终的工程特性重要度。以某企业一款装载机产品服务系统工程特性重要度分析为例,验证了所提方法的有效性。

关键词: 产品服务系统, 工程特性, 质量功能展开, 云模型, 相对偏好关系