Static Cloud Vector for Computing the General Quality performance of Outsourcing components in Complex Product Supply Chain
Yuan Liu, Liuqi Zhan, Jingjing Hao · 2024
In the supply chains for complex products, customized and digitally crafted outsourcing components sourced globally from small samples face challenges in quality inspection. Subjective and objective factors lead to ambiguity and randomness in data, forming the cloud area of quality performance. This study introduces a novel data-driven cloud model to quantify this quality performance. We develop a static quality cloud vector and propose the Signal-to-Noise (SN) ratio to assess data stability. The weight of the cloud vector is designed based on the SN of the cloud. A case study in aircraft supply chains validates our model's feasibility and effectiveness, ultimately aiding manufacturers in better understanding and controlling outsourced quality levels.