When advanced automation technologies are applied in the semiconductor industry, advanced equipment control has become increasingly feasible; hence it is now easier to improve overall manufacturing performance, including cycle time, productivity, and product quality. Currently, Fault Detection and Classification (FDC) is a newly deployed system that supports tool control in the semiconductor industry. Based on comprehensive and real time tool sensor data of an FDC infrastructure, numerous novel multivariate applications have been developed to further enhance tool and manufacturing productivity. However, when most multivariate applications were designed and pilot run in production environment, they all encountered a similar challenge - manufacturability. Because of insufficiency of manufacturing know-how and manufacturability, most multivariate applications cannot be widely deployed to an entire FAB for all types of tools and processes. This work investigates various multivariate applications and presents a novel methodology for improving manufacturability design of multivariate applications in the semiconductor industry.