The goal of smart factories is to improve productivity and reduce production costs, but it is more important to attain manufacturing competitiveness through improvements to product quality and yield. As product functions become more advanced and processing becomes increasingly miniaturized, the yields of micro-manufacturing processes have become an important management factor, determining the production cost and quality of a product. Micro-manufacturing processes generally pass through many stages to produce a product; therefore, it is difficult to find the process or piece of equipment where a fault has occurred. As such, it is difficult to realistically ensure high yields. This paper presents an S-EES (smart-equipment engineering system) construction and big data analysis methodology for manufacturing to increase product yield and quality in a smart factory environment. It also presents plans for acquiring the data needed for big data analysis of a manufacturing site and for constructing the system. To improve product yield, it is necessary to analyze the fault factors causing low yield; similarly, the critical processes and equipment that affect these fault factors must be identified and managed. However, interrelations exist between pieces of equipment, and complex faults are caused by the downstream as well as upstream in the processing sequence that a certain lot passes through. Because of this, yield management is important but also difficult. This study finds the fault-responsible processes and machines that affect yields by using a method that utilizes PLS-VIP (partial least squares with variable importance of projection) and association rules in micro-manufacturing line processes, and it classifies these processes and machines as single factors or cumulative factors. In addition, it applies the specific methodology to an actual site, extracts the fault-responsible processes and machines, and confirms the effects of important processes and equipment on yields.