# Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance
## 2 Literature Review  
According to many studies, Taiwan is the fifth largest exporter of machinery and machine tools in the world, and also the seventh largest producer [6; 7]. Its main production factory is located in the central region of Taiwan, so the central region of Taiwan is an important industrial center which have various machine tools and machinery [5]. Moreover, Taiwan's machinery and machine tool industry has formed a complete industrial chain driven by a high degree of clustering effect, and has a key position in the industry [5; 6]. In addition, the German government proposed Industry 4.0 in 2011 to promote a fully networked smart manufacturing production environment by connecting information, communication technologies and digital manufacturing technologies, whose core lies in the combination of technologies such as Cyber-Physical technological systems, Cloud manufacturing, Internet of Things (IOT), and additive manufacturing [18]. Using the Cyber-Physical technological systems to promote fully networked smart manufacturing and intelligent production, with the Internet of Things (IOT) and big data analysis development rapid evolution of emerging technologies, it will drive the innovation of the global machinery and machine tool industry, through the integrating and applying related technologies achieve the goal of smart manufacturing [3; 12]. According to some studies, the machinery and machine tool industry needs to improve its market competitiveness. That can increase its operational flexibility because, with the exception of some critical parts and components in which they excel, the majority of the parts and components are typically outsourced [19]. Furthermore, machinery and machine tools are typically made up of hundreds of parts. All component products must be of high quality. To ensure the quality of the final product, the quality of all component products must meet the quality level requirements [4]. After receiving the order, the machine tool manufacturer first designs the mechanism to meet the needs of the customer, then assembles the outsourced parts and components and sells them to the machining factory after the test to process some parts and components to ensure the quality of the entire industrial chain's processes, including machine tools and assembled components, as well as the quality of products processed by machine tools. According to some studies, indicators represent the performance of machinery and machine tools numerically in a unitless manner and are generally used as a tool to evaluate whether the process quality meets the requirements of engineers or customers [20; 21; 22]. In order to analyze the operation performance of the complete industrial chain, this article sets up evaluation indicators for the performance of machinery and machine tools. Since the indicators' parameters are unknown, basing the selection solely on the point estimates of the indicators may increase the likelihood that sampling mistakes will result in incorrect conclusions [9; 12]. As a result, the entire value chain of the industry will benefit from the development of a fuzzy evaluation and improvement decision-making model for machine operation performance based on the confidence interval of the index. Additionally, some academics noted that the machine tool industry can create models for the evaluation and analysis of production data, gather pertinent production data via a network platform, and analyze the data using the model that has been built. This will benefit the entire industry chain, including the company itself and the enhancement of process quality for all suppliers or outsourcers. The rate of process scrapping and rework can be decreased as well as social costs such as carbon emissions brought on by product after-sales repairs when the quality level of the outsourcer's manufacturing process is enhanced. It develops into a \"Green Outsourcer\" and contributes to a sustainable and green economy [23].