## 2 Related Work
### Log Data Analysis  
Real time prediction in digital twins helps with making appropriate decisions in short and medium term scenarios based on the current state of the process, and uses digital systems in a virtual space rather than manufacturing systems in a real space [14]. In such a smart factory, process prediction technology can be used to analyze and evaluate the production line before implementing the process offline. It can also be used as a  
Figure 1: Digital twin framework. proactive model to prevent risk factors that may occur in the process. By using these two methods, managers who set the plant's operation plan can make more efficient and reliable decisions, and it is possible to prevent plans from being negatively affected by unexpected external factors. In recent years, the demand for customized products has increased significantly in the market, and accordingly, the manufacturing process is also changing at a rapid pace. The existing approaches to digital twins are based on a model structure manufactured by company or consulting experts. However, recent research has focused on data driven technologies such as event log data rather than expert consulting, which requires a relatively long time and a large cost to respond to the rapidly changing consumption structure and system characteristics. Research using the process mining method to track and determine various characteristics is becoming more popular [15]. Process mining techniques can estimate the relationship between the change in outcome according to the state or parameters that implement the state. Therefore, process mining can be used to estimate the outcome or cause of a process in manufacturing scenarios [16]. Ref. [17] tried to determine why delays occurred by analyzing the event log data and using decision trees to find the cause of bottlenecks in the process. Ref. [18] tried to predict the next process based on events that occurred previously in a business process using a natural language processing model. Through this, the author tried to adjust the factors that hinder the operation of the process based on the process mining technique. Ref. [19] proposed a method for automatically discovering manufacturing systems and creating digital twins based on process mining. The author applied the proposed method to the actual manufacturing line and proved that the results were effective. The author of [20] tried to implement a digital twin using the process mining technique in building manufacturing, and through this, he tried to predict the factors that could fail in the current process. This enables tactical decision-making by preventing factors that disrupt the entire process in advance. Ref. [21] tried to implement a manufacturing digital twin based on process mining technology. In addition, the author tried to efficiently control the assets of the facility by combining RTLS (Realtime Locating System) with the digital twin. This led to optimization of the process operation and was intended to reduce the waste factor of operation. As mentioned above, process mining technology can explain the correlation between process results and causes by considering event log data. Log data in the manufacturing process are accumulated in information systems at a faster and more rapid pace with the development of IoT technology. These data are recorded and stored on the server every day. Detailed documentation of these processes can help with predictions, but it remains difficult to fully understand the data. The log summarizes all processes generated in the manufacturing system as an event, and is structured in the format shown in Table 1. Activities are tracked according to a sequential order ID, which typically contains information such as a timestamp and associated costs, including the start time and end time. There may be multiple cases that follow the same trace, but each case is separate and considered as a different event.