## 3 MSP Algorithms and Master Data in Production Planning System
### Forecasting Variable Master Data  
Research has revealed that most manufacturers have experienced consequences related to additional production resources, extended lead times, reduced product quality, and low performance owing to data management issues. This indicates that data errors are common in business processes [21]. Master data management plays a highly important role in production planning. In the production process, baseline information such as the production volume and production time comprise the core information required to establish production plans and operate efficiently. Among the master data, tact time and yield mostly have constant values. However, these display inconsistent and unstable characteristics in an altered manufacturing environment, such as manual processes, newly developed processes, or the addition of new facilities [22]. Rather than manage the master data as user-managed baseline information, machine-learning algorithms are used to make predictions based on process data such as performance, to establish production plans [23]. Accurate forecast data can optimize the production schedule guidance to obtain the optimization of the total production time. Therefore, ensuring the proper use of existing resources to meet the basic requirements of the production schedule has important theoretical significance for the actual production of the enterprise [24]. The target master data to be predicted are defined as follows. 1. Tact Time: The time required to produce one product. In the production process, tact time is an important factor that determines the production speed of the production line. For efficient production, it is necessary to optimize the tact time of the production line and establish a production plan based on it. Keeping accurate and up-to-date information about tact time through master data management ensures that production plans are well-aligned with actual production. 2. Yield: Indicates how many of the products produced during the production process meet the quality standards. Yield directly affects product quality and production performance, so it is an important factor to consider when planning production. Figure 3 shows that the existing IT system is implemented such that users master data information through the UI, whereas the new IT system collects and analyzes data through sensors located in resources other than the users. It predicts the current data as well as the future data to be used for planning. Manufacturing processes require flexibility and the capability to reconfigure themselves to address critical challenges. To achieve this, it is important to obtain relevant information in real time to make strategic decisions, optimally utilize available resources, and remain competitive in the market. A method to achieve this is to use machine-learning to predict and provide effective data. Machine-learning algorithms are utilized to improve production planning and address scheduling challenges [25]. It is challenging for conventional job-planning techniques to obtain effective optimal solutions to complex distributed resource-planning problems using individual numerical analysis methods [26]. Thus, machine-learning algorithms have been used to develop methods to improve production planning. In this study, the MSP algorithm is used in the API to predict the values of the two types of master data. The objective is to establish a production plan and compare it with the existing master data plan, and thereby help users finalize the production plan. Artificial Intelligence (AI) is an emerging branch of data analysis, widely used when trying to obtain intrinsic relationships between data [27]. This is a new way of analyzing data to understand relationships. It utilizes machine learning algorithms as a prediction method. Machine learning algorithms can be broadly categorized into supervised learning, unsupervised learning, and reinforcement learning [19]. Supervised learning is a method that uses input data and data with correct answers (labels) to train a model.