###### Abstract  
With the increasing adoption of smart factories in manufacturing sites, a large amount of raw data is being generated from manufacturers' sensors and Internet of Things devices. In the manufacturing environment, the collection of reliable data has become an important issue. When utilizing the collected data or establishing production plans based on user-defined data, the actual performance may differ from the established plan. This is particularly so when there are modifications in the physical production line, such as manual processes, newly developed processes, or the addition of new equipment. Hence, the reliability of the current data cannot be ensured. The complex characteristics of manufacturers hinder the prediction of future data based on existing data. To minimize this reliability problem, the M5P algorithm, is used to predict dynamic data using baseline information that can be predicted. It combines linear regression and decision-tree-supervised machine learning algorithms. The algorithm recommends the means to reflect the predicted data in the production plan and provides results that can be compared with the existing baseline information. By comparing the existing production plan with the planning results based on the changed master data, it provides data results that help production management determine the impact of work time and quantity and confirm production plans. This means that forecasting data directly affects production capacity and resources, as well as production times and schedules, to help ensure efficient production planning. production planning; predictive modeling; master data; machine learning; M5P Algorithm 2023137829. 13 June 202329 June 202329 June 202333 June 20233  
be used to improve the quality of data, optimize processes, and make better decisions [5]. Since the Industrial Revolution, the mass production system has been discarded completely in favor of multi-product, low-volume production to satisfy the complex and diverse needs of customers. Moreover, the life-cycle of products has been shortened. The complexity of the manufacturing process from demand to production to shipment has increased exponentially with technological advances. Manufacturers have factories in multiple locations that provide products through multiple supply chains. In this increasingly complex manufacturing environment with multiple tradeoffs, production planning within a given capacity should be performed appropriately to satisfy various constraints and demands. In the current competitive and dynamic business environment, reliable data has become even more important with the advancement of Internet of Things (IoT) and cloud technology. Furthermore, predicting and utilizing data from numerous sources remains a key challenge in the current situation. Production planning has a wide range of influences on SCM. The objective of SCM is to improve efficiency, quality, productivity, and customer satisfaction. It plays an important role in achieving these objectives [6]. Production planning optimizes the use of raw materials and resources, coordinates production volumes and schedules, and efficiently manages the production process. It can help identify and improve problems in areas such as inventory management, production line efficiency, and productivity. Thereby, it provides factors that can have a positive impact [7]. It learns baseline information data (which form the basis of production planning according to performance data) and makes predictions using machine learning, compares with and reflects on existing master data, establishes plans through the production planning system, and provides indicators to help manage production. Section 1 presents the background and purpose of the study, as well as the methodology and organization of the study. Section 2 describes the importance of master data from the perspective of the manufacturing industry before outlining the contents of the study. Section 3 describes the algorithms applied from a technical point of view and discusses previous research.