The entire industry is increasing the use of big data analysis using artificial intelligence technology due to the Fourth Industrial Revolution. The value of big data is increasing, and the same is true of the production technology. However, small and medium -sized manufacturers with small size are difficult to use for work due to lack of data management ability, and it is difficult to enter smart factories. Therefore, to help small and medium -sized manufacturing companies use big data, we will predict the gross production time through machine learning. In previous studies, machine learning was conducted as a time and quantity factor for production, and the excellence of the ExtraTree Algorithm was confirmed by predicting gross product time. In this study, the worker's proficiency factors were added to the time and quantity factors necessary for production, and the prediction rate of LightGBM Algorithm knowing was the highest. The results of the study will help to enhance the company's competitiveness and enhance the competitiveness of the company by identifying the possibility of data utilization of the MES system and supporting systematic production schedule management.

Production management in the automobile parts industry is carried out according to the production plan of the customer, so it is important to prevent shortages in product supply. As the product composition became increasingly complex, the MES System was built for the purpose of efficient production plan management and inventory management, but its utilization is low. This study analyzed the problems of the MES system and sought to improve it. Through previous studies, it was confirmed that the inventory management of the pull approach that actually occurred in the warehouse is more suitable than the push approach based on the forecast of the warehouse for the volatility, complexity, and uncertainty of orders in the auto parts industry. To realize this, we tried distributed MRP by using the ADO function of VBA to link the standard information of the MES system with Excel and change the structure of the BOM table. Through this, it can help increase the accuracy of production planning and realize efficient inventory management, thereby increasing the utilization of the MES system in the auto parts industry and enhancing the competitiveness of the company.