This study aims to digitize production planning by utilizing prediction models based on production data and reducing human intervention to increase efficiency. Production data obtained from a real manufacturing system through the Manufacturing Execution System (MES) interface was analyzed using an artificial neural network (ANN) algorithm, and future production quantities were predicted. By integrating the production forecast results into the Enterprise Resource Planning (ERP) system, it was aimed to automatically direct the production processes. Thus, production decisions can be automatically made by the system based on past data. As a result of the implementation, dynamic and data-driven decision-making processes in production management were facilitated through the forecast outputs integrated into the ERP system. This prediction-based approach is more flexible compared to traditional production planning methods and enables quicker responses from the production system. Consequently, this study presents an innovative approach that contributes to digital transformation within the scope of Industry 4.0 and serves as an example for decision support systems in production management. With this study, the development of predictive systems that operate with real-time data flow is aimed for the future.