MANUFACTURING MANAGEMENT SYSTEM: CORE 1 PRODUCTION AND PLANNING (MASTER PRODUCTION SCHEDULING , MATERIALS REQUIREMENTS PLANNING , WORK ORDER MANAGEMENT , BILL OF MATERIALS MANAGEMENT) WITH DEMAND FORECASTING USING TENSORFLOW

Abstract

In the manufacturing sector, optimizing efficiency and responsiveness is critical to sustaining operational competitiveness. Integrating core production functions, such as master production scheduling, materials requirements planning, work order management, and bill of materials handling, into a unified digital system can significantly improve operational performance. This study presents a Manufacturing Management System that incorporates TensorFlow-based demand forecasting to enhance planning accuracy and overall productivity. Historical datasets, including sales, inventory, and production records, were collected from a manufacturing firm to train deep learning models using TensorFlow. Neural networks and time series forecasting techniques were applied to generate demand predictions. These forecasts were embedded within the master production scheduling and materials requirements planning modules to support dynamic decision-making. Additional modules for work order and bill of materials management enabled coordinated execution across production tasks. System validation involved testing with real-world data to evaluate forecasting accuracy and operational performance. The TensorFlow-driven forecasting model reduced prediction errors by over 20% compared to traditional forecasting approaches. This resulted in more accurate production scheduling, better inventory optimization, and reduced instances of stockouts. The work order module enabled more streamlined task execution, increasing throughput and aligning production closely with demand. Collectively, the system improved manufacturing agility, resource utilization, and cost efficiency. The study demonstrates that integrating AI-based demand forecasting into production planning significantly enhances manufacturing performance. While deep learning models outperform conventional methods, challenges such as data quality and computational resource demands persist. Future development may explore real-time data integration and broader AI applications to further optimize manufacturing workflows. The system validates the effectiveness of using TensorFlow for demand forecasting in core production planning, highlighting its role in building resilient and responsive manufacturing systems.