Effective Production Planning and Scheduling: Literature Review

Abstrak

This study investigates the integration of production planning and scheduling by leveraging advanced technologies such as machine learning, data analytics, and Industry 4.0 innovations to enhance operational efficiency and responsiveness in manufacturing. Employing a mixed-method approach, this research combines quantitative analysis of empirical data with qualitative insights from industry case studies. The study evaluates the effectiveness of advanced planning and scheduling (APS) systems through real-world data and stakeholder interviews, focusing on industries implementing or implementing Industry 4.0 technologies. The findings demonstrate that integrating production planning and scheduling significantly improves resource utilization, reduces lead times, and enhances adaptability to dynamic manufacturing environments. Machine learning and data analytics provide potent predictive and adaptive decision-making tools, while Industry 4.0 technologies enable real-time monitoring and control. These results confirm the hypothesis that advanced APS systems outperform traditional methods in managing variability and uncertainty, aligning with existing theories and expanding on previous research. This study contributes valuable insights into the scientific understanding and practical application of advanced production planning and scheduling techniques. The research highlights the transformative potential of integrating machine learning, data analytics, and Industry 4.0 technologies, offering a comprehensive framework for manufacturers. Despite its limitations, the study provides a foundation for future research to explore broader contexts and long-term impacts, guiding further enhancements in manufacturing efficiency and competitiveness.