DAYDREAMING FACTORIES: A DIGITAL TWIN APPROACH FOR FACTORY OPTIMIZATION USING DISCRETE EVENT SIMULATION (DES) TOOLS AND INDUSTRY 4.0 TECHNIQUES

Abstract

The advent of Industry 4.0, marked by the integration cyber-physical systems and the Internet of Things (IoT), has revolutionized the manufacturing sector. Digital Twin (DT) technology is central to this transformation, which serves as a virtual replica of physical assets and processes. This thesis explores implementing and optimizing a DT system in a rubber products manufacturing company, leveraging simulation-based planning and scheduling to enhance operational efficiency. Employing a mixed-methods approach, the study integrates qualitative and quantitative data from company records, expert interviews, and direct observations to develop a detailed simulation model using Simio software. The DT model incorporates real-time data, enabling dynamic adjustments and providing a robust framework for simulating various scenarios. Validation methods, including face validation and sensitivity analysis, ensure the model's reliability. Key findings indicate significant improvements in scheduling accuracy, resource utilization, and on-time delivery rates. The DT's adaptability to variability and disruptions enhances operational efficiency and customer satisfaction. Risk-based planning within the DT framework allows for comprehensive risk management, mitigating the impact of potential disruptions. This research contributes to digital twins and Industry 4.0 by demonstrating a practical DT implementation in a real-world setting. It highlights the potential for DTs to enhance traditional manufacturing systems and provides a framework for future implementations. The study's implications include improved operational efficiency, enhanced decision-making, and strategic planning capabilities. Future research should focus on developing standardized DT evaluation frameworks, improving interoperability, addressing security and privacy concerns, and exploring the long-term sustainability of DT implementations. These advancements will further the application of DT technology, fostering continuous improvement and innovation in manufacturing.