The document discusses the use of multiple simulation methods, specifically System Dynamics Simulation (SDS) and Discrete Event Simulation (DES), for evaluating and optimizing the performance of manufacturing systems. It highlights a case study on a printed laminate film manufacturing line, showcasing how these methodologies can uncover productivity enhancements, resource utilization issues, and throughput variations. The integration of SDS and DES is presented as a robust approach to inform decision-making and drive continuous improvement in manufacturing operations.

1. MULTIPLE SYSTEM DYNAMICS AND
DISCRETE EVENT SIMULATION FOR
MANUFACTURING SYSTEM
PERFORMANCE EVALUATION
Dario Antonelli, Paweł Litwin and Dorota Stadnicka

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4. ABSTRACT

5. Abstract
 This presentation explores the application of multiple simulation methods, including
System Dynamics Simulation (SDS) and Discrete Event Simulation (DES), for
evaluating and improving the performance of manufacturing systems. By utilizing these
simulation techniques, companies can gain valuable insights into system behavior,
identify potential bottlenecks, and optimize overall productivity.

6. Importance of Simulation in Manufacturing Systems
 Simulations play a vital role in analyzing and optimizing manufacturing systems. They
provide a virtual environment to assess different scenarios, evaluate the impact of
changes, and make informed decisions before implementing them in the real world.
Simulation allows companies to minimize risks, reduce costs, and enhance efficiency
in their manufacturing processes.

7. System Dynamics Simulation (SDS)
 System Dynamics Simulation focuses on modeling and understanding the dynamic
behavior of complex systems. It utilizes continuous functions and differential equations
to simulate the interactions between various components and feedback loops within
the manufacturing system. SDS helps to identify causal relationships, predict system
behavior, and explore the implications of different policies and interventions.

8. Discrete Event Simulation (DES)
 Discrete Event Simulation is widely used for analyzing material flow and process-
oriented aspects of manufacturing systems. It models individual events and their
sequencing, allowing for a detailed examination of the system's behavior in discrete
time intervals. DES enables the evaluation of resource allocation, scheduling
strategies, and the optimization of production control

9. Advantages of Combined SDS and DES Approach
 Integrating System Dynamics Simulation (SDS) with Discrete Event Simulation (DES)
offers several advantages. SDS captures the dynamic behavior of the manufacturing
system, while DES focuses on specific events and their impact on the overall system
performance. By combining these methods, companies can gain a comprehensive
understanding of system dynamics and make data-driven decisions to improve
performance.

10. Case Study: Manufacturing Line for Printed Laminate Films
 In this case study, we analyze a manufacturing line for printed laminate films to
demonstrate the effectiveness of the SDS and DES approach. The study examines
both manual and automatic work processes, simulating input material preparation with
SDS and production line operations with DES. The results provide insights into
potential improvements and optimization strategies.

11. Simulation Results: Productivity Analysis
 Graph: Bar chart comparing the productivity of different production scenarios (manual
vs. automatic) in the manufacturing line for printed laminate films. The y-axis
represents productivity, and the x-axis shows the different scenarios.
 The simulation results indicate that the automatic production scenario outperforms the
manual production scenario in terms of productivity. The graph highlights the
significant improvement achieved by implementing automation in the manufacturing
process.

12. Simulation Results: Resource Utilization Analysis
 Graph: Line chart showing the utilization of resources (e.g., machines, labor) over time
in the manufacturing line for printed laminate films. The y-axis represents resource
utilization, and the x-axis represents time.
 Text: The simulation results demonstrate the fluctuation in resource utilization
throughout the production process. By analyzing the graph, we can identify periods of
high resource utilization and potential bottlenecks that may impact overall system
performance. This information enables us to optimize resource allocation and improve
productivity.

13. Simulation Results: Throughput Analysis
 Graph: Line chart depicting the throughput of the manufacturing line for printed
laminate films over time. The y-axis represents throughput, and the x-axis represents
time.
 Text: The simulation results reveal the variation in throughput over time, reflecting the
dynamic nature of the manufacturing system. By analyzing the graph, we can identify
periods of high throughput and potential constraints that limit overall production
capacity. This analysis helps in optimizing production control and improving overall
system performance.

14. Implementation and Continuous Improvement
 Simulation results serve as a valuable guide for implementing changes and continuous
improvement initiatives in manufacturing systems. By incorporating the insights gained
from simulation models, companies can optimize resource allocation, streamline
processes, and improve overall system performance. Regular updates and
refinements to the simulation models ensure that they remain accurate and relevant.

15. Conclusion
 In conclusion, the integration of System Dynamics Simulation (SDS) and Discrete
Event Simulation (DES) provides a powerful framework for evaluating, optimizing, and
improving manufacturing system performance. By leveraging the capabilities of these
simulation methods and analyzing the simulation results, companies can make
informed decisions, enhance productivity, and gain a competitive edge in the
marketplace.

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