Almost everything in our day-to-day lives can be modeled as a graph, including production factories that have yet to move to technological and digitized manufacturing.
This delayed change can be due to a lack of investment or because production methods don't adjust well to automated processes. In the first part of this session, José will dive into modeling a diverse and highly human-dependent production line using graph theory and Neo4j to visualize it. This will allow us to identify bottlenecks and patterns in our modeled production line. In the second part, you'll see how to use graph theory and Discrete Event Simulations (DES) to predict the delivery dates for orders and packages. The final goal is to contrast the DES approach with more conventional mechanisms for predictions like machine learning and why it is a valid solution for factories with low technological advances.
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