| dc.description.abstract | This study focuses on applying digital twins to supply chain of ABC coffee
company that combines optimization techniques, machine learning, and simulation to
improve decision-making processes. The optimization focuses on aligning the supply
chain's inputs, like customer expectations, product details, suppliers, production, and
distribution center specifics, with the aim to enhance efficiency and reduce costs.
Greenfield Analysis and Network Optimization are central components of Optimization,
indicating a methodical approach to designing and improving the supply chain network.
Machine learning is applied to predict potential disruptions by area, with a specific
mention of events like those experienced during COVID-19. This predictive capability
is essential for proactive supply chain resilience. The simulation component tests the
system's responses to various scenarios, assessing the outcomes of disruptions and
optimization strategies. The result of the simulation dictates whether the current model
is sufficient or if further recommendations are necessary to improve the system. In
essence, the model from this study outlines a robust supply chain management strategy
that is data-driven, adaptable to change, and resilient to disruptions. | en_US |
