Integrated AI Control Towers and Digital Twin Simulations for Resilient Supply Chain Management
DOI:
https://doi.org/10.22399/ijcesen.5005Keywords:
Supply Chain Resilience, Digital Twin, Control Tower, Predictive Analytics, Disruption Management, Real-Time SimulationAbstract
The strategic configuration of agile and resilient supply chain operations has now become urgent in an environment marked with increased volatility in the world, uncertainty, and an escalating rate of supply chain disruption events. The paper evaluates how AI-enabled supply chain control towers together with the sophisticated digital twin simulations can create an intelligence-based ecosystem of real-time and anticipatory decision-making. We construct a multi-level theoretical framework that links the data ingestion, predictive and prescriptive analytics, visualization, and the simulation-based planning to closed-loop feedback mechanisms based on the literature of Industry 4.0 and digital twin. Through an experimental study based on a simulation, we indicate quantifiable increase in accuracy of demand forecasting, disruption identification and response time, resource efficiency, and sustainability-related outcomes. The reporting of all the metrics of performance is based on the experimental simulations by the authors. Of these advantages, issues of adopting it continue to be challenging, among them data interoperability among heterogeneous platforms, algorithm transparency, and cybersecurity risks. To close these gaps, the paper presents a research agenda of how to achieve these gaps by designing ethical AI, real-time adaptive learning, and cross-industry standards to make AI scaling deployment possible. On the whole, AI controlled towers and digital twin integration can be seen as a promising avenue of more resilient, responsive, and yet more autonomous supply chain networks.
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