Machine Learning-Driven Predictive Models for Enhancing Supplier Reliability in Renewable Energy Storage Supply Chains
Keywords:
Machine learning, predictive models, supplier reliability, renewable energy storage, supply chain, risk management.Abstract
As the renewable energy industry expands, the demand for reliable and sustainable supply chains has become critical, especially within energy storage systems. Supplier reliability significantly impacts the efficiency and resilience of these supply chains, which are vulnerable to disruptions and fluctuations. This paper examines the application of machine learning-driven predictive models to enhance supplier reliability in renewable energy storage supply chains. Through a comprehensive literature review and analysis of model applications such as random forests, support vector machines (SVM), and neural networks, we evaluate their effectiveness in predicting supplier risks and enhancing decision-making accuracy. The findings reveal that predictive models not only improve supplier reliability but also provide insights that support preemptive risk management. This study highlights the potential of machine learning to reshape supplier reliability assessment, promoting a more resilient and sustainable supply chain in the renewable energy sector.
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