Neural Network-based Optimization Framework for Green Supply Chain Management

The Green Supply Chain optimization is an important challenge because environmental sustainability, cost effectiveness, and supply chain flexibility interact in complicated ways. Rule-based systems and statistical models often used in traditional supply chain management are not able to dynamically respond to changing market demand and sustainability limitations. To overcome these drawbacks, this study introduces a neural network-based optimization framework that incorporates real-time data processing and predictive analytics to maximize supply chain efficiency while reducing environmental footprint. The model assesses critical performance indicators like carbon footprint, energy usage, recycling percentages, and cooperation with suppliers in a way that provides an equitable trade-off between economic performance and ecological sustainability. Experimental outcomes illustrate that the model greatly enhances decision-making precision, cost-saving, and sustainability performance measures, surpassing traditional supply chain optimization methods. Outcomes based on real SCM Datasets prove that the devised model achieves an accuracy of 98%, surpassing conventional models in efficacy and reliability. Providing a scalable and smart solution, this framework can help develop green logistics, environmentally friendly supply chain methods, and industrial sustainable practices.