Supply Chain Optimization with Integrated Twin Learning Systems Empowering Sustainability

Abstract

In The modern supply chain management systems are highly associated with IoT and AI environments. Traditional analytical systems to be revitalized with optimized strategies to support efficient decision making systems. The proposed framework model is an Integrated Twin Learning System with adaptive sustainability addressing. Twin Learning Systems configured with high adaptive decision making with feedback assisted self learners. Making traditional supply chains to proactive respondents to demand and supply risks with environmental monitor ability. Well focused over process optimization, service collaboration efficiency, cost effectiveness and sustainability objectives. Our model enhances supply chain resilience in various real time environments.