Artificial intelligence, machine learning, and deep learning for sustainable and resilient supply chain and logistics management

Integrating artificial intelligence (AI) and machine learning (ML) into logistics and supply chain management is crucial for enhancing resilience and efficiency in today's unpredictable global market. This research explores the latest advancements and applications of AI and ML technologies that are transforming logistics and supply chain operations. AI-driven predictive analytics and real-time data processing have enabled companies to anticipate disruptions, optimize routes, and improve demand forecasting accuracy. Machine learning algorithms are essential in identifying patterns and anomalies within large datasets, supporting proactive decision-making and risk management. Current trends indicate a growing use of AI in autonomous delivery systems, which aim to reduce human error and improve delivery times. Additionally, AI-enhanced blockchain technology is becoming more popular for its ability to increase transparency and traceability across the supply chain, ensuring ethical sourcing and reducing fraud. AI in inventory management has significantly minimized overstocking and stockouts by providing accurate inventory levels and automating replenishment processes. Furthermore, AI-powered supply chain management systems are increasingly adopted to streamline supplier selection and performance evaluation, creating more resilient supplier networks.