Harnessing the Power of AI and Big Data in Supply Chain Resilience and Efficiency

The rapid advancement in technology requires that supply chains are developed and improved continuously. This paper looks into how the artificially intelligent technologies (AI) and the big data analytics (BDA) can be integrated to simultaneously improve the efficiency and resilience of the supply chains withstanding disruption. While most research focuses on assessing these technologies individually, this study utilizes a combined approach in a newly revealed manner. Literature published from 2014 to 2025 was subjected to a systematic literature review to find current developments, challenges and gaps, which were used in this paper. With the data provided, the strongest potential deficits identified were for AI and BDA to assist with productivity and decision making, more specifically in regard to risk forecasting and anomaly identification. Nonetheless, other challenges, such as data integration and privacy as well as ethical issues, pose problems as well. There is a more prominent gap that this study identifies stems from the advancement of technology and their practical use, resulting in a demand for frameworks to facilitate deployments. A critique draws attention to the existing gaps within sophisticated approaches such as intricate risk evaluation and AI model validation. This paper offers an integrated approach to predicting supply chain disruptions by fully utilizing all features of AI combined with BDA, while adopting the best practices for their implementation to strengthen the guidance for both researchers and practitioners