Strengthening Supply Chain Resilience Through AI and Big Data - A Systematic Review

Abstract

The growing complexity of global supply chains, exacerbated by ongoing economic changes such as inflation, recession, and fluctuating logistics costs, underscores the critical need to enhance supply chain resilience. Artificial Intelligence (AI) and Big Data (BD) have emerged as strategic variables for ameliorating adaptability by improving risk prediction, enabling real-time decision-making, and increasing operational responsiveness. However, their deployment is still challenged by a number of barriers, especially those connected with data quality issues, cybersecurity threats, and limited interoperability. Therefore, this study conducts a systematic literature review (SLR) following the PRISMA guidelines, based on a selection of 34 peer-reviewed articles published between 2019 and 2024. The SLR is complemented by a bibliometric analysis performed using Biblioshiny, a web-based graphical interface of the Bibliometrix package in RStudio, to identify major research streams, influential contributors, and international research collaborations. The findings indicate a strong academic interest in the application of AI and BD to supply chain resilience, particularly in areas such as predictive analytics, process automation, and supply chain responsiveness. Nevertheless, significant obstacles remain, such as high implementation costs and organizational resistance. This research is an attempt to bring to the limelight the connection between a structured synthesis of the literature, research gaps, and strategic recommendations to enhance the contribution of AI and BD to resilient supply chain management in times of economic disruptions.