The Challenges of Integrating AI and Robotics in Sustainable WMS to Improve Supply Chain Economic Resilience

The integration of artificial intelligence (AI) and robotics into the warehouse management system (WMS) has substantially advanced supply chain (SC) operations, offering notable improvements in efficiency, accuracy, and economic resilience.In warehousing environments, AI algorithms and robotized systems enable rapid and precise product retrieval from storage while optimizing routing and packaging, thereby reducing order preparation time and enhancing delivery reliability.The implementation of these advanced technologies also results in fewer errors, improved customer satisfaction, and streamlined SC processes, empowering organizations to better manage inventory and respond swiftly to fluctuating market demands.Such innovations allow for reduced operating costs, enhanced productivity, and increased sustainability.Autonomous mobile robots (AMRs), automated guided vehicles (AGVs), and drones, among other cutting-edge solutions, are increasingly incorporated into the WMS to minimize physical labor and mitigate workplace injuries.Despite these benefits, considerable challenges remain, including the high initial costs and requisite technical expertise for ongoing maintenance.The integration of new AI and robotic technologies into pre-existing systems necessitates careful evaluation, substantial employee training, and process adaptation.Nonetheless, these technologies play a crucial role in fostering environmentally and socially sustainable operations within warehouses and broader SCs, contributing to reduced carbon emissions and the elimination of hazardous tasks for human workers.This study aims to identify the most effective AI and robotic technologies for a sustainable WMS, with recommendations tailored to maximize SC value through automation.A detailed examination of existing warehouse practices is essential to pinpoint areas where automation can yield the most substantial impact and deliver long-term resilience and value for SCs.