The Application of Intelligent Information Systems Driven by 6G Big Data in Product Sales Traceability

Healthcare supply chains are complex networks that span geographical and organizational boundaries and are the foundation of many vital everyday services. Most track and trace systems now in use are centralized, which creates issues with data privacy, authenticity, and transparency in supply chains related to healthcare. In this study, we suggested using generative artificial intelligence to optimize the healthcare supply chain. The primary objectives are to precisely forecast the need for medical supplies, ensure that the right supplies are on hand when required, optimise delivery routes, and foster trust and openness in the movement of medical supplies across the supply chain. This paper offers novel ideas for using Conditional Generative Adversarial Networks to transform healthcare supply chain management. Using complex data patterns, Krill Herd Optimization (CGANs-KHO) is presented as a potent technique for very precise medical supply-demand predictions. The supply chain procedures are smoothly linked with Enhanced Q-learning with Genetic Algorithms (EQL-GA), guaranteeing accurate medical supply stocking across a range of healthcare institutions. Dijkstra's algorithm is used to optimize delivery routes, ensuring that medical supplies are moved between the central warehouse and other healthcare institutions in a timely and effective manner. Proof of Authority (PoA)-enabled private blockchains increase trust and transparency in the supply chain. Using smart contracts to automate safe supply chain agreements, this blockchain monitors the transfer of medical supplies. This increases stakeholder trust and validates transactions. The performance measures, which include Demand, Inventory level, Route, Time against Quality of Medical Supplies, and Transaction versus Transparency.