Vol. 8 No. 05 (2026): Volume 08 Issue 05

Pharmaceutical supply chains represent one of the most operationally demanding environments for artificial intelligence deployment, requiring continuous demand forecasting, expiration tracking, procurement automation, and multi-location coordination under conditions that permit minimal margin for error. This paper presents the design, implementation, and real-world deployment outcomes of Restocks AI, an autonomous multi-agent AI platform engineered to replace manual pharmaceutical supply chain operations at national scale. The system integrates predictive inventory engines, multi-agent decisioning frameworks, distributed task-processing infrastructure, and real-time data pipelines to autonomously manage end-to-end supply chain operations across geographically dispersed pharmacy networks. The platform was deployed across two pharmaceutical organizations in Uzbekistan spanning a combined 427 pharmacy locations across all 14 administrative regions, including one of the country's largest private pharmacy networks (150+ locations) and the largest government-operated pharmaceutical institution (277 locations). Deployment results demonstrated 70% reduction in manual operational workload, 30% acceleration of replenishment cycles, 65% reduction in pharmaceutical expiration waste, 70% reduction in stockout incidents, and a combined financial impact of 812 billion Uzbek sum. These results demonstrate that autonomous multi-agent AI systems can effectively manage pharmaceutical supply chains at national scale, offering a replicable architecture with significant implications for healthcare infrastructure, public health outcomes, and the broader application of multi-agent AI in critical industries.

The current data warehousing infrastructure heavily depends on the ETL (Extract, Transform, Load) process to deliver timely and accurate information to support analytics and decision-making processes. Nevertheless, conventional ETL scheduling methodologies, such as AutoSys and Linux-based scripting, tend to be rigid, rule-based, and inflexible to handle dynamic workload management, thereby leading to wastage of resources, delays, and inefficiencies. This paper novel study of AutoCortex, an autonomous job orchestration framework which improves ETL scheduling efficiency through intelligent, adaptive and self-optimizing mechanisms.The AutoCortex framework combines Linux shell scripting, AutoSys job scheduling, decision models to facilitate real-time monitoring, predictive scheduling and automated dependency of complex ETL workflows. The AutoCortex framework will utilize historical ETL execution information, system resource utilization and job dependencies to dynamically manage ETL job priorities, optimize execution sequences, and eliminate performance bottlenecks. AutoCortex will also include fault tolerance mechanisms such as automated recovery, alerting and self-healing to enhance system reliability.

Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, This research assessed persistent unavailability electrical energy problem of Local Government Service Commission (LGSC) Alagbaka in Akure, Ondo State, Data were collected from Benin Electricity Distribution Company (BEDC), Akure and LGSC log books Alagbaka. Frequency and duration of electricity supply were obtained from the BEDC and LGSC log books. Results revealed that the existing public utility and existing generators sources feed the LGSC; the LVP and associated CBs of the LGSC network are functioning very well; the average public supply is averagely 4hrs per each working day as against the required 8hrs per day amounting to 50% availability through the year; total connected load is 113.4 kVA. Conclusively, a proposed generator or inverter capacity is designed to 79.4 kVA considering appropriate standards of demand load, diversity factor and future expansion; solar inverter is recommended for the LGSC operation due to its overall benefits existing literature established in this research; and 80 kVA solar inverter is recommended for the LGSC operation.  Load survey should be carried out before sizing of a generator/Inverter for any edifice. Appropriate standards should be considered in sizing a power supply unit source.