Automated Inventory Management Systems with IoT Integration to Optimize Stock Levels and Reduce Carrying Costs for SMEs: A Comprehensive Review

Automated inventory management systems integrated with Internet of Things (IoT) technology represent a transformative approach for Small and Medium-sized Enterprises (SMEs) in optimizing their stock levels and reducing carrying costs. A literature review also shows that there is progress in developing automated solutions, such as IoT sensors, real- time data analytics, and cloud-based applications that improve inventory control. A study shows that the adoption of IoT in automation has seen improvement in the accuracy of inventory, a reduction in stockouts, and carrying costs among SMEs. Research suggests operational improvements including reduced downtime from real time tracking, reverse logistics systems with intuitive demand forecasting for ideal stock replenishment and automation of reordering systems while effectively managing working capital. Materials and Methods: Research methodology encompassed a comprehensive analysis of peer-reviewed literature, case studies, and empirical research focusing on automated inventory management systems with IoT integration in SMEs. Criteria for choosing the literature included articles focusing on the outcomes of IoT implementation, technical integration, and performance of inventory systems. Data extraction main concern was on the efficiencies of the SC such as inventory accuracy, carrying costs, stockouts, and ROI. Descriptive methods used involved comparisons between pre-implementation and post- implementation data, use of statistical tools for measurement of performance enhancement and assessment of factors important in the deployment of the system. Results Implementation of IoT-integrated automated inventory management systems demonstrated significant improvements across multiple performance metrics. Studies reported average inventory accuracy improvements of 25-35%, reduction in carrying costs [1] by 20-30%, and decrease in stockout incidents by 35-45%. Real-time monitoring capabilities led to improved demand forecasting accuracy by 40%, while automated reordering systems reduced manual processing time by 60%. Cloud-based platforms enabled better inventory visibility and control, resulting in working capital optimization of 15-25%. SMEs implementing integrated systems reported enhanced supplier collaboration, reduced lead times, and improved customer satisfaction levels. Cost-benefit analyses indicated positive ROI within 12-18 months of system deployment. Discussion Analysis reveals several key factors contributing to successful implementation of automated inventory management systems with IoT integration. Critical success factors include proper system architecture design, effective change management strategies, and comprehensive staff training programs. Integration challenges primarily revolve around initial investment costs, technical expertise requirements, and system interoperability concerns. Studies show that the greater gains are experienced by SMEs with higher ITOR and those engaged in intricate supply chain operations. Literature review also points to differences in implementation strategies across different sectors and in their recovery, with manufacturing and retail sectors having the highest levels of adoption and improvement. Conclusion IoT-integrated automated inventory management solutions should be adopted by SMEs and are affirmed by ample data to help optimize stock status and minimize carrying costs. The tangible benefits that could be noted are accuracy in inventory, less operational costs, visibility of supply chain, and optimal use of working capital. In appointing the factors affecting the implementation success, it requires the assessment of the technical feasibility and organisational readiness as well as the management of change strategies. Research shows that dynamic technology factors will continue to improve the system’s performance and increased availability to SMEs. Due to advancement in IoT technology, artificial intelligence and cloud computing, there are potential factors that may boost the performance and cost of automatically Managed inventory systems in the future.