Agile, IoT, and AI: Revolutionizing Warehouse Tracking and Inventory Management in Supply Chain Operations

Aim: The way traditional supply chains operate has shown limited success in coping with changes in supply chain requirements related to inventory tracking and warehouse management. The research evaluates the collaborative effects of Agile methods with IoT devices and AI capabilities to optimize these processes. Methods: This study examines three vital aspects of data science deployment within a consumer product distribution company that uses IoT sensors (RFID tags and Texas Instruments CC2650) for real-time data combined with AI analytics (Random Forest and reinforcement learning in Python) through an Agile (2-week cycles via Jira) deployment approach for inventory management. A twelve-month project employs AI modeling based on Python and utilizes Scrum sprints as its methodology. Results: The systematic study produced three significant results which include a 25% higher inventory turnover rate, 20% fewer tracking errors, and 15% lower operating costs. Strong solutions emerge from the combination of Agile with IoT and AI and demonstrate promising capabilities for enhancing supply chain resilience at a large-scale level. Conclusion: Practical applications from the research follow some practical suggestions and directions for upcoming scientific investigations into blockchain technology implementation. Recommendation: The research presents real-world implications for medium firms and recommendations for blockchain-based secure data-sharing studies to advance supply chain functions.