IoT-Enabled Supply Chain Management: Enhancing Efficiency and Transparency
Abstract
The adoption of IoT in supply chain management is improving real-time tracking, inventory management, and logistics optimization. This paper explores IoT applications in RFID-based tracking, AI-driven demand forecasting, and automated warehouse operations. It discusses challenges such as data integration, cybersecurity threats, and regulatory compliance. Case studies highlight successful IoT-driven supply chain transformations, demonstrating increased transparency, reduced costs, and enhanced decision-making capabilities.
References
Kolla, V. R. K. (2020). Forecasting the Future of Crypto currency: A Machine Learning Approach for Price Prediction. International Research Journal of Mathematics, Engineering and IT, 7(12).
Kolla, V. R. K. (2018). Forecasting the Future: A Deep Learning Approach for Accurate Weather Prediction. International Journal in IT & Engineering (IJITE).
Kolla, V. R. K. (2016). Analyzing the Pulse of Twitter: Sentiment Analysis using Natural Language Processing Techniques. International Journal of Creative Research Thoughts.
Kolla, V. R. K. (2015). Heart Disease Diagnosis Using Machine Learning Techniques In Python: A Comparative Study of Classification Algorithms For Predictive Modeling. International Journal of Electronics and Communication Engineering & Technology.
Kolla, V. R. K. (2020). Paws And Reflect: A Comparative Study of Deep Learning Techniques For Cat Vs Dog Image Classification. International Journal of Computer Engineering and Technology.
Chavva, M., & Veera, S. (2023). Dynamic Cost-Aware Language Models: A Real-Time Framework for Optimizing Cloud Resource Recommendations. International Journal of Machine Learning for Sustainable Development, 5(2), 1-15.
Chavva, M., & Veera, S. (2022). Leveraging Large Language Models (LLMs) for Automated Cloud Solution Design and Architecture: A New Paradigm in Cloud Computing. International Journal of Sustainable Development in Computing Science, 4(4), 1-20.
