• Yordany Hernandez Cortes

This article explores the integration of IoT and ML within an intelligent supply chain ecosystem, aiming to enhance decision-making, predictive maintenance, and real-time monitoring. By utilizing IoT sensors and devices, we collect vast amounts of data across the supply chain, from raw material sourcing to final product delivery. Machine learning algorithms process this data to identify patterns, predict demand, and optimize logistics. The intelligent ecosystem developed in this study demonstrates significant improvements in key performance indicators, including reduced lead times, minimized costs, and increased adaptability to market fluctuations. This paper provides a comprehensive framework for implementing IoT and ML in supply chain operations, highlighting the transformative impact of these technologies on achieving a more efficient, responsive, and intelligent supply chain.

"ADVANCED SUPPLY CHAIN OPTIMIZATION: LEVERAGING IOT AND MACHINE LEARNING WITHIN AN INTELLIGENT ECOSYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.d426-d433, June-2024, Available :http://www.jetir.org/papers/JETIR2406352.pdf

"ADVANCED SUPPLY CHAIN OPTIMIZATION: LEVERAGING IOT AND MACHINE LEARNING WITHIN AN INTELLIGENT ECOSYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppd426-d433, June-2024, Available at : http://www.jetir.org/papers/JETIR2406352.pdf