• Yordany Hernandez Cortes

This research aims to explore the transformative impact of IoT-enabled digital twins and machine learning on supply chain management, focusing on strategies for real-time decision-making. The study investigates how these advanced technologies can enhance efficiency, reduce costs, and improve the responsiveness of supply chains. The research employs a mixed-methods approach, combining qualitative and quantitative data collection and analysis. It includes a comprehensive literature review, case studies of leading companies implementing these technologies, and the development of a conceptual framework. Quantitative data is gathered through surveys and interviews with supply chain professionals, and the data is analyzed using statistical methods to identify trends and correlations. Additionally, machine learning models are developed and tested on real-world supply chain data to demonstrate their effectiveness in real-time decision-making.

"TRANSFORMING SUPPLY CHAINS WITH IOT-ENABLED DIGITAL TWINS AND MACHINE LEARNING: STRATEGIES FOR REAL-TIME DECISION MAKING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.d416-d425, June-2024, Available :http://www.jetir.org/papers/JETIR2406351.pdf

"TRANSFORMING SUPPLY CHAINS WITH IOT-ENABLED DIGITAL TWINS AND MACHINE LEARNING: STRATEGIES FOR REAL-TIME DECISION MAKING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppd416-d425, June-2024, Available at : http://www.jetir.org/papers/JETIR2406351.pdf