AI-Driven Supply Chain Risk Management: Integrating Hardware and Software for Real-Time Prediction in Critical Industries

Authors: Abhinav Balasubramanian, Niranjana Gurushankar

DOI: https://doi.org/10.5281/zenodo.14565873

Short DOI: https://doi.org/g8w63m

Country: USA

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Abstract: Supply chain disruptions pose significant challenges to critical industries, demanding proactive risk management strategies. This research explores a novel approach by integrating AI-driven hardware and software components for real-time prediction and mitigation of supply chain risks. The study emphasizes the synergy between advanced hardware, such as IoT sensors and RFID tags for real-time data acquisition across the supply chain, and sophisticated AI algorithms implemented through software solutions. This integrated framework enables continuous monitoring of critical parameters, including environmental conditions, location tracking, and product quality. By analyzing this real-time data, the AI system identifies potential disruptions, predicts their impact, and facilitates proactive interventions. This research contributes to the development of resilient and adaptive supply chains in critical industries by combining the strengths of both hardware and software advancements.

Keywords: Artificial Intelligence, Supply Chain Risk Management, Real-time Prediction, Hardware-Software Integration, IoT, Machine Learning, Predictive Analytics, Anomaly Detection, RFID, Sensors, Robots, AGV’s, Cobots.

Paper Id: 231926

Published On: 2020-05-12

Published In: Volume 8, Issue 3, May-June 2020

Cite This: AI-Driven Supply Chain Risk Management: Integrating Hardware and Software for Real-Time Prediction in Critical Industries - Abhinav Balasubramanian, Niranjana Gurushankar - IJIRMPS Volume 8, Issue 3, May-June 2020. DOI 10.5281/zenodo.14565873

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