Assessment of Supply Chain Risk in Manufacturing Sector

ABSTRACT This paper explores the diverse risks impacting supply chains within the manufacturing sector, particularly in an increasingly globalized and technologically interconnected world. Using real-world data and industry case studies, it evaluates key risk categories including operational disruptions, financial volatility, geopolitical tensions, and cyber threats. The analysis highlights both the vulnerabilities and resilience strategies of modern manufacturing firms, offering insights into how proactive risk management can sustain competitive advantage. Charts and risk matrices visually support the discussion. The paper concludes with practical mitigation frameworks to guide firms toward more agile and secure supply chains. In the dynamic landscape of the manufacturing industry, sourcing decisions play a Pivotal role in determining operational efficiency, cost-effectiveness, and overall business resilience. This abstract provides a concise overview of the evaluation of sourcing risk within the manufacturing sector, highlighting key factors, methodologies, and implications. Manufacturers face multifaceted challenges in sourcing, ranging from geopolitical instability and supply chain disruptions to fluctuating market demands and regulatory constraints. Evaluating these risks demands a holistic approach that integrates quantitative and qualitative assessments. Traditional risk assessment frameworks often focus on financial metrics, supplier stability, and operational contingencies. However, contemporary approaches recognize the need for broader considerations, including environmental sustainability, ethical sourcing practices, and social responsibility. This abstract delves into the methodologies employed for assessing sourcing risk, encompassing both pre-emptive risk identification and ongoing risk management strategies. It explores the role of advanced analytics, including predictive modelling and simulation, in forecasting and mitigating potential disruptions. Previous Road Accident Detection Using AI and ML.