INTELLIGENCE-DRIVEN SUPPLY CHAIN RISK MANAGEMENT AND RESILIENCE STRATEGIES.

Abstract

This research article focuses on applying intelligence-driven strategies to enhance supply chain risk management and build resilience. The study utilizes secondary data analysis through SPSS software, with a sample size of 500 organizations operating in diverse industries. The objective is to provide a comprehensive understanding of the descriptive and inferential analysis of supply chain risks and the effectiveness of resilience strategies. The descriptive analysis explores various dimensions of supply chain risks, such as demand volatility, supplier reliability, transportation disruptions, and regulatory compliance. Additionally, it investigates the prevalence and impact of risks in different industries and geographical regions. This analysis aims to identify the critical risk factors that pose significant challenges to supply chain operations. The inferential analysis assesses the relationships between risk factors and their influence on supply chain resilience. The research investigates the effectiveness of intelligence-driven strategies, including real-time monitoring, predictive analytics, and advanced technologies like artificial intelligence and machine learning, in mitigating risks and enhancing supply chain resilience. Furthermore, the study examines the role of collaboration among supply chain partners in improving resilience and reducing vulnerabilities. The findings from this research provide valuable insights into the current state of supply chain risk management practices and the effectiveness of resilience strategies in diverse industries. The results contribute to the existing body of knowledge by identifying best practices, key risk factors, and potential areas for improvement. Moreover, they assist practitioners and decision-makers in developing proactive risk management strategies and fostering resilience in their supply chain operations.