A Qualitative Perspective on AI-Driven Risk Management in Sustainable Supply Chains

Abstract

This study explores the transformative role of artificial intelligence (AI) in risk management within sustainable supply chains, highlighting its impact on enhancing efficiency, resilience, and sustainability. As organizations face increasing complexities in global supply chains, the integration of AI technologies emerges as a critical strategy for proactive risk management. The research employs a qualitative approach, utilizing thematic analysis to examine the perceptions and experiences of industry professionals regarding AI's applications in managing supply chain risks. Key findings indicate that AI significantly enhances predictive capabilities, allowing organizations to anticipate disruptions and implement proactive measures, ultimately fostering resilience. Additionally, the automation of processes and optimization of logistics contribute to operational efficiencies, leading to reduced costs and environmental impact. The alignment of AI initiatives with sustainability objectives is increasingly evident, as organizations leverage data analytics to assess environmental implications and promote sustainable practices. However, challenges related to data integration, quality, and ethical considerations pose barriers to the effective implementation of AI technologies. The importance of stakeholder collaboration is emphasized, as crossorganizational partnerships facilitate the sharing of data and insights, enhancing overall supply chain resilience. This research underscores the necessity of addressing the challenges associated with AI deployment while recognizing its potential to revolutionize risk management practices in sustainable supply chains. By embracing AI technologies, organizations can position themselves strategically to navigate the complexities of modern supply chains, achieve sustainability goals, and maintain a competitive edge in the market.

Keywords: Artificial Intelligence, Risk Management, Sustainable Supply Chains, Operational Efficiency, Predictive Capabilities, Stakeholder Collaboration, Data Integration