Supply Chains (SCs) face increasing exposure to disruptive events that can interrupt the flow of goods and information, referred to as Supply Chain Disruptions (SCDs), compromising SC performance. In the Supply Chain Management context, Supply Chain Resilience (SCR) emerges as the adaptive capability that enables firms to prepare for, respond to, and recover from SCDs, returning to normal operations or transitioning to a more favourable state. Analytics (commonly categorized as descriptive, predictive, and prescriptive) have been theoretically linked to enhanced SCR; yet, limited empirical research has examined how firms actually apply these analytics categories across SC functions and how they contribute to recognizing and managing SCDs in practice. 

This thesis explores the application of descriptive, predictive, and prescriptive analytics in SCM within the manufacturing industry, aiming to understand how analytics support SCR across predisruptive and post-disruptive phases. A qualitative multiple case study was conducted across five firm within the manufacturing industry in Sweden, complemented by document studies in triangulation of the information. 

The findings reveal that descriptive analytics dominate across all firms and functions, serving as the foundation for monitoring, visualization, and reporting through tools such as ERP systems, Excel, Power BI, and Qlik. Predictive and prescriptive analytics remain unevenly adopted and largely limited to specific SC functions, particularly purchasing. SCD recognition emerges as a multi-source process where analytics play a complementary role alongside external inputs and SC communication. Across SCR phases, analytics contribute more substantially to the post-disruptive phase (especially during response) than to proactive prevention, revealing a gap between the theoretical potential of advanced analytics and their practical application. Overall, the findings suggest that enhancing SCR depends not on adopting increasingly sophisticated analytics, but on aligning analytics applications with organizational needs and integrating them effectively across SC functions.