In Quest of Sustainable Performance Advantage in the Fertilizer Manufacturing Firms: A Multi-Method Driven Supply Chain Risk Assessment Framework

Purpose: In an era of escalating supply chain disruptions ranging from geopolitical conflicts to cyber threats, this study presents a structured framework for identifying, prioritizing and mitigating risks that threaten operational resilience. It offers a practical framework for identifying, prioritizing and managing supply chain risks that impede firm performance and resilience. Design/Method/Approach: Using a mixed-methods approach, we combine qualitative insights with the Analytical Hierarchy Process (AHP) to assess and rank key vulnerabilities based on their severity, likelihood and interdependence. Guided by expert evaluations and pairwise comparisons using Expert Choice 11, we categorize risks into operational, logistical, technological and external domains and assign weighted priorities to each. The methodology not only pinpoints high-impact risks but also reveals how these risks cascade across the organizational supply chains and affect the firm performance outcomes. Findings/Results: The results of this study offer clear and evidence-based strategies for the fertilizer manufacturing firms, especially the small and medium firms, to transit from reactive problem-solving to proactive risk planning. Originality: By integrating theoretical rigor with practical application, this study addresses a notable gap in the literature and equips decision-makers with a scalable and actionable risk mitigation tool. Research Implications: Key contributions include an AHP validated risk taxonomy and recommendations that align with real-world resource constraints. Ultimately, this research reframes supply chain risk management as a strategic enabler of agility & competitive advantage. The future studies may build on this work by incorporating AI-driven analytics and industry-specific scenarios to enhance predictive accuracy & relevance in an increasingly complex supply chain ecosystem