This study examines the barriers to incorporating Artificial Intelligence (AI) in supply chain management within Pakistan. Its primary objectives are to identify, hierarchically structure, and analyze the interrelationships among the obstacles impeding AI adoption, ultimately providing actionable recommendations for overcoming these challenges. Data were collected through an extensive literature review and expert interviews involving 26 specialists from academia and industry. The study employs Interpretive Structural Modeling (ISM) to map the complex interdependencies between the identified barriers and utilizes fuzzy Cross-impact matrix multiplication applied to classification (MICMAC) analysis to classify these barriers based on their driving and dependence powers. Key findings reveal that foundational issues such as Infrastructure Limitations, Data Challenges, and Security Concerns form the operational base that constrains AI integration. In contrast, independent barriers - specifically Government Support and Policy, along with Collaboration and Ecosystem Development - exert a significant influence over the entire system. The analysis indicates that while many barriers, including a shortage of a skilled workforce and ambiguous regulatory frameworks, are outcomes of systemic issues, addressing the independent barriers can trigger a cascading improvement across the system. The novelty of this study lies in its integrated methodological approach, combining ISM and fuzzy MICMAC, which allows for a detailed understanding of the hierarchical and causal relationships among barriers - a perspective that is less studied in the existing literature. The implications of this study are twofold: for policymakers, the study highlights the need for robust regulatory frameworks and enhanced public-private collaborations; for industry practitioners, it provides a strategic framework to prioritize investments in digital infrastructure, workforce development, and cybersecurity measures.
Khanzada et al. (Thu,) studied this question.