Navigating Socio-Technical Dynamics of AI-Enabled Traceability Implementation in the Dairy Industry: A Fuzzy-DEMATEL Approach

Traceability in the dairy supply chain helps ensure high transparency, quality assurance, and regulatory compliance. At the same time, the implementation of traceability in the dairy sector faces several challenges; therefore, this paper examines the factors influencing the implementation of AI-enabled traceability in the Indian dairy industry. The Fuzzy-Decision-Making Trial and Evaluation Laboratory (DEMATEL) method investigated the relationship among identified factors and categorized them into cause-and-effect groups. The analysis highlighted “Lack of Standards and Regulations” and “High Cost of Infrastructure” with D-R values of 1.26 and 1.08 as the most influential causal factors, while “Supply Chain Integration” with a D+R value of 11.52 emerged as the most impacted effect factor. The factors underscore the critical need to develop strategic solutions to address the traceability challenges faced by the dairy industry. The proposed framework enhances socio-technical theory by explicitly outlining the complex interactions required to efficiently implement AI-enabled traceability. The findings provide policymakers, industry stakeholders, and researchers with valuable insights into prioritizing interventions, allocating resources, and fostering collaboration to improve dairy traceability practices.