An evaluation of traceability dynamics in dairy supply chains through causal modeling in emerging economies

Traceability capability to track the history, location, and application of dairy products is crucial for ensuring food safety, quality, and transparency across supply chains. However, its development in emerging economies, particularly in Iran, remains limited due to infrastructural and technological challenges. This study addresses this gap by identifying and analyzing the key factors that influence traceability in Iran’s dairy sector, which plays a critical role in national nutrition and public health. Using a hybrid approach, the fuzzy Delphi method was first applied to refine a set of 19 factors extracted from the literature, validating 14 context-relevant elements based on expert consensus. Subsequently, the fuzzy DEMATEL method, designed to model causal relationships under uncertainty, was used to determine interdependencies among these factors. The results highlight food safety and quality, supply chain process management, data analysis and forecasting, and data integration as the most influential drivers of traceability. Meanwhile, competitive advantage, sourcing transparency, and environmental sustainability were found to be dependent outcomes. This research contributes a contextualized, expert-based framework tailored to the Iranian dairy industry and offers practical implications for improving transparency, reducing waste, and building consumer trust. The methodology and findings are transferable to other developing country contexts facing similar challenges. • Identify critical drivers of traceability in dairy supply chains of developing economies. • Refine traceability factors through expert-driven supply chain evaluation. • Apply causal modeling to uncover key relationships in traceability systems. • Reveal the role of food safety and data integration in shaping traceability. • Offer context-specific insights for transparent and efficient supply chains.