Risk analysis of factors affecting the safety and haleness of beef in the supply chain using Bayesian networks

Background and Objective: Food safety and compliance with halal standards constitute strategic priorities within the cattle supply chain. However, structural complexities and inherent uncertainties often render effective risk management a significant challenge. This research aims to develop a systematic framework using Bayesian Network (BN) analysis to identify, model causal relationships, and quantitatively evaluate hazards.
Methodology: The research process involved extracting key risk factors, establishing probabilistic dependency structures via Conditional Probability Tables (CPTs), and prioritizing them based on their impact on safety and halal indicators. Expert panel data and secondary sources were utilized for model calibration and validation.

Findings: Results from sensitivity analysis revealed that factors such as "lack of animal health records" and "unhygienic transportation" serve as critical nodes, playing a pivotal role in the chain-like propagation of risks. Probabilistic inference indicates that focusing on the management of these root drivers leads to a systemic reduction of dependent risks throughout the entire network.
Conclusion: Beyond enhancing transparency and traceability in the supply chain, this approach provides a dynamic tool for crisis prediction and significantly strengthens consumer trust.