Adaptive Bayesian-Feedback Framework for ERP Purchase Module-Based Halal Authentication in Imported Meat Supply Chains

Background: Imported halal meat supply chains face substantial challenges caused by fragmented certification systems, information gaps, and uncertainties. These factors challenge procurement management and hinder the assurance of halal compliance. Conventional Enterprise Resource Planning (ERP) systems are mainly designed for transaction processing and offer limited support for flexible decision-making. Methods: An Adaptive Bayesian-Feedback Framework was developed that integrates Digital Halal Authentication, Evidence-Based Decision Support, Bayesian inference, adaptive feedback learning, and the Markov Decision Process (MDP) within the ERP Purchasing Module. The framework was evaluated using 30 imported meat shipment cases, expanded into 300 procurement transaction records. Results: Implementation of the framework reduced halal authentication processing time from 2509 s to 290 s, representing an 88.44% decrease and an approximately 8.65-fold improvement in processing speed. The framework also enabled continuous revisions to evidence on vendors and procurement risks and improved the consistency of decision-making amid uncertain supply chain circumstances. Conclusions: The framework develops a unified decision-support system comprising adaptive halal authentication, evidence-based decision support, and intelligent ERP. This integration improves procurement governance, traceability, transparency, and uncertainty management in imported halal meat supply chains.