DATA-DRIVEN QUALITY ASSURANCE SYSTEMS FOR FOOD SAFETY IN LARGE-SCALE DISTRIBUTION CENTERS

Abstract

Ensuring food safety within large-scale distribution centers has become a critical priority in modern supply chain management, particularly as global logistics networks grow increasingly complex and data-intensive. This study examines the design, implementation, and optimization of data-driven quality assurance (QA) systems that leverage advanced analytics, Internet of Things (IoT) sensors, and machine learning algorithms to monitor and control food safety parameters in real time. By integrating predictive data models with automated quality inspection frameworks, organizations can significantly reduce contamination risks, improve traceability, and maintain regulatory compliance across diverse storage and transportation environments. The study systematically reviews 112 peer-reviewed papers published between 2017 and 2022, identifying key technological trends such as blockchain-enabled traceability, AI-based anomaly detection, temperature and humidity monitoring via IoT networks, and cloud-based decision support systems for risk assessment. Findings reveal that data-driven QA architectures not only enhance operational transparency but also enable proactive responses to deviations in food quality, thereby minimizing waste and ensuring consumer safety. The paper further highlights the challenges associated with data integration, cybersecurity, and scalability when deploying such systems across multinational logistics networks. Ultimately, this review provides a comprehensive framework for developing resilient, intelligent, and adaptive QA systems that align with evolving global standards for food safety in large-scale distribution centers.