High-quality cold-chain logistics are key to effectively managing the quality of temperature-sensitive foods. Hence, this study investigates the service quality of such logistics, using a real-life case of temperature-sensitive milk delivery.
This study focuses on developing business analytics for quality control in cold-chain perishable-food logistics, grounded in normal accident theory and stakeholder theory, and tests them using real-world data.
The empirical business-analytics results indicate that cargo locations, logistics status and delivery times are the essential factors that influence the quality of temperature-sensitive milk.
This study confirms that a combination of normal accident theory and stakeholder theory can be usefully applied to the development of strategies for managing perishable-food logistics. As such, its proposed business analytics provide a fresh foundation for research on logistics quality management.
The balance between efficiency and service quality in a logistics system should be assessed carefully, and logistics companies should collaborate with their stakeholders and can help to mitigate potential cold-chain risks.
This pioneering analytical study explores the essential quality issues that confront cold chains and demonstrates how to extract vital insights from temperature-sensitive food logistics monitoring data. As such, it represents a noteworthy contribution to the field of logistics quality management.
