A cost-effective over-temperature alarm system for cold chain delivery.

Managing over-temperature alarms in cold chain logistics (CCL) is crucial for temperature monitoring systems. However, existing research in this area primarily focuses on improving identification accuracy while overlooking the associated cost implications. This study addresses this gap by proposing a cost-effective over-temperature alarm system using an artificial neural network (ANN) model that integrates multi-source data (MSD). This article demonstrates that utilizing ANN and MSD leads to significantly improved recognition accuracy compared to using single-source data while keeping the cost increase to a minimum. The paper found recognition accuracy to be 97.4% with three feature values and 98.6% with six feature values. Sensitivity analyses reveal that factors such as the initial food temperature, cumulative open time, data acquisition interval, and algorithm significantly impact the recognition accuracy of the over-temperature alarm system. The location of sensors, however, has a slight effect. Furthermore, the choice of the number of feature values and the granularity of the data influences the cost of temperature management and recognition accuracy. This study provides valuable insights into the factors influencing recognition accuracy, aiding in designing cost-effective over-temperature alarm systems for food supply chain management. [Display omitted] • A cost-effective over-temperature alert system is developed utilizing artificial neural networks. • The model achieves over 98% accuracy in cold chain logistics unstable environmental temperatures. • By selecting algorithms and features, the system can reduce sensor quantity while maintaining accuracy. [ABSTRACT FROM AUTHOR]