Predictive model for temperature monitoring in reefer containers

Monitoring the temperature of perishable products, particularly in shipments sensitive to temperature variations, poses a challenge for professionals in the cold chain. Temperature plays a critical role in preserving food quality, and improper variations can accelerate the deterioration of transported products. A major challenge during transportation is managing the temperature within reefer containers. Solutions to support this monitoring have become increasingly important due to the high value of the shipment. This study aimed to develop a predictive model for monitoring the internal temperature of reefer containers. The proposed model draws an analogy to a thermal model based on heat flow, adapted to products with similar thermal properties. With the support of APIs, data on weather conditions were collected to feed the model. The methodology was tested and validated under real conditions, yielding results that track the actual internal temperature variations recorded by the sensor. The data generated provide important information for monitoring the container's internal temperature and for decision-making, aimed at maintaining the integrity of the transported products.