Towards a sustainable and resilient digital cold supply chain, environment sensing technologies and Big data analytics combined with Artificial Intelligence can extract critical information and provide a systematic framework for product optimization and safety enhancement. Our research focuses on a descriptive analysis framework of a real-world dairy food distribution system, using historical data collected from embedded sensors inside refrigerated trucks. The dataset consists of 5,972 records that present the distribution schedule, the collected temperatures and the refrigerator’s open door window in seconds. The measurements are taken both on-route and while loading/unloading from various vehicles for two periods one in the winter and one in the summer. A Big data analysis approach combining statistical analysis and heterogenous data manipulation was conducted to extract critical results regarding truck refrigeration efficiency and dairy products safety zone.  Additionally, Pearson analysis was performed to evaluate the correlation between temperature differences and the open-door condition. The results indicated that in the summer period, temperature fluctuations are intensified both on-route and at the delivery point compared with the winter period, and the operating time of the refrigerator is extended. Moreover, refrigeration performance differentiated amongst the vehicles due to the truck’s thermal characteristics i.e. inappropriate thermal insulation or even the human factor. The contribution of this research is to conduct a descriptive analysis of critical measurements comparing system characteristics between the summer and winter periods and to propose an effective analytical framework for optimizing the efficiency of the overall system in a real-world distribution use case.Towards a sustainable and resilient digital cold supply chain, environment sensing technologies and Big data analytics combined with Artificial Intelligence can extract critical information and provide a systematic framework for product optimization and safety enhancement. Our research focuses on a descriptive analysis framework of a real-world dairy food distribution system, using historical data collected from embedded sensors inside refrigerated trucks. The dataset consists of 5,972 records that present the distribution schedule, the collected temperatures and the refrigerator’s open door window in seconds. The measurements are taken both on-route and while loading/unloading from various vehicles for two periods one in the winter and one in the summer. A Big data analysis approach combining statistical analysis and heterogenous data manipulation was conducted to extract critical results regarding truck refrigeration efficiency and dairy products safety zone.  Additionally, Pearson analysis was performed to evaluate the correlation between temperature differences and the open-door condition. The results indicated that in the summer period, temperature fluctuations are intensified both on-route and at the delivery point compared with the winter period, and the operating time of the refrigerator is extended. Moreover, refrigeration performance differentiated amongst the vehicles due to the truck’s thermal characteristics i.e. inappropriate thermal insulation or even the human factor. The contribution of this research is to conduct a descriptive analysis of critical measurements comparing system characteristics between the summer and winter periods and to propose an effective analytical framework for optimizing the efficiency of the overall system in a real-world distribution use case.