## 4 Case Study--Analysis of Disruptions
### Results Analysis, Conclusions, and Recommendations  
The result of the system's operation was the generation of a report containing logistics process statistics and information about detected irregularities (deviations from the assumptions). Below are excerpts from the results (Figure 4). During the analyzed period, 874 cycles were identified, with 34.5% of them being fresh cycles and 65.5% being frozen cycles. In the analyzed company, shipments are sent seven days a week. The company sends more frozen deliveries than fresh, and on weekends, deliveries tend to have a more supplemental nature rather than regular (Figure 5). This results from underestimating the demand, as most stores place orders with the DC for goods in quantities that match the in-store demand for stock. The duration of cycles is challenging to determine because the actual cycle time does not correspond to what is recorded by the TMS (transport management system). The cycle time for delivery is measured by TMS systems as follows: the start time of the cycle corresponds to the time the container/order is scanned when the goods are dispatched from the DC to the delivery vehicle. The end time of the cycle, on the other hand, refers to the re-scanning of the container/order when it is unloaded at the store. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
Most entrepreneurs understand and analyze the delivery cycle in this way. However, this approach is incorrect because this interpretation is characteristic only for deliveries other than the cold chain, such as dry food product deliveries. Cold chain deliveries, on the other hand, have the cycle starting from the moment the loaded container is sealed  
Figure 4: Container uses statistics—weekly. Figure 5: The average number of cycles on different days of the week (from Monday on the left bar to Sunday on the right bar). until it is reopened during unloading. This is because the goods inside the container must be transported under specific temperature conditions from the end of loading, marked by closing the container's doors, to the moment unloading begins, marked by opening the doors. As a result, in the analyzed company, the study has shown that the actual cycle is significantly longer, around 13 h for fresh deliveries and about 10 h for frozen deliveries (Figure 6). According to the company, the delivery time should be around 6 h. The difference is that because the company does not include the waiting time for transportation from the moment the goods are loaded into the container to the moment of their release, which is the time spent in the delivery area. This waiting time is approximately 4 h. The results of the analysis of the delivery cycle duration are presented in the chart below. Furthermore, the company is unable to determine how long the containers with goods wait for unloading at the stores. The unloading time at the stores is not enforced by the delivery--the containers are returned to the distribution center (DC) during the next delivery opportunity. So, store employees have time to unload, which usually occurs when there is less store traffic, and employees can attend to the delivered goods. It might also happen that employees intentionally delay the unloading to make use of the cooling system inside the container, which is supposed to maintain the proper conditions inside. By delaying the unloading, they are lowering the costs of cooling and maintaining the newly delivered goods in the store. However, they are risking potential damage to the goods due to the breach of transport conditions, and they would also require more energy to cool it. Therefore, these savings might be misleading. If store employees were informed about the right time for unloading, the goods wouldn't be damaged due to transport condition violations, and the company could save on energy costs during the maintenance of proper conditions in the store.