## 4 Results and Discussion
### Preventive and Mitigation Strategies  
The predictive analysis conducted in this study reveals key insights into the factors contributing to pallet collapses during transportation. Therefore, based on the identified influential features, several preventative strategies can be implemented to reduce cargo loss.  
Firstly, optimizing packaging specifications is crucial. The study identified both \"Container_Height\" and \"Container_Diameter\" as significant predictors of pallet collapse. To address this, companies should standardize packaging dimensions to ensure stability. By adhering to optimal container sizes and avoiding excessive height or diameter, the likelihood of imbalances that could lead to collapses is reduced. Additionally, reinforcing packaging through the use of double-walled or specially designed containers for high-risk shipments can further enhance stability. Testing packaging designs for their ability to withstand various transport conditions is also recommended.  
Figure 5: Feature importance, within the RandomForests6040 model, as measured by Impurity Decrease.  
Temperature control is another critical factor. The \"Temperature_Delivery_Date\" feature was found to impact the risk of pallet collapse significantly. To mitigate this risk, it is essential to implement measures for maintaining optimal temperature conditions during transport. This could involve utilizing temperature-controlled trucks or containers and ensuring regular monitoring to adhere to specified temperature ranges. Integrating temperature logging systems into the shipping process can provide real-time data, allowing for prompt corrective actions if temperature deviations occur.  
Reviewing and adjusting shipping routes based on the analysis of the \"Destination_Country\" feature can also help reduce risks. It is important to analyse shipping routes and destination-specific factors that may contribute to increased risk, such as exposure to extreme weather conditions or rough terrains. Developing a risk assessment framework based on historical data for different destination countries can guide adjustments in shipping strategies and packaging solutions according to the risk profile of each destination.  
Improving handling procedures is another vital strategy. Enhancing training programs for handling and loading procedures will ensure that personnel are well informed about best practices for securing and managing loads. This can significantly reduce the chances of pallet collapses. Additionally, investing in high-quality handling equipment and performing regular maintenance will prevent equipment failures during loading and unloading.  
Leveraging predictive analytics for proactive measures is also recommended. Developing early warning systems that utilize predictive models to flag high-risk shipments based on identified features can trigger preventive actions, such as additional packaging or routing adjustments, before shipment dispatch. Continuous monitoring of predictive models as new data become available allows for real-time adjustments to strategies, improving overall risk management.  
Finally, collaborating with industry partners can enhance these efforts. Sharing best practices and learning from others' experiences can lead to improved standards and innovative solutions. Engaging with stakeholders and partners can foster collaborative efforts to address common challenges in transportation and logistics.  
By implementing the aforementioned strategies, businesses can proactively address the factors contributing to pallet collapses and effectively reduce cargo loss during transportation. The insights derived from predictive modelling will support informed decision making and enhance the efficiency and safety of the supply chain.