## 3 Methodology  
As one can conclude from the previous section, the literature reveals several gaps in addressing specific challenges related to pallet stability in logistics, particularly in the glass industry. While general research has been focused on broad aspects of cargo handling, such as route optimization and securing techniques, detailed studies on pallet stability remain sparse. Existing works often lack granularity in predicting pallet collapse and fail to account for the unique challenges posed by fragile goods. Additionally, previous research has not sufficiently integrated data-driven approaches or machine learning techniques to address these specific issues effectively. Therefore, this study aims to fill these gaps by providing targeted predictions and strategies to mitigate pallet instability mainly focused on the glass industry. The primary objective of this research was twofold: firstly, to develop and apply a predictive model capable of identifying potential incidents of pallet collapse using advanced data analytics and machine learning techniques; secondly, to devise and recommend targeted preventative strategies based on the insights derived from these predictive models to mitigate the risk of cargo loss. This approach involved analysing a range of variables to identify the key factors that significantly contribute to pallet collapse. By pinpointing these critical variables, the study aimed to enable proactive measures and preventive actions that effectively reduce the likelihood of such incidents. To achieve these objectives, this study employed a systematic approach guided by the CRISP-DM methodology, as outlined by [PERSON] and [PERSON] [28], which facilitated a structured and rigorous execution of the data mining process through its six phases, each crucial to the development and implementation of the predictive models and strategies:  
* Business Understanding: The study began by clearly defining the specific problem of pallet collapse within the glass packaging industry, understanding its implications for logistics and business operations. The key business goals were established, focusing on reducing cargo loss and improving supply chain efficiency through predictive analytics. * Data Understanding: Relevant data from a leading glass manufacturer were collected, including variables related to pallet geometry, environmental conditions, and transportation details. The data were thoroughly explored to identify patterns and assess their quality, ensuring that the collected dataset was suitable for the modelling phase. * Data Preparation: This phase involved cleaning and preprocessing the data to address any inconsistencies or missing values. Features relevant to predicting pallet collapse were carefully selected and engineered to enhance the models' predictive capabilities. The data were also split into training and testing sets to facilitate unbiased model evaluation. * Modelling: In this phase, machine learning algorithms, specifically Decision Tree and Random Forest models, were applied to the enhanced data. The models were trained to predict the likelihood of pallet collapse based on the selected features. Hyperparameter tuning was conducted to optimize the performance of these models, focusing on metrics such as Precision, Recall, and F1-score. * Evaluation: The models' performance was rigorously evaluated against the test data to ensure their effectiveness in predicting pallet collapse. The evaluation criteria were aligned with the study's objectives, emphasizing the models' ability to generalize and accurately predict unseen cases. * Deployment: Finally, the insights gained from the predictive models were translated into actionable recommendations. These preventative strategies were designed to address the key risk factors identified in the analysis, such as optimizing pallet stacking patterns and enhancing packaging materials.