## 3 Methodology
### Data Understanding  
Given that the problem at hand entailed a binary classification task, distinguishing between instances with a high probability of experiencing issues related to collapsed pallets and those without, it was imperative to gather transportation information about both scenarios. This involved collecting data on situations that resulted in pallet collapse incidents as well as instances where no problems occurred during transportation.  
To ensure comprehensive data coverage, information on transport, logistics, and product characteristics was gathered from a multinational company specializing in the production and distribution of glass containers for the food and beverage industries, Table 1. Additionally, the values corresponding to the average daily temperature at the delivery point were obtained from Meteostat (see [[https://meteostat.net/pt/](https://meteostat.net/pt/)]([https://meteostat.net/pt/](https://meteostat.net/pt/)), accessed on 21 March 2024), a reputable meteorological data provider.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline Material\_Ref & Code of the shipped material \\ \hline Destination\_Country & Transport destination country \\ \hline Distance & Distance between the origin and destination points, in kilometres \\ \hline Temperature\_Delivery\_Date & Average daily temperature at the delivery point, in Celsius degrees \\ \hline Delivery Date - Picking Date & Duration, in days, that the material remains inside the vehicle before the commencement of transport \\ \hline Warehousing\_Time & Number of days of the material inside the warehouse \\ \hline External\_Warehouse(Y/N) & Binary variable taking value 1 (one) if the material was shipped from an external warehouse, and 0 (zero) otherwise \\ \hline Carrier\_ID & Internal code assigned to the carrier responsible for the transport \\ \hline SAP\_Customer\_number & Internal code designated for the customer which is to be filled out by the transport \\ \hline WOOD\_PALLET & Specific type of wood pallet being used \\ \hline LAYER\_SEPARATOR & Specific type of layer separator being used in the pallet \\ \hline Foil\_thickness & Thickness, in millimetres, of the foil wrapping the pallets \\ \hline Larger\_Side & Measurement, in millimetres, from the leftmost container to the rightmost container along the largest side of the pallet \\ \hline Smaller\_side & Measurement, in millimetres, from the leftmost container to the rightmost container along the smallest side of the pallet \\ \hline Container\_Diameter & Maximum diameter, in millimetres, of the material \\ \hline Container\_Bearing\_Diameter & Diameter, in millimetres, of the bottom surface of the material \\ \hline Container\_Weight & Weight, in grams, of the material \\ \hline Container\_Height & Height, in millimetres, of the material \\ \hline Pallet\_Height & Height, in millimetres, of the pallet \\ \hline Truck\_Weight & Weight, in kilograms, of the vehicle \\ \hline Complaint (Y/N) & Target binary variable, taking a value of 1 (one) if the transportation resulted in a problem related to collapsed \\  & pallets, and a value of 0 (zero) if no such problem occurred \\ \hline \hline \end{tabular}
\end{table}
Table 1: Collected variables and respective descriptions.  
The data collection process spanned from 2017 to 2023 and led to a highly imbalanced dataset. Specifically, the dataset included over 1 (one) million instances from the majority class, representing situations where no problems were encountered. In contrast, the minority class, which denoted instances of collapsed pallets, comprised only 526 examples.  
In the context of this specific problem, the imbalance observed in the dataset is inherent to the nature of the problem itself, rather than being a result of any shortcomings in the data collection process. Specifically, the frequency of transportation instances without any collapsed pallets far exceeds those where such incidents were observed.