## 1 Introduction  
Cargo loss within logistics systems poses significant financial challenges for businesses, incurring not only the costs associated with replacing damaged or lost goods but also additional expenses such as incident management, increased insurance premiums, missed business opportunities, and potential reputation damage [1]. The urgency of addressing these losses is stressed by their widespread impact across various sectors, particularly in industries where goods are susceptible to damage during transport, such as in the ceramic [2] and the glass industries [3]. However, previous research on logistics has been predominantly focused on broad solutions, such as route optimization and general cargo securing techniques, without sufficiently addressing the specific issue of pallet stability during transportation. This is a critical gap, as the collapse of pallets can lead to significant product loss and safety hazards, especially in industries with fragile goods like the glass industry. For instance, the work presented in [PERSON] [4] is focused on optimizing transportation routes, while the work [PERSON] [5] is about general cargo securing methods. These works, although valuable, fail to account for the particular risks associated with unstable pallets, particularly in the glass industry where product fragility and unique packaging requirements are major factors. Moreover, many previous studies lack the granularity required to effectively predict and prevent the collapse of pallets because of insufficient data or overly generalized approaches. For example, the studies presented in [PERSON] (2018) and [PERSON] (2018) explore ways to improve cargo handling but do not dive deep into the specific variables that lead to pallet instability in glass product transport. Thus, the complexity of the problem is often underestimated and, as a result, previous solutions fall short when it comes to mitigating pallet collapse incidents. The instability and collapse of pallets during transport can lead to significant product damage and loss, triggering the cascading financial effects previously mentioned, which may be difficult to estimate due to the lack of data or access to them ([PERSON], 2018). However, these losses extend beyond property damage and can result in severe human consequences. Inadequate cargo securing for road transport has led to significant fatalities and injuries. For example, in 2014, approximately 1200 individuals in Europe lost their lives in accidents directly linked to improperly secured cargo ([PERSON], 2018). These examples highlight the severe consequences of pallet instability and collapse, and stress the necessity for more research focused on overcoming this problem. In response to the current literature gaps, this study aimed to specifically address the problem of pallet collapse, mainly in the glass industry due to its particular vulnerability to such a problem. Thus, this study fills the research gap by leveraging data analytics and machine learning techniques to predict pallet collapse, thereby offering a more tailored solution to this overlooked problem in the logistics of fragile goods. Data analytics and machine learning, whose studied techniques are detailed in the following section, have been widely used to solve various problems, such as detecting and classifying vehicle-deck collisions on railway bridges ([PERSON], 2018), optimizing gate assignments in airports ([PERSON], 2018), identifying vehicles to reduce traffic infractions ([PERSON], 2018), and predicting ice resistance to ensure safe ship navigation in icy regions ([PERSON], 2018). Here, it is used to predict pallet stability during transportation in the glass industry, and to identify and mitigate factors contributing to pallet collapse.