# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 1 Introduction  
Different results in recent years have shown how vulnerable supply chains can be [1, 2]. Due to the domino effect, individual actors in the supply chain are often not the only ones affected by an interruption but rather the entire network [3, 4, 5, 6]. The tsunami in Japan in 2004, hurricane Katrina in the US in 2005, the volcanic eruption in Iceland in 2010, and the current COVID-19 pandemic are good examples demonstrating how numerous networks and even entire industries can be impacted by the negative effects of certain events (e.g., [7, 8]). In order to prevent disturbances, companies should apply Supply Chain Risk Management (SCRM) [4, 5, 9, 10]. Supply Chain Risk Management is 'a part of Supply Chain Management which contains all strategies and measures, all knowledge, all institutions, all processes, and all technologies, which can be used on the technical, personal, and organizational level to reduce supply chain risk' [11] (p. 157). The aim is to increase the transparency and robustness of value-added processes in order to counteract any supply chain interruptions or even avoid them altogether [12, 13, 14]. Here, a distinction must be made between reactive and proactive SCRM. Reactive SCRM refers to all activities that are carried out after the occurrence of a supply chain risk in order to keep the extent of damage as low as possible [15]. In contrast, proactive SCRM includes all activities carried out prior to the risk occurring in order to proactively protect the company from supply chain risks [10]. Many companies do not focus on supply chain risks. In addition to a lack of time and personnel resources, the reasons often given are a lack of standards with regards to system compatibility and data consistency, as well as technical difficulties in integrating risk management software into existing information and communication systems [16]. One of the greatest challenges is to receive information about deviations from the planned process at an early stage in order to ensure timeliness of logistics processes within the supply chain [17]. The early identification of different supply chain risks is crucial for the timely introduction of countermeasures in order to avoid supply chain disruptions [4; 11]. SCRM must predict potential causes of these process disruptions and analyze interruptions that have occurred in the past in order to minimize financial losses and process failures throughout the supply chain. Here, machine learning (ML) can help to detect risks early on [18; 19; 20; 21; 22; 23]. Machine Learning can broadly be defined as an algorithm that generates outputs based on available data without first programming the respective learning outcome [24]. Instead, the ML algorithm 'learns' and iteratively assimilates its perception to the underlying real-world phenomena represented in the input data. The current type regarding ML is based on an amalgamation of several trends, which reinforce each other, making ML a powerful tool in an array of fields and practical use-cases. First, the availability of vast amounts of digital data is a necessary condition for the application of most ML algorithms--particularly for Deep Learning approaches [25], which utilize multi-layered Artificial Neural Networks to enable the most sophisticated, modern use-cases (e.g., the state-of-the-art Natural Language Processing system GPT-3) [26]. In general, the increased availability of data in previous years has been able to empower the application of ML. Supply chain networks generate over 1.6 billion new data points each month, supplying a number of data streams that can be used as inputs in an ML system [27; 28]. Second, computational power has proven to be a major driver of progress in AI. Major breakthroughs were enabled through significant improvements in computational performance [29]. Third, algorithmic advances play a relevant role in enabling efficient and scalable applications.