# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 2 Methodology and Findings
### Current Status of ML Application in SCRM in Scientific Literature
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Study Type** & **Scope of Application** & **Considered Risks** & **SCRM Focus** \\ \hline [PERSON] et al. [56] & Conceptual work & transport & transport risks & indirect \\ [PERSON] et al. [60] & Conceptual work & transport & transport risks & indirect \\ [PERSON], [PERSON], [PERSON] [18] & Conceptual work & production & supplier risks & direct \\ [PERSON] and [PERSON] [61] & Case Study & production & production risks & indirect \\ [PERSON] et al. [62] & Conceptual work \& use case & production & production risks & indirect \\ [PERSON] and [PERSON] [63] & Conceptual work & transport & food quality/transport risks & indirect \\ [PERSON] et al. [57] & Case study & transport & supplier risks & direct \\ [PERSON] et al. [58] & Conceptual work & transport & supplier risks & direct \\ [PERSON] et al. [64] & Conceptual work & supply chain & quality risks & indirect \\ [PERSON] and [PERSON] [65] & Conceptual work & production & supply risks & indirect \\ [PERSON] [19] & Conceptual work & supply chain & supply risks & direct \\ [PERSON] et al. [66] & Conceptual work & procurement & information risks & indirect \\ [PERSON] et al. [20] & Survey & production & transport risks & indirect \\ [PERSON] et al. [67] & Conceptual work & supply chain & sales risks & indirect \\ [PERSON] et al. [22] & Conceptual work & supply chain & supply risks & direct \\ [PERSON] et al. [59] & Conceptual work & supply chain & supplier risks & direct \\ [PERSON] et al. [23] & Conceptual work & supply chain & supply risks & direct \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of publications with an ML SCRM application example. more mature than other areas of the supply chain. Also, the technological development of retrofittable sensors, which can be attached to transport containers enables the collection of extensive data from the entire supply chain, so that the identified practical examples reflect this technological trend [56; 72]. The examples described provide a rather low level of detail and, with few exceptions, do not allow any conclusions to be drawn about the company or the characteristics associated with it (e.g., [57; 61]). Furthermore, it can be seen from the analyzed contributions that ML is used in SCRM primarily to improve risk identification. Different data is used, which can be of either an internal company origin--such as order date, supply time delta, contracted supply time, supplier ID, etc.--or an external origin, such as GPS data, weather data, or entries in social media. Here, too, the analysis shows that the contributions rarely mention which data sources were actually used in the analysis [18; 56; 57; 62; 63]. In the contributions, the application of ML is primarily aimed at identifying production, transport, and supply and suppliers risks in order to be able to quickly counteract potential supply chain problems (see Table 1). In addition, ML is used in these examples to quickly identify the risks associated with suppliers [18; 58; 59]. Sales and quality risks, on the other hand, are hardly ever examined in any of these scientific contributions [63].