# 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  
The results of the systematic literature analysis have shown that the link between ML and SCRM has been increasingly investigated over the last ten years. The initial focus has been on individual risks associated with computational intelligence, swarm intelligence, and simulations [53; 54]. Only by beginning research on neural networks, such as the developed AlexNet architecture [55], will the number of scientific contributions increase significantly (see Figure 1). In the last two years in particular, there has been an increase in the number of published contributions as ML has become more widely integrated in companies [56; 57; 58; 59]. Figure 1 shows the number of published articles using the search string described in Section 2.1. A total of 533 articles were found, consisting of 49% conference proceedings, 44% journal articles, 3% books or chapters within books, and 4% others (e.g., editorials). A more detailed examination of the journal articles has shown that no journal to date has dealt with the topic in a comprehensive manner (see Figure 2). Instead, the relevant articles are scattered across different journals, ranked primarily in B.  
Figure 1: Historical series of published papers on ML in SCRM. As described in Section 2.1, after careful selection, 23 articles were considered relevant. The contributions were analyzed in detail and evaluated with respect to their focus and the risks considered. The results showed that the authors in the articles rarely consciously make a connection to SCRM. For this reason, the link concerning ML and SCRM is often only made indirectly. For example, [PERSON] et al. (2018) use an ML based forecasting model to predict future temperature for perishable food supply chain to minimize food quality and safety risks during transport. To do so, [PERSON] et al. (2018) use ML to detect the direction of passive RFID tags so that products entering or leaving a gate can be correctly identified. The aim is to improve the efficiency of RFID-based product traceability, thereby reducing transport risks. [PERSON] et al. (2018) are currently developing a framework that uses data-driven artificial intelligence (AI) techniques to predict delivery delays in a multi-level manufacturing supply chain, using the example of a multi-stage supply chain in aerospace. [PERSON] and [PERSON] (2018), on the other hand, present an integrated, comprehensive planning system (Artificial Intelligence Planner), which uses AI techniques to improve data analysis and decision support for production planning, and thus reduce production risks. The case study was conducted in a concrete construction company in the UK. [PERSON] et al. (2018) present a time series model (exponential smoothing with covariates) that takes into account both historical data and environmental business information to develop robust demand forecasts to reduce production risks. The model was tested at the chemical company BASF. [PERSON] and [PERSON] (2018) use climatic, agricultural, and economic data as well as an ML algorithm to construct a Bayesian Network which can be used to optimize specific hazard categories for food safety, thereby avoiding food quality and transport risks. The authors use the Rapid Alert System for Food and Feed for the data. [PERSON] et al. (2018) use historical data available to an Original Equipment Manufacturer for ML application and prediction, thereby avoiding supply disruptions. [PERSON] et al. (2018) combine simulations and ML to explore applications for data-driven decision support in the selection of robust suppliers. The use of ML algorithms supports supplier selection and thus leads to a reduction of supplier risks. [PERSON] et al. (2018) use ML techniques to predict fraud in an intelligent supply chain. They enable an assessment and classification of whether a transaction can be classified as normal or fraudulent to reduce product quality risks.