# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 2 Methodology and Findings  
### Systematic Literature Review  
An overview of current contributions where ML is applied for SCRM should be created, using a systematic literature review (SLR). In order to use the results of the literature review to answer the research questions, the evaluation of the contributions considered the following:  
* What examples of ML application in SCRM have already been described in literature? * Which risks could be influenced by the integration of ML into SCRM? The literature analysis was carried out in two parts, with the first part being based on [PERSON] et al. [38]. In the first part of the analysis, an SLR was conducted in the scientific database, SCOPUS, for publications (up until December 2020) using a search for titles and abstracts which include'supply chain' AND 'crisis' OR risk* OR vulnerability* OR uncertainty* OR disruption* OR disaster* OR disturbance* OR catastrophe OR peril OR hazard OR resilience OR robustness OR safety OR flexibility OR agility AND'machine learning' OR 'artificial intelligence' OR 'predictive analytics'. The search string was previously matched with search terms used in relevant SLRs in SCRM and ML [9; 12; 13; 14; 11; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. The combination resulted in 533 hits. In the subsequent title screening, the authors found that only in a few cases did the titles indicate that the subject areas SCRM and ML were treated, so that a selection based on a title screening was not considered to be appropriate. Instead, the authors linked title screening directly to abstract screening and thus considered 109 articles to be relevant. Most of the rejected contributions were publications that were not relevant to the research project. Some of the publications contained the keywords, but the content did not refer to supply chains. Other journal articles did not meet the scientific standards and were, therefore, not included in the analysis. A further condition for contributions to be considered was that they were written in English. The last step in the SLR included a complete review and analysis of the remaining articles. Publications that deal with general mathematical optimization models or whose topic is too far away from the focus of this paper were not included in the analysis. The final selection of relevant papers included 23 publications, which the authors identified as relevant for the research project. The results of the SLR showed a research gap; only a few examples of ML applied in SCRM have thus far been considered in the scientific literature. This is mainly due to the fact that although ML has been used in supply chain management for some time, its integration into SCRM is still a very young research field. In addition, the few examples described in the scientific literature contain a low level of detail, such that it was barely possible to extract meaningful answers to the above research questions. For this reason, the authors extended the scope of the literature analysis. After these initial assessments, the literature analysis focused on the scientific consideration of the field, ML in SCRM, and the second section on the analysis of applied examples in existing business practices. For this purpose, the additional information on the previously identified examples was sought in practical journals and the internet to increase the level of detail. To structure this approach, a multi-vocal literature review (MLR) was employed. The method initially appears in an educational context [43]. The authors at that time focused on case study research, but, in 1991, [PERSON] had already emphasized that this constraint limitation should be expanded [44]. Accordingly, the method has recently been adopted to a diverse field of primarily technologically focused research areas, among them blockchain [45], cloud computing [46], cloud data migration [47], and many studies in software development (e.g., [48], and especially [49] as a secondary study).