# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 4 Conclusions  
In this paper, we answered the research questions about which supply chain areas ML has already been considered for implementation to improve SCRM, about what the primary risks considered in the use-cases are, and how ML might shape SCRM. For this purpose, the existing scientific literature was analyzed and synthesized to investigate how ML can be incorporated into SCRM, and which risks can be addressed with it. We have followed a two-step approach consisting of a systematic literature review and a multi-vocal literature review to generate a more holistic view of our research field. We selected 533 contributionsin this topic area with the help of the systematic literature review and analyzed a total of 23 publications in-depth. The results of the systematic literature review clearly showed that only a few examples of ML in SCRM have been described in any meaningful detail within the scientific literature to date and that the existing examples focus on supply chain areas of production, transport, and the entire supply chain. The results also showed that the application examples are primarily related to the early identification of production, transport, and supply risks in order to be able to counteract potential supply chain problems quickly. At the same time, the evaluation has shown that, to-date, few examples of ML exist which contain a sufficient level of detail to allow for more in-depth analysis. The very manageable number of scientific contributions clearly reflects the research gap of integrated ML in the SCRM. Nevertheless, through the case studies analyzed, we were able to identify the added value that the integration of ML in SCRM can provide such as increased flexibility and response time, the integration of new data sources (e.g., social media or weather data), or higher reliability and precision compared to conventional analysis technologies. From the results of the systematic literature analysis, a set of four propositions have been developed to show how ML might shape and improve SCRM. Among them is the need to develop new assessment standards and to change the qualification requirements for supply chain risk managers associated with the use of ML; this can be used as impetus for further research. In order to better understand the positive impact of ML on SCRM, we advocate the further publication of detailed use cases. More use cases are needed to better demonstrate the benefits of using ML in SCRM to decision makers. Companies should therefore be encouraged to publish their approaches and share experiences with researchers. They should also enable researchers to conduct empirical studies in which the positive impact on different supply chain risks is verified. Only through an intensive exchange of knowledge for better solution finding can progress be made in this field, from which both practitioners and researchers can benefit. We also note that reliance on good database search and retrieval is essential for a systematic literature review. The results of this research are limited by the search engines, whose volume of contributions change regularly. The use of alternate terminology as well as the lack of precision of the search term can be listed as further deficiency. Sometimes authors use different definitions to define the same things [100]. Another limitation is the limited number of available use cases. The number of case studies should be continuously updated and evaluated. Furthermore, not all use cases could be discussed with company representatives in order to obtain additional information. To deepen the analysis, further interviews should be conducted in the future. Conceptualization, M.S. and S.L., methodology M.S. and S.L., formal analysis M.S. and S.L. ; visualization, M.S. ; writing--original draft preparation, M.S. and S.L. ; writing--review and editing, M.S. and S.L. ; supervision M.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.