# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 3 Discussion  
As described previously, ML is already generating value for SCRM in practical use-cases. The results of the literature analysis demonstrate that ML in SCRM enables different approaches for using and integrating data. ML is able to consider multiple, complex data sets, an array of strategic choices, and various scenarios [84]. Building complex models was also possible previously, but increasingly data volumes are becoming too large to be processed with traditional technology [85]. Additionally, ML approaches are able to process structured data, unstructured data, and a combination of both as shown in [PERSON] et al. ([59], see also [86]). This is particularly valuable as the quantity and frequency of unstructured digital data is experiencing tremendous growth, and as such, only extracting data from structured sources would, therefore, severely limit SCRM's potential. For instance, data sources, such as social media, are promising origins for analyses [87]. To develop such cases, the availability of data is crucial. This is also becoming easier, as digitized business processes and the application of data-generating sensors has been normalized across industries [72]. We should not, however, omit the challenges inherent in collecting this data--ensuring adequate data quality is a necessary precondition. Establishing a governance structure and incorporating data quality measures are thus critical success factors for big data analytics in general and ML in SCRM in particular [88]. The continuous evaluation of unstructured data cannot be accomplished using IT tools alone; it also requires the support of experts (i.e., data scientists) who perform the technical data analyses and provide decision support. This includes both the manual cleansing of raw data in order to improve data quality as well as the programming of algorithms to automatically utilize real-time data. Therefore, companies need to hire qualified people to maintain AI-Risk-Management Systems [85]. As two areas of responsibility, Data Science and SCRM are merged, and their competencies and responsibilities must be redefined and clearly assigned in order to avoid errors in data preparation, provision, and evaluation. In addition, one of the risk manager's core tasks in the future will be to consider the results of data analyses as a forecasting aid in management decision--as shown in the BUFAB und Volkswagen examples. The focus of the tasks will consequently progress towards stronger data-based decision-making, i.e., drawing the right conclusions from the results of ML integration. Consequently, the requirements on the qualifications of the supply chain risk manager do vary, as this individual should also have an understanding of mathematical methods of analysis [89]. Drawing from the above discussion, we propose:  
**Proposition 1**. : _The integration of ML into SCRM leads to changes in the qualification requirements of supply chain risk managers._  
It has been demonstrated that humans have more trust in their own capabilities than in the capabilities of AI [90], and, as such, humans tend to make decisions without the  
Figure 4: Focused risks in the ML in SCRM practical application examples. help of AI [90]. Consequently, stronger control mechanisms should be built into ML-based decisions. At the same time, the decision-making process should be systematically and analytically prepared, as shown in the BUFAB example, in order to make the derivations comprehensible and, thus, promote acceptance among employees. In addition, the automated research and evaluation of real-time information leads to a reduction in the manual activities of the supply chain risk manager. This leaves them more time for analytical and strategic tasks (i.e., their focus shifts from an operations-centered view towards medium to long-term strategies with an SCRM orientation, as shown in the HELLA example) [76]. Furthermore, utilizing ML approaches in SCRM catalyzes a trend towards a more proactive mentality.