# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 3 Discussion
The potential to include real-time data makes quicker reactions possible [6] and bridges the path towards prescriptive decisions while considering risks in advance [5]. This improvement in flexibility and response time [6] can lead to reduced time demands on data analysis and initiate necessary actions, culminating in the identification of fraud and hazards in real time [84]. The collection of real-time data within the enterprise, and especially within the supply chain, as shown in the FedEx example, means that companies need to pay more attention to risk avoidance strategies as their response time to risk will increase. The perspective of risk assessment as well as the associated risk avoidance measures shift from a reactive SCRM approach to a more proactive approach due to the inclusion of ML. Consequently, the integration of ML also requires the introduction of new assessment standards which include the integration of proactive measures. These measures should consider: to what extent is it economically sensible to avoid risks? For example, when does the effort involved in avoidance exceed the follow-up costs caused by a risk that has occurred? The traditional assessment criteria, \"probability of occurrence\" and \"extent of damage\", are not sufficient here as a basis for decision-making. Threshold values for deviations from the normal state should be defined, starting from when the intervention of an employee is required (though there are challenges in doing so, see e.g., [91; 92]). Drawing from the above discussion, we propose:  
**Proposition 2**. : _The integration of ML into SCRM requires new evaluation standards._  
Additionally, the quality of SCRM decisions can be improved. Using traditional approaches, which involve multiple points of human-supply chain contact, intuition and feelings are often used as decision making tools, increasing the risk of planning fallacies or other biases entering the equation [93]. Relying more on algorithmic decision-making or incorporating data-driven requirements into the judgement process of the SCRM professional reduces this risk, resulting in higher reliability and precision [85]. This precision can be improved further by utilizing algorithms to sort potential risks based on their priority [87]. One should not, however, be blind to the fact that algorithmic bias is an issue and must be considered, particularly in the black-box nature of ML algorithms such as Artificial Neural Nets--despite recent advances in Explainable AI (XAI)-research [94]. Assuming objectivity in decision making because an algorithm was used, falls short of this aim, and as such implementing transparency measures to ensure algorithmic accountability is necessary [95]. Companies must expand their monitoring and data-mining techniques for SCRM in order to ensure high quality data and efficient control measures have been taken [96]. Having implemented these and other necessary prerequisites for data security and privacy concerns, the automatization of decisions and processes is the tantalizing promise of ML in SCRM [84]. The FedEx use case touched on above manages this by automatically rerouting packages due to changing external circumstances in real-time. Similarly, ML is able to identify customers with a high-risk profile, automatically engaging various checks and responses, reviewing only a subset of cases manually [97]. These benefits provide tangible value to companies, leading to a more efficient and effective use of resources along the supply chain [36]. However, the results of the literature analysis have clearly highlighted a research gap. Since few application examples are available, companies need more guidance on how to integrate ML into SCRM. What are the first steps they need to take? What approach should they follow? Should existing data be analyzed to determine which risks can be reduced, or should risks be the starting point, and data for problem solving purposes be generated afterwards? What level of maturity should the SCRM have reached before ML can be integrated?