In addition, ML is used especially in the sales area to better predict customer behavior. McDonald’s, which suggests further additional sales to the customer during the purchase transaction, processes information using ML and considers weather data, times of day, local traffic, events in the vicinity, and historical sales data [81].

By addressing supply and demand imbalances and triggering automated responses using ML applications, companies cannot only improve the customer experience, but also limit sales risks. They can also reduce costs through better forecasting of freight and warehouse processes and improve collaboration with logistics service providers [82].

In addition, examples of ML applications in the production area could also be found. Robert Bosch GmbH, a manufacturer of industrial technology and consumer goods, uses ML to test components on the test bench and to recognize error patterns based on collected data, thus distinguishing relevant from non-relevant error messages. As a result, risks regarding product quality and delays in the process flow can be reduced [83].

Drawing from the above discussion, we propose:

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 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. 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:

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].

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? Science should provide appropriate recommendations for implementation and action.

In order to sensitize managers to the topic, more publications of use cases from both science and real-world applications are required. As a starting point for further research, existing examples of ML in supply chain management could be analyzed and evaluated against the background of SCRM in order to illustrate the cross-functional added value of ML applications. Another starting point for future research is an empirical review of the improvement of SCRM by ML, which is still pending. Drawing from the above discussion, we propose:

Several supply chain areas can benefit simultaneously from the results of the ML deployment. The use cases of the CEP companies show that the integration of ML not only reduces transport and delivery risks, but also intensifies the customer relationship through an improved information supply. By integrating ML into SCRM, the company can strengthen its position vis-à-vis the customer, since it knows its strengths and weaknesses better. Thus, the cooperation between the individual actors in the supply chain can be improved at the same time. The integration of ML into SCRM can also be an important lever in price negotiations with customers or insurance companies. It can also help to set oneself apart from competitors [4].

ML-based results can help communicating supply risks with organizational decision-makers. Informing decision-makers early on about supply chain risk levels has implications for manager cognition and how they adapt decision-making strategies based on risk knowledge [98]. According to Pournader et al. [99], this is an important component of behavioral SCRM. ML consequently supports decision-making and mitigates problems related to managers’ cognition and potential biases.

Finally, it should be noted that the integration of ML into the SCRM also requires a critical examination. For example, it must be considered that ML-based risk management systems require high initial investments [85]. In addition to the IT infrastructure, the storage of data (capacity) and the tracing of failure-causes are expensive [28]. There are also legal concerns about the massive collection or use of data for risk management [72]. Thus, new legal regulations for the use of AI/Big Data could follow in the future, which have to be considered in the process phases of risk management.