# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 2 Methodology and Findings
### Analysis of Identified Practical Use Cases  
As described in Section 2.1, examples of real-world ML supply chains were researched in a multi-vocal literature review. For the analysis, only practical examples with usable information were considered. Figure 3 shows the identified practical use cases. In the following, selected practical examples are described in more detail. The trading company BUFAB Sweden AB offers its customers a full-service solution for sourcing and logistics for C-Parts ([[https://www.bufab.com](https://www.bufab.com)]([https://www.bufab.com](https://www.bufab.com)), accessed on 21 May 2021). If the company receives an order from a customer, the purchasing staff then seeks to obtain offers from potential suppliers. The search and contacting of these suppliers as well as the waiting periods during communication takes approx. 40% of the available work time; whereby, a hit rate of only 7-10% is reached [73]. By using relevant data, such as product price, delivery compliance/performance, product quality/reliability, lead-time, or the stable delivery of goods, BUFAB excludes non-qualified suppliers from bid solicitation and thus supply risks can be proactively reduced or even eliminated completely [58; 68; 73]. Consequently, requests for quotes are only sent to suppliers who have also filled out purchase requisitions. Recommendations for improved supplier efficiency and performance can be made, considering not only price, product quality, product quantity, and service, but also risk factors regarding uncertainty, vulnerability, and possible supply disruptions [68]. According to [PERSON] [19], an extension of this approach would be to use ML for risk identification when evaluating text documents, such as news articles, in order to make as realistic assessments of supplier situations as possible. Figure 3: Practical examples of ML in SCRM. The car manufacturer Volkswagen has also developed a so-called bidder list generator, which uses ML to precisely determine possible suppliers. In this way, delays and risks in purchasing can be reduced (www.volkswagen.com, accessed on 21 May 2021; interview with expert from procurement). According to [PERSON] (2018) and Qu et al. (2018) ML could also be used in procurement in the context of contract management as well as in the determination of optimal prices. The automotive supplier HELLA, one of the largest trade organizations for vehicle parts and accessories in Europe, develops and manufactures lighting technology and electronic products for the automotive industry (www.hella.com, accessed on 21 May 2021). HELLA transfers material planning to internal and external suppliers within 24 months. Until now, delivery performance has been evaluated retroactively according to predefined criteria in supplier logistics. In order to be able to predict the reliability of deliveries, HELLA has tested the use of ML techniques. Here, call-off history, incoming goods history, advanced shipping notification history, purchasing and supplier data, as well as supplier and material master data were used in ML to forecast the reliability of delivery quantities for a single supplier-material-plant relationship for a defined period of 30 days. The results have shown that the reliability of critical supply quantities can be correctly predicted with an accuracy between 75% and 80%. Therefore, the use of ML in SCRM can reduce supply chain interruptions ([PERSON], 2018) (interview with expert from SCRM). In addition to the interface consideration to suppliers, practical examples for the use of ML could be found, which aim at a reduction of transportation risks. For example, the courier--express and parcel (CEP) service providers--such as Fedex, DHL, or UPS, use ML techniques to optimize transport processes. They improve the transparency of their supply chain with the ML-based systems, composed of IoT and scanning devices used to generate a huge data pool.