# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 2 Methodology and Findings
### Current Status of ML Application in SCRM in Scientific Literature
[PERSON] and [PERSON] (2018)  
Figure 2: Journals that have published papers concerning ML in SCRM. present a data-driven analysis framework that integrates ML technologies and temporal aggregation mechanisms to predict the requirements of intermittent electronic components and, thereby, to reduce supply risks. The empirical study was conducted in a distribution company for electronic components. [PERSON] [19] has designed a conceptual model in which ML is used to identify delivery risks using text documents. [PERSON] et al. [66] present a knowledge-based infrastructure system to collect information on procurement and on the selection of supplier network partners with the help of both ML and neural networks to reduce information risks. The prototype developed by the authors was tested in the Chinese company GPP Limited, which manufactures plastic toys and exports its products to customers in the USA, Europe, and Japan. [PERSON] et. al. [20] provide an overview of ML techniques and describe how they can be used to reduce transportation risks by assessing the safety of oil and gas pipelines. [PERSON] et al. [67] present a conceptual method for a predictive and adaptive omni-channel supply chain management for the retail industry. ML and simulation-based optimization are applied to minimize uncertainty and incompatibility between supply and demand. [PERSON] et al. [22] use ML to model disruptive events and their impact on the supply chain to identify potential risks in a timely manner. [PERSON] et al. [59] discuss whether and to what extent supply chain maps can be automatically generated by evaluating unstructured texts in natural language, such as news reports or blog posts, in order to reduce supplier risks. [PERSON] et al. [23] present a vaccine blockchain system as well as ML technologies, which are based on blockchain, to enable vaccine traceability and prevent vaccine record fraud, thereby reducing supply risks. However, the contributions of [PERSON] [42], [PERSON] et al. [41], and [PERSON] et al. [68] which provide a systematic literature analysis do not provide practical examples. [PERSON] et al. [69] describe the potential of internet technologies for dealing with perishable products, while the [PERSON] publication [70] discusses the potential added value that AI can bring to agriculture in the next decade; both also fail to provide concrete examples of possible applications. Finally, [PERSON] et al. [71] discuss the application of AI adoptions in the field of SCRM. Based on a qualitative study in India they propose a research model on the implementation of AI in SCRM at an organizational level. These latter publications are therefore not listed in Table 1.  
The results of the systematic literature analysis clearly show that only a few examples of ML in SCRM have been described in any meaningful detail within the scientific literature to date [18; 57; 61; 62; 65]. The existing few examples focus on supply chain areas of production [18; 20; 61; 62; 65], transport [56; 57; 58; 63], and the entire supply chain [19; 22; 23; 59; 64; 67]. This is not surprising for production divisions, where automation and built-in sensor technology for data acquisition has already been implemented for years and is, therefore,