# Supplier Risk in Supply Chain Risk Management: An Updated Conceptual Framework
## 4 Results and Discussion
### Research Profiling
The largest cluster is defined as cluster #0, which is supplier selection, corroborating CitNet Explorer findings. The most apparent reason for the predominance of the supplier selection cluster is that several authors consider it as the most critical dimension and the foundation of supplier management [6; 21]. Indeed, selecting an efficient supplier base can be a decisive factor in an organization's success [1; 58; 3]. However, we also propose a complementary empirical explanation: given the challenges that researchers face in accessing industrial environments [12], many companies may feel more comfortable sharing supplier performance data (without disclosing supplier names) than granting access to their internal production processes or strategic guidelines. As a result, supplier selection may become a more feasible topic for empirical studies. Moreover, it is possible to develop supplier selection simulations without relying on real industrial data. Among the main metrics presented by CiteSpace to assist in understanding the structural characteristics of the network, the Modularity Q property presented a value of 0.9244, which indicates that the network of references obtained is divided into loosely coupled clusters. The Weighted Mean Silhouette property presented a value of 0.9895, suggesting a high homogeneity level between the clusters. Such characteristics can be expected from a recent research theme that is still in the development phase. Table 1 lists those 15 main clusters ordered by size, homogeneity (Silhouette), average years of publication, and terms used in the title. It is observed that the most representative nucleus revolves around 2017 (cluster #0), led by the area of research in supplier selection. Other relevant issues were found between 2011 and 2019, so the data reaffirms the recent effervescence of the topic. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster \#** & **Size** & **Silhouette** & \
\begin{tabular}{c} **Average** \\ **Year** \\ \end{tabular} &
\begin{tabular}{c} **Label** \\ \end{tabular} \\ \hline
0 & 37 & 0.993 & 2017 & supplier selection \\
1 & 36 & 0.964 & 2013 & production-distribution planning \\
2 & 30 & 1 & 2006 & supplier chain management \\
3 & 30 & 0.952 & 2016 & risk management \\
4 & 26 & 1 & 2019 & csr alignment \\
5 & 25 & 1 & 2011 & resilient supply chain \\
6 & 23 & 0.996 & 2014 & multi-attribute auction \\
7 & 22 & 1 & 2017 & supply chain \\
8 & 21 & 1 & 2007 & automotive industry \\
9 & 19 & 1 & 1999 & supply chain management \\
10 & 18 & 1 & 2007 & supplier code of conduct \\
11 & 17 & 0.972 & 2012 & service suppliers \\
12 & 16 & 1 & 2005 & Simulation \\
13 & 16 & 1 & 2008 & pharmaceutical industry \\
14 & 12 & 1 & 2009 & analytic hierarchy process (ahp) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Clusters. Figure 9: Network of references. Then, the keyword network was generated, as shown in Figure 10. The main goal is to shed light on the keywords most associated with the research theme to deepen the research profile investigation concerning research topics and methods that authors use. This graphic representation shows the 10 keywords most used in the articles, regardless of the previous area clusters, and shows the connections among keywords. Therefore, in addition to basic terms that are commonly associated with this field of knowledge, such as purchasing and supply chain, it is possible to infer the following:  
1. The term supplier selection (#1) is the most relevant, after the generic term purchasing, which corroborates our previous findings. It also relates directly to the dimension \"selection\" from the supplier management approach as listed by [1]. 2. The term simulation (#2) refers to a research method that involves applying quantitative methods, logical modeling tools, and similar methods. Thus, it is the most used research method in the field. The main reason for the extensive use of simulation in the literature is its ability to model dynamic environments such as supply chains and to quantify risks [45; 48].