# Risk Assessment in Supplier Selection for Intelligent Manufacturing Systems Based on PLS-SEM
## 2 Literature Overview
Therefore, from the perspective of IMSS supplier risk prediction, this paper carries out the research on the risk evaluation of intelligent manufacturing system solution suppliers, so as to provide reference for intelligent manufacturing enterprises to select high-quality IMSS suppliers. PLS-SEM (Least squares structural equation model) is a model that can estimate a large number of potential variables and indicators in a small sample size ([PERSON]/[PERSON] and [PERSON] 2008) [16]. For complex models, PLS-SEM identifies factors by introducing a flexible residual covariance structure, and makes robust prediction under the background of small sample size, asymmetric distribution and interdependent observation ([PERSON] 1998a, 1998b) [17; 18]. PLS-SEM mainly has the following advantages: 1 PLS-SEM does not require the index data to obey the normal distribution. The sample size is smaller and the analysis is accurate. The sample size of PLS-SEM is only 30 to 100. 2 PLS-SEM infiltrates statistical scientific methods, such as multiple linear regression, principal component analysis and correlation analysis, and can reasonably and effectively analyze and predict the impact of various factors on the IMSS suppliers evaluation model. 3 PLS-SEM can better deal with the problem of multicollinearity, explain the correlation between variables [19], and ensure that different factors have different interpretation connotations. [PERSON], [PERSON], [PERSON], and [PERSON] [20] found that the characteristics of PLS-SEM make it an alternative to the CB-SEM method for complex modeling ([PERSON], [PERSON], [PERSON], et al., 2009; [PERSON], 2012) [21; 22]. [PERSON], [PERSON] and [PERSON] [17] validated the large-level model by providing robust solutions. Many scholars use the PLS-SEM method to solve the evaluation problem. In order to improve the environmental performance of environmental suppliers, [PERSON] and [PERSON] established the PLS-SEM model to measure the performance of environmental suppliers and test the reliability and validity of the structural model [23]. [PERSON], [PERSON], and [PERSON] used PLS-SEM to analyze the importance of accounting standards to the future research and development of management accounting [24]. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], [PERSON] established a PLS-SEM model for customer satisfaction evaluation regarding mobile travel, based on the American customer satisfaction index system [25]. It can be seen that PLS-SEM is more suitable for complex model evaluation. Focusing on the needs of IMSS supplier risk evaluation in manufacturing enterprises, this paper uses the PLS-SEM theoretical model in statistics to carry out research. The main contributions are as follows: On the one hand, it selects a new research perspective to carry out IMSS suppliers risk evaluation of intelligent manufacturing enterprises, from the perspective of IMSS suppliers risk prediction. On the other hand, it expands the application of PLS-SEM in the field of intelligent manufacturing supplier risk evaluation. The contents are arranged as follows: Taking the observable risk influencing factors as the intermediary, this paper constructs the IMSS suppliers risk evaluation model through the PLS-SEM method, quantifies the implied risk of IMSS suppliers, uses power BI software to visualize the risk index inspection process and results, helps decision makers intuitively understand the implied risk of candidate IMSS suppliers, and provides decision-making reference for intelligent manufacturing enterprises to select high-quality IMSS suppliers.