Identifying and Analyzing Supply Chain Risks of Saipa Automobile Company using the Coso Model and Social Network Analysis (SNA)

This paper aimed at identifying and analyzing supply chain risks of Saipa automotive company to determine those seemly critical and the appropriate decision for each category to be made. To this end, first, according to company’s documents and interviews with experts, and using theme analysis method, the identification and categorization of supply chain risks are addressed. In the second step, by using of SNA approach, the most important risks in terms of the effects they have on emerging other risks in the risks’ relationship network are determined. The results are analyzed using the IPM matrix and the necessary decisions are made according to this matrix. According to the results, 48% of the total risks are categorized in financial-economic, suppliers, information, and transportation categories. Therefore, it seems that paying particular attention to these areas can result in significant improvement in system’s status. The automotive industry is the second largest industry in Iran, the survival of which is of great importance for the country. Today, various factors, such as fluctuations in the foreign exchange market, have led to uncertainties in this industry. In addition, other factors, including increasing the variety of products and services, reducing product life cycle, demand fluctuations, rising costs, technological changes, political issues, financial instability, and natural disasters have also increased the uncertainty and risk in the industry’s supply chain. On the other hand, the automotive industry has faced many risks owing to its long supply chain, in which diverse companies interact with each other. Hence, the supply chain risk management of this industry to identify and evaluate the risks and reduce their adverse effects is counted as a critical issue on which many researchers have been embarked. So far, various models, such as Fault Tree Analysis (FTA) (Zhang et al., 2016) and Failure Modes and Effects Analysis (FMEA) (Liu & Zhou, 2014) have been developed as risk analysis tools. However, in these models, each risk, its significance, and its impact on the performance of the company or supply chain has been deemed as a single concept regardless of various possible relationship among different kinds of risks in the system. Regarding the promising relationships among different types of risks, some have used Analytic Hierarchical Process (ANP) method (Talebi & Iron, 2015) to evaluate and prioritize the risks. The main problem is that this method is only applicable when the number of risks is low, thus the pairwise comparison of the risks would be difficult while the inconsistency is high. Whereas, if the number of identified risks is high (usually in the case of long supply chains such as the automotive supply chain), a risk will be counted as critical in co-occurrence with other risks. Thus, in addition to detecting the risks, identifying their communication network and the features of such a network is also important in analyzing and presenting solutions. Social Network Analysis (SNA) is an approach which emphasizes on the pairs of points and their relationships instead of focusing just on single points and their features. On the other hand, Coso is a new approach providing a comprehensive framework for managing and evaluating risks. To this end, in order to overcome the deficiencies of the existing models, it has been attempted to exploit the capabilities of SNA and to innovatively conduct it in the framework of the steps taken by the Coso risk management model, hoping to develop a new approach in risk evaluation and to apply it for assessing Saipa automotive supply chain risk. The current study has been carried out in two stages. First, an initial framework has been proposed to categorize the existing risks in the supply chain of Saipa automotive regarding the sources of risks. After two stages of refinement, a list of risks have been obtained by using expert opinions. The process of identifying the risks based on the Coso model framework and the theme analysis method are as follows: 1-       Investigating the Saipa control environment (internal environment) by reviewing company’s documents and interviewing with experts (Step 1 of the Coso model); 2-       Target setting as to risk identification after conducting interviews with experts (step 2 of the COSO model). 3-       Detecting risk sources in the organization by interviewing experts and using the theme analysis method (Step 3 of the Coso model). The second phase of the research is quantitative, in which the following steps have been followed: 4-       Risk evaluation (Step 4 of the COSO Model). Risk assessment has been conducted from two perspectives. One in terms of the importance of the risks in their relationship network, and the other in terms of the impact of the risks on the performance of the Saipa Supply Chain. First, by using SNA approach, the communication network among the identified risks has been depicted, then the key risks associated with other risks in the network have been identified using the concepts of degree and betweenness centrality. Afterwards, a survey of experts has been conducted to determine the impact of risks on the supply chain performance. 5-       At the last step, the identified risks have been classified in IPM matrix. Risk categorization as for importance dimension has done with respect to SNA output and upon performance dimension based the survey output.   Upon the results of the qualitative part of the study, 93 kinds of risks were identified as to Saipa automotive supply chain. Afterwards, the identified risks were branded in four categories as presented in Figure 1.  Fig. 1- Pareto diagram of risk categorization risks on the concepts of coso model  In the quantitative stage, the network causal relationship network of the identified risks were depicted and the degree of centrality and betweenness centrality measures were calculated in UCINET software. The more degree of centrality in the target network implies the risks most influenced by other risks (in degree) and the risks most affects other risks to be created. Betweenness centrality indicates the risks through which many risks lead to creating other risks.   Fig. 2- Network of Causal Relationships among Saipa Supply Chain Risks Finally, the IPM matrix was used to label the identified risks. In this matrix, the importance of the risks was determined based on centrality measures, so that risks with centrality degree higher than their mid-range (115) and those with betweenness centrality higher than their mid-range (31.9) were determined and their common points were taken as important risks. Besides, the risks were divided in two categories of high and low performance upon the cut- point of 0.5 for the scores in this dimension. Thus, the identified risks were assigned to each cell of the IPM matrix as presented in Figure 3, where the distribution of the risks was 36.6% in “keeping the current situation”, 16.1% in “critical”, 28% in “resource waste”, and 19.4% in “indifference” cells. In this paper, SNA approach is conducted in the framework of the steps taken by the Coso risk management model to develop the current risk evaluation models and to identify critical risks in Saipa automotive supply chain by applying the developed framework in practice. According to the results, 48% of the total critical risks were categorized in financial-economic, suppliers, information, and transportation categories. Therefore, it seems that paying particular attention to these areas can result in significant improvement in system’s status. References Zhang, M., Song, W., Chen, Z., & Wang, J. (2016). Risk assessment for fire and explosion accidents of steel oil tanks using improved AHP based on FTA. Process Safety Progress, 35(3), 260-269. Liu, J., & Zhou, Y. (2014), Improved FMEA Application to Evaluation of Supply Chain Vulnerability, 7th International Joint Conference on Computational Sciences and Optimization (CSO) Location: Beijing, China. Talebi, D., & Iron, F., (2015). Identification of Risk Factors of Supply Chain and Supplier Selection with Analytical Network Process (Case: Automobile Indastry), Industrial Management Perspective, 17, 31- 43.