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Modeling Automotive Supply Chain Risks over Time Horizon: Hybrid of Nonlinear Bayesian Averaging Models and Panel Time-Variable Parameters

Document Type : Research Paper

Authors

1 PhD student, Department of Industrial Management, of Faculty of Management,CT.C ,Islamic Azad University, Tehran, Iran

2 Associate Professor, Department of Logistics and Supply Chain Engineering, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

3 Assistant Professor, Department of Industrial Management, Faculty of Management, CT.C, Islamic Azad University, Tehran, Iran

Abstract

The highly accurate difference between nonlinear Bayesian averaging models and classical models indicates the failure of classical models. Classical models do not have the ability to determine the optimal model and always follow a predetermined pattern. Accordingly, to improve this gap, a hybrid of nonlinear Bayesian averaging models and panel time-varying parameters was used to model the risk of the automotive industry. The time period of the present study is from 2011 to 2023, and in this study, information from 57 companies active in the automotive industry on the Tehran Stock Exchange was analyzed. A total of 119 risks affecting the automotive industry supply chain were identified. Based on nonlinear Bayesian averaging approaches, 15 unsystematic risk indicators and 13 systematic risks were identified as the most important risks in the supply chain. After identifying the factors, an attempt was made to examine these factors over time in the automotive industry supply chain based on the TVP-PFAVR approach. Given that the significant proportion of systematic risk in the supply chain is higher than that of unsystematic risk, the stability of the economic and business environment, good governance, and political environment should be on the agenda for management stability. In fact, stabilization policies in the form of demand-side policies, including monetary-fiscal policies, should be included in the Central Bank's mandate to reduce the economic-financial risks of the automotive industry supply chain. Given the significance of financing constraint indicators in creating systematic risk, ranking automotive industry companies is strongly recommended to optimally allocate financial resources among these companies.
Introduction
Today, uncertainty is increasing and change is occurring rapidly; disruptions are imminent. All markets and industries may experience different types of disruption. Supply chain disruptions are unplanned events that may occur and affect the normal (or expected) flow of materials (Ghadir et al., 2022). These disruptions may occur at one level of a supply chain and quickly spread throughout the entire supply chain or even to other supply chains (Rezaei-Vandchali et al., 2020). The critical effects of disruptions on the performance of supply chains prompt researchers to focus on the management of supply chain disruptions and identify a wide range of risks (Sharma, 2021).
In this regard, supply chains have realized that in order to have a competitive advantage in the long term, they should improve their abilities to respond to and reduce a wide range of supply chain risks (Barianis et al., 2019). Therefore, the identification of supply chain risks increasingly attracts the attention of academics and professionals in industry, because identifying, evaluating, reducing, and monitoring possible disruptions in the supply chain leads to reducing the negative impact of risk events on supply chain operations (Munir, 2020; Yang et al., 2021).
The automobile industry is one of the important sectors of the national economy, and its proper performance can lead to sustainable economic development. In fact, among the country's industries, the automobile industry is known as a primary industry, and due to the issue of sanctions, its supply chain is facing crises and various risks as a result. Considering these issues, the need to design a supply chain risk management system that is in harmony with the characteristics of this industry is felt more than ever.
Methods
This research belongs to the category of analytical applied research. In this article, in order to determine the factors affecting the supply chain, systematic factors and non-systematic factors affecting the supply chain are obtained. A complete list of variables affecting systematic and non-systematic risks in the automotive industry on the Tehran Stock Exchange is provided for calculation in estimation models. In this research, the information of 57 companies in the automotive and parts manufacturing industry, active in this industry, has been used. Considering that the types of risks affecting the supply chain of the automobile industry affect the activities and financial ratios of the company, this article identifies the important economic risks. Unlike previous research that generally used survey tools, in the current paper, real information from these companies is used.
Results
Based on theoretical and empirical foundations, 119 risks are identified in the form of non-systematic risks and systematic risks. After identifying the factors, an attempt was made to examine these factors over time in the automotive industry supply chain based on the TVP-PFAVR approach. In this article, based on nonlinear Bayesian averaging approaches, 15 unsystematic risk indicators and 13 systematic risks were identified as the most important risks to the supply chain.
Conclusion
The purpose of the current research was to present a model for the supply chain risks of automotive industries listed on the Tehran Stock Exchange using approaches based on Bayesian averaging. The period of the current research was from 2011 to 2023. In this research, the information of 57 companies active in the field of the automobile industry on the Tehran Stock Exchange was used. In order to determine the optimal model, Bayesian averaging and weighted least squares were used. Out of 119 risks, based on nonlinear Bayesian averaging approaches, 15 unsystematic risk indicators and 13 systematic risks were identified as the most important risks to the supply chain.

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Industrial Management Studies
Volume 23, Issue 77
August 2025
Pages 271-328
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