Bayesian Network Based on Cross Bow-tie to Analyze Differential Effects of Internal and External Risks on Sustainable Supply Chain

In recent years, Iran&rsquos manufacturing industries have faced a unique set of challenges, with risks stemming not only from global uncertainties (regular risks) but also from the impact of sanctions. Moreover, the unprecedented COVID-19 pandemic since 2020 has further complicated international relations and industrial operations. This study proposes a novel supply chain risk analysis approach for Iran&rsquos manufacturing industries, utilizing the bow-tie (BT) analysis and Bayesian network methodologies. By conducting risk assessments and implementing corrective actions, the aim is to establish a sustainable supply chain for these industries. To enhance accuracy, risks are categorized into two main groups: internal and external. However, the traditional BT model cannot address both categories simultaneously. Therefore, a cross BT structure is introduced, and the Bayesian network is employed to enable a concurrent analysis of the cross BT structure. To handle data uncertainty, linguistic variables and Dempster&ndashShafer evidence theory are utilized. The findings indicate that risks associated with sanctions rank predominantly higher, while those specific to the COVID-19 pandemic fall within intermediate positions. Notably, this observation may be attributed to the intricate interplay between pandemic-related risks and other influential factors, particularly those arising from sanctions, within Iran&rsquos manufacturing industries.