The wooden furniture industry, as one of the key sectors in Iran’s production oriented economy, plays a significant role in job creation, export development, and sustainable growth. Given the complexity of its supply chain and its vulnerability to various risks, the identification and systematic analysis of risks are of particular importance. This study aims to conduct a comprehensive assessment of supply chain risks in the wooden furniture industry of Malayer County using Bayesian Networks and the RPN method. Research data were collected through interviews with 20 manufacturers, questionnaires, and analysis performed using Agena Risk software. Risks were examined across four main stages of the supply chain: pre production, production, domestic sales, and export. A conceptual model was developed, and a tornado diagram was applied for sensitivity analysis. To enhance analytical accuracy, dependency structures among risks were modeled within the Bayesian network, enabling dynamic evaluation of the probability and impact of each risk under different scenarios. The most influential indicators in each of the four stages were identified, leading to the development of four primary scenarios and one comprehensive scenario. Sensitivity analysis results revealed that risks related to managerial knowledge, economic factors, government support, and environmental constraints are the most critical across different stages. Scenario analysis further indicated that the export market sales stage exerts the greatest influence on the entire supply chain. Based on the findings, practical strategies for risk mitigation were proposed for each stage. This study can support improved decision making in supply chain management and enhance the overall performance of the country’s furniture industry.