Abstract

The economic feasibility of medicinal herb processing facilities is critical to the sustainable growth of the herbal medicine industry. This study explores the application of Bayesian and artificial intelligence (AI) methodologies to evaluate the financial viability of such facilities. Bayesian models provide probabilistic assessments by incorporating prior knowledge and dynamically updating predictions based on new data, effectively addressing uncertainties in raw material costs, market demand, and production variables. AI models complement this approach by leveraging machine learning algorithms to analyze large datasets, identify patterns, and predict outcomes across various operational scenarios. The integration of these methodologies offers a comprehensive framework for evaluating feasibility, surpassing the limitations of traditional deterministic approaches. Findings highlight the importance of securing high-quality raw materials, adopting energy-efficient technologies, and diversifying market strategies to improve economic outcomes. The study emphasizes the value of scenario analysis, flexible business strategies, and advanced technologies in navigating the complex challenges of this industry. Recommendations include enhancing supply chain management, investing in automation, and aligning operations with emerging consumer trends. This research advances the understanding of economic evaluation in medicinal herb processing and provides actionable insights for stakeholders, paving the way for the sustainable development of the sector. Future research directions are proposed, including incorporating sustainability metrics and exploring blockchain technology for supply chain transparency.