Dynamic Supply Chain Modeling: A Case Study of the Bioeconomy

Abstract: This study applies established economic tools to examine investment and production decisions in early stage bioeconomy supply chains characterized by uncertainty and technological immaturity. The analysis focuses on macroalgae based co production of protein concentrates and mucilage thickeners within a two stage supply chain structure that includes learning effects at each stage. Price uncertainty is modeled using Geometric Brownian Motion, while investment timing and production decisions are evaluated using Real Options Valuation. The framework is designed to assess when entry becomes economically viable and how optimal production evolves as knowledge and cumulative output increase. The empirical application relies on observed trade data for China and the United States from 2016 to 2023 for both products, allowing simulation of future price paths and comparison between markets with different structural characteristics. The results indicate that projects which appear unprofitable under static evaluation can become economically viable when sequential investment, learning, and flexibility are properly considered. The analysis also shows that market stability, price levels, and the pace of learning play a central role in shaping long term outcomes. Rather than proposing a new methodology, the study demonstrates how existing economic frameworks can be effectively applied to analyze emerging bioeconomic activities. The findings offer practical insights for researchers, policymakers, and investors seeking to evaluate projects based on new biological resources under conditions of uncertainty and establish a sustainable system.