AI-Driven Predictive Analytics for Kapok Supply Chain Governance

The kapok (Ceiba pentandra) fiber industry plays a vital role in Indonesia’s rural bioeconomy, particularly in regions with high production intensity such as Pasuruan Regency. Despite its economic potential and alignment with the green economy agenda, the industry faces increasing volatility due to seasonal harvest cycles, climate-induced disruptions, global demand fluctuations, and exchange rate instability. These conditions necessitate an adaptive and predictive approach to supply chain risk governance. We evaluated the performances of predictive analytics models, including linear regression, random forest, gradient boosting, XGBoost 3.2.0 libraries, K-nearest neighbors, and stacking regressor. Using multi-year monthly data on production volume, residual stock, and exchange rates, the stacking regressor was the most accurate model, achieving the lowest root mean square error and highest R2 values. The results bridge the gap by applying predictive analytics to a resource-based, seasonal small industry sector. Practically, the results also enable leveraging AI in strengthening the long-term sustainability of agribusiness supply chains.