A Prescriptive Analytics Framework for Optimizing the Indonesian Salt Supply Chain Using System Dynamics and Data-Driven Decision Support

Abstract

The Indonesian salt supply chain is characterized by persistent price volatility, supply-demand imbalances, and limited decision-support tools for policy development. Current models are mostly descriptive or predictive and provide forecasts with little actionable advice. This study presents a prescriptive analytics framework that couples a calibrated system dynamics model with a mixed-integer linear programming optimization model to assist in monthly decision-making regarding buffer stock release, import quota allocation, and distribution routing. The system dynamics model was calibrated using historical data (2020–2025), and the key stocks, flows, and feedback loops in the Indonesian salt supply chain were captured. The optimization model minimizes the weighted objective function of price volatility, service level, and logistics cost under inventory balance, capacity, and non-negativity constraints. An evaluation with 100 stochastic scenarios shows that the proposed framework reduces the average monthly price volatility by 23.4% and improves the service level from 81.6% to 94.1% relative to the baseline heuristic policies, with a 6.3% increase in logistics cost. A sensitivity analysis indicates that a buffer stock capacity of at least 15% of the annual consumption is required to maintain price stability. The proposed framework offers a data-driven decision-support approach for translating supply chain forecasts into actionable policy recommendations.