Development of decision support tool using system dynamics modeling for coal supply chain

Abstract

The uncertainties in the energy and industry sector cascade coal demand uncertainties, which challenge coal company's supply chain process. Hence understanding the relationship between the planning, production, blending, transport, and sales process are crucial for effective decision making. These relations are also vital to creates a business plan as a decision support tool to define their abilities to meet the demand. The research presents system dynamics modeling as decision support tool in coal business supply chain. The proposed approach utilizes a coal company's production, transportation, blending, and sales data combined with future scenarios simulation. Actual data validate the model to ensure its accuracy. Illustrations about how much coal producers should produce specific coal based on demand are presented in the model. Inversely, the model also predicted the demand fulfillment based on company’s coal production and availability. Based on the result and analysis, the transport model has reached 73% accuracy based on the Absolute Accuracy Calculation, RSME of 65,365.07 and 21% error rate from MAPE. The stockpile component has 50.04% for absolute accuracy, 115,220.59 for RMSE calculation, and 49.95% for MAPE calculation. Three scenarios are introduced into the model including, diversification scenario, 2022 supply-demand scenario, and 2023 supply-demand scenario. The component with higher accuracy tends to have similar behavior in all the scenarios. Meanwhile, there are some differences in the behavior in the component with lower accuracy. But in overall, with this system dynamics model the company can increase their ability to creates more faster and accurate production, sales, and future scenario planning with more precise calculation. This happen because they have more detailed information about their supply chain condition, with visualization of the future behavior.