REVOLUTIONIZING SMALL-SCALE LNG BUSINESS: OPTIMAL STRATEGIES FOR AN ADAPTIVE AND SUSTAINABLE SUPPLY CHAIN - Nnadikwe Johnson (Jurnal Komputer Indonesia (Ju-Komi), 2025)
This groundbreaking research tackles the intricate challenges facing the small-scale LNG market, including logistical complexities, high operational costs, limited infrastructure, fluctuating demand, and environmental concerns. By harnessing the power of machine learning techniques, such as reinforcement learning, recurrent neural networks, online learning, and graph theory, we develop a revolutionary intelligent system for optimizing LNG pickup and delivery routes. Our innovative approach transforms the selection and planning process, yielding unprecedented efficiency gains, cost reductions, and faster delivery times. Our linear regression model reveals a significant relationship between LNG supply chain cost and independent variables, with a coefficient of determination (R-squared) of 0.85. The time series analysis shows a trend coefficient of 0.05, indicating a steady increase in LNG supply chain performance metrics. The ARIMA model demonstrates a strong autoregressive component, with a coefficient of 0.80. Our multiple linear regression model shows that transportation cost, storage cost, demand, and supply are significant predictors of LNG supply chain cost, with an R-squared of 0.90. The stochastic frontier analysis estimates an efficiency score of 0.85, indicating room for improvement in the LNG supply chain. The vector autoregression model reveals significant relationships between LNG supply chain performance metrics, with an AIC of 120.56. The generalized autoregressive conditional heteroskedasticity model estimates a significant ARCH coefficient of 0.20 and GARCH coefficient of 0.70, indicating volatility clustering in LNG supply chain performance metrics. The panel data model shows that transportation cost and storage cost are significant predictors of LNG supply chain cost, with an R-squared of 0.88. Our machine learning model achieves an R-squared of 0.92, outperforming traditional statistical models. By implementing optimization strategies, we achieve a 15% reduction in transportation costs, a 20% reduction in transportation times, a 12% increase in tank utilization, an 8% reduction in transportation costs through using larger vessels, a 6% reduction in transportation costs through optimizing routes, and a 4% reduction in overall supply chain costs through improving demand forecasting and supply chain planning.