Impact of Macroeconomic Indicators on Supply Chain Logistics: A Comparative Simulation Study

Abstract

Supply chain logistics operations are increasingly susceptible to macroeconomic fluctuations, creating significant challenges for global trade networks. This study investigates the quantitative impact of five critical macroeconomic indicators, GDP growth rate, inflation, fuel prices, interest rates, and exchange rate volatility on supply chain logistics performance through a comprehensive comparative simulation framework. We develop a multi-model approach combining linear regression and nonlinear neural network components to capture both direct and indirect relationships between macroeconomic variables and logistics metrics, including transportation costs, inventory levels, and delivery performance. Monte Carlo simulations are employed across 1,000 scenarios to evaluate system resilience under varying economic conditions. Our analysis reveals that fuel price volatility exhibits the strongest correlation with logistics costs (r = 0.847), while exchange rate fluctuations significantly impact international shipping delays. The proposed framework demonstrates superior predictive accuracy with MAPE values below 8.5% across all tested scenarios. These findings provide actionable insights for supply chain managers to develop robust strategies against macroeconomic uncertainties and contribute to the theoretical understanding of economic-logistics interdependencies.