Integrating Economic and Financial Statistics in Developing Predictive Models for Logistics Optimization

This paper explores the integration of economic and financial statistics into predictive models aimed at optimizing logistics operations. It highlights how macroeconomic indicators, financial data, and operational logistics data can be combined to improve forecasting accuracy, route optimization, and supply chain risk management. A systematic literature review methodology underpins the conceptual framework, synthesizing best practices and identifying gaps. The study proposes a layered framework to facilitate effective data collection, integration, and predictive analysis, enabling more informed, resilient logistics decision-making, particularly in emerging markets.