Reduction of Transportation Cost: Examinig the Moderating Role of Artificial Intelligence (AI) Implementation in Bangladesh

Abstract

This study investigates the role of logistics strategies Route and Node Optimization, Shipment Consolidation, and Demand Forecasting in reducing transportation costs in the logistics sector of Bangladesh. It also explores the moderating effect of Artificial Intelligence (AI) on the effectiveness of these strategies. With the increasing complexity of logistics operations and the growing need for cost-efficient practices, this research highlights how traditional logistics methods can be augmented with advanced AI technologies to optimize operations and achieve significant cost reductions. Data were collected through structured surveys involving 300 respondents from logistics companies in Bangladesh. Using Principal Component Analysis (PCA) and Multiple Regression Analysis, the relationships between the independent variables (logistics strategies), the moderating variable (AI implementation), and the dependent variable (Transportation Cost Reduction) were evaluated. The results reveal that Shipment Consolidation had the most significant impact on transportation cost reduction, followed by Demand Forecasting and Route and Node Optimization. AI implementation was found to positively moderate these relationships, enhancing the efficiency of each strategy. The findings underscore the transformative potential of AI in logistics operations, particularly in developing economies. The study contributes to both theory and practice by providing actionable recommendations for integrating AI into logistics to achieve cost efficiency. Future research directions are suggested, including exploring longitudinal impacts and expanding the study scope to other regions.