An Analysis of Smart Routing Management Influence on Carbon Dioxide Emission Reduction in FMCG Distribution within Bangkok Metropolitan Region

Abstract

This research consists purposes were 1. to compare carbon dioxide (CO₂) emissions from fast-moving consumer goods (FMCG) transportation across different delivery time windows and 2. to examine the influence of transportation management factors namely travel distance, engine idle time and the level of intelligent route management on CO₂ emissions in the Bangkok Metropolitan Region. This study employed a quantitative research design. The research instrument was a data recording form obtained from the Telematics system. The sample consisted of truck trips used for the transportation of fast-moving consumer goods (FMCG) within the study area. A total of 400 trips were selected using stratified sampling based on delivery time periods. The data collected from the research instrument were analyzed using descriptive statistics, including mean and standard deviation. Hypothesis testing was conducted using One-Way Analysis of Variance (One-Way ANOVA) and Multiple Linear Regression Analysis. The results indicate that 1) delivery time windows significantly affect CO₂ emissions at the .01 level, with peak hours producing the highest emissions, followed by off-peak periods, and the lowest emissions occurring during nighttime and 2) travel distance, engine idle time and the level of intelligent route management significantly influence CO₂ emissions, jointly explaining 78.4% of the variance (R² = 0.784). The findings suggest that optimizing delivery time windows, implementing intelligent routing technologies and reducing engine idle time are critical strategies for minimizing greenhouse gas emissions in the logistics sector. This study recommends that logistics operators integrate digital technologies with the development of employees’ green skills to enhance operational efficiency and support sustainable logistics development.