Business Administration and Management
Using an AHP-GIS based framework to develop a resilient and competitive logistics network: A study of container yard location covering multimodal railway links in Northeastern Thailand
Name and surname of author:
Prin Weerapong, Jakub Dyntar
Keywords:
Supply chain management, multi criteria decision analysis, analytic hierarchy process, geographic information system, site selection, container yard
DOI (& full text):
Anotation:
This study examines the optimal location for container yard development in Northeastern Thailand to enhance multimodal transport connectivity and strengthen national competitiveness in regional and global trade. Utilizing the analytic hierarchy process (AHP) and geographic information system (GIS), the study evaluated 18 railway stations based on five key factors: logistics suitability (13.5%), infrastructure readiness (14.6%), government support (22.5%), railway network capabilities (16.9%), and investment economics (32.5%). The findings identify Nata Station in Nong Khai Province as the most strategically advantageous location, with the highest composite weighted Z-score of 0.8775. The station features a road network density of 0.85 km per square kilometer and a railway density of 0.32 km per square kilometer, facilitating efficient cross border freight transportation. The establishment of a container yard in this optimal location is expected to reduce logistics costs, improve supply chain efficiency, and enhance cross border trade, particularly with member states of the Association of South East Asian Nations (ASEAN) trading bloc and China. This development reinforces the role of Thailand as a regional logistics hub, fostering industrial expansion and economic integration. Furthermore, government backed policies, including investment incentives and infrastructure development, contribute to building a resilient and competitive logistics network. The study provides a strategic framework for policymakers and private sector stakeholders to leverage logistics infrastructure in strengthening the long-term competitiveness of Thailand in global trade.
This study examines the optimal location for container yard development in Northeastern Thailand to enhance multimodal transport connectivity and strengthen national competitiveness in regional and global trade. Utilizing the analytic hierarchy process (AHP) and geographic information system (GIS), the study evaluated 18 railway stations based on five key factors: logistics suitability (13.5%), infrastructure readiness (14.6%), government support (22.5%), railway network capabilities (16.9%), and investment economics (32.5%). The findings identify Nata Station in Nong Khai Province as the most strategically advantageous location, with the highest composite weighted Z-score of 0.8775. The station features a road network density of 0.85 km per square kilometer and a railway density of 0.32 km per square kilometer, facilitating efficient cross border freight transportation. The establishment of a container yard in this optimal location is expected to reduce logistics costs, improve supply chain efficiency, and enhance cross border trade, particularly with member states of the Association of South East Asian Nations (ASEAN) trading bloc and China. This development reinforces the role of Thailand as a regional logistics hub, fostering industrial expansion and economic integration. Furthermore, government backed policies, including investment incentives and infrastructure development, contribute to building a resilient and competitive logistics network. The study provides a strategic framework for policymakers and private sector stakeholders to leverage logistics infrastructure in strengthening the long-term competitiveness of Thailand in global trade.
Section:
Business Administration and Management
APA Style Citation:
Weerapong, P., & Dyntar, J. (2026). Using an AHP-GIS based framework to develop a resilient and competitive logistics network: A study of container yard location covering multimodal railway links in Northeastern Thailand. E&M Economics and Management, 29(2), 156–176. https://doi.org/10.15240/tul/001/2026-2-011