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Research Article Open access CC BY 4.0

Evaluating the Role of Two-Axle Trucks in Rice Distribution Efficiency: Evidence from South Sulawesi, Indonesia

Hakzah Hakzah, A. Irmayani Pawelloi, Muhammad Aksar, Ahmad Yauri Yunus, Sri Dewi

Journal of Engineering Research and Reports · pp. 322–334 · Published 14 May 2026

10.9734/jerr/2026/v28i51898

Abstract

South Sulawesi Province, Indonesia, is one of the major rice-producing regions where transportation plays an important role in maintaining the stability of agricultural supply chains. This study analyzes the performance of two-axle truck transportation in rice distribution within Sidenreng Rappang Regency using an integrated logistics evaluation approach. A quantitative descriptive method was employed using survey data collected from 120 respondents consisting of rice entrepreneurs and transport operators. The Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA) were applied to evaluate service performance and user satisfaction, while a gravity-based distribution model was used to analyze rice distribution flows toward Parepare and Makassar cities. The results indicate that two-axle trucks are predominantly used because of their operational efficiency, accessibility, and compatibility with regional road conditions. The CSI value of 81.5% indicates that users are generally very satisfied with the transportation services. However, the IPA results reveal that several service attributes still require improvement, particularly driver insurance and safety assurance. The gravity-based distribution model also shows that Parepare has a stronger distribution intensity than Makassar due to lower transportation resistance and shorter distribution distance. This study contributes to agricultural logistics and transportation planning by integrating customer satisfaction analysis with spatial freight distribution modeling to evaluate rural rice transportation systems in developing regions.

Rice distribution two-axle trucks freight transport CSI IPA gravity-based distribution model.

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