Development of a system for parcel delivery applications for cross-provincial rail transportation

Abstract

 The transportation system is crucial to the nation regarding its economy, society, and environment. An efficient, safe, dependable, and cost-effective transport infrastructure will significantly enhance the country's business competitiveness. This research evaluates the performance of the supply chain and examines the construction of a system for parcel delivery applications in cross-provincial rail transportation. This initiative involves entrepreneurs striving for excellence in logistics management, specifically in the creation of a parcel delivery system for cross-provincial rail transport from Bangkok to Nong Khai. It entails the development of a prototype module to assess the efficiency of cross-border freight transport services provided by Thai logistics entrepreneurs, focusing on time resolution, transportation quality, transportation volume, and service quality. The examination of variables associated with rail transport service suppliers and recipients involves the collection and analysis of both qualitative and quantitative data. The outcomes of the statistical model analysis present the estimate, standard error (SE), 95% confidence intervals, standard coefficient (β), z-value, and p-value. Experimental results of different variables (CS → iOT) Estimate: 0.666, meaning iOT has a positive influence on CS (Customer Satisfaction) at a level of approximately 0.666 units. p < .001: The effect is statistically significant (99% confidence level). The 95% confidence interval: [0.568, 0.7628] confirms the confidence that the estimate is in this range. (iOT → System) Estimate: 0.853, meaning the System has a positive effect on iOT at a level of approximately 0.853. p < .001: The effect is statistically significant. The 95% confidence interval: [0.670, 1.0353] and (iOT → Notification) Estimate: -0.285, indicating a slightly negative effect of Notification on iOT. p = .023: The effect is statistically significant (95% confidence level). The 95% confidence interval: [-0.530, -0.0393] Conclusion: The iOT variable clearly has a significant (positive) influence on CS and System, but the effect on Notification is slightly negative. The significant p-values (< .05) indicate that these relationships are not due to chance.