Delivery Accuracy, Robotics, and Logistics Information System Integration in Operational Performance Through Sorting Optimization
DOI:
https://doi.org/10.59261/bustechno.v7i4.828Keywords:
Delivery Accuracy, Logistics Information System, Logistics Operational Performance, Robotic Technology, Sorting Process OptimizationAbstract
Background: Logistics transformation at Sentral Pengolahan Pos (SPP) Jakarta, a high-volume mail-processing hub within PT Pos Indonesia (Persero), is taking place amid rising shipment volumes and persistent challenges in national logistics performance.
Objective: This study examines the relationships between delivery accuracy, robotic technology implementation, and logistics information system integration and logistics operational performance, with sorting process optimization tested as a mediating variable.
Method: The study employed a quantitative explanatory, cross-sectional design using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. Primary data were collected through a 5-point Likert-scale questionnaire administered to 80 employees and operators at SPP Jakarta using total sampling (census).
Results: All ten hypothesized structural paths were statistically supported. Delivery accuracy, robotic technology implementation, and logistics information system integration were positively and significantly associated with sorting process optimization and logistics operational performance. Robotic technology implementation showed the largest path coefficient for sorting process optimization (0.471), while logistics information system integration showed the largest direct path coefficient for logistics operational performance (0.434). Sorting process optimization demonstrated statistically significant but limited partial mediation, with Variance Accounted For (VAF) values ranging from 20.31% to 26.05%, indicating that most of the modeled relationships remained direct rather than being transmitted through the mediator.
Conclusion: The findings suggest that PT Pos Indonesia should strengthen robotic technology utilization, information system interoperability, and sorting process optimization as complementary operational capabilities. Because the data are cross-sectional and perception-based, the results should be interpreted as structural associations rather than definitive causal effects.
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