Continuous improvement model for logistics operations efficiency and organizational performance

Abstract

The efficiency of logistics operations requires systematic mechanisms to identify deviations, reduce non-value-added activities, and improve performance. This research aimed to design and computationally evaluate a quantitative continuous improvement model by integrating the PDCA cycle, Kaizen, and key performance indicators. A synthetic baseline of 500 observations was generated and subsequently evaluated under conservative, moderate, and intensive scenarios, resulting in 1,500 simulation records. The model considered cycle time, productivity, service level, delivery compliance, operational errors, and non-value-added activities. Results showed overall improvements of 7.08%, 13.69%, and 22.38%, respectively. Pre-post comparisons using the Wilcoxon signed-rank test showed significant differences across the evaluated indicators, accompanied by large effect sizes. Sensitivity analysis confirmed the stability of model behavior under variations in the random component. Overall, the findings support the functional consistency and computational reproducibility of the proposed model and provide a quantitative framework for subsequent empirical validation in real logistics operations