Measuring the efficiency of logistics channels

Abstract

The researcher aims to study Measuring the efficiency of logistics channels with the purpose of research to study the testing of performance measurement tools (Performance) of logistics channels and evaluate the testing of that tool and bring effective performance measurement tools to use in the operation of logistics channels in the future. By collecting data from 515 persons Tran Slink Co., Ltd. employees and using the questionnaire as a tool. Statistics used to analyze the data were t-test, F-test (ANOVA and MANOVA), multiple correlation analysis, and multiple regression analysis. The results showed the relationship between the independent and dependent variables By analyzing multiple regression (Multiple regression analysis), a method for selecting variables into the equation so that the equation can predict the maximum variable criteria. There is a method for selecting variables by using Forward regression analysis. It can be concluded that the variables selected into the equation are quality dimensions using Item Fill Rate indicators. And the cost dimension by using the Logistics cost per unit and Cash Conversion Cycle indicators has a multiple correlation coefficient (Multiple R) equal to 0.842, a coefficient of determination (R Square) equal to 0.709, and an adjusted coefficient (R Square). 0.674 Standard error in the system (Standard Error) 26.830 The results of the correlation test between the efficiency of logistics channels (Y1) found that the quality dimension using the indicator Item Fill Rate (X1) had a P-value equal to 0.001, less than 0.05 (0.000 < 0.05). And the cost dimension using Logistics cost per unit indicator and Cash Conversion Cycle (X3) has a P-value equal to 0.001, less than 0.05 (0.000 < 0.05), that is, the main assumption is rejected. Ho The H1 sub-hypothesis is accepted, implying that the quality dimension using the Item Fill Rate metric and the cost dimension using the Logistics cost per unit and Cash Conversion Cycle metrics influence the efficiency of the logistics channel. It can be written as a regression equation as follows: Y=103.789 + 1.455 (X1) + 3.035 (X3).