Ordinary Least Squares Regressionfor Predict Container Delay

Abstract

In Indonesia, many manufacturing companies use logistics services, commonly referred to as transporters, to distribute goods. In practice in the field experienced many obstacles transporter. One of them is during the door to the port process. Process complex business is one of the reasons the door's length to the port process, and one same factor effect is during the loading process in the warehouse Sender. So that the door to the port process can be faster, factors which effects must be minimized. In this research, the author uses two variables: delay in the loading warehouse as variable X or independent variable, and process delay door to port as they are variableY or dependent. This study aims to determine how much the influence of the Xvariable on the Y variable. Equation OLS regression from this study obtainedY = 2.2564 + 1.3546X+0. The Independent variable that affects the dependent variable based on OLS regression gets anR 2 value of 0.9584. This shows that the warehouse's delay capability (X) influencing the door to port delay (Y) is 96%. Researchers also used the MAPE method to calculate system accuracy value. The MAPE value obtained is 1.676%, which means that the system accuracy value is quite good, namely amounted to 98.324%.