工业工程与管理

The reduction of loan processing cycle time gives relief in a lot of financial and non-financial activities to customers and to companies. Thus, the purpose of this research is to minimize and analyze the loan processing cycle time of Development Bank of Ethiopia so that processing cycle time gets crushed. Project owners get frustrated when their loan application takes longer processing time. As the loan processing cycle time gets longer and longer, project owners or promoters lose enormous amount of money and energy waiting for the loan approval day. Thus, the study was conducted to improve this delay of loan processing time using data from the Bank and ARENA simulation software. In modeling this system, an ARENA software simulation model was developed, verified, and validated to determine the daily loan processing cycle time and potential problem areas for the various request levels in the case company to shorten the length of processing time. The ARENA simulation software used to improve the current delay of the loan processing as if they work in real worlds. It has helped the analysis to simulate and showed how it works in the real world. Based on the results of this study, it was found that the average loan processing cycle time is more than 45 days. After analyzing alternative scenarios using ARENA simulation software, it was obtained that the loan processing cycle time to be less than 39 days.

Factor analysis or sometimes referred to as variable analysis has been extensively used in classification problems for identifying specific factors that are significant to particular classes. This type of analysis has been widely used in application such as customer segmentation, medical research, network traffic, image, and video classification. Today, factor analysis is prominently being used in fault diagnosis of machines to identify the significant factors and to study the root cause of a specific machine fault. The advantage of performing factor analysis in machine maintenance is to perform prescriptive analysis (helps answer what actions to take?) and preemptive analysis (helps answer how to eliminate the failure mode?). In this paper, a real case of an industrial rotating machine was considered where vibration and ambient temperature data was collected for monitoring the health of the machine. Gaussian mixture model-based clustering was used to cluster the data into significant groups, and spectrum analysis was used to diagnose each cluster to a specific state of the machine. The significant features that attribute to a particular mode of the machine were identified by using the random forest classification model. The significant features for specific modes of the machine were used to conclude that the clusters generated are distinct and have a unique set of significant features.