Increasing Productivity in a Maintenance Management System Using Data Mining Techniques; An Urban Bus Network Case Study

Increasing Productivity in a Maintenance Management System Using Data Mining Techniques; An Urban Bus Network Case Study

Undoubtedly analyzing maintenance data, which comprises a big portion of databases in todayʹs firms, provides them with a tremendous competitive edge both in manufacturing and service sectors. As maintenance management is a costly and inevitable part of the organization, ensuring that the maintenance activities are performed in an effective manner, is of outmost importance. In other words, organizations can precede with the cost reduction operations, for instance, if and only if the unproductive maintenance activities and processes can be identified. Subsequently, rectifying or removing these kinds of activities or taking other means of modification can help enterprises and organizations to reduce their costs. Data mining is known to be an excellent tool which helps the decision makers to discover the hidden knowledge and patterns when dealing with a large amount of data. This study uses data mining techniques, namely clustering and association rules, to identify the unproductive maintenance activities in a maintenance system. This will support the maintenance decision makers to set goals to make amendments in the maintenance systems under their supervisions. The data from the maintenance system comprising of independent components (an urban bus network) were used both as a case study and to illustrate the method used