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The purpose of this paper is to analyze abnormal behavior patterns in a maintenance outsourcing process. Based on the results, the managers can focus on the abnormal behavior and the direction of the investigation can be narrowed. The abnormal behavior can be identified more easily.

Maholanobis Distance (MD) and Decision Tree (DT) are integrated to analyze for abnormal behavior patterns. To prevent abnormal behaviors, a maintenance outsourcing case must be passed by several managers in different departments. In this research, some criteria for pairs of managers are calculated first. Based on the criteria, the MDs of these pairs can be calculated. Pairs are categorized by their MDs. Any pair whose MD is higher than a threshold is labeled “abnormal” while the remaining are labeled “normal”. After oversampling the minority class of abnormal, a DT is built by Classification and Regression Trees (CART) based on the labeled dataset. Finally, the combination of criteria for abnormal categories is extracted from the tree.

Through the results from the DT, the combinations of criteria provide obvious characteristics of cases that are categorized as abnormal, and then provide a direction for investigators. Thus, the range of investigation can be narrowed. The empirical results show that the result of the proposed integrated methodology is helpful for abnormal behavior pattern analysis.

This research is intended to help an organization to enhance their investigation in a large number of maintenance outsourcing cases. About 8,000 cases are collected for analysis.

The integration of MD and DT for analyzing abnormal behavior patterns in a maintenance outsourcing process is not found in the literature. Moreover, the empirical results show that the proposed integrated methodology is helpful in a real application.