Evaluating the efficiency of system integration projects using data envelopment analysis (DEA) and machine learning

Data envelopment analysis (DEA), a non-parametric productivity analysis, has become an accepted approach for assessing efficiency in a wide range of fields. Despite its extensive applications, some features of DEA remain unexploited. We aim to show that DEA can be used to evaluate the efficiency of the system integration (SI) projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning. In this methodology, we generate the rules for classifying new decision-making units (DMUs) into each tier and measure the degree of affecting the efficiencies of the DMUs. Finally, we determine the stepwise path for improving the efficiency of each inefficient DMU.