Every organization needs an evaluation system to measure this usefulness in order to know the performance and usefulness of its units, and this issue is more important for financial institutions, including companies based on information technology. Data envelopment anal More
Every organization needs an evaluation system to measure this usefulness in order to know the performance and usefulness of its units, and this issue is more important for financial institutions, including companies based on information technology. Data envelopment analysis is a non-parametric method for measuring the efficiency and productivity of decision making units (DMUs). On the other hand, data mining techniques allow DMUs to explore and discover meaningful information, which was previously hidden in large databases. This paper proposes a general framework combining data envelopment analysis with regression trees to evaluate the efficiency and productivity of DMUs. The result of the hybrid model is a set of rules that can be used by policy makers to discover the reasons for efficient and inefficient DMUs. As a case study using the proposed method to investigate the factors related to productivity, a sample including 18 branches of Iranian insurance in Tehran was selected and after modeling based on the advanced input-oriented LVM model with poor accessibility in data coverage analysis with Undesirable output was calculated and with the decision tree technique, rules are extracted to discover the reasons for productivity increase and productivity regression.
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