Benchmarking of Academic Departments using Data Envelopment Analysis (DEA)
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Departmental rankings are a primary factor in assessing the organizational performance and quality. Peer Assessment Scores are one of few metrics by which academic departmental performances are quantified. There are different metrics to determine performances of academic departments, however, it does not indicate whether the departments are performing at their full potential with the resources available. Therefore, it is critical for university departments to assess the performance efficiency, and understand whether the resources are used efficiently. In this thesis, output-oriented Data Envelopment Analysis (DEA) models are used to evaluate the efficiency of the departments relative to its peers, and benchmark departments are provided for the less efficient departments. Furthermore, an Investment Model is developed based on DEA that helps decision makers with a support-system in deciding how much resources should be dedicated to increase one or more inputs in order to increase maximum potential efficiency of the departments, when an investment budget is provided. We examined the proposed models with an illustrative case study of eighteen Industrial Engineering Departments in the USA. Five inputs and one output were considered for the case study. The inputs were Number of Faculty, Research Expenditure per Faculty, Undergraduate Students per Faculty, Number of Graduate Students, and Average H-Index, whereas the only output was Peer Assessment Score. In addition, the results were discussed along with the sensitivity analysis. Finally, conclusions of the work were mentioned, along with the opportunities for future developments.
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