Integrated performance evaluati̇on method study and performance based department ranki̇ng: A case study

Enterprise companies, which make future strategic plan, need to view overall performance. In this direction, monitoring decision-making unit (DMU) performances is a critical issue. Therefore, it should be fair and consistent performance evaluation and prepared open and clear reports. To handle with these requirements, this study focusses on establishing a comprehensive method of performance evaluations (PE). As a framework of the study, in scope of effectivity and efficiency aspects, five main solutions are suggested to solve five main problems in PE. In the paper, football theorem is selected as a strategic concept. Then, right PE tool are selected in terms of strategic frame of companies firstly. In this study, we examine Balanced Scorecard (BSC) approach that considers not only financial but also non-financial topics to watch overall performance. BSC has key performance indicators (KPI) that show objectives and actualizations belonging to related DMU, and BSC also provides proper and summary reports including different perspectives. The next step is determination of KPI's weights. Analytic Hierarchy Process (AHP) is applied to determine KPI priorities. As a result of first two steps, effectivity could be followed easily for a company. Because BSC evaluates DMU'S separately, it does not provide satisfied comparison among different DMU's. Therefore, we propose Data Envelopment Analyses (DEA) that is linear program based non-parametric approach to follow efficiency. However, DEA works correctly for only homogeneous DMU's. As a third step, classification process is applied to ensure homogeneity. Then, using BSC KPI's as outputs and DMU budgets as input, DEA model is run for each class. As a last step, we separate DMUs into categories using effectivity and efficiency score obtained from previous stages. To determine category numbers, Hierarchical Clustering Analyze (AHCA) method is used and group elements are selected with applying K-Means Clustering Analyze technique. At the end, two case studies are given to show how developed model is applied within an enterprise company.