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). Five main stages exist as a framework of the study. Firstly, right PE tool are selected in terms of strategic frame of companies. 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. Because BSC evaluates DMU'S separately, it does not provide satisfied comparison among different DMU's. In here, we propose data envelopment analyses (DEA) that is linear program based non-parametric approach. 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 efficiency score obtained from fourth stage. 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, a case study is given to show how developed model is applied within a company that uses only BSC tool for PE currently. It could be said that this integrated methodology is more efficient and reliable for decision-making process.
Departments are assessed using performance evaluation systems (PES) based on their important performance criteria. The sum of the weighted KPIs’ actualization scores creates the departments' performance ratings, which are used to rank departments from highest to lowest performing. However, this strategy does not account for departmental variances. We used multi-attribute decision-making (MADM) techniques in this study to ascertain the performance of several divisions inside a business. Our study's structure is divided into two distinct parts. In the first stage, pair-wise evaluation matrices are built and attribute weights are established using the Analytic Hierarchical Process (AHP) to determine attribute precedence. The second step employs the Elimination and Choice Expressing the Reality (ELECTRE) methodology to rank the departments using the attribute weights generated in the first stage using the AHP method. At the conclusion of the paper, a case study is presented to demonstrate how departments are organized in accordance with performance management principles within a corporation that use the balance-scorecard program for performance evaluation.
Companies follow their objectives with some critical success factors (CSF), and they know their bottlenecks and strong points. This provides decision support for them, but this method ignores overall performance and ranking issues. In this study, a comprehensive methodology is recommended to find out an effective solution to the performance evaluation problem for making strategic performance management. Two methods are used from different areas as a framework. To select the higher-performing departments, Data Envelopment Analyses (DEA) is used as a linear programming-based main method. Moreover, a Multi-Criteria Decision Making (MCDM) method is proposed, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), to increase the discrimination power of DEA and eliminate undesirable results because of determining weight bounds. These two methods are combined, and a comprehensive solution model is presented in the study. In the end, a case study is given for a real-life example, an integrated DEA-PROMETHEE method is applied to the case. When the case results are examined, the proposed model produces more logical weight values and better results.
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