A Linear Programming Based Multi-Criteria Approach for Performance Ranking: A Case Study

Abstract

In general, companies follow their objectives with some critical success factors (CSF), and they know their bottlenecks and strong points. This provides decision aids 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 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 discrimination power of DEA and eliminate undesirable results because of determined 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, combined DEA-PROMETHEE method is applied to the case. When the case results are examined, proposed model produces more logical weight values and better results.