Designing fuzzy multi criteria decision making model for best selection of areas for Improvement in EFQM (European Foundation for Quality Management) model

Abstract

In a growingly competitive business environment, numerous organizations adopt the total quality management (TQM) approach, to struggle for business excellence. To monitor the progress towards business excellence, thousands of organizations across the world use self-assessment on a regular basis. There are a few popular business excellence models, that provide standard criteria against which an organization can measure its performances. European Foundation for Quality Management (EFQM) is the most popular ones. The EFQM Excellence Model was introduced at the beginning of 1992, as the framework for assessing organizations for the European Quality Award. It is now the most widely used organizational framework in Europe and across the world and it has become the basis for the majority of international, national and regional Quality Awards. It is a practical tool that can be used as a guide to identify areas for Improvement. However, the current EFQM model has some drawbacks and problems which are not able to identify the priorities in Area for Improvement (AFI). For organizations with limitations of time, budget and resources and that cannot implement all the AFIs, some standards or indexes and limitations should be defined for prioritizing and choosing the AFIs. Using fuzzy multi criteria decision making model, the AFIs’ can be identified. Therefore, this work will develop a more comprehensive method of evaluating, assessing and determining the AFIs’ in the EFQM model. The results showed that, the new comprehensive developed model is more valid and acceptable and the experts verified the model for selecting of AFIs’ in EFQM in practice. The developed model was used in a case study and extracted results from it and were analyzed from different points of view. Key words: European quality award, EFQM, business excellence model, area for improvement, fuzzy logic, fuzzy multi criteria decision making.