Hassan, R.R.; Abu Talib, M.; Dweiri, F.; Roman, J. An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study. Appl. Sci.2024, 14, 2569.
Hassan, R.R.; Abu Talib, M.; Dweiri, F.; Roman, J. An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study. Appl. Sci. 2024, 14, 2569.
Hassan, R.R.; Abu Talib, M.; Dweiri, F.; Roman, J. An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study. Appl. Sci.2024, 14, 2569.
Hassan, R.R.; Abu Talib, M.; Dweiri, F.; Roman, J. An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study. Appl. Sci. 2024, 14, 2569.
Abstract
The integration of artificial intelligence (AI) into the European Foundation for Quality Management (EFQM) business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering (with k=2). This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. The research paper's main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices.
Keywords
Operational excellence; EFQM; Artificial Intelligence; ISO/IEC 23053 standard
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
