# An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study
## 1 Introduction  
### Theoretical Background  
Organizations primarily focused on quality and Total Quality Management (TQM) measures to improve their operations before the emergence of business excellence models like the [PERSON] model and the European Foundation for Quality Management (EFQM) model [1]. These approaches were foundational in promoting a culture of continuous improvement and customer satisfaction. However, business excellence has become a prominent topic in the past few decades, and various models and frameworks have been developed to assess and enhance organizational performance beyond just quality [2].  
Figure 1 shows the evolution of quality management concept. Quality inspection (Quality 1.0, 1900s) represented the early days of quality management and inspected finished products to identify defects. Quality was associated with the physical properties of products [3]. Quality control (Quality 1.0, 1920s) emphasized product inspection. During the production process, it introduced more systematic methods for defect identification and correction [3]. Later, quality assurance (Quality 2.0, 1950s) included both product and process quality. This stage witnessed the emergence of ISO standards, which providedguidelines for ensuring consistent quality in manufacturing and other industries. In the 1980s, Total Quality Management (TQM, Quality 3.0, 1980s) included not only product and process quality but also the entire organization's culture and operations. It focused on customer satisfaction, continuous improvement, and employee involvement. During this era, various ISO standards were developed, and business excellence models like the EFQM gained prominence [3]. Beginning in 2017, Quality 4.0 (2017) represents the latest stage in the evolution of quality management, accompanying the fourth industrial revolution (Industry 4.0). It extends the principles of TQM to include not only product, process, and company aspects but also integrate customer and supplier needs. Quality 4.0 leverages technologies such as artificial intelligence (AI) and machine learning (ML) to improve quality management practices [4].  
Quality management standards (Quality 3.0 and Quality 4.0) are integrated into organizations aiming to enhance not only their products and processes, but also their customers' and suppliers' satisfaction and happiness [3]. The EFQM is a business excellence model that supports the preceding goals. Therefore, it is crucial to have outstanding EFQM output scores to ensure that the organizations' targets are achieved. This research paper introduces a novel integrated AI framework following the ISO/IEC 23053 standard to predict these EFQM output scores, hence enhancing operational excellence [5]. The AI framework gives decision makers in organizations feedback to improve their weakness points before applying for the EFQM excellence award. Therefore, the purpose of this research's framework is to save time and cost for organizations, and provide them with great implications aiming to improve their processes and overall performance.  
In the following sections, the materials and methods used in this research are discussed. This section is divided into EFQM, AI, and ISO/IEC 23053 sub-sections. Later on, the Results and Discussion Section demonstrates all results obtained from the research's AI framework when applied to old and new EFQM score datasets as AI model training inputs. In the final section, conclusions and future work are outlined.