# An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study
## 3 Results and Discussions
### New EFQM Model Results  
The same AI framework used in the previous section is used when the new EFQM dataset is given for the sake of comparison, knowing that the new EFQM has only seven input criteria rather than nine. Different AI models were used to train the framework with the new EFQM dataset. Table 3 shows the output accuracy for these AI models used with the new EFQM.  
Figure 27 shows the elbow diagram when the new EFQM is used to choose the suitable k-value for the k-means clustering algorithm, which is where the curve starts to decrease, and before it is steady. In this case, k can be chosen to be between 2 and 5. K = 2 was chosen for accuracy issues. As discussed earlier in the old EFQM section, Random Index adjusted for chance was used to compare similarities between clusters.  
ARI (NewEFQM) = 0.5913 for K = 2,  
ARI (new EFQM) = 0.41 for K = 3.  
Figure 26: Three “Low” outputs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**AI Model** & **Decision Tree** & **Linear Regression** & **Gradient Boosting** & **Random Forest** & **Support Vector** & **K-Means** \\  & & & & **Regression** & & **Machine** & **Clustering** \\ \hline Output Accuracy & 57.20\% & 58.54\% & 60.34\% & 53.2\% & 13.65\% & 59.13\% \\ \hline \multirow{3}{*}{Hyperparameters} & Default & Default & Default & & Default & \\  & Random\_state = 0 & Learning rate = 0.1, & Random\_state = 0 & Default & \\  & Max\_depth = 2 & alpha, max\_depth = 3 & Max\_depth = 2 & Max\_depth = 2 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Output accuracy for different AI models used with new EFQM.  
Figure 27: Elbow diagram for determining k-value for new EFQM.  
Figure 28 shows the correlation between the new EFQM's dimensions. It shows that the new EFQM dimensions are more correlated with each other than the old EFQM dimensions. There are many strong correlations (0.9), such as rhose between \"Organizational Culture and Leadership\" and \"Purpose, Vision & Strategy\". Figure 29 shows very low accuracy when using the new EFQM model and applying the k-means clustering. This value remained low even when the k-value was changed.  
When testing the framework, if the EFQM output score is \"Low\", the framework returns the lowest EFQM input criterion with recommendations to decision makers for self-improvement. Using this framework saves time and money before anorganization applies for the EFQM excellence award.  
The basic significance of this research is to improve the operational excellence in public and private organizations. This research is innovative since it adds to the knowledge body the new concept of injecting AI following the ISO/IEC 23053 standard into the EFQM, leading to operational excellence. This can be of great value for organizations seeking to enhance their performance and attain recognition in the field of quality management.  
This research paper guides the way for future studies. Benchmarking between different business excellence models can be studied when using different machine learning techniques. Quality 4.0, or the new EFQM can also be used to predict operational excellence using different AI techniques. The model can be applied to different industries worldwide, and then benchmarking can be performed.  
Writing--review & editing, R.R.H.; Supervision, M.A.T., F.D. and J.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data used for training and testing the k-means unsupervised machine learning were obtained from a confidential UAE government entity.  
A special thank you to all hidden soldiers who participated in this research paper, starting with my supervisors who offered help throughout the whole paper. Thanks to [PERSON] for her continuous technical support with AI techniques.  
The authors declare no conflicts of interest.