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THE INTEGRATION OF AI IN OPERATIONAL EXCELLENCE FRAMEWORKS: A CASE STUDY APPROACH
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Background: Organizations actively use Artificial Intelligence (AI) integration with Operational Excellence (OpEx) frameworks to improve their performance levels. Present scientific studies fail to understand AI's potential for process optimization and efficiency boost coupled with productivity growth, particularly within industrial operational excellence frameworks.
Objective: This investigation examines AI integration's effects on operational performance through the evaluation of AI implementation practices operational technology systems and worker competency levels while studying process optimization and operational results. This study analyzes variable relationships while identifying the key contributors toward operational excellence.
Methodology: This research adopted a quantitative method that incorporated structured questionnaires rated with Likert-scale items. A total of 355 professionals from different industries which use both AI and operational excellence frameworks took part in this research including representatives from manufacturing, healthcare, and retail. The research used descriptive statistics and normality tests together with reliability analysis (Cronbach's Alpha) correlation analysis and Principal Component Analysis (PCA) to analyze the data.
Results: Research results demonstrate AI implementation along with technological infrastructure and employee skills create robust positive correlations that drive operational performance outcomes. All constructs demonstrated reliable measurement according to Cronbach's Alpha results. Independent variable distributions were mainly normal according to the Shapiro-Wilk test yet some needed analysis through non-parametric methods. Process efficiency and technological readiness emerged as critical drivers of AI implementation based on PCA analysis which found that the first two components explained greater than 55% of the data variation.
Conclusion: AI technology stands as the foundation for boosting operational performance when utilized in OpEx frameworks. Research demonstrates that organizations need to develop solutions regarding employee training and technical systems before they can unlock maximum benefits from AI implementation. The research delivers practical advice on which organizations need to implement AI effectively for continuous operational process enhancement. Longitudinal research studies combined with industry-specific examination of AI applications need attention to develop a better understanding of operational excellence enabled by artificial intelligence.
Creative Business & Social Research
Title: THE INTEGRATION OF AI IN OPERATIONAL EXCELLENCE FRAMEWORKS: A CASE STUDY APPROACH
Description:
Background: Organizations actively use Artificial Intelligence (AI) integration with Operational Excellence (OpEx) frameworks to improve their performance levels.
Present scientific studies fail to understand AI's potential for process optimization and efficiency boost coupled with productivity growth, particularly within industrial operational excellence frameworks.
Objective: This investigation examines AI integration's effects on operational performance through the evaluation of AI implementation practices operational technology systems and worker competency levels while studying process optimization and operational results.
This study analyzes variable relationships while identifying the key contributors toward operational excellence.
Methodology: This research adopted a quantitative method that incorporated structured questionnaires rated with Likert-scale items.
A total of 355 professionals from different industries which use both AI and operational excellence frameworks took part in this research including representatives from manufacturing, healthcare, and retail.
The research used descriptive statistics and normality tests together with reliability analysis (Cronbach's Alpha) correlation analysis and Principal Component Analysis (PCA) to analyze the data.
Results: Research results demonstrate AI implementation along with technological infrastructure and employee skills create robust positive correlations that drive operational performance outcomes.
All constructs demonstrated reliable measurement according to Cronbach's Alpha results.
Independent variable distributions were mainly normal according to the Shapiro-Wilk test yet some needed analysis through non-parametric methods.
Process efficiency and technological readiness emerged as critical drivers of AI implementation based on PCA analysis which found that the first two components explained greater than 55% of the data variation.
Conclusion: AI technology stands as the foundation for boosting operational performance when utilized in OpEx frameworks.
Research demonstrates that organizations need to develop solutions regarding employee training and technical systems before they can unlock maximum benefits from AI implementation.
The research delivers practical advice on which organizations need to implement AI effectively for continuous operational process enhancement.
Longitudinal research studies combined with industry-specific examination of AI applications need attention to develop a better understanding of operational excellence enabled by artificial intelligence.
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