Abstract: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Continuous Improvement (CI) frameworks is redefining the foundations of automotive manufacturing under the Industry 4.0 paradigm. Traditional method-ologies such as Kaizen, Lean Six Sigma, and Total Quality Management (TQM) have long provided structured approaches for quality enhancement, waste reduction, and process stability. However, the emergence of AI introduces new capabilities—advanced analytics, predictive modeling, and intelligent automation—that transform these static frameworks into dynamic, data-driven ecosystems. This study conducts a systematic literature review following the PRISMA protocol, covering publications from 2010 to 2024 across Scopus, Web of Science, and OpenAlex. After filtering and de-duplication, 13,080 documents were analyzed. Data were categorized by AI methodologies (computer vision, neural networks, deep learning), industrial use cases (quality inspection, predictive maintenance, process optimization, scheduling, and supply chain planning), and key performance metrics such as Overall Equipment Effectiveness (OEE), Mean Time Between Failures (MTBF), parts per million (ppm), lead time, and service level. The analysis reveals substantial and measurable performance improvements. AI-driven systems achieve an aver-age 15% gain in production efficiency, while computer vision enables automated defect detection, improving first-pass yield and reducing scrap. Predictive maintenance reduces unplanned downtime, increasing equipment availability and reliability. These benefits depend strongly on digital maturity and integration within enterprise systems—particularly Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP), and Product Lifecycle Management (PLM) which together ensure real-time data flow, process synchronization, and traceability across production operations. The primary barriers to adoption include data quality and governance issues, lack of workforce expertise, model explainability in safety-critical environments, and the complexity of integrating AI solutions into legacy systems. These factors hinder large-scale deployment despite proven technical advantages. This study proposes an applied framework for integrating AI within CI initiatives, aligned with the DMAIC (Define–Measure–Analyze–Improve–Control) cycle and the emerging Quality 4.0 architecture. It highlights managerial enablers such as data readiness, digital governance, and cross-functional collaboration, while identifying research gaps related to implementation costs, time-to-value, and long-term performance measurement. The findings demonstrate how AI transforms CI from reactive optimization to proactive, self-improving systems capable of sustaining excellence in modern automotive manufacturing.

Sara OULED LAGHZAL and EL OUADI Abdelmajid. “Integrating Artificial Intelligence into Continuous Improvement for Automotive Manufacturing”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161187

@article{LAGHZAL2025,
title = {Integrating Artificial Intelligence into Continuous Improvement for Automotive Manufacturing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161187},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161187},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Sara OULED LAGHZAL and EL OUADI Abdelmajid}
}