Intelligent 5S Audit System Achieves 0.75 Alignment with Human Assessments, Enabling Continuous Automotive Improvement

The pursuit of efficient and objective industrial organisation remains a key challenge for modern manufacturing, and researchers are now applying artificial intelligence to refine established methodologies like the 5S system. Rafael da Silva Maciel and Lucio Veraldo Jr, from the Institute of Science and Technology at the Federal University of São Paulo, lead a team that developed an automated 5S audit system utilising large-scale language models. This innovative approach assesses the five core principles of 5S, sorting, setting in order, shining, standardising, and sustaining, through intelligent image analysis, offering a significant step towards Industry 4. 0 standards. The team demonstrates strong alignment between automated and human assessments, achieving a substantial 50% reduction in audit time and a remarkable 99. 8% decrease in operating costs compared to traditional manual audits, establishing a new, scalable paradigm for continuous improvement in automotive manufacturing.

This work represents a significant opportunity to improve industrial organisation audits in the automotive chain, making them more objective, efficient and aligned with Industry 4. 0 standards.

AI Auditing Cuts Automotive Manufacturing Costs

This research details the development and validation of an automated 5S audit system using advanced language models in an automotive manufacturing context. The study demonstrates the feasibility and benefits of integrating AI with the traditional 5S methodology for continuous improvement in operational efficiency and quality. Key findings reveal that the AI system successfully replicated human auditor assessments with a strong level of agreement, achieving a 99. 8% reduction in audit operating costs compared to manual audits. The AI system enables more frequent and continuous monitoring of 5S conditions, leading to faster identification of deviations and improved responsiveness. This practical application of AI in a real-world manufacturing setting offers a new paradigm for continuous improvement initiatives.

Automated 5S Auditing Reduces Time and Cost

This work presents a breakthrough in industrial auditing, delivering an automated 5S system for automotive manufacturing environments. The research team developed a system based on large-scale language models capable of assessing workplace organisation, order, cleanliness, and discipline through intelligent image analysis. Validation of the system’s reliability yielded strong agreement between automated assessments and those performed by human auditors, demonstrating its potential for widespread adoption. The results demonstrate a significant acceleration of the audit process, achieving a 50% reduction in traditional audit time while maintaining consistent assessment quality.

Furthermore, the automated system delivers a substantial reduction in operating costs, achieving a 99. 8% decrease compared to traditional manual audits. These findings confirm the potential for widespread implementation across automotive plants of varying sizes, offering a scalable solution for continuous improvement. The research establishes a new paradigm for integrating lean systems with artificial intelligence, offering benefits beyond simple cost reduction. The system’s consistent application of 5S principles supports adherence to crucial automotive quality certifications. By automating the audit process, the team enables more frequent assessments, facilitating proactive identification of non-conformities and driving ongoing operational performance improvements.

AI Audits Cut Automotive Costs Dramatically

This research demonstrates the successful integration of 5S principles with artificial intelligence technologies within automotive manufacturing, establishing a new approach to industrial auditing. By developing an automated 5S audit system based on large-scale language models, the team achieved notable speed and consistency in replicating human assessments of workplace organisation, order, cleanliness, and team discipline. Validation using statistical measures confirmed strong agreement with experienced human auditors, with any discrepancies being explainable and offering opportunities for system refinement. The findings indicate a substantial reduction in operational audit costs, approximately 99. 8%, and a rapid return on investment, highlighting the economic benefits of this AI-enhanced approach. This work contributes significantly to continuous improvement initiatives by enabling daily monitoring, immediate analysis, and data-rich decision-making, effectively complementing traditional audit methods.