Abstract--The evolution of the 5S methodology with the support of artificial intelligence techniques represents a significant opportunity to improve industrial organization audits in the automotive chain, making them more objective, efficient and aligned with Industry 4.0 standards. This work developed an automated 5S audit system based on large-scale language models (LLM), capable of assessing the five senses (Seiri, Seiton, Seiso, Seiketsu, Shitsuke) in a standardized way through intelligent image analysis. The system's reliability was validated using Cohen's concordance coefficient (κ = 0.75), showing strong alignment between the automated assessments and the corresponding human audits. The results indicate that the proposed solution contributes significantly to continuous improvement in automotive manufacturing environments, speeding up the audit process by 50% of the traditional time and maintaining the consistency of the assessments, with a 99.8% reduction in operating costs compared to traditional manual audits. The methodology presented establishes a new paradigm for integrating lean systems with emerging AI technologies, offering scalability for implementation in automotive plants of different sizes. The global automotive industry faces growing competitiveness challenges demanding maximized operational efficiency and production quality. The 5S methodology, recognized worldwide as the foundation for workplace organization and cleanliness, plays a strategic role in operational excellence.

Manufacturing energy consumption data contains important process signatures required for operational visibility and diagnostics. These signatures may be of different temporal scales, ranging from monthly to sub-second resolutions. We introduce a hierarchical machine learning approach to identify automotive process signatures from paint shop electricity consumption data at varying temporal scales (weekly and daily). A Multi-Layer Perceptron (MLP), a Convolutional Neural Network (CNN), and Principal Component Analysis (PCA) combined with Logistic Regression (LR) are used for the analysis. We validate the utility of the developed algorithms with subject matter experts for (i) better operational visibility, and (ii) identifying energy saving opportunities.

Emerging artificial intelligence technologies have the prospect to transform the automobile sector. Artificial intelligence is promptly shifting from a secretive and out-of-reach device utilized through large tech, the military, and social media behemoths to a near-ubiquitous requirement for manufacturers and groups throughout an increasingly extensive range of industries. Industrial and automobile manufacturing are two of the most interesting areas of AI research, with more and more accessible, inexpensive, and advantageous AI-powered applied sciences already exhibiting the potential to radically seriously change the panorama of how matters are made. The best of this new technology of AI structures and options are meant for real-world utility in real-world workplaces, doing a virtually limitless variety of jobs faster, easier, and with greater efficiency. In addition, this equipment drastically decreases, if it no longer eliminates, meeting errors whilst growing security and efficiency.

Over the last several weeks, there have been a couple of major announcements from two of the world's largest automotive brands that show how IoT can help them avoid recalls and warranty claims. After conducting several years of its own research and testing, the first came from GM, who announced that it recalls six million vehicles in the U.S. after the National Highway Traffic Safety Administration denied its recall appeal, saying the carmaker had not established the recall was unnecessary. The most recent came from Ford, which announced it took steps to rein in rising warranty costs. Part of the new plan to offset these costs involves the company charging suppliers upfront for half of the cost of warranty-related issues. Second, it's the damage to the OEMs' brands when massive recalls or spikes in warranty claims occur.

Whilst the term artificial intelligence (AI) may conjure images of futuristic utopia and modern-day visionary technologies; the concept has actually been a societal forethought for longer than we think. Meanwhile, turn the clocks back considerably further to Ancient Egypt, and you'll find robotic-inspired animated ceremonial statues. Artificial intelligence is, in fact, centuries-old, and its implementation has long been a desire of the human race. Fast-forward to today and the omnipresence of robotics is remarkable. Not only are robots applied to large-scale industrial manufacturing chains (both assembling cars and integrated with vehicles themselves), but they're also found much closer to home on a smaller scale; hoovering our floors, mowing our lawns and, in some cases, stocking our shelves at local supermarkets1.