## 1 Introduction
The following section presents a comparative analysis of the efficiency and adaptability of machine learning (ML) algorithms in quality control, contrasting them with conventional statistical approaches. ML algorithms have demonstrated proficiency in handling substantial datasets from various sources, detecting nonlinear relationships and complex patterns that traditional statistical methods might overlook [24; 25]. Studies have shown that Random Forests, for example, are more effective than conventional methods such as [PERSON]'s T\({}^{2}\) control charts in predicting manufacturing performance, underscoring their superior efficiency in quality control tasks [24]. Furthermore, machine learning enables automation, thereby reducing the need for human intervention and improving product quality by providing rapid and reliable information for quality assessment [26]. The adaptability of ML is evident in its real-time data adjustment capabilities, which allow continuous parameter tuning based on ongoing analysis. This has been demonstrated in the QU4 LITY project for bearing testing. Furthermore, machine learning's predictive capabilities allow it to anticipate potential issues and adapt to changing conditions, which is crucial for consistent performance in dynamic systems such as enterprise resource planning (ERP) [27]. The continuous learning aspect of machine learning models also allows for accuracy and reliability improvements over time, a flexibility that static statistical models lack [28]. Although machine learning algorithms offer considerable advantages, traditional statistical methods remain relevant, particularly in contexts where the data are relatively straightforward or where interpretability is a priority. However, as the data produced by manufacturing processes becomes increasingly complex, the efficiency and adaptability of machine learning become increasingly valuable. The incorporation of machine learning (ML) into quality control procedures not only enhances operational efficiency but also facilitates predictive maintenance and risk management. To illustrate, industries that use IoT sensors with ML models can proactively address issues based on patterns in vibration or temperature data, thus reducing downtime and maintenance costs [29]. Despite these advances, challenges persist, including the need for substantial, high-quality training data,concerns about algorithm transparency, and the importance of skilled personnel to interpret ML outputs [29]. Implementing ML in quality control improves efficiency and adaptability over traditional methods, aligning with the demands of complex, data-intensive manufacturing environments and advancing toward more resilient production processes. A review of the literature makes it evident that digital tools, including machine learning, cyberphysical systems, and data analytics, are significant in the context of modern quality management. The automation of quality control processes, the enhancement of operational efficiency, and the dynamic adjustment of production parameters based on real-time data are becoming pivotal elements that enable manufacturing companies to achieve superior product quality, minimise defects, and optimise the utilisation of resources. Notwithstanding these advances, there remain several significant gaps in the existing literature that require further investigation. One of the main challenges is the restricted scalability of ML solutions for smaller enterprises. [PERSON] et al. [30] posit that small and medium enterprises (SMEs) encounter obstacles in the complete implementation of sophisticated predictive technologies due to limitations in technical and financial resources. Further research is required to develop more accessible and scalable solutions that can be easily implemented in diverse enterprises. Although ML offers considerable promise in quality management, significant gaps remain that impede the comprehensive deployment of these technologies.