## 5 Application Example: Guidelines for an SSPC  
SPC is an essential tool for manufacturing, enabling real-time monitoring and control of production processes to identify and rectify issues before they impact product quality. Traditional SPC relies on control charts and statistical tools to visualize process variations and detect anomalies. However, implementing and managing traditional SPC can be challenging, particularly in complex manufacturing environments [46]. SSPCs offer a modern alternative, leveraging artificial intelligence (AI) and machine learning (ML) to automate tasks and provide deeper insights into production processes [47]. It boasts several advantages over traditional SPCs, including (i) enhanced efficiency, as SSPC automates tedious tasks like data collection and analysis, freeing up personnel for higher-value activities [48]; (ii) improved accuracy, as AI and ML identify problems with superior precision [49]; and (iii) boosted visibility, as SSPC offers deeper insights into production processes, enabling more informed decision-making [50].  
SSPC signifies the integration of cutting-edge technologies, such as artificial intelligence (AI), natural language processing (NLP), and machine learning (ML), into conventional SPC frameworks. This transformative approach not only facilitates real-time monitoring and analysis of production processes but also endows the system with predictive capabilities and adaptive control, thereby contributing to overall operational excellence in advanced manufacturing [41]. The importance of incorporating advanced technologies is particularly pronounced in advanced manufacturing systems, ensuring heightened quality, minimized defects, and enhanced overall efficiency [14].  
The utilization of AI, NLP, and ML assumes a pivotal role in enhancing the interpretative capacities of data. These technologies play a significant role in proactive decision-making and continuous improvement within manufacturing operations, aiding in the identification of patterns and anomalies [51]. Notably, NLP facilitates improved communication with existing SPC systems, allowing users to interact through natural language commands, thereby enhancing user-friendliness and accessibility for individuals without extensive technical expertise [52]. Consequently, the collaborative integration of traditional SPC methodologies with smart technologies establishes a dynamic framework that ensures the integrity of production processes and cultivates a responsive and adaptive manufacturing environment [28].  
An SSPC encompasses a real-time monitoring function [27], wherein AI algorithms analyze data from the process, enabling immediate detection of variations or anomalies [53]. Additionally, SSPC incorporates predictive analytics skills provided by ML procedures, anticipating potential issues before they manifest and thereby saving time, preserving profits, and ensuring better overall performance [54]. Another distinguishing feature of SSPC is its proficiency in pattern recognition within production data, foreseeing unexpected trends and identifying erratic or compromising behavior [55]. Moreover, SSPC embraces adaptive control abilities, dynamically adjusting process parameters based on real-time data analysis. Lastly, SSPC often integrates advanced data visualization techniques, presenting graphical representations that are instrumental in comprehending complex patterns and trends.