## 1 Introduction  
Quality has become a key factor for competitiveness in the industry, and Six Sigma is one of the most effective methodologies for improving quality and reducing variability in production processes. Six Sigma is a methodology based on statistical analysis, aiming to reduce process variability to achieve the highest possible level of quality. The primary goal of Six Sigma is to reach a defect rate of 3.4 defects per million opportunities, which results in an almost perfect process [1, 2, 3]. The printing industry, although technologically advanced, faces numerous challenges in maintaining production stability and quality. Introducing the Six Sigma methodology can bring significant benefits. Although Six Sigma was initially developed for production processes in other industries, its application in the printing industry is becoming more prevalent and holds great potential for improving performance and achieving production process stability. Variability in the printing process can be caused by various factors such as material variations, machine settings, and human factors. By implementing the Six Sigma method ology, it is possible to identify and reduce these variations, leading to more consistent production. The optimization process involves defining Critical to Quality (CTQs) points and analyzing process capability. Through the analysis of these points, it is possible to identify the main sources of variability and take steps toward their elimination. This paper explores the possibilities of applying an AI-driven Six Sigma methodology in graphic production, with a particular focus on the offset printing process. In contrast to traditional applications of Six Sigma, which are typically employed as a framework for optimizing the entire production process, this study introduces the principles of Six Sigma as a numerical indicator for evaluating the quality and consistency of individual printed sheets. The Sigma level serves not only as a quantitative measure to assess the stability and predictability of the offset printing process but also as a basis for the identification and continuous optimization of key process parameters. By integrating statistical methods with the Random Forest model, this study provides a data-driven framework for determining optimal parameter combinations to achieve the highest possible sigma level corresponding to first-class print quality. This approach provides practitioners with an accurate tool to monitor and improve print quality in real time, actionable insights on parameter adjustments, and a methodology to stabilize offset printing processes. It also ensures that quality assessment is both objective and accurate, minimizing reliance on subjective assessment methods. By combining robust statistical analysis and machine learning, this methodology provides a scalable solution that can be adapted to a variety of production environments. It also bridges the gap between theoretical advances and real-world applications in graphic production by enabling consistent, high-quality results and reducing production variability. The Six Sigma methodology encompasses the definition of critical product or process characteristics, measurement of current performance, data analysis to identify the causes of variability, implementation of improvements, and the establishment of control mechanisms to ensure the maintenance of achieved results. The introduction of the Six Sigma methodology in the graphic industry requires adaptation to the specific characteristics of that industry, but its basic principles can be applied with the goal of achieving more stable and efficient production processes. Thus, an analysis of existing research and theories was conducted to identify key factors affecting production variability and propose measures for AI-driven optimization and standardization of the offset printing process. Numerous studies explore the application of the Six Sigma methodology and its modalities across various industries.