###### Abstract
Manufacturing processes utilize sensors and smart devices to collect data and drive the adoption of artificial intelligence (AI) and predictive analytics [10]. Companies are highlighting the need for strategic transformation and are increasingly considering the integration of AI [11]. There is extensive research using improved statistical process control and artificial intelligence techniques. For instance, [PERSON] et al. (2023) [12] integrated SPC with machine learning techniques to predict future system behavior and intelligently detect anomalies. Real-time monitoring and quality control of water resource projects use the combination of SPC and artificial intelligence in a dam construction project. Similarly, the adoption of AI-driven workforce management has been highlighted in areas such as risk management, business, marketing, and information exchange [13]. Furthermore, research in [14] presented a method that converts the unstructured reviews into a set of statistical control tables, analyzes how the average and variation in attribute importance and performance change over time, and identifies whether changes are a natural event, aberration, or a gradual trend. The authors of [15] enhanced the accuracy of \"control chart recognition\" (CCPR) by utilizing control chart patterns for specific factors in the manufacturing process. A CCPR-improved automatic statistical process control was designed with several complex features. [PERSON] [16] proposed a method for monitoring a process with categorical observations, utilizing an approach that considers uncertainty and vagueness. In addition,Ruelas-Santoyo et al. [17] presented the development of a methodology that integrates the multivariate cumulative sum control chart and the multilayer perceptron artificial neural network for the detection and interpretation of the source(s) of variation generated in the manufacturing processes. Moreover, [PERSON] et al. [18] proposed a new method that uses a multi-label convolutional neural network to construct a classifier for concurrent patterns of a control chart. This study used data from previous studies to evaluate the effectiveness of the proposed method with appropriate multi-label classification metrics. The results of the study show that the recognition performance of a multi-label convolutional neural network is better than that of traditional machine learning algorithms. Furthermore, SPC and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, the authors of [19] proposed an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. [PERSON] et al. [20] proposed a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boosting or AdaBoost (AB) algorithms from ML models. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. Each of the above references improves or strengthens control graphs, but maintains the configuration of control graphs, which are inputs to different neural networks or fuzzy systems that recognize patterns or detect and interpret the source of variation.