The growing adoption of Industry 4.0 technologies has increased the need for flexible and data-driven tools to enhance Statistical Quality Control (SQC). In this context, this study aims to analyze the potential contributions of four R packages—qcc, qcr, qicharts2, and SixSigma—to modern quality management systems. The purpose is to describe how these existing tools, when used together, can support real-time monitoring, process characterization, and capability analysis within digitalized industrial environments. The research follows an analytical and descriptive approach, comparing the functionalities, outputs, and complementarities of each package. qcc is identified as the foundational tool for classical control chart construction; qcr extends its scope through enhanced graphical flexibility and integrated capability analysis; qicharts2 introduces automated detection of instability signals based on Shewhart rules; and SixSigma focuses on structured process characterization and capability evaluation under the DMAIC framework. Results show that combining these packages provides a comprehensive environment for process monitoring and decision support, enabling automation, interoperability, and reproducibility. The study concludes that these R-based tools collectively bridge traditional SQC techniques with Industry 4.0 principles, reinforcing connectivity, transparency, and continuous improvement, while facilitating the transition from reactive quality control toward intelligent and adaptive process management.