In the flexographic industry, companies often face a recurring challenge: low quality performance in their production lines. This issue is primarily caused by deviations in CTP plates, inadequate monitoring of fading, and the absence of an accurate and automated inspection system. These deficiencies lead to color variations, graphic misalignments, rework, and replacements, ultimately resulting in the rejection of batches by end customers due to a high percentage of nonconforming products. To address this, a review of recent studies was conducted, enabling the development of a model aimed at improving quality performance. The model integrates methodologies such as Lean Six Sigma, applying tools like Standardized Work and the Self-Quality Matrix to standardize procedures, as well as Statistical Process Control (SPC) and Design of Experiments (DoE) to stabilize printing parameters. It also incorporates emerging technologies such as the Internet of Things (IoT) and Andon systems to automate inspection processes. In this regard, the study proposes a model that combines traditional tools with technological solutions to enhance the quality performance of flexographic production lines. This proposal responds to the urgent need to reduce rework and replacements, which currently account for up to 6.61% of annual revenue as cost of poor quality. The model was validated through simulations using Simio software and later implemented in a packaging production line within a company in the sector. The results showed a 10.17% increase in the quality performance indicator (from 89.74% to 99.91%) and a reduction in nonconforming products from 10.25% to 0.0001%.