Reduction of Defects in Flexographic Material Production: An Innovative Approach Based on Artificial Intelligence and Optimal Work Methods

The graphic industry faces an ongoing problem: printing defects, which, despite their impact, have not been widely studied. Globally, the demand for commercial printing is expected to grow by 24%. In Peru, this sector plays a significant role in the economy. According to INEI, it is part of the manufacturing industry, contributing 12.5% to the national GDP and employing 8.5% of the Economically Active Population (EAP). Additionally, in 2021, it saw a 7.3% increase in sales. This study proposes an innovative model to tackle this issue by combining AI-driven digital technologies with industrial engineering tools. Defects affect more than just finances; they also lead to customer loss and damage to a company's reputation. Key causes include poor design procedures, printer malfunctions, failure to follow proper storage practices, and prolonged storage, all of which contribute to defective prints and the use of degraded raw materials. In response, various improvements have been implemented, including method studies, work standardization, planned maintenance, adherence to good storage practices, and the use of Economic Order Quantity (EOQ) and Demand Driven Material Requirements Planning (DDMRP). The main contribution of this study is the identification and validation of a model that helps graphic companies overcome challenges in quality and order fulfillment. The model was tested using Arena simulation software and implemented in a Peruvian company specializing in commercial printing. The results showed a 12.03% decrease in defect rates (from 17.59% to 5.56%), an increase in the meantime between failures by 400.7 hours (from 46.35 to 447.05 hours), and a reduction in defective raw materials from 23.81% to 11.11%. Additionally, the capacity of the printing and storage processes has been optimized. According to the Process Capability Analysis, the capability of the printing process increased from 0.73 to 3.22 and that of the storage process from 0.54 to 2.09, making them capable processes that ensure a high level of quality. In conclusion, the improvement model narrowed the technical gap, boosting quality in the graphic sector, and leading to greater customer satisfaction and higher profits.