Modelo teórico de otimização da gestão da qualidade por meio de análise de dados industriais simulados

Abstract

Quality management in industrial environments still relies on manual records, a traditional method that presents significant structural limitations. The use of paper forms for part counting, nonconformity registration, and communication between production and quality leads to transcription errors, illegible records, operator variability, information delays, and data loss, compromising traceability and reducing the reliability of information used for decision-making. These shortcomings directly affect the monitoring of performance indicators and hinder operational standardization, emphasizing the need for digital solutions capable of ensuring greater agility and accuracy in data flow. In response to this scenario, this study develops and evaluates a theoretical model for optimizing quality management through the digitalization and integration of industrial data, simulating its application within a production recording process. The methodology involved analyzing the limitations of the manual system, developing an automated digital model, and comparing both approaches using performance indicators and quality tools such as PDCA, Ishikawa, Pareto, and 5 Whys. The simulated results showed reductions above 85% in registration errors, communication delays, and rework, while significantly increasing accuracy, standardization, and traceability. The root-cause analysis demonstrated that the structural issues of the manual method are directly related to the lack of standardization, dependence on individual interpretation, and absence of electronic integration. Therefore, the proposed digital model is concluded to be an effective solution for mitigating operational failures, accelerating information flow, and reinforcing data-driven decision-making, in alignment with Industry 4.0 principles. The study demonstrates that the digitalization and integration of industrial data are essential steps toward making production processes more reliable, efficient, and sustainable.