Abstract With the increasing complexity of industrial processes, there is a growing demand for integrated quality control solutions. Quality control in industrial processes is continually sought after, and the tools applied for this purpose have long been known and adopted by managers. This study arose from the need to overcome the limitations of individual quality control tools, such as Statistical Process Control, the PDCA cycle, the Ishikawa Diagram, and the 5W2H method, which, although effective in their specific functions, do not provide a comprehensive approach to effectively managing continuous industrial processes. The objective of this study was to integrate these tools into a methodology that systematically monitors, diagnoses, and corrects failures efficiently. The methodology was applied to a case study in the sugar manufacturing industry, specifically in the liming process, demonstrating its effectiveness in stabilizing the process and improving the final product quality. Control charts showed that the process was out of control, with a capability of 1.02, below the acceptable limit of 1.33. After implementing the proposed corrective actions, the process stabilized, and the capability increased to 1.45, with a significant reduction in process variability. The integration of these tools enhanced each one’s problem-solving potential, demonstrating effectiveness in continuous improvement and the management of continuous industrial processes. This methodology not only proved effective in the presented case study but also shows potential for adaptation in other industrial sectors such as pharmaceuticals, food, and manufacturing, especially in the context of Industry 4.0, where integration with real-time monitoring technologies and artificial intelligence could further optimize quality control and process efficiency.
Leite et al. (Thu,) studied this question.
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