Enhancing packaging line efficiency through statistical quality control: a case study

This article explores the application of statistical quality control methods in ensuring the weight consistency and overall quality of packages produced on a high-volume packaging line. The primary objective of the study was to maintain production standards by controlling the defect rate, defined by an acceptable quality level (AQL) of 1%. To achieve this, a rigorous quality control process was implemented, incorporating a systematic sampling plan that involved selecting 50 packages from each production batch. A maximum of 2 defective packages was established as the acceptance criterion, aligning with industry standards for defect tolerance. The weight measurements were systematically collected and analyzed across 10 production batches, with a target weight of 500 grams for each package. The data obtained was evaluated to assess compliance with predefined quality thresholds and to detect any significant variations or deviations from the target weight. Key metrics, such as defect rates, process capability indices, and statistical trends, were analyzed to identify potential causes of variability, including equipment performance, material inconsistencies, or environmental factors. The findings demonstrated the effectiveness of employing statistical quality control techniques in monitoring and improving process stability. By identifying and addressing areas of concern, the study highlights how proactive quality control practices can minimize defects, reduce waste, and ensure consistent product quality. Furthermore, the results underscore the critical role of structured sampling plans and data analysis in achieving production efficiency and enhancing customer satisfaction. This approach emphasizes the importance of integrating statistical methods into manufacturing processes as a strategic tool for continuous improvement and operational excellence.

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Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).