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

• Autorzy: Sahin Aybuke

Identyfikatory

DOI

10.30657/aek.2024.10.5

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

statistical quality control; acceptable quality level; process capability

PL

kontrola jakości statystyczna; akceptowalny poziom jakości; zdolność procesu

• Wydawca: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji
• Czasopismo: Archives of Engineering Knowledge
• Rocznik: 2024
• Tom: T. 10, Nr 1
• Strony: 24--30
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Sahin Aybuke

• Sakarya University, Kemalpasa, Esentepe Kampusu, Universite Caddesi, 54050 Serdivan/Sakarya, Turkey

Bibliografia

• 1. Antony, J., Banuelas, R., 2002. Key Ingredients for the Effective Implementation of Six Sigma. Measuring Business Excellence, 6(4), 20– 27.
• 2. Chandra, M., Schindler, T., 2014. Statistical Quality Control in Weight Consistency Monitoring. Journal of Food Engineering, 121(2), 223–229.
• 3. Deming, W. E.. 1982. Out of the Crisis. MIT Press.
• 4. Dodge, H.F., Romig, H.G., 1959. Sampling Inspection Tables: Single and Double Sampling. John Wiley & Sons.
• 5. Duncan, A. J., 1986. Quality Control and Industrial Statistics (5th ed.). Irwin Professional Publishing.
• 6. Govindaraju, K., Soundararajan, V., 1996. Operating Characteristic Curves for Variable Sampling Plans. Journal of Quality Technology, 28(3), 255–265.
• 7. Juran, J.M., 1992. Juran on Quality by Design: The New Steps for Planning Quality into Goods and Services. Free Press.
• 8. Kume, H., 1992. Statistical Methods for Quality Improvement. Productivity Press.
• 9. Montgomery, D.C., 2019. Introduction to Statistical Quality Control (8th ed.). Wiley.
• 10. Montgomery, D.C., Runger, G.C., 2018. Applied Statistics and Probability for Engineers (7th ed.). Wiley.
• 11. Shankar, R., Prabhu, B.S., 2018. Optimizing Sampling Plans to Reduce Inspection Costs. International Journal of Production Research, 56(14), 4561–4578.
• 12. Wheeler, D.J., 2010. Understanding Statistical Process Control (3rd ed.). SPC Press.
• 13. Wheeler, D.J., Chambers, D.S., 1992. Understanding Statistical Process Control. SPC Press.
• 1. Antony, J., Banuelas, R., 2002. Key Ingredients for the Effective Implementation of Six Sigma. Measuring Business Excellence, 6(4), 20– 27.
• 2. Chandra, M., Schindler, T., 2014. Statistical Quality Control in Weight Consistency Monitoring. Journal of Food Engineering, 121(2), 223–229.
• 3. Deming, W. E.. 1982. Out of the Crisis. MIT Press.
• 4. Dodge, H.F., Romig, H.G., 1959. Sampling Inspection Tables: Single and Double Sampling. John Wiley & Sons.
• 5. Duncan, A. J., 1986. Quality Control and Industrial Statistics (5th ed.). Irwin Professional Publishing.
• 6. Govindaraju, K., Soundararajan, V., 1996. Operating Characteristic Curves for Variable Sampling Plans. Journal of Quality Technology, 28(3), 255–265.
• 7. Juran, J.M., 1992. Juran on Quality by Design: The New Steps for Planning Quality into Goods and Services. Free Press.
• 8. Kume, H., 1992. Statistical Methods for Quality Improvement. Productivity Press.
• 9. Montgomery, D.C., 2019. Introduction to Statistical Quality Control (8th ed.). Wiley.
• 10. Montgomery, D.C., Runger, G.C., 2018. Applied Statistics and Probability for Engineers (7th ed.). Wiley.
• 11. Shankar, R., Prabhu, B.S., 2018. Optimizing Sampling Plans to Reduce Inspection Costs. International Journal of Production Research, 56(14), 4561–4578.
• 12. Wheeler, D.J., 2010. Understanding Statistical Process Control (3rd ed.). SPC Press.
• 13. Wheeler, D.J., Chambers, D.S., 1992. Understanding Statistical Process Control. SPC Press.

Uwagi

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