Statistical analysis and prediction of the product complaints

• Autorzy: Knop Krzysztof , Ziora Robert

Identyfikatory

DOI

10.2478/czoto-2022-00011

• Języki publikacji: PL

Abstrakty

EN

The article presents the results of the analysis of cardboard packaging complaints based on selected quality tools and statistical tools for the purpose of a rough assessment of the effectiveness of corrective and preventive actions taken by the surveyed company and for predictive purposes. The analysis was performed in terms of two research periods - 1 year and quarters, and from the point of view of total complaints and external - customer complaints. Data on the number of products complained of as well as financial losses incurred by the company on this account were analysed. The article presents the potential of both classic quality tools and statistical tools for the purposes of in-depth analysis of complaints data and for predictive purposes and subsequent risk analysis. The critical complaint was indicated - complaint code 403 - overprint. The number of complained products to be expected in the next quarter of the new year was determined. The article shows that the corrective and preventive actions taken by the company have not yet brought the expected result in the form of reducing the number of products complained by customers during the quarters surveyed.

Słowa kluczowe

PL

zarządzanie jakością; opakowania kartonowe; narzędzia jakości; analiza statystyczna

EN

quality management; cardboard packaging; complaints; quality tools; statistical analysis; prediction

• Wydawca: De Gruyter
• Czasopismo: System Safety : Human - Technical Facility - Environment
• Rocznik: 2022
• Tom: Vol. 4, nr 1
• Strony: 99--115
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Knop Krzysztof

• Czestochowa University of Technology, Poland

• https://orcid.org/0000-0003-0842-9584

autor

Ziora Robert

Bibliografia

• 1. Andrássyová Z., Žarnovský J., Álló Š., Hrubec J., 2013. Seven New Quality Management Tools, Advanced Materials Research, 801, 25-33.
• 2. Chernoff H., 1973. The Use of Faces to Represent Points in K-Dimensional Space Graphically, Journal of the American Statistical Association, 68, 342, 361-368.
• 3. Daniel, W.W., 1990. Page's test for ordered alternatives, Applied Nonparametric Statistics (2nd ed.), PWS-Kent, Boston, 279-284.
• 4. Hamrol A., 2005. Quality management. Science and Practice, PWN, Warszawa.
• 5. Hamrol A., Kujawińska A., Bożek M., 2020. Quality inspection planning within a multistage manufacturing process based on the added value criterion, The International Journal of Advanced Manufacturing Technology, 108, 1-14.
• 6. Ingaldi M., 2021. Assessment of the service provision process as a business process management tool, Polish Journal of Management Studies, 23, 1, 204-223.
• 7. Ji B., Ameri F., Cho H., 2021. A non-conformance rate prediction method supported by machine learning and ontology in reducing underproduction cost and overproduction cost, International Journal of Production Research, 59.
• 8. Krynke M., Mielczarek K., Kiriliuk O., 2021. Cost Optimization and Risk Minimization during Teamwork Organization, Management Systems in Production Engineering, 29, 2, 145-150.
• 9. Makarov R., 2015. Sheet-Glass Quality Improvement Based on Statistical Analysis of Glass-Production Monitoring, Glass and Ceramics, 71, 350-352.
• 10. Marmolejo-Ramos F., Tian T., 2010. The shifting boxplot. A boxplot based on essential summary statistics around the mean, International Journal of Psychological Research, 3.
• 11. Mizuno S., 1988. Management for Quality Improvement: The 7 New QC Tools, Woodland Hills, CA (USA): Productivity Pr. Inc.
• 12. Nayatani Y., Eiga T., Futami R., 2006. The seven QC tools: New tools for a new era, Environmental Quality Management, 4, 1, 101-109.
• 13. Pacana A., Czerwińska K., 2020. Improving the quality level in the automotive industry, Production Engineering Archives, 26, 4, 162-166.
• 14. Pavletic D., Sokovic M., Paliska G., 2008. Practical Application of Quality Tools, International Journal for Quality Research, 2.
• 15. Performance Review Institute ed., 2006. Root Cause Corrective Action Booklet, Performance Review Institute
• 16. Potkány M., Kamodyová P., Stasiak-Betlejewska R., Lesníková P., 2021. Nature and potential barriers of facility management in manufacturing enterprises, Polish Journal of Management Studies, 23, 1, 327-340.
• 17. Stasiak-Betlejewska R., Czajkowska A., 2017. Quantification of the Quality Problems in the Construction Machinery Production, MATEC Web of Conferences, 94.
• 18. Schiffauerova A., Thomson V., 2006. Managing cost of quality: Insight into industry practice. The TQM Magazine, 18.
• 19. Webber L., Wallace M., 2012. Quality Control for Dummies, Wiley Publishing, Hoboken, NJ.
• 20. Więckowska B., 2021. User's Guide – PQStat, PQStat Software.
• 21. Tarí J., Sabater V., 2004. Quality tools and techniques: Are they necessary for quality management? International Journal of Production Economics, 92, 3, 267-280.
• 22. Tashi T., Mbuya V.B., Gangadharappa H., 2016. Corrective action and preventive actions and its importance in quality management system: A review, International Journal of Pharmaceutical Quality Assurance, 7, 1-6.
• 23. Tomic B., Spasojević-Brkić V., 2011. Effective root cause analysis and corrective action process, Journal of Engineering Management and Competitiveness (JEMC), 1, 1/2, 16-20.
• 24. Tóth G., 2010. The replacement of the Neumann trend test and the Durbin–Watson test on residuals by one‐way ANOVA with resampling and an extension of the tests to different time lags, Journal of Chemometrics, 24, 140-148.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).