Multivariate nonconformity analysis for paving stone manufacturing process improvement

• Autorzy: Knop Krzysztof

Identyfikatory

DOI

10.2478/mspe-2022-0042

• Języki publikacji: EN

Abstrakty

EN

The article presents the result of multidimensional analysis of 'Behaton' type paving stones' nonconformities for improving the production process by improving the quality of the final product. Statistical tools, including SPC tools and quality tools, both basic and new, were used to analyse nonconformities in the spatial-temporal system, i.e. according to the type of nonconformity and according to the examined months. The purpose of using the data analysis tools was to thoroughly analyse the cases of nonconformities of the tested product, obtain information on the structure of these nonconformities in the various terms, and information on the stability and predictability of the numerical structure of nonconformity over time. Potential causes influencing a large percentage of paving stone defects were identified, factors and variables influencing the most frequently occurring nonconformities were determined, and improvement actions were proposed. As a result of the multidimensional and multifaceted analyses of paving stone nonconformities, it was shown that in the structure of nonconformity there were cases that were unusual in terms of the number of occurrences, and the lack of stability in the number of nonconformities in terms of the examined months was proven. Three critical nonconformities of the tested product were identified: side surface defects, vertical edge defects, and scratches and cracks. It was determined that the most important factor causing a large percentage of nonconformity was the time of shaking and vibrating the concrete, which was significantly related to the technical condition of the machines, and the most important reason for a large percentage of paving stone nonconformity was the lack of efficient maintenance. Machine, method, and man turned out to be the most important categories of problem factors and specific remedial actions were proposed. A multidimensional look at the structure of paving stone nonconformity as well as the factor and causes causing them has brought a lot of valuable information for the management staff of the analysed company, thanks to which it is possible to improve the production process and improve the quality of the final product.

Słowa kluczowe

EN

SPC tools; QC tools; nonconformity analysis; statistical analysis; improvement

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2022
• Tom: nr 4 (30)
• Strony: 331--341
• Opis fizyczny: Bibliogr. 46 poz., rys., tab.

Twórcy

autor

Knop Krzysztof

• Czestochowa University of Technology, Faculty of Management, Department of Production Engineering and Safety Armii Krajowej 19B, 42-200 Czestochowa, Poland

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

Bibliografia

• [1] ISO 9001:2015. Quality management systems. Fundamentals and vocabulary.
• [2] A. Mitra. Fundamentals of quality control and improvement. 4th Edition. Hoboken (NJ): John Wiley & Sons, 2016. ISBN 978-1118705148.
• [3] Ch. A. Cianfrani, J. E. West. ISO 9001:2015 Explained. Fourth Edition. Milwaukee (WI): ASQ Quality Press, 2015. ISBN 978-0873899017.
• [4] K. Knop. Analysis and Quality Improvement of the UV Printing Process on Glass Packagings. In: Ulewicz R., Hadzima (Eds.), Quality Production Improvement - QPI 2021, B., Warsaw: De Gruyter, 2021, pp. 314-325. doi: 10.2478/cqpi-2021-0031.
• [5] K. Teplická, D. Hrehová, M. Ševela. "Improvement the processes in order production in construction industry with the orientation on processes performance". Polish Journal of Management Studies, Vol. 24, No. 1, 2021, pp. 407-427. doi: 10.17512/pjms.2021.24.1.24.
• [6] E. Nedeliaková, M. P. Hranický, M. Valla. "Risk identification methodology regarding the safety and quality of railway services". Production Engineering Archives, Vol. 28, No. 1, 2022, pp. 21-29. doi: 10.30657/pea.2022.28.03.
• [7] A. Hamrol. Quality management with examples. Warsaw: PWN, 2005. ISBN: 978-83-011-7466-8.
• [8] D. Siwiec, A. Pacana. "Method of improve the level of product quality". Production Engineering Archives, Vol. 27, No. 1, 2021, pp. 1-7. doi: 10.30657/pea.2021.27.1.
• [9] K. Knop. "Analysis of Risk of Nonconformities and Applied Quality Inspection Methods in the Process of Aluminium Profiles Coating Based on FMEA Results". Production Engineering Archives, Vol. 16, 2017, pp. 16-21. doi: 10.30657/pea.2017.16.04.
• [10] W. Biały. "Application of quality management tools for evaluating the failure frequency of cutter-loader and plough mining systems". Archives of Minning Sciences, Vol. 62, Iss. 2, 2017, pp. 243-252. ISSN 0860-7001. doi: 10.1515/amsc-2017-0018.
• [11] S. Sousa, N. Rodrigues, E. Nunes. "Application of SPC and Quality Tools for Process Improvement". Procedia Manufacturing, Vol. 11, 2017, pp. 1215-1222. doi: 10.1016/j.promfg.2017.07.247.
• [12] D. Pavletic, M. Sokovic, G. Paliska. "Practical Application of Quality Tools”. International Journal for Quality Research, Vol. 2, 2008.
• [13] D. C. Montgomery. Statistical Quality Control. 7th Edition. Hoboken (NJ): Wiley, 2012. ISBN 978-1118146811.
• [14] D. Spiegelhalter. The Art of Statistics: How to Learn from Data. New York: Basic Books, 2021. ISBN 978-1541675704.
• [15] Z. Holcomb. Fundamentals of Descriptive Statistics. 1st Edition. New York: Routledge, 1997. ISBN 9781884585050.
• [16] E. Kozień. "Quality Improvement in Production Process". Conference Quality Production Improvement – CQPI, vol. 1, no. 1, 2019, pp. 596-601. doi: 10.2478/cqpi-2019-008.
• [17] S. Grabowska. "Improvement of the Production Process in the Industry 4.0 Context". Multidisciplinary Aspects of Production Engineering, Vol. 1, 2018, pp. 55-62. doi: 10.2478/mape-2018-0008.
• [18] S. Jevgeni, E. Shevtshenko, R. Zahharov. "Framework for Continuous Improvement of Production Processes and Product Throughput". Procedia Engineering, Vol. 100, 2015. doi: 10.1016/j.proeng.2015.01.398.
• [19] P.A. Tavallali, M.R. Feylizadeh, A. Amindoust. "Presenting a mathematical programming model for routing and scheduling of cross-dock and transportation". Polish Journal of Management Studies, Vol. 22, No. 1, 2020, pp. 545- 564. doi: 10.17512/pjms.2020.22.1.35.
• [20] J. Li, Ch. T. Papadopoulos, L. Zhang. "Continuous improvement in manufacturing and service systems". International Journal of Production Research, Vol. 54, No. 21, 2016, pp. 6281-6284. doi: 10.1080/00207543.2016.1228235.
• [21] N. Ivanov. "A Study on Optimization of Nonconformities Management Cost in the Quality Management System (QMS) of Small-sized Enterprise of the Construction Industry". Procedia Engineering, Vol. 153, 2016, pp. 228-231. doi: 10.1016/j.proeng.2016.08.107.
• [22] S. Rallabandi, R.G. Satyanarayana, R. Srikanth Reddy Rikkula. "Utility of quality control tools and statistical process control to improve the productivity and quality in an industry". International Journal of Reviews in Computing, Vol. 5, January, 2011. ISSN 2076-3328.
• [23] M. Donauer, P. Peças, A. Azevedo. Nonconformity root causes analysis through a pattern identification approach. In: A. Azevedo (Ed.), Advances in Sustainable and Competitive Manufacturing Systems, Switzerland: Springer International Publishing, 2013, pp. 851-863. doi: 10.1007/978- 3-319-00557-7_70.
• [24] D. Okes, Root Cause Analysis: The Core of Problem Solving and Corrective Action. ASQ Quality Press, 2009. ISBN 978- 0873897648.
• [25] A. Pacana, K. Czerwińska, "Improving the quality level in the automotive industry", Production Engineering Archives, Vol. 26, No. 4, 2020, pp. 162-166. doi: 10.30657/pea.2020.26.29.
• [26] J. Tarí, V. Sabater. "Quality tools and techniques: Are they necessary for quality management?". International Journal of Production Economics, Vol. 92, No. 3, 2004, pp. 267- 280. doi: 10.1016/j.ijpe.2003.10.018.
• [27] D. Siwiec, A. Pacana. "The use of quality management techniques to analyse the cluster of porosities on the turbine outlet nozzle". Production Engineering Archives, Vol. 24, 2019, pp. 33-36. doi: 10.30657/pea.2019.24.08.
• [28] K. Knop. "The Use of Quality Tools to Reduce Surface Defects of Painted Steel Structures". Manufacturing Technology, Vol. 21, No 6, 2021, pp. 805-817. doi: 10.21062/mft.2021.088.
• [29] S. Mizuno. Management for Quality Improvement: The 7 New QC Tools. Woodland Hills, CA (USA): Productivity Pr. Inc., 1988. ISBN 9780915299294.
• [30] Y. Nayatani, T. Eiga, R. Futami. "The seven QC tools: New tools for a new era". Environmental Quality Management, Vol. 4, No. 1, 2006, pp. 101-109. doi: 10.1002/tqem.3310040111.
• [31] Z. Andrássyová, J. Žarnovský, Š. Álló, J. Hrubec. "Seven New Quality Management Tools". Advanced Materials Research, Vol. 801, 2013, pp. 25-33.
• [32] W. Brylicki. Kostka brukowa z betonu wibroprasowanego. Kraków: Polski Cement Sp. z.o.o., 1998.
• [33] G. Łój. Europejskie standardy dla kostki brukowej. Stowarzyszenie Producentów Brukowej Kostki Drogowej, Warszawa: Stowarzyszenie Producentów Cementu 2005.
• 34] R. Makarov. "Sheet-Glass Quality Improvement Based on Statistical Analysis of Glass-Production Monitoring". Glass and Ceramics, Vol. 71, Iss. 9-10, 2015, pp. 350-352.
• [35] F. Marmolejo-Ramos, T. Tian. "The shifting boxplot. A boxplot based on essential summary statistics around the mean”. International Journal of Psychological Research, Vol. 3, No. 1, 2010. doi: 10.21500/20112084.823.
• [36] H. Chernoff. "The Use of Faces to Represent Points in K-Dimensional Space Graphically". Journal of the American Statistical Association, Vol. 68, No. 342, 1973, pp. 361-368.
• [37] A. Boldizsar. "Statistical analysis of road freight transport in Catalonia". Production Engineering Archives, vol. 28, no. 1, 2022, pp. 40-49. doi: 10.30657/pea.2022.28.05.
• [38] P. Lillrank, J. Kujala. "Managing common and specific causes of quality problems in project-based organisations". International Journal of Productivity and Quality Management, Vol. 1, No. 1-2, 2005, pp. 56-68. doi: 10.1504/IJPQM.2006.008373.
• [39] D.R. Bamford, R.W. Greatbanks. "The use of quality management tools and techniques: a study of application in everyday situations". International Journal of Quality & Reliability Management, Vol. 22, No. 4, 2005, pp. 376-392. doi: 10.1108/02656710510591219.
• [40] R. Ulewicz, D. Kleszcz, M. Ulewicz. "Implementation of Lean Instruments in Ceramics Industries". Management Systems in Production Engineering, Vol. 29, No. 3, 2021, pp. 203-207. doi: 10.2478/mspe-2021-0025.
• [41] M. Krynke, T. N. Ivanova, N. F. Revenko. "Factors, Increasing the Efficiency of Work of Maintenance, Repair and Operation Units of Industrial Enterprises". Management Systems in Production Engineering, Vol. 30, No. 1, 2022, pp. 91-97. doi: 10.2478/mspe-2022-0012.
• [42] S.T. Dziuba, M. Ingaldi, M. Kadlubek. Use of quality management tools for evaluation of the products quality in global economy. Globalization and its socio-economic consequences, Žilina: Žilinská univerzita, 2016, pp. 425-432.
• [43] M. Potkany, J. Zavadsky, R. Hlawiczka, P. Gejdos, J. Schmidtova. "Quality Management Practices in Manufacturing Enterprises in the Context of Their Performance". Journal of Competitiveness, Vol. 14, No. 2, 2022, pp. 97- 115. doi: 10.7441/joc.2022.02.06.
• [44] M. Potkany, P. Gejdos, P. Lesnikova, J. Schmidtova. "Influence of Quality Management Practices on the Business Performance of Slovak Manufacturing Enterprises". Acta Polytechnica Hungarica, Vol. 17, No. 9, 2022, pp. 161-180. doi: 10.12700/APH.17.9.2020.9.9.
• [45] M. Ingaldi, S.T. Dziuba. Supervisor’s Assessment as an Element Effecting Technological Process in Chosen Metallurgical Company. 25th Anniversary International Conference on Metallurgy and Materials, Ostrava, Tanger, 2016, pp. 1822-828.
• [46] G. Tóth. "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, Vol. 24, 2010, pp. 140-148. doi: 10.1002/cem.1293.

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).