Analysis procedure of inspection errors based on MSA attribute study data set for the improvement purposes: Part 1 - methodolodgy

• Autorzy: Knop Krzysztof

Identyfikatory

DOI

10.2478/cqpi-2020-0017

• Języki publikacji: EN

Abstrakty

EN

The article presents an authorship version of the analysis procedure of data set from MSA Attribute Study for the purposes related to the reduction of conformity assessment errors and improvement of production process effectiveness. The MSA manual does not include any clear guidelines on how to eliminate errors or guidelines on how to analyse data sets from attribute study to eliminate errors. The article attempts to fill the gap identified in this field. In this article (Part 1), the author outlines the key features of own methodology of analysis data from MSA attribute study. In this article, which is one of the two parts, a research problem has been identified. It was emphasised that the influence on the reduction of the effectiveness of the production process have errors committed by the controllers in the alternative assessment of the product's conformity with the requirements, i.e. errors of I and II type, in particular, II type errors, which should be first eliminated. A traditional approach to research analysis and evaluation of alternative inspection system practised in the MSA manual was presented. Four key assumptions that were adopted for the research goal were presented. Author's procedure for analysis of errors from the attribute study data set is to point to the direction of activities in the field of error analysis, emphasise intolerance to any error, assume to use the root causes analysis and the coaching sessions to reach the root causes of conformity errors. In the second, final article in the series (Part 2), the author illustrates how, step by step, the procedure could be used in practice. It also presents the advantages and limitations of its own procedure.

Słowa kluczowe

EN

MSA; errors in inspection; procedure; key assumptions

PL

MSA; błędy w inspekcji; procedura; kluczowe założenia

• Wydawca: De Gruyter
• Czasopismo: Quality Production Improvement - QPI
• Rocznik: 2020
• Tom: Vol. 2, Iss. 1
• Strony: 150--161
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Knop Krzysztof

• Czestochowa University of Technology, Poland

Bibliografia

• 1.AIAG, 2010. Measurement Systems Analysis (MSA), Reference Manual, 4th Edition.
• 2.Akutsu M., 1995. Ergonomic Design Approach for Improving Industrial Visual Inspection - Study on Structure and Optimum Feeding Rate in Visual Sorting Inspection. The Japanese Journal of Ergonomics, 31, 426-427.
• 3.Bożek M., Kujawińska A., Rogalewicz M., Diering M., Gosciniak P., Hamrol A., 2017. Improvement of catheter quality inspection process, MATEC Web of Conferences, 121.
• 4.Bożek M., Rogalewicz M., 2013. The inefficiency of the final control causes the low efficiency of the manufacturing process, Engineering of Machines, 18, 1, pp. 84-56.
• 5.Cohen J., 1960. A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 10, 3746.
• 6.Diering M., Dyczkowski K., 2016. Assessing the Raters Agreement in the Diagnostic Catheter Tube Connector Production Process Using Novel Fuzzy Similarity Coefficient, Proceedings of the 2016 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 228-232.
• 7.Diering M., Dyczkowski K., Hamrol A. 2015. Estimating the level of conformity of assessments in visual inspection - problems in determining Kappa coefficients [in Polish], Publishing House of the Polish Society for Production Management, pp. 257-268.
• 8.Durivage M. A., 2015. Practical Attribute and Variable Measurement Systems Analysis (MSA). A Guide for Conducting Gage R&R Studies and Test Method Validations, ASQ Quality Press.
• 9.Gangala C., Modi M., Manupati VK., Varela MLR., Machado J., Trojanowska J., 2017. Cycle Time Reduction in Deck Roller Assembly Production Unit with Value Stream Mapping Analysis, Advances in Intelligent Systems and Computing, 571, pp. 509-518.
• 10.Grabowska M., Takala J., 2018. Assessment of Quality Management System Maturity, Lecture Notes in Mechanical Engineering, Advances in Manufacturing, Springer Inter. Pub., Cham, pp. 889-898.
• 11.Grissinger M., 2003. Failure Mode and Effects Analysis Can Help Guide ErrorPrevention Efforts, Pharmacology & Therapeutics, 28, 1, p. 8.
• 12.Gwet K. L., 2014. Handbook of Inter-Rater Reliability: The Definitive Guide to Measuring the Extent of Agreement Among Multiple Raters. 4th Edition, LLC, Gaithersburg, USA.
• 13.Hamrol A., 2015. Strategies and practices of efficient operation. Lean, Six Sigma and others [in Polish], Warsaw, Publisher: PWN.
• 14.Hamrol A., 2000. Process diagnostics as a means of improving the efficiency of quality control, Production Planning & Control, 11, 8, pp. 797-805.
• 15.Hinckley C. M., 2003. Make no mistake - Errors can be controlled, Quality and Safety in Health Care, 12, 5, pp. 359-365.
• 16.Hubbard D. W., 2014. How to Measure Anything: Finding the Value of Intangibles in Business. 3rd Edition, Hoboken, NJ, USA: Wiley.
• 17.Klaput P., Vykydal D., 2018. Effect of the number of non-conforming samples on the Kappa indicator values, MATEC Web of Conferences 183, pp. 1-6.
• 18.Knop K., Borkowski S., 2011. The estimation of alternative control efficiency with the use of the Cohen’s kappa coefficient, Management and Production Engineering Review, 2, 3, pp. 19-27.
• 19.Knop K., Ingaldi M., Smilek-Starczynowska M., 2018. Reduction of Errors of the Conformity Assessment During the Visual Inspection of Electrical Devices, Lecture Notes in Mechanical Engineering, Advances in Manufacturing, Springer Inter. Pub., Cham, pp. 857-867.
• 20.Kujawinska A., Vogt K., Diering M., Rogalewicz M., Waigaonkar S.D., 2018. Organization of Visual Inspection and Its Impact on the Effectiveness of Inspection, Lecture Notes in Mechanical Engineering, Advances in Manufacturing, Springer Inter. Pub., Cham, pp. 899-909.
• 21.Liker J. K., Meier D., 2006. The Toyota Way Fieldbook. A Practical Guide for Implementing Toyota's 4Ps, New York, London: McGraw-Hill.
• 22.Lughofer E., 2011. Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications, Springer Verlag, Berlin Heidelberg.
• 23.Martin M., 2013. Business Efficiency For Dummies, Hoboken, NJ, USA: Wiley.
• 24.Munoz S. R., Bangdiwala S. I., 1997. Interpretation of Kappa and B statistics measures of agreement, Journal of Applied Statistics, 24, 1, pp. 105-112.
• 25.Nowicka-Skowron M., Ulewicz R., 2015. Problems in the implementation of lean concept in the metal industry companies, 25th Anniversary International Conference on Metallurgy and Materials, pp. 1962-1967.
• 26.Reinfuss R., 2012. MBO - a simple and effective management technique for your company [in Polish], Gliwice, Publisher: Onepress, Helion.
• 27.Rosandich R.G., 1997. Intelligent Visual Inspection. Using artificial neural networks, London, UK: Chapman & Hall.
• 28.Sim J., Wright Ch. C., 2005. The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements, Physical Therapy, 85, 3, pp. 257-268.
• 29.Smith B., 1993. Making war on defects: six sigma design, IEEE Spectrum, 30, 9, pp. 43-7. Starzyńska B., Szajkowska K., 2018.
• 30.Diering M., Rocha A., Reis L. P., A Study of Raters Agreement in Quality Inspection with the Participation of Hearing Disabled Employees, Lecture Notes in Mechanical Engineering, Advances in Manufacturing, Springer Inter. Pub., Cham, pp. 881-888.
• 31.Stoklosa P., 2019. Feel the PISMOEA by nose, so elements of the measurement system [in Polish], available on: https://www.pronost.pl/artykuly/46-spc-msametrologia/189-poczuj-pismoea-nosem-czyli-elementy-systemu-pomiarowego, date of access: 27.02.2020.
• 32.Tang W., Hu J., Zhang H., Wu P., He H., 2015. Kappa coefficient: a popular measure of rater agreement, Shanghai Arch Psychiatry, 27, 1, pp. 62-67.
• 33.Tran TA, Luu-Nhan K., Ghabour R., Daroczi M., 2020. The use of Lean Six-Sigma tools in the improvement of a manufacturing company - case study, Production Engineering Archives 2020, 26, 1, 30-35. DOI: 10.30657/pea.2020.26.07.
• 34.Webber L., Wallace M., 2007. Quality Control for Dummies, Hoboken, NJ (USA): Wiley Publishing.
• 35.Wheeler D. J., 1999. Understanding Variation: The Key to Managing Chaos. 2nd Revised Edition, Knoxville, Tennessee, USA: SPC Press.
• 36.VDA 5, 2011. Capability of Measurement Processes, 2nd Edition, Berlin: VDA.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).