Enhancing Process Stability and Quality Management: A Comprehensive Analysis of Process Capability Indices
DOI:
https://doi.org/10.34021/ve.2023.06.04(5)Keywords:
quality management; stability; process capability indices; Process Capability Index (Cp); Corrected Process Capability Index (Cpk); Process Performance Index (Pp); Process Performance Corrected Index (Ppk); long-term variability; short-term variability; dynamic viscosityAbstract
Process Capability Indices such as Process Capability Index (Cp) and Corrected Process Capability Index (Cpk), along with Process Performance Index (Pp) and Process Performance Corrected Index (Ppk), are most commonly used tools in quality management within manufacturing processes. They determine whether a process is capable of producing products within established tolerance limits, addressing both short-term and long-term variability. Despite widespread use, the analysis of Process Capability Indices often overlooks special variability within processes, which may lead to misleading interpretations of a process's capability, especially when the determination of tolerance limits is inadequately conducted. This article aims to verify the hypothesis that current analyses of Process Capability Indices fail to consider special variability, which could mislead the interpretation of a process's ability to meet its specifications. Statistical analyses were conducted using the Minitab software, based on dynamic viscosity measurements from the production process of solvent-based paint, to explore the implications of special variability on the interpretation of Process Capability Indices. The study revealed that while Process Capability Indices are useful for identifying quality management opportunities, their effectiveness is limited when special variability is present, often resulting in misinterpretations of a process's true capabilities. The findings highlight the need for methodologies that incorporate considerations of all forms of variability to ensure accurate process capability assessments. This gap in traditional analyses can affect the strategic decision-making in quality management, suggesting a critical area for further research and methodological development. The research confirms the need for a revised approach in analyzing Process Capability Indices, advocating for advanced methods that accurately reflect all forms of variability to improve quality management practices. Future research should focus on developing these methodologies to ensure more reliable and effective use of Process Capability Indices in quality management.
Downloads
References
Anguo, X. (2004). The Conception of Quality in Marketing. Journal of Gansu Radio & Tv University, 4, 23-26.
Meng, F., & Yang, J. (2023). Process capability analysis of Taguchi index Cpm based on generalized p‐value. Quality and Reliability Engineering International, 39(6), 2311-2329.
Singh, G., & Verma, A. (2017). A Brief Review on injection moulding manufacturing process. Materials Today. Proceedings, 4, 1423-1433. https://doi.org/10.1016/J.MATPR.2017.01.164.
Zonnenshain, A., & Kenett, R. S. (2020). Quality 4.0—the challenging future of quality engineering. Quality Engineering, 32(4), 614-626.
Gubreley, S., Gupta, A., & Upadhyay, S. K. (2024). Bayes Estimation of Capability Index Using Three-Parameter Weibull Distribution. Reliability: Theory & Applications, 19(1 (77)), 523-530.
Haievskyi, O., Kvasnytskyi, V., Haievskyi, V., Szymura, M., & Sviridova, L. (2023). Refinement of the process capability index calculation. Journal of Engineering Sciences (Ukraine), 10(2), B8–B15, https://doi.org/10.21272/jes.2023.10(2).b2.
Daniels, L., Edgar, B., Burdick, R., & Hubele, N. (2004). Using Confidence Intervals to Compare Process Capability Indices. Quality Engineering, 17, 23-32. https://doi.org/10.1081/QEN-200028666.
Steiner, S., Abraham, B., & MacKay, J. (1997). Understanding process capability indices. Waterloo, Ontario.
Saha, M. (2022). Applications of a new process capability index to electronic industries. Communications in Statistics: Case Studies, Data Analysis and Applications, 8(4), 574–587. https://doi.org/10.1080/23737484.2022.2107962.
Ahmed, S. E. (2005). Assessing the process capability index for non-normal processes. Journal of Statistical Planning and Inference, 129(1-2), 195-206.
Sediyama, J., Alassane, D., Silva, R., & Júnior, J. (2023). Consistencies of the capability indices based on the normal probability distribution. Gestão & Produção, 30, Article e5722. https://doi.org/10.1590/1806-9649-2022v29e5722.
Kumar, S., Dey, S., & Saha, M. (2019). Comparison between two generalized process capability indices for Burr XII distribution using bootstrap confidence intervals. Life Cycle Reliability and Safety Engineering, 8, 347-355. https://doi.org/10.1007/s41872-019-00092-1.
Mahapatra, A. P. K., Song, J., Shao, Z., Dong, T., Gong, Z., Paul, B., & Padhy, I. (2020). Concept of process capability indices as a tool for process performance measures and its pharmaceutical application. Journal of Drug Delivery and Therapeutics, 10(5), 333-344.
Nawaz, K. (2011). Quality Management, Quality of Management and Quality Culture. Biblioteca RegieLive.
Sofijanova, E. (2020). Understanding and quality control through a new product. Journal of Economics, 5, 1-10. https://doi.org/10.46763/joe205.2001s.
Asness, C., Frazzini, A., & Pedersen, L. (2018). Quality Minus Junk. Review of Accounting Studies, 24, 34-112. https://doi.org/10.1007/s11142-018-9470-2.
Pramudya, M. R., Sudirman, S., & Rosnawati, R. (2021). Effect of Service Quality on Patient Satisfaction at Mabelopura Health Center. International Journal of Health, Economics, and Social Sciences (IJHESS), 3(2), 140-149. https://doi.org/10.56338/ijhess.v3i2.1476.
Abbas, R., Sadiq, S., Gul, H., & Khan, S. (2022). Quality indicators of Physical therapy practice in Pakistan. Pakistan Journal of Medical and Health Sciences, 16(5). https://doi.org/10.53350/pjmhs22165000.
Gunasekaran, A., Subramanian, N., & Ngai, W. (2019). Quality management in the 21st century enterprises: Research pathway towards Industry 4.0. International Journal of Production Economics, 207, 125-129. https://doi.org/10.1016/J.IJPE.2018.09.005.
Ali, S., Shin, W., & Song, H. (2022). Blockchain-Enabled Open Quality System for Smart Manufacturing: Applications and Challenges. Sustainability, 14(18), 11677. https://doi.org/10.3390/su141811677.
Saxena, M. M., & Srinivas Rao, K. V. N. (2019). Quality management, total quality management, and six sigma. International Journal of Scientific and Technology Research, 8(12), 394-399.
Aslam, M., Rao, G., Ahmad, L., & Jun, C. (2020). A new control chart using GINI CPK. Communications in Statistics - Theory and Methods, 51, 197 - 211. https://doi.org/10.1080/03610926.2020.1746971.
Saha, M., Dey, S., Yadav, A., & Kumar, S. (2019). Classical and Bayesian inference of Cpy for generalized Lindley distributed quality characteristic. Quality and Reliability Engineering International, 35, 2593 - 2611. https://doi.org/10.1002/qre.2544.
Borgoni, R., & Zappa, D. (2020). Model‐based process capability indices: The dry‐etching semiconductor case study. Quality and Reliability Engineering International, 36(7), 2309-2321.
Schrock, E., & Lefevre, H. (2020). Process Capability. The Good and the Bad News About Quality. https://doi.org/10.1201/9781003065937-20.
Chowdhury, M. R. (2013). Process capability analysis in pharmaceutical production. International Journal of Pharmaceutical and Life Sciences, 2(2), 85-89.
Godina, R., Pimentel, C., Silva, F. J. G., & Matias, J. C. (2018). Improvement of the statistical process control certainty in an automotive manufacturing unit. Procedia Manufacturing, 17, 729-736.
Dobránsky, J., Pollák, M., & Doboš, Z. (2019). Assessment of production process capability in the serial production of components for the automotive industry. Management systems in production engineering, 27(4), 255-258.
Ostadi, B., Taghizadeh Yazdi, M., & Mohammadi Balani, A. (2021). Process capability studies in an automated flexible assembly process: A case study in an automotive industry. Iranian Journal of Management Studies, 14(1), 1-37.
González Álvarez, R., Barrera García, A., Guerra Morffi, A. B., & Medina Mendieta, J. F. (2022). Stability assessment and process capability analysis in a food pasta company. Visión de futuro, 26(1), 231-251.
Yogi, K. (2018). Assessment of Process Capability: the case of Soft Drinks Processing Unit. IOP Conference Series: Materials Science and Engineering, 330. https://doi.org/10.1088/1757-899X/330/1/012064.
Chen, M. S., Wu, M. H., & Lin, C. M. (2014). Application of indices Cp and Cpk to improve quality control capability in clinical biochemistry laboratories. Chin. J. Physio, 57(2), 63-68.
Pawar, H. U., Bagga, S. K., & Dubey, D. K. (2021). Investigation of production parameters for process capability analysis: A case study. Materials Today: Proceedings, 43, 196-202.
Saied, E. K., Besees, A. Y., Wazeer, A., & Abd-Eltwab, A. A. (2020). Process Performance Analysis In Cement Industry. International Journal of Scientific & Technology Research. 9(06):844-860.
Szczepańska-Woszczyna, K., Vysochyna, A., & Kwilinski, A. (2024). Public Health Efficiency and Country Competitiveness: Empirical Study in Pre-Pandemic and Pandemic Periods. Forum Scientiae Oeconomia, 12(1), 151–166. https://doi.org/10.23762/FSO_VOL12_NO1_8.
Chen, Y., Lyulyov, O., Pimonenko, T., & Kwilinski, A. (2023). Green development of the country: Role of macroeconomic stability. Energy & Environment, 0. https://doi.org/10.1177/0958305X231151679
Hussain, H.I., Haseeb, M., Kamarudin, F., Dacko-Pikiewicz, Z., & Szczepańska-Woszczyna, K. (2021). The role of globalization, economic growth and natural resources on the ecological footprint in Thailand: Evidence from nonlinear causal estimations. Processes, 9, 1103. https://doi.org/10.3390/pr9071103.
Dacko-Pikiewicz, Z. (2019). Building a family business brand in the context of the concept of stakeholder-oriented value. Forum Scientiae Oeconomia, 7, 37-51. https://doi.org/10.23762/FSO_VOL7_NO2_3.
Kwilinski, A. (2019). Implementation of Blockchain Technology in Accounting Sphere. Academy of Accounting and Financial Studies Journal, 23, 1-6.
Kwilinski, A., Lyulyov, O., & Pimonenko, T. (2023). Unlocking Sustainable Value through Digital Transformation: An Examination of ESG Performance. Information, 14, 444. https://doi.org/10.3390/info14080444.
Kwilinski, A., Lyulyov, O., & Pimonenko, T. (2023). Spillover Effects of Green Finance on Attaining Sustainable Development: Spatial Durbin Model. Computation, 11, 199. https://doi.org/10.3390/computation11100199.
Kwilinski, A., Lyulyov, O., & Pimonenko, T. (2024). Reducing Transport Sector CO2 Emissions Patterns: Environmental Technologies and Renewable Energy. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100217. https://doi.org/10.1016/j.joitmc.2024.100217.
Kwilinski, A. (2024). Understanding the Nonlinear Effect of Digital Technology Development on CO2 Reduction. Sustainable Development, 1-15. https://doi.org/10.1002/sd.2964.
Sulich, A., & Zema, T. (2023). The Green Energy Transition in Germany: A Bibliometric Study. Forum Scientiae Oeconomia, 11(2), 175–195. https://doi.org/10.23762/FSO_VOL11_NO2_9.
Mesagan, E. P., & Olunkwa , N. C. (2020). Energy Consumption, Capital Investment and Environmental Degradation: The African Experience. Forum Scientiae Oeconomia, 8(1), 5–16. https://doi.org/10.23762/FSO_VOL8_NO1_1.
Kwilinski, A. (2023). E-Commerce and Sustainable Development in the European Union: A Comprehensive Analysis of SDG2, SDG12, and SDG13. Forum Scientiae Oeconomia, 11, 87-107. https://doi.org/10.23762/FSO_VOL11_NO3_5.
Dabrowski, D., Dabrowski, J., Zamasz, K., & Lis, M. (2023). Driving the Image of an Electricity Supplier through Marketing Activities. Forum Scientiae Oeconomia, 11(4), 83–98. https://doi.org/10.23762/FSO_VOL11_NO4_4.
Kwilinski, A., Rebilas, R., Lazarenko, D., Stezhko, N., & Dzwigol, H. (2023). The Impact of the COVID-19 Pandemic on the Evolution of Investment Markets in Central and Eastern Europe. Forum Scientiae Oeconomia, 11(4), 157-186. https://doi.org/10.23762/FSO_VOL11_NO4_8.
Kwilinski, A., Lyulyov, O., & Pimonenko, T. (2023). The Role of Country's Green Brand and Digitalization in Enhancing Environmental, Social, and Governance Performance. Economics and Environment, 87(4), 613. https://doi.org/10.34659/eis.2023.87.4.613.
Wróblewski, Ł., & Lis, M. (2021). Marketing Mix of Cultural Institutions on the Cross-Border Market of a City Divided by a Border – An Analysis and Evaluation. Polish Journal of Management Studies, 23(2), 555-572. https://doi.org/10.17512/pjms.2021.23.2.33.
Dacko-Pikiewicz, Z. (2019). The Selected Aspects of Strategic Management in the City Divided by the Border in the Context of the Development of the Cross-Border Market of Cultural Services. Polish Journal of Management Studies, 19(1), 130-144. https://doi.org/10.17512/pjms.2019.19.1.10.
Minitab 21.4,1 Statistical Software. (2023). https://www.minitab.com/content/dam/www/en/uploadedfiles/documents/read me/MinitabReadMe_en.pdf
Alagić, I. (2021, May). Minitab Application as Statistical Tool for Lean Six Sigma. International Conference on New Technologies, Development and Applications (pp. 422-430). Cham: Springer International Publishing.
Safdar, S., Ahmed, E., Jilani, T., & Maqsood, A. (2019). Process Capability Indices under Non-Normality Conditions using Johnson Systems. International Journal of Advanced Computer Science and Applications, 10(3). https://doi.org/10.14569/ijacsa.2019.0100338.
Kenyon, G. N., & Sale, E.A. (2010). Calculating Process Capability Index with Limited Information. Conference: POMS.
Mahapatra A. P. K., Song, J., Shao, Z., Dong, T., Gong, Z., Paul, B., & Padhy, I. (2020). Concept of process capability indices as a tool for process performance measures and its pharmaceutical application. Journal of Drug Delivery and Therapeutics, 10(5), 333- 344. http://dx.doi.org/10.22270/jddt.v10i5.4288.
Table of Control Chart Constants. https://www.bessegato.com.br/UFJF/resources/table_of_control_chart_constants_ old.pdf
Chen, K., Wang, K., & Chang, T. (2017). A novel approach to deriving the lower confidence limit of indices Cpu, Cpl, and Cpk in assessing process capability. International Journal of Production Research, 55, 4963-4981. https://doi.org/10.1080/00207543.2017.1282644.
Balamurali, S., & Mahalingam, U. (2019). Determination of an efficient variables sampling system based on the Taguchi process capability index. Journal of the Operational Research Society, 70, 420-432. https://doi.org/10.1080/01605682.2018.1441637.
Balamurali, S., & Usha, M. (2017). Developing and designing of an efficient variables sampling system based on the process capability index. Journal of Statistical Computation and Simulation, 87, 1401-1415. https://doi.org/10.1080/00949655.2016.1267735.
Alotaibi, R., Dey, S., & Saha, M. (2022). Estimation and Confidence Intervals of a New PCI for Logistic-Exponential Process Distribution. Journal of Mathematics, 2022.
Qiu, P. (2018). Some perspectives on nonparametric statistical process control. Journal of Quality Technology, 50, 49 - 65. https://doi.org/10.1080/00224065.2018.1404315.
Yang, Y., Li, D., & Qi, Y. (2018). An Approach to Non-normal Process Capability Analysis Using Johnson Transformation. 2018 IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE), 495-498. https://doi.org/10.1109/CCSSE.2018.8724679.
Yang, Y., & Zhu, H. (2018). A Study of Non-Normal Process Capability Analysis Based on Box-Cox Transformation. 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA), 240-243. https://doi.org/10.1109/ICCIA.2018.00053.
Chen, P., Wang, B., & Ye, Z. (2019). Yield-based process capability indices for nonnormal continuous data. Journal of Quality Technology, 51, 171 - 180. https://doi.org/10.1080/00224065.2019.1571342.
Senvar, O., & Sennaroglu, B. (2016). Comparing performances of clements, box-cox, Johnson methods with weibull distributions for assessing process capability. Journal of Industrial Engineering and Management, 9(3), 634-656.
Anthony, A., Marco, R., & Aldo, C. (2020). The Box-Cox Transformation: Review and Extensions. Statistical Science, 36(2), 239 - 255.
Hamasaki, T., & Kim, S. Y. (2007). Box and Cox power-transformation to confined and censored nonnormal responses in regression. Computational statistics & data analysis, 51(8), 3788-3799.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
