Statistical Quality Control in General Manufacturing: Principles, Methods and Contemporary Applications

References

[1] Antony, J. (2006). Six Sigma for service processes. Business Process Management Journal, 12(2), 234–248. https://doi.org/10.1108/14637150610657558

[2] Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: An overview. Quality and Reliability Engineering International, 23(5), 517–543. https://doi.org/10.1002/qre.829

[3] Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for experimenters: Design, innovation and discovery (2nd ed.). Wiley-Interscience.

[4] Deming, W. E. (1986). Out of the crisis. MIT Press.

[5] Dodge, H. F., & Romig, H. G. (1959). Sampling inspection tables: Single and double sampling (2nd ed.). Wiley.

[6] Erto, P., Pallotta, G., & Park, S. H. (2008). An example of data technology product: A control chart for Weibull processes. International Statistical Review, 76(2), 157–166. https://doi.org/10.1111/j.1751-5823.2008.00047.x

[7] Farahani, A., Tohidi, H., & Shoja, A. (2019). An integrated optimization of quality control chart parameters and preventive maintenance using Markov chain. Advances in Production Engineering & Management, 14(1), 5–14. https://doi.org/10.14743/apem2019.1.308

[8] Hoerl, R. W., & Snee, R. D. (2012). Statistical thinking: Improving business performance (2nd ed.). Wiley.

[9] International Organization for Standardization. (2015). ISO 9001:2015 Quality management systems—Requirements. ISO.

[10] Juran, J. M., & De Feo, J. A. (Eds.). (2010). Juran's quality handbook: The complete guide to performance excellence (6th ed.). McGraw-Hill.

[11] Kumar, U. D., Crocker, J., Knezevic, J., & El-Haram, M. (2000). Reliability, maintenance and logistic support: A life cycle approach. Kluwer Academic Publishers.

[12] Montgomery, D. C. (2020). Introduction to statistical quality control (8th ed.). Wiley.

[13] Montgomery, D. C., Runger, G. C., & Hubele, N. F. (2011). Engineering statistics (5th ed.). Wiley.

[14] Mykytka, E. F., & Ramberg, J. S. (1994). Fitting a distribution to data using an alternative to moments. Proceedings of the 1994 Winter Simulation Conference, 734–737.

[15] Nembhard, H. B., & Mastrangelo, C. M. (1998). Integrated process control for startup operations. Journal of Quality Technology, 30(3), 201–211. https://doi.org/10.1080/00224065.1998.11979850

[16] Pande, P. S., Neuman, R. P., & Cavanagh, R. R. (2000). The Six Sigma way: How GE, Motorola and other top companies are honing their performance. McGraw-Hill.

[17] Pyzdek, T., & Keller, P. (2018). The Six Sigma handbook (5th ed.). McGraw-Hill Education.

[18] Quesenberry, C. P. (1997). SPC methods for quality improvement. Wiley.

[19] Ryan, T. P. (2011). Statistical methods for quality improvement (3rd ed.). Wiley.

[20] Shewhart, W. A. (1931). Economic control of quality of manufactured product. Van Nostrand. (Republished by ASQ, 1980)

[21] Snee, R. D. (1990). Statistical thinking and its contribution to total quality. The American Statistician, 44(2), 116–121. https://doi.org/10.1080/00031305.1990.10475703

[22] Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi's quality engineering handbook. Wiley-Interscience.

[23] Woodall, W. H. (2006). The use of control charts in health-care and public-health surveillance. Journal of Quality Technology, 38(2), 89–104. https://doi.org/10.1080/00224065.2006.11918593

[24] Yang, K., & El-Haik, B. S. (2009). Design for Six Sigma: A roadmap for product development (2nd ed.). McGraw-Hill.

[25] Zhang, N. F. (1998). A statistical control chart for stationary process data. Technometrics, 40(1), 24–38. https://doi.org/10.1080/00401706.1998.10485479

@article{1718896,
      author = {Girjesh Kumar},
      title = {Statistical Quality Control in General Manufacturing: Principles, Methods and Contemporary Applications},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {1970-1979},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718896.pdf},
      abstract = {Statistical Quality Control (SQC) represents a cornerstone methodology in modern manufacturing, providing systematic, data-driven frameworks for monitoring, analyzing and improving product quality and process performance. This paper presents a comprehensive examination of SQC as applied to general manufacturing environments, covering foundational statistical principles, the full taxonomy of SQC tools—including descriptive statistics, Statistical Process Control (SPC), acceptance sampling and design of experiments—and their integration into contemporary manufacturing paradigms such as Lean, Six Sigma and Industry 4.0. Drawing on established theoretical frameworks and documented industrial applications, the paper analyzes the effectiveness, limitations and evolving challenges of SQC implementation. Particular attention is given to control chart methodologies, capability indices, measurement system analysis and the role of digital technologies in transforming quality monitoring. Findings indicate that the strategic deployment of SQC tools yields measurable reductions in process variability, defect rates and production costs while simultaneously enhancing customer satisfaction and regulatory compliance. The paper concludes with a synthesis of best practices and directions for future research in smart quality control systems.},
      keywords = {Statistical Quality Control, Statistical Process Control, Control Charts, Process Capability, Acceptance Sampling, Six Sigma, Manufacturing Quality, Industry 4.0},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1718896}
  }