Research Article

Impact analysis of critical success factors on the benefits from statistical process control implementation

Fabiano Rodrigues Soriano; Pedro Carlos Oprime; Fabiane Letícia Lizarelli

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The Statistical Process Control - SPC is a set of statistical techniques focused on process control, monitoring and analyzing variation causes in the quality characteristics and/or in the parameters used to control and process improvements. Implementing SPC in organizations is a complex task. The reasons for its failure are related to organizational or social factors such as lack of top management commitment and little understanding about its potential benefits. Other aspects concern technical factors such as lack of training on and understanding about the statistical techniques. The main aim of the present article is to understand the interrelations between conditioning factors associated with top management commitment (Support), SPC Training and Application, as well as to understand the relationships between these factors and the benefits associated with the implementation of the program. The Partial Least Squares Structural Equation Modeling (PLS-SEM) was used in the analysis since the main goal is to establish the causal relations. A cross-section survey was used as research method to collect information of samples from Brazilian auto-parts companies, which were selected according to guides from the auto-parts industry associations. A total of 170 companies were contacted by e-mail and by phone in order to be invited to participate in the survey. However, just 93 industries agreed on participating, and only 43 answered the questionnaire. The results showed that the senior management support considerably affects the way companies develop their training programs. In turn, these trainings affect the way companies apply the techniques. Thus, it will reflect on the benefits gotten from implementing the program. It was observed that the managerial and technical aspects are closely connected to each other and that they are represented by the ratio between top management and training support. The technical aspects observed through SPC application directly affect the benefits from the program.


Quality control, Quality improvement, SPC, Structural Equations Model


Ahmed, A. F. T. A. B., Khan, I., & Ghosh, M. K. (2012). SPC implementation in pharmaceutical industry for material flow management. Interscience Management Review, 2(3), 57-60.

Antony, J., & Taner, T. (2003). A conceptual framework for the effective implementation of statistical process control. Business Process Management Journal, 9(4), 473-489.

Antony, J., Balbontin, A., & Taner, T. (2000). Key ingredients for the effective implementation of statistical process control. International Journal of Productivity and Performance Management, 49(6), 242-247.

Basilevsky, A. T. (1994). Statistical factor analysis and related methods: theory and applications. New York: John Wiley & Sons.

Bhote, K. R. (1987). SPC made easier, simpler, more statistically powerful. Target, 3(3), 12-20.

Biolchini, J. C. A., Mian, P. G., Natali, A. C. C., Conte, T. U., & Travassos, G. H. (2007). Scientific research ontology to support systematic review in software engineering. Advanced Engineering Informatics, 21(2), 133-151.

Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of Systems and Software, 80(4), 571-583.

Burr, I. W. (1976). Statistical quality control methods (16th ed.). New York: CRC Press.

Caulcutt, R. (1996). Statistical process control (SPC). Assembly Automation, 16(4), 10-14.

Chen, S. H., Yang, C. C., Lin, W. T., & Yeh, T. M. (2008). Performance evaluation for introducing statistical process control to the liquid crystal display industry. International Journal of Production Economics, 111(1), 80-92.

Cochran, W. G. (1977). Sampling techniques. New York: John Wiley and Sons.

Does, R. J. M. M., Trip, A., & Schippers, W. A. J. (1997). A framework for implementation of statistical process control. International Journal of Quality Science, 2(3), 181-198.

Elg, M., Olsson, J., & Dahlgaard, J. (2008). Implementing statistical process control: an organizational perspective. International Journal of Quality & Reliability Management, 25(6), 545-560.

Forza, C. (2002). Survey research in operations management: a process-based perspective. International Journal of Operations & Production Management, 22(2), 152-194.

Fryer, K. J., Antony, J., & Douglas, A. (2007). Critical success factors of continuous improvement in the public sector. The TQM Magazine, 9(5), 497-517.

Garcia, J. G. (1995). Análise de la información mercadológica através de la estatística multivariante. Ciudad de Mexico: Alambra Mexicana.

Gordon, M. E., Philpot, J. W., Bounds, G. M., & Long, W. S. (1994). Factors associated with the success of the implementation of statistical process control. The Journal of High Technology Management Research, 5(1), 101-121.

Grigg, N., & Walls, L. (2007a). Developing statistical thinking for performance improvement in the food industry. International Journal of Quality & Reliability Management, 24(4), 347-369.

Grigg, N., & Walls, L. (2007b). The role of control charts in promoting organisational learning: New perspectives from a food industry study. The TQM Magazine, 19(1), 37-49.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2009). Análise multivariada de dados (6. ed.). Porto Alegre: Bookman Editora.

Hair, J. F., Hult, T. M., Ringle, C. M., & Sarstedt, M. A. (2014). Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: SAGE.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.

Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Planning, 45(5), 320-340.

Hayes, G. E., & Romig, H. G. (1977). Modern quality control. Los Angeles: Bruce.

Hoerl, R. W., & Snee, R. D. (2010). Closing the gap. Quality Progress, 43(5), 52-53.

Jobson, J. (1992). Applied multivariate data analysis: categorical and multivariate methods. New York: Springer Science & Business Media.

Judi, H. M., Jenal, R., & Genasan, D. (2009). Some experiences of quality control implementation in Malaysian companies. European Journal of Scientific Research, 27(1), 34-45.

Laohavichien, T., Fredendall, L. D., & Cantrell, R. S. (2011). Leadership and quality management practices in Thailand. International Journal of Operations & Production Management, 31(10), 1048-1070.

Lee, H. J. (2004). The role of competence-based trust and organizational identification in continuous improvement. Journal of Managerial Psychology, 19(6), 623-639.

Lim, S. A. H., & Antony, J. (2013). A conceptual readiness framework for statistical process control (SPC) deployment. In Proceedimgs of the Industrial Engineering and Engineering Management (IEEM), 2013 IEEE International Conference (pp. 300-304), Bangkok.

Lim, S. A. H., Antony, J., & Albliwi, S. (2014). Statistical Process Control (SPC) in the food industry: a systematic review and future research agenda. Trends in Food Science & Technology, 37(2), 137-151.

Mahanti, R., & Evans, J. R. (2012). Critical success factors for implementing statistical process control in the software industry. Benchmarking: An International Journal, 19(3), 374-394.

Marin-Garcia, J. A., Val, M. P., & Martin, T. B. (2008). Longitudinal study of the results of continuous improvement in an industrial company. Team Performance Management, 14(1/2), 56-69.

Mason, B., & Antony, J. (2000). Statistical process control: an essential ingredient for improving service and manufacuring quality. Managing Service Quality: An International Journal, 10(4), 233-238.

Montgomery, D. C. (2010). A modern framework for achieving enterprise excellence. International Journal of Lean Six Sigma, 1(1), 56-65.

Montgomery, D. C. (2014). Introdução ao controle estatístico da qualidade (4. ed.) Rio de Janeiro: Grupo Gen-LTC.

Oakland, J. S. (2008). Statistical process control (5th ed.). Oxford: Routledge.

Phyanthamilkumaran, S. Z., & Fernando, Y. (2008). The role of cultural change in the relationship between critical factors with the success of Statistical Process Control (SPC) projects. Proceedings of Annual London Conference on Money, Economy and Management, 3(4), 1-11.

Putri, T. N., & Yusof, S. R. M. (2008). Critical success factors for implementing quality engineering (QE) in Malaysian’s and Indonesian’s automotive industries: a proposed model. International Journal of Automotive Industry and Management, (2), 1-15.

Rantamäki, J., Tiainen, E. L., & Kässi, T. (2013). A case of implementing SPC in a pulp mill. International Journal of Lean Six Sigma, 4(3), 321-337.

Rohani, J. M., Yusof, S. M., & Mohamad, I. (2009). The relationship between statistical process control critical success factors and performance: A structural equation modeling approach. In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1352-1356), Hong Kong.

Rohani, J. M., Yusof, S. R. M., & Mohamad, I. (2010). The development of a survey instrument for measuring a relationship between statistical process control success factors and performance. Jurnal Mekanikal, (30), 1-16.

Rungasamy, S., Antony, J., & Ghosh, S. (2002). Critical success factors for SPC implementation in UK small and medium enterprises: some key findings from a survey. The TQM Magazine, 14(4), 217-224.

Rungtusanatham, M., Anderson, J. C., & Dooley, K. J. (1999). Towards measuring the “SPC implementation/practice” construct: Some evidence of measurement quality. International Journal of Quality & Reliability Management, 16(4), 301-329.

Sharma, R., & Kharub, M. (2014). Attaining competitive positioning through SPC: an experimental investigation from SME. Measuring Business Excellence, 18(4), 86-103.

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207-222.

Woodall, W. H. (2000). Controversies and contradictions in statistical process control. Journal of Quality Technology, 32(4), 341-350.

Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376-387.

Xie, M., & Goh, T. N. (1999). Statistical techniques for quality. The TQM Magazine, 11(4), 238-242.

Yamamoto, Y., & Bellgran, M. (2013). Four types of manufacturing process innovation and their managerial concerns. Procedia CIRP, 7, 479-484.

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