There are many situations in which the control of two or more related quality characteristics is necessary. Monitoring these quality characteristics independently can lead to confounding results. Distortion in the process-monitoring procedure increases as the number of quality characteristics increases. Industrial processes with two or more correlated quality characteristics, require the use of Multivariate Statistical Process Control and Multivariate Capability Analysis. This study employed Multivariate Statistical Quality Control techniques to analyze the soybean meal production at Hule and Sons Nigeria Limited. Sample data of the production process from the Department of Quality Assurance of the company was obtained. Quality characteristic of interest were percentage of crude residual oil in the meal after extraction using solvent extraction method; percentage moisture content; free fatty acid (FFA); amount of phosphorus and the flash point. The study investigates the stability and capability of the multivariate process using advanced statistical process control techniques. Hoteling’s T2 square control chart, applied on the transformed data, indicated statistical control with no out-of-control signals detected as all the points fall below the upper control limit of 13.19. In contrast, Robust principal component analysis (ROBPCA) identified one observation exceeding the 95% control limit which suggests the presence of an assignable variation which the conventional method may overlook. Furthermore, the orthogonal distance (OD) chart revealed two samples outside the 95% control threshold, although all the OD values were almost all zeros, indicating potential collinearity or numerical issues in orthogonal projections. The process capability indices were MC_p= 0.3864; MC_pk=0.3451; MC_pm=0.0086 and MC_pmk=0.0076, signifying a substantial deviation from the desired performance standard. These results highlight the limitations of relying solely on traditional control charts and emphaze the importance of incorporating robust multivariate techniques for more sensitive and reliable process monitoring in high-dimensional data.

Akinola,, A. A. (2009). Total quality management and business success. Ibadan: Greenline Publishers. Awariefe, C., & Ogbereyivwe, O. (2024).Time series modelling and forecasting foreign direct investment using linear and nonlinear models: The case of Nigeria. Journal of Basics and Applied Sciences Research, 2(1), 46–53. https://doi.org/10.33003/jobasr-2024-v2i1-33 Biegel, T., Bauer, M., & Riedl, M. (2022). Fault detection in multivariate processes using deep autoencoder-based monitoring. IEEE Transactions on Industrial Informatics, 18(4), 2765–2774. https://doi.org/10.1109/TII.2021.3075642 Chang, S. I., & Ghafarias, P. (2025). Artificial intelligence and machine learning in statistical process monitoring: A comprehensive review. Computers & Industrial Engineering, [Volume pending]. Grigg, N. P. (1997). Monitoring and controlling quality in food processing operations. Total Quality Management, 8(6), 365–376. https://doi.org/10.1080/0954412979560 Henze, N., & Zirkler, B. (1990). A class of invariant consistent tests for multivariate normality. Communications in Statistics - Theory and Methods, 19(10), 3595–3617. https://doi.org/10.1080/03610929008830400 Jackson, J. E. (1991). A user's guide to principal components. New York: Wiley. Jensen, W. A., Jones-Farmer, L. A., Champ, C. W., & Woodall, W. H. (2006). Effects of parameter estimation on control chart performance: A review. Journal of Quality Technology, 38(4), 349–364. Marilyn, M. H., & Robert, D. H. (2007). Statistical thinking: Improving business performance (2nd ed.). Duxbury Press. Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons. Obadara, E. O., & Alaka, A. A. (2013). Accreditation and quality assurance in Nigerian universities. Journal of Education and Practice, 4(8), 124–130. Raza, S. A., & Payam, S. (2009). Statistical process control as a quality control tool. Journal of Quality and Technology Management, 5(1), 21–34. Ryan, T. P. (1989). Statistical methods for quality improvement. New York: Wiley. Shewhart, W. A. (1931). Economic control of quality of manufactured product. Van Nostrand. Smith, G. M. (2004). Statistical process control and quality improvement (5th ed.). Pearson Prentice Hall. Smith, S. (2004). Control charts and process monitoring. Journal of Quality Technology, 36(1), 2–12. Sullivan, J. H., & Woodall, W. H. (1996). A comparison of multivariate control charts for individual observations. Journal of Quality Technology, 28(4), 398–408. Tracy, N. D., Young, J. C., & Mason, R. L. (1992). Multivariate control charts for individual observations. Journal of Quality Technology, 24(2), 88–95. Wang, F. K., & Chen, K. S. (1998). Process capability indices for multivariate processes. Computers & Industrial Engineering, 35(1–2), 45–48. Wang, F. K. (2005). An extension of process capability index to multivariate processes. International Journal of Production Research, 43(12), 2551–2560. Xekalaki, E., & Perakis, K. (2002). Multivariate capability indices: A review. Quality and Reliability Engineering International, 18(2), 111–118.