Monitoring Process Variability and Root Cause Analysis in Paper Box Production

  • Authors

    • Rohayu Mohd Salleh
    • Ng Jin Chuan
    • Sabariah Saharan
    https://doi.org/10.14419/ijet.v7i4.30.22377

    Received date: November 29, 2018

    Accepted date: November 29, 2018

    Published date: November 30, 2018

  • Hotelling’s T2, Multivariate Statistical Process Control, MYT decomposition

  • Abstract

    In this paper, monitoring procedure for process variability in multivariate setting based on individual observations which is a combination of (i) Hotelling’s T2 control chart in detecting out of control signal and (ii) implementation of Mason, Young and Tracy (MYT) decomposition and structure analysis technique for root cause analysis is introduced. The advantages of this procedure will be shown by using the case of a paper box production process in one of the Malaysian manufacturing companies. The successful application of this multivariate approach could act as a stimulant for most industries to imitate in process monitoring. Moreover, the computation efficiency in root cause analysis enables quality’s multiple characteristics to be monitored simultaneously. Based on the findings, the core issue that needs to be a matter of concern by the management team is the closure tap of the box. This process variation should be solved immediately to avoid the products’ quality from further deteriorating.

  • References

    1. Department of Statistics Malaysia, Monthly Manufacturing Statis-tics Malaysia, December 2016, (2017).
    2. Deloitte, 2016 Global Manufacturing Competitiveness Index, (2017)
    3. Chi SC (1982), Accelerated Industrialization and Employment Op-portunities in Malaysia. Geoforum, 13, 11–18.
    4. Rasiah R (1995), Foreign capital and industrialization in Malaysia. Germany: Springer.
    5. Jomo KS (2013), Industrializing Malaysia: Policy, Performance, Prospects. New York: Routledge.
    6. Feltz CJ & Shiau JH (2001), Statistical Process Monitoring Using an Empirical Bayes Multivariate Process Control Chart. Quality and Reliability Engineering International 17(2), 119–124.
    7. Singh R & Gilbreath G (2002), A Real-Time Information System for Multivariate Statistical Process Control. International Journal of Production Economics 75(1), 161–172.
    8. Kourti T (2005), Application of Latent Variable Methods to Pro-cess Control and Multivariate Statistical Process Control in Indus-try. International Journal of Adaptive Control and Signal Pro-cessing 19(4), 213–246.
    9. Goldoost M, Ebrahimi J & Fallah S (2015), Identification of the Out-of Control Variables in the Process of Producing Needle Valves in Gas Industries. International Journal of Advanced Re-search, 3(9), 224–230.
    10. Rahman MN, Zain RM, Nopiah ZM, Ghani JA, Deros BM, Mo-hamad N & Ismail AR (2009), The Implementation of SPC in Ma-laysian Manufacturing Companies. European Journal of Scientific Research, 26(3), 453–464.
    11. Montgomery DC (2008). Statistical Quality Control: A Modern Introduction. 6th edition. USA: John Wiley & Sons.
    12. Kirdar AO, Green KD & Rathore AS (2008), Application of Multi-variate Data Analysis for Identification Resolution of a Root Cause for a Bioprocessing Application. Biotechnology Progress, 24(3), 720–726.
    13. Adepoju A, Yahaya A & Asiribo OE (2015), Hotelling T2 Decom-position: Approach for Five Process Characteristics in a Multivari-ate Statistical Process Control. American Journal of Theoretical and Applied Statistics, 4(6), 432–437.
    14. Adams BM, Woodall WH, Lowry CA (1992), The Use (and Mis-use) of False Alarm Probabilities in Control Chart Design. In: Lenz HJ., Wetherill GB, Wilrich PT (eds) Frontiers in Statistical Quality Control 4, Physica, Heidelberg.
    15. Doggett AM (2005), Root Cause Analysis: A Framework for Tool Selection. The Quality Management Journal, 12(4), 34–45.
    16. Abubakar SS, Asiribo OE, Yahaya A & Dikko HG (2014), Detect-ing Assignable Signals via Decomposition of T2 Statistics. Interna-tional Journal of Innovative Research in Science, Engineering and Technology, 3(3), 10535–10543.
    17. Sullivan JH & Woodall WH (1996), A Comparison of Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 28(4), 398–408.
    18. Mason RL & Young JC (1999), Improving the sensitivity of T2 sta-tistics in multivariate process control. Journal of Quality Technolo-gy, 31(2), 155–165.
    19. Tong LI, Wang CH & Huang CL (2005), Monitoring defects in IC fabrication using Hotelling’s T2 control chart. IEE Trans. Semicon-ductor Manufacturing, 18(1), 140–148.
    20. Rooney JJ & Heuvel LNV (2004), Root Cause Analysis for Begin-ners. Quality Progress, 37(7), 45–53.
    21. Preuss PG (2003), School Leader's Guide to Root Cause Analysis: Using Data to Dissolve Problems. New York: Routledge.
    22. Carroll JS, Rudolph JW & Hatakenaka S (2002), Lessons Learned from Non-Medical Industries: Root Cause Analysis as Culture Change at a Chemical Plant. Quality and Safety in Health Care, 11(3), 266–269.
    23. Ward CJ, Frey N & Fisher D (2012), Root Cause Analysis. Instruc-tional Leader – Principal Leadership, 13(20), 59–61.
    24. Mason RL, Tracy ND & Young JC (1995), Decomposition of T2 for multivariate control chart interpretation. Journal of Quality Technology, 27, 99–108.
    25. Agog NS, Dikko HG & Asiribo OE (2014) Decomposing Ho-telling’s T2 Statistic Using Four Variables. International Journal of Innovative Research in Science, Engineering and Technology, 3(4), 11449 – 11454.
    26. Yahaya A & Adepoju AA (2016), Monitoring and Identification of Influential Variables in Industrial Design Process. Nigerian Journal of Scientific Research, 15(1), 164–172.
    27. Hair JF, Black WC, Babin BJ, Anderson RE & Tatham RL (2006), Multivariate Data Analysis. 6th edition. New Jersey: Pearson Pren-tice Hall.
    28. Koch I (2014), Analysis of Multivariate and High-Dimensional Da-ta. New York: Cambridge University Press.
    29. Capilla C (2009), Application and Simulation Study of the Ho-telling’s T2 Control Chart to Monitor a Wastewater Treatment Pro-cess. Environmental Engineering Science, 26(2), 333–342.
    30. Mason RL & Young JC (2002), Multivariate Statistical Process Control with Industrial Application. USA: ASA-SIAM.
    31. Bentler PM (1983), Simultaneous Equation Systems as Moment Structure Models: With an Introduction to Latent Variable Models. Journal of Econometrics, 22(1), 13–42.
    32. Cudeck R (1989), Analysis of correlation matrices using covariance structure models. Phychological Bulletin, 105(2), 317 – 327.
    33. Salleh RM & Djauhari MA (2012), Female Shrouded Connector Production Process Variability Monitoring: A Robust Approach. ASM Science Journal, 6(1), 1 – 13.

  • Downloads

  • How to Cite

    Salleh, R. M., Chuan, N. J., & Saharan, S. (2018). Monitoring Process Variability and Root Cause Analysis in Paper Box Production. International Journal of Engineering and Technology, 7(4.30), 492-497. https://doi.org/10.14419/ijet.v7i4.30.22377