Research Article Open Access

MODEL BUILDING FOR AUTOCORRELATED PROCESS CONTROL: AN INDUSTRIAL EXPERIENCE

M. A. Djauhari1, S. L. Lee1 and Z. Ismail1
  • 1 Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia

Abstract

We show that many time series data are governed by Geometric Brownian Motion (GBM) law. This motivates us to propose a procedure of time series model building for autocorrelated process control that might consist of two steps. First, we test whether the process data are governed by GBM law. If it is affirmative, the appropriate model is directly given by the properties of that law. Otherwise, we go to the standard practice at the second step where the best model is constructed by using ARIMA method. An industrial example will be reported to demonstrate the advantages of that procedure. In that example, a comparison study with ARIMA method will be reported to illustrate the effectiveness and efficiency of the GBM-based model building.

American Journal of Applied Sciences
Volume 11 No. 6, 2014, 888-898

DOI: https://doi.org/10.3844/ajassp.2014.888.898

Submitted On: 12 January 2014 Published On: 29 March 2014

How to Cite: Djauhari, M. A., Lee, S. L. & Ismail, Z. (2014). MODEL BUILDING FOR AUTOCORRELATED PROCESS CONTROL: AN INDUSTRIAL EXPERIENCE. American Journal of Applied Sciences, 11(6), 888-898. https://doi.org/10.3844/ajassp.2014.888.898

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Keywords

  • Box-Jenkins Method
  • Control Charts
  • Log Normal Distribution
  • Statistical Process Control
  • Stochastic Process