Authors: Azadeh Adibi; Douglas C. Montgomery; Connie M. Borror
Addresses: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ 85281, USA ' School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ 85281, USA ' School of Mathematical and Natural Sciences, Arizona State University at the West Campus, P.O. Box 37100, Phoenix, AZ 85069, USA
Abstract: In some applications where the quality characteristic of interest is a profile, it is possible to use multiple linear regressions to model process quality. In this paper, we propose using a P-value approach to evaluate performance of multivariate profiles in Phase II. The average run length (ARL) is used to evaluate performance of the proposed method under different shifts in the model parameters. In our proposed approach, P-values for all observed levels within a sample are calculated. If any P-value is less than a specific threshold, the chart signals out of control. The main advantage of this approach is its ease of implementation in practice. Performance of the proposed method is compared to another commonly used method involving the T² control charts. Simulation results indicate that the proposed P-value approach performs quite well under the various conditions considered. Given that it is a straightforward and easy to implement approach, as well as a monitoring scheme that only requires a single control chart, the P-value method is quite competitive in practice.
Keywords: Phase II monitoring; average run length; ARL; profile monitoring; P-value; multivariate profiles; process modelling; process quality; control charts; simulation; statistical process control; SPC.
International Journal of Quality Engineering and Technology, 2014 Vol.4 No.2, pp.133 - 143
Published online: 14 Apr 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article