Important research themes from volume 26 2010


  • Douglas C. Montgomery

The aim of this journal is to provide information on new developments in many aspects of the quality and reliability engineering field to professionals by publishing research articles, review papers, tutorials, and applications/case studies. It is appropriate to examine the most recent volume and identify some important themes, highlight some of the papers that contribute to those themes, and discuss what we hope to see in the future.

Statistical Process Control and related topics continue to be an important area of research and applications. The journal published many noteworthy papers in this area, including papers on multivariate process monitoring by Kim and Adams1, methods for detecting special causes in integrated process control by Reynolds and Park2, and robust CUSUM methods for monitoring the mean and variance by Reynolds and Stoumbos3. Pehlivan and Testik4 and Ozsan5 contributed important papers on the EWMA. Han et al.6 compared several methods for detecting increases in Poisson rates. This is an important problem in public health monitoring and syndromic surveillance as well as business and industry. Noorossana et al.7 authored a paper on multivariate profile monitoring and provided an application in calibration. Perry8 wrote on monitoring autocorrelated processes with polynomial drift.

Designed experiments and robust design are an integral part of both product design and process improvement. The paper by Johnson and Montgomery9 is a tutorial on constructing optimal designs for nonlinear models. Recent development in computing and applications in commercial software have made constructing Bayesian D-optimal designs for nonlinear models practical and these authors demonstrate the advantages of this approach relative to using standard designs for linear models in the nonlinear setting. Arda Vanli et al.10 and Dehlendorff11 provided papers on aspects of experiments with computer models. The use of computer models in engineering design and development is growing rapidly, and the design of experiments to efficiently and effectively use these models is an important topic. Ma et al.12 presented a paper on three-level and mixed-level orthogonal arrays. Goh and Lam13 wrote on problem-based learning and its applications in designed experiments. Gupta et al.14 showed how experiments with signal–response systems could be analyzed using generalized linear mixed models. This paper appeared in our special issue on design for six sigma. Arnouts et al.15 wrote an instructive paper on design and analysis of strip-plots experiments. This is an important paper because the strip-plot problem arises relatively often in industrial experimentation and is often not recognized by the experimenter. Hamada and Hamada16 propose an algorithm for analyzing fractional factorial designs with complex aliasing. Nonregular designs such as Plackett–Burman designs and many orthogonal arrays have alias structures where either all or many effects are not completely confounded so that it may be possible to estimate more effects than could typically be done with a regular design of the same size. However, algorithms such as the authors propose will typically be required.

Bisgaard and Khachatryan17 contributed an interesting paper on the variogram, a little-known but highly useful tool that has applications in identification and characterization of time series. Their paper provides a straightforward method for constructing large-sample confidence intervals for the variogram.

The journal also published several interesting case studies, including the papers by Vanhatalo18 and Amiri et al.19. Case studies are important. We have a high standard for these papers. They must represent the best of contemporary practice either through the application of new methodology or the application of existing methods in unique environments. Quality and reliability engineers accumulate, generate, and deploy knowledge to improve products, processes, and services which provide benefit to society. Case studies facilitate the generation and widespread adoption of new methods or provide insight about how existing methods can be used in novel situations. These types of papers perform a valuable service to our profession. We encourage potential authors of case studies to contribute to our journal.