Improving Reliability in Multistage Processes with Autocorrelated Observations

Abstract

Multistage process surveillance is considered to effectively improve the product reliability in manufacturing or service operations. To this end, the process output is commonly inspected under specific conditions and the values of the reliability-related quality characteristic are measured. However, in some cases, the observations from the process output are autocorrelated. This brings about the situation where the use of existing monitoring schemes is futile. Therefore, a class of survival analysis regression models called the proportional hazards (PH) model has been modified to justify the effect of cascade property in line with the autocorrelation issue. Subsequently, three monitoring procedures have been devised in both the presence and absence of a censoring mechanism. The problem of unobserved heterogeneity is also addressed and remedial action has been discussed using frailty models. The performance analysis reveals that the cumulative sum (CUSUM) control chart outweighs the other two competing monitoring schemes. An example is given to illustrate the application and performance of the proposed control charts in real practice. Finally, the impact of ignoring autocorrelation has been studied which confirms the significant effect of autocorrelation on the performance of the process control.

Key Words:

• Autocorrelated observations
• CUSUM control chart
• EWMA control chart
• frailty models
• multistage process
• proportional hazards model.

Additional information

Notes on contributors

Shervin Asadzadeh

Shervin Asadzadeh received his Ph.D. in Industrial Engineering from K.N. Toosi University of Technology in Tehran, Iran. His is currently an Assistant Professor in Islamic Azad University (North Tehran Branch). He has been teaching Design of Experiments, Time Series Analysis, Statistical Quality Control, Quality Management and Productivity, and Engineering Statistics at K.N. Toosi University of Technology, Allameh Tabataba’i University and Islamic Azad University. His primary research interests include Statistical Quality Control, Reliability, Survival Data Analysis, Applied Statistics, Robust Statistics and Simulation. He is a member of National Elites Foundation of Iran, Iranian Institute of Industrial Engineering and Iranian Statistical Society.

Abdollah Aghaie

Abdollah Aghaie is a Professor of Industrial Engineering at K. N. Toosi University of Technology in Tehran, Iran. He received his BSc from Sharif University of Technology in Tehran, MSc from New South Wales University in Sydney and Ph.D. from Loughborough University in the U.K. His main research interests are in Modeling and Simulation, Quality Management and Control, Social Networks, Knowledge Management, Risk Management, Internet Marketing and Ergonomics.

Hamid Shahriari

Hamid Shahriari is an Associate Professor in the Department of Industrial Engineering at K.N. Toosi University of Technology in Tehran, Iran. He received his Ph.D. in Industrial Engineering from the Arizona State University, USA. His research interests include quality control, applied multivariate statistics, engineering statistics, and data analysis. He is a member of IIIE, IITC, and ISA.

Seyed Taghi Akhavan Niaki

Seyed Taghi Akhavan Niaki is Professor of Industrial Engineering at Sharif University of Technology, Iran. His research interests are in the areas of Simulation Modeling and Analysis, Applied Statistics, Multivariate Quality Control, and Operations Research. Before joining Sharif University of Technology, he worked as a systems engineer and quality control manager for Iranian Electric Meters Company. He received his Bachelor of Science in Industrial Engineering from Sharif University of Technology in 1979, his Master’s and his Ph.D. degrees both in Industrial Engineering from West Virginia University in 1989 and 1992, respectively. He is the editor of Scinetia Iranica Transaction E, a board member to several international journals, and a member of anju.