Monitoring and Diagnosing Multistage Processes: A Review of Cause Selecting Control Charts

Authors

  • Abdollah Aghaie Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran.
  • Shervin Asadzadeh Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran.
  • Su-Fen Yang Department of Statistics, National Chengchi University, Taiwan.
Abstract:

A review of the literature on cause selecting charts (CSCs) in multistage processes is given, with a concentration on developments which have occurred since 1993. Model based control charts and multiple cause selecting charts (MCSCs) are reviewed. Several articles based on normally and non-normally distributed outgoing quality characteristics are analyzed and important issues such as economic design, autocorrelated processes and adaptive design parameters of cause selecting charts are discussed. The results reveal that cause selecting charts outperform traditional Shewhart charts for individuals when the process steps are dependent, in view of the relationship between input and output quality characteristics. A new method for modeling and simulating a multistage process is proposed which can prove to be more reasonable in real practice. Finally, various directions for future research are given.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Control Charts for Autocorrelated Processes : a Review

One of the primary tools used in statistical quality control is the control charts. The standard assumptions that are usually cited in justifying the use of control charts are that the data generated by the in control process are independent and identically distributed. However the independency assumption is not realistic in practice. Data sets collected from industrial processes may have corre...

full text

Monitoring Industrial Processes with Robust Control Charts

• The Shewhart control charts, used for monitoring industrial processes, are the most popular tools in Statistical Process Control (SPC). They are usually developed under the assumption of independent and normally distributed data, an assumption rarely true in practice, and implemented with estimated control limits. But in general, we essentially want to control the process mean value and the p...

full text

Developing A Cause Selecting Control Chart for Monitoring Two–Stage Processes with Poisson Quality Characteristic

Nowadays, most of products are the results of different dependent process steps. Due to the cascade property in most of these processes, using the traditional control charts for monitoring these processes is not suitable. To solve this problem, Cause selecting Charts (CSCs) are developed to monitor multistage processes. These control charts have usually been developed when quality characteristi...

full text

Cause-selecting Charts based on Proportional Hazards and Binary Frailty Models (Quality Engineering Conference Paper)

Monitoring the reliability of products in both the manufacturing and service processes is of main concern in today’s competitive market. To this end, statistical process control has been widely used to control the reliability-related quality variables. The so-far surveillance schemes have addressed processes with independent quality characteristics. In multistage processes, however, the cascade...

full text

A Review and Evaluation of Statistical Process Control Methods in Monitoring Process Mean and Variance Simultaneously

In this paper, first the available single charting methods, which have been proposed to detect simultaneous shifts in a single process mean and variance, are reviewed. Then, by designing proper simulation studies these methods are evaluated in terms of in-control and out-ofcontrol average run length criteria (ARL). The results of these simulation experiments show that the EWMA and EWMS methods ...

full text

Univariate and multivariate control charts for monitoring dynamic-behavior processes: a case study

The majority of classic SPC methodologies assume a steady-state (i.e., static) process behavior (i.e., the process mean and variance are constant) without the influence of the dynamic behavior (i.e., an intended or unintended shift in the process mean or variance). Traditional SPC has been successfully used in steady-state manufacturing processes, but these approaches are not valid for use in d...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 2  issue 3

pages  214- 235

publication date 2008-12-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023