Effective Control Charts for Monitoring Multivariate Process Dispersion

2011 ◽  
Vol 28 (4) ◽  
pp. 409-426 ◽  
Author(s):  
Chia-Ling Yen ◽  
Jyh-Jen Horng Shiau ◽  
Arthur B. Yeh
Keyword(s):  
Control Charts ◽  
Effective Control ◽  
Multivariate Process ◽  
Process Dispersion

Comparing Robustness of EWMA Dispersion Control Chart for Non-Normal Process

2014 ◽  
Vol 912-914 ◽  
pp. 1189-1192
Author(s):  
Hai Yu Wang
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Normal Process ◽  
Comparison Analysis ◽  
Run Length ◽  
Monitoring Process ◽  
Process Dispersion

This article discusses robustness to non-normality of EWMA charts for dispersion. Comparison analysis of run length of four kinds of EWMA charts to monitoring process dispersion is provided to evaluate control charts performance and robustness. At last robust EWMA dispersion charts for non-normal processes are proposed by this way.

Multivariate process dispersion monitoring without subgrouping

2019 ◽  
Vol 47 (9) ◽  
pp. 1652-1675
Author(s):  
Abdul Haq ◽  
Michael B. C. Khoo
Keyword(s):  
Multivariate Process ◽  
Process Dispersion

Memory-Type Control Charts for Monitoring the Process Dispersion

2013 ◽  
Vol 30 (5) ◽  
pp. 623-632 ◽  
Author(s):  
Nasir Abbas ◽  
Muhammad Riaz ◽  
Ronald J. M. M. Does
Keyword(s):  
Control Charts ◽  
Process Dispersion ◽  
Memory Type

scholarly journals Creating And Working With Control Charts In Excel

2016 ◽  
Vol 10 (1) ◽  
pp. 23-44
Author(s):  
Paul F. Schikora
Keyword(s):  
Distance Education ◽  
Quality Control ◽  
Control Charts ◽  
Theoretical Basis ◽  
Effective Control ◽  
Interactive Mode ◽  
Quality Control Charts ◽  
Hands On ◽  
Problem Data ◽  
The Web

With the growth in distance education offerings, instructors who now teach quantitative material via the web have been faced with many challenges.  Foremost has been the need to develop appropriate methods for teaching such material to students who are not physically in the classroom.  Methodologies that have traditionally been taught in a highly interactive mode in the classroom must now be presented effectively in a far more asynchronous environment.  Tutorials and detailed handouts are one way to accomplish this. We present a written tutorial for creating quality control charts using Excel.  The tutorial guides students through the process of creating X-bar and R charts in such a way as to reinforce the theoretical basis of quality control already taught.  Students apply their knowledge in hands-on activity, learn how to improve Excel’s default charts to create visually effective control charts, and learn to reuse/recycle their work to easily create additional charts for different sets of problem data.

scholarly journals A Robust Control Chart for Monitoring Dispersion

2013 ◽  
Vol 2013 ◽  
pp. 1-5
Author(s):  
Maoyuan Zhou ◽  
Wei Geng
Keyword(s):  
Robust Control ◽  
Control Chart ◽  
Control Charts ◽  
Nonparametric Test ◽  
Dispersion Parameters ◽  
Monitoring Process ◽  
Location Parameters ◽  
Process Dispersion ◽  
Change Point Model ◽  
Process Location

Most robust control charts in the literature are for monitoring process location parameters, such as mean or median, rather than process dispersion parameters. This paper develops a new robust control chart by integrating a two-sample nonparametric test into the effective change-point model. Our proposed chart is easy in computation, convenient to use, and very powerful in detecting process dispersion shifts.

Exponentially weighted control charts to monitor multivariate process variability for high dimensions

2017 ◽  
Vol 55 (17) ◽  
pp. 4948-4962 ◽  
Author(s):  
Nadeera Gnan Tilshan Gunaratne ◽  
Malihe Akhavan Abdollahian ◽  
Shamsul Huda ◽  
John Yearwood
Keyword(s):  
Control Charts ◽  
High Dimensions ◽  
Process Variability ◽  
Multivariate Process ◽  
Exponentially Weighted

scholarly journals On the reliability of Shewhart-type control charts for multivariate process variability

2017 ◽  
Author(s):  
Maman A. Djauhari ◽  
Rohayu Mohd Salleh ◽  
Zunnaaim Zolkeply ◽  
Lee Siaw Li
Keyword(s):  
Control Charts ◽  
Process Variability ◽  
Multivariate Process

SHORT RUNS MULTIVARIATE CONTROL CHART FOR PROCESS DISPERSION

2005 ◽  
Vol 12 (02) ◽  
pp. 127-147 ◽  
Author(s):  
MICHAEL B. C. KHOO ◽  
S. H. QUAH ◽  
H. C. LOW ◽  
C. K. CH'NG
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Job Shops ◽  
Data Set ◽  
Control Charting ◽  
Standard Scale ◽  
Production Run ◽  
Process Dispersion ◽  
Manufacturing Techniques ◽  
Control Limits

The multivariate Hotelling's T2 control chart is designed to be used in a mass production for processes where data to estimate the mean vector and covariance matrix as well as the computation of control limits are available before a production run. Recent years have seen a trend in manufacturing industries to produce smaller lot sizes, a.k.a., low volume production which is a result of increased importance given to just-in-time (JIT) manufacturing techniques, synchronous manufacturing and the reduction of in-process inventory and costs. This new manufacturing environment is also referred to as short runs production or short runs. In a short runs environment, it is difficult or perhaps impossible to establish a reliable historical data set in setting valid control limits and in estimating process parameters due to the availability of insufficient data for a particular process because production runs are usually short and change frequently from one process to another. There is also a need to start charting at or very near the beginning of the run in such a case. Another problem encountered in a short runs production such as in job shops is that there are many different types of measurements so that many different control charts are needed. Standardized control charts that allow different statistics to be plotted on the same chart are extremely useful in short runs. Control charts with standard scale simplify the control charting process in a short runs environment. In this paper, we address the multivariate short runs problems for process dispersion based on individual measurements by presenting the required formulas so that the chart can be used from the start of production, whether or not prior information for estimating the chart's limit and its parameter is available. The proposed chart plots standardized statistics for multiple parts on the same chart. This paper extends the work of the authors in Ref. 13.

Long story short: an introduction to the short-term and longterm Six Sigma quality and its importance in laboratory medicine for the management of extra-analytical processes

2018 ◽  
Vol 56 (11) ◽  
pp. 1838-1845 ◽  
Author(s):  
Cristiano Ialongo ◽  
Sergio Bernardini
Keyword(s):  
Six Sigma ◽  
Control Charts ◽  
Clinical Chemistry ◽  
Laboratory Medicine ◽  
Effective Control ◽  
Full Potential ◽  
Short Term ◽  
Performance Requirements ◽  
Analytical Phase

Abstract There is a compelling need for quality tools that enable effective control of the extra-analytical phase. In this regard, Six Sigma seems to offer a valid methodological and conceptual opportunity, and in recent times, the International Federation of Clinical Chemistry and Laboratory Medicine has adopted it for indicating the performance requirements for non-analytical laboratory processes. However, the Six Sigma implies a distinction between short-term and long-term quality that is based on the dynamics of the processes. These concepts are still not widespread and applied in the field of laboratory medicine although they are of fundamental importance to exploit the full potential of this methodology. This paper reviews the Six Sigma quality concepts and shows how they originated from Shewhart’s control charts, in respect of which they are not an alternative but a completion. It also discusses the dynamic nature of process and how it arises, concerning particularly the long-term dynamic mean variation, and explains why this leads to the fundamental distinction of quality we previously mentioned.

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