NDLI: Using p values to design statistical process control charts

Content Provider	Springer Nature Link
Author	Li, Zhonghua Qiu, Peihua Chatterjee, Snigdhansu Wang, Zhaojun
Copyright Year	2012
Abstract	Conventional Phase II statistical process control (SPC) charts are designed using control limits; a chart gives a signal of process distributional shift when its charting statistic exceeds a properly chosen control limit. To do so, we only know whether a chart is out-of-control at a given time. It is therefore not informative enough about the likelihood of a potential distributional shift. In this paper, we suggest designing the SPC charts using p values. By this approach, at each time point of Phase II process monitoring, the p value of the observed charting statistic is computed, under the assumption that the process is in-control. If the p value is less than a pre-specified significance level, then a signal of distributional shift is delivered. This p value approach has several benefits, compared to the conventional design using control limits. First, after a signal of distributional shift is delivered, we could know how strong the signal is. Second, even when the p value at a given time point is larger than the significance level, it still provides us useful information about how stable the process performs at that time point. The second benefit is especially useful when we adopt a variable sampling scheme, by which the sampling time can be longer when we have more evidence that the process runs stably, supported by a larger p value. To demonstrate the p value approach, we consider univariate process monitoring by cumulative sum control charts in various cases.
Starting Page	523
Ending Page	539
Page Count	17
File Format	PDF
ISSN	09325026
Journal	Statistical Papers
Volume Number	54
Issue Number	2
e-ISSN	16139798
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-04-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bootstrap Cumulative sum control charts Process monitoring Self-starting Variable sampling Statistics for Business/Economics/Mathematical Finance/Insurance Probability Theory and Stochastic Processes Economic Theory Operations Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

EWNA charts for monitoring the mean and the autocovariances of stationary processes

R-charts for the exponential, Laplace and logistic processes

Quality surveillance with EWMA control charts based on exact control limits

The influence of parameter estimation on the ARL of Shewhart type charts for time series

A unification and some corrections to Markov chain approaches to develop variable ratio sampling scheme control charts

Introducing model uncertainty by moving blocks bootstrap

Fast initial response features for EWMA control charts

The use of Andrews curves for detecting the out-of-control variables when a multivariate control chart signals

Hotelling’s T2 control chart with double warning lines

Using p values to design statistical process control charts

Similar Documents

EWNA charts for monitoring the mean and the autocovariances of stationary processes

R-charts for the exponential, Laplace and logistic processes

Quality surveillance with EWMA control charts based on exact control limits

The influence of parameter estimation on the ARL of Shewhart type charts for time series

A unification and some corrections to Markov chain approaches to develop variable ratio sampling scheme control charts

Introducing model uncertainty by moving blocks bootstrap

Fast initial response features for EWMA control charts

The use of Andrews curves for detecting the out-of-control variables when a multivariate control chart signals

Hotelling’s T2 control chart with double warning lines

Using p values to design statistical process control charts