Cumulative Sum Charts and Charting for Quality Improvement

Hawkins, Douglas M.; Olwell, David H.

doi:10.1007/978-1-4612-1686-5

Cited by 640 publications

(581 citation statements)

References 0 publications

Supporting

Mentioning

552

Contrasting

Unclassified

Order By: Relevance

“…Moustakides (1986), Ritov (1990), Gan (1991), and Yashchin (1993 explore the optimality properties of the CUSUM. Optimality in this sense refers to detecting when a process has shifted from a single known distribution to another known distribution (Hawkins and Olwell, 1998). In short, the CUSUM is optimal for detecting the persistent shifts for which they are tuned (Hawkins and Olwell, 1998).…”

Section: Discussion Of Cusum Optimality Propertiesmentioning

confidence: 99%

“…as n approaches infinity (Hawkins and Olwell, 1998). Using this relation, probability limits are used to determine the upper and lower control limits for the Poisson selfstarting Shewhart style control chart.…”

Section: Is a Poissonmentioning

confidence: 99%

“…This goal is not entirely attainable due to the range of possible values for A i (Hawkins and Olwell, 1998 The software package ANYGETH.exe is used in this research to determine the upper and lower control limits (Hawkins and Olwell, 1998).…”

Section: Self-starting Poisson Bayesian Cusum Control Chartmentioning

confidence: 99%

“…L i R where z is one of the M + 1 states and R i,z is the transition probability from state z to state i (Hawkins and Olwell, 1998) and Olwell, 1998). The result of the equations used to determine ARL is that, given H and K, the ARL can be calculated.…”

Section: Self-starting Poisson Bayesian Cusum Control Chartmentioning

confidence: 99%

See 3 more Smart Citations

Statistical Monitoring of Suicides in the U.S. Armed Forces

Martin¹

2004

PsycEXTRA Dataset

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. FUNDING NUMBERS PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Naval Postgraduate School Monterey, CA 93943-5000 PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES)N/A ABSTRACTThis study models DoD suicides as a Poisson process to detect departures from usual variation using a self-starting control chart scheme. Methods are implemented in a Microsoft Excel spreadsheet with Visual Basic macros for ease of use. Persistent shifts in the process mean are detected in the following months for each service component.

show abstract

Section: Discussion Of Cusum Optimality Propertiesmentioning

confidence: 99%

Section: Is a Poissonmentioning

confidence: 99%

Section: Self-starting Poisson Bayesian Cusum Control Chartmentioning

confidence: 99%

Section: Self-starting Poisson Bayesian Cusum Control Chartmentioning

confidence: 99%

See 2 more Smart Citations

Statistical Monitoring of Suicides in the U.S. Armed Forces

Martin¹

2004

PsycEXTRA Dataset

View full text Add to dashboard Cite

show abstract

“…The CUSUM chart can be adjusted with the reference value k. The k-value needs to be chosen relative to the size of the shift that is to be detected (MONTGOMERY 1997, HAWKINS andOLWELL 1998). MONTGOMERY (1997), HAWKINS and OLWELL (1998) recommended a k-value that is chosen to be half the size of the shift that needs to be detected.…”

Section: Cusum Chartsmentioning

confidence: 99%

Control charts applied to pig farming data

Engler

Tölle

Timm³

et al. 2009

Arch. Anim. Breed.

View full text Add to dashboard Cite

Statistical control charts are effective tools to reveal changes in a production process. The CUSUM (cumulative sum) and the EWMA (exponentially weighted moving average) control chart are used to detect small deviations in a process. Data from two sow herds, herd A and herd B, were collected from 1999 to 2004. Farm A had an average number of 530 breeding sows, Farm B had an average of 370 breeding sows. Both herds were diagnosed with Porcine Reproductive and Respiratory Syndrome (PRRS). The weekly means of the number of piglets weaned (NPW), the pre-weaning mortality (PWM) and return to service rate (RSR) were analysed with different settings of the CUSUM as well as the EWMA control chart to reveal a shift in the production process. For the pre-weaning mortality and the number of piglets weaned, the two charts detected a change in the process 4 weeks (Farm A) and 2 weeks before (Farm B) PRRS was diagnosed. The CUSUM and the EWMA chart revealed a shift in the return to service rate on Farm A 3.5 months before PRRS was detected. On Farm B, the signal occurred 6 weeks before the infection was detected. The CUSUM and the EWMA control charts were effective tools for detecting small deviations in sow herd data. Compared with EWMA, the use of the CUSUM chart is more straightforward and the settings are more easily handled. The CUSUM chart is therefore the preferred option for use in practice.

show abstract

Multivariate Cumulative Sum ( CUSUM ) Chart

Testik¹,

Runger²

2007

Encyclopedia of Statistics in Quality and Reliability

View full text Add to dashboard Cite

Cumulative sum (CUSUM) control charts are important tools for the real‐time, on‐line monitoring of production processes. There are several multivariate extensions to the univariate CUSUM control chart. This work discusses the multivariate extensions to the CUSUM control chart and their uses. Basic formulas and notation are provided and the advantages and disadvantages of each are compared. References are given to allow the interested reader to investigate further.

show abstract

Cumulative Sum Charts and Charting for Quality Improvement

Cited by 640 publications

References 0 publications

Statistical Monitoring of Suicides in the U.S. Armed Forces

Statistical Monitoring of Suicides in the U.S. Armed Forces

Control charts applied to pig farming data

Multivariate Cumulative Sum ( CUSUM ) Chart

Contact Info

Product

Resources

About