CHAPTER 8 BOOK COMPANION - American College of … 2E/HC... · CHAPTER 8 BOOK COMPANION. ... Taguchi, Genichi . List of Organizations . Organizations . ... Crossing the Quality Chasm

1 Copyright 2012 Health Administration Press

CHAPTER 8

BOOK COMPANION

Quality management became imperative for the manufacturing sector in the 1970s and 1980s, for

service organizations in the 1980s and 1990s, and, finally, for the healthcare industry in the

1990s, culminating with the Institute of Medicine report To Err Is Human. This report detailed

alarming statistics on the number of people harmed by the healthcare system and called for major

improvement in the quality of healthcare as related to patient safety. The report recognized the

need for systemic changes and called for innovative solutions to ensure improvement.

The healthcare industry is facing increasing pressure not only to increase quality but also

to reduce costs. This chapter provides an introduction to quality management tools and

techniques that are available and being used successfully by healthcare organizations. Chapter 8

covers the following major topics:

Defining quality

The costs of quality

Quality programs including TQM/CQI, ISO 9000, the Baldrige criteria, and Six Sigma

Six Sigma tools and techniques, including the DMAIC process, the seven basic quality tools,

statistical process control, and process capability

Other quality tools and techniques including quality function deployment, Taguchi methods,

and poka-yoke

After completing this chapter, the reader should have a basic understanding of quality,

quality programs, and quality tools. This understanding should enable readers to use the tools

and techniques to begin improving quality in their organizations.

The author invites readers comments, recommended readings, website suggestions, or


any other material to be added to this webpage for this chapter or any other chapters. Please click

here to send an e-mail. Be sure to include Healthcare Operations Management, 2nd edition in

the subject line.

Downloadable Resources

PowerPoint Slides for Chapter 8

To access a PowerPoint presentation covering the key points of Chapter 8, click on the icon

below to open/close the attachments panel:

HC Ops 2 slides - Chapter 8.ppt

Excel Templates

SPC Charts

Statistical process control (SPC) is a statistics-based methodology for determining when a

process is moving out of control. All processes have variation in output. Some of the variation

is caused by factors that can be identified and managed (assignable or special), and some of the

variation is inherent in the process (common). SPC is aimed at discovering variation resulting

from assignable causes so that adjustments can be made and bad output is not produced.

In SPC, samples of process output are taken over time, measured, and plotted on a control

chart. From statistics theory, the sample means will follow a normal distribution. From the

central limit theorem, 99.7 percent of sample means will be within +/ 3 standard errors of the

overall mean and 0.3 percent will be outside those limits. A sample mean outside the +/ 3

standard error limits will be obtained only 3 times out of 1,000 if the process is working as it

should. These +/ 3 standard error limits are the control limits on a control chart.

To access a template for quality control, click on the icon below to open/close the

mailto:[email protected]?subject=Healthcare%20Operations%20Management,%202nd%20edition


attachments panel:

Ch. 8 Quality Template.xls

The template contains mean charts, a range chart, a p chart, a c chart, process capability

measurement, and a normal distribution.

Quality Function Deployment

Quality function deployment (QFD) is a structured process for identifying customer needs and

wants and translating them into a product or process that meets those needs. The tool is most

often used in the development phase of a new product or process, but also can be used to

redesign an existing product/process. Typically, it will be found in a Design for Six Sigma

(DFSS) project, where the goal is to design the process to achieve Six Sigma results. The QFD

process uses a matrix called the house of quality to organize data in a usable fashion. To access a

template for a QFD exercise, click on the icon below to open/close the attachments panel:

Ch. 8 QFD Template.xls

Examples and Problem Data from the Text

Chapter 8 contains a number of examples and problems. They are featured in the files attached to

this PDF. Click on the icons to access them:

Riverview Drug Prescription Process Map (You need Visio software to open this file.):

Riverview Drug Prescription Process ppt.vsd


Riverview Clinic Wait Time QC Example:

Turnaround Time, Problem 1, Data:

Riverview Customer Satisfaction, Problem 2, Data:

Websites of Interest

The quality movement in the United States and around the world has gained substantial

momentum as can be seen by the extensive array of websites devoted to advancing this science.

The authors have selected the following sites as excellent examples.

Videos (available online)

National Campaign Aims to Curb Hospital MistakesPBS NewsHour with Jim Lehrer on the

100,000 Lives Campaign

The Original Dr. Deming Style Red Bead ExperimentThis training video accompanies a

training product available from this site. It also provides an introduction to TQM. See

Chapter 2 for links to more Deming videos.

Podcasts (Audiocasts)

Diagramming Processes for Six Sigmaa presentation of the various functions in Visio that can

be used in Six Sigma; from Microsoft

RV clinic QC.xls

Problem 1 TAT data.xls

Problem 2 Riverview CS data.xls

http://www.pbs.org/newshour/bb/health/july-dec06/lives_08-18.htmlhttp://www.redbead.com/http://www.microsoft.com/Office/asx/visio/OperationsSixSigma.asx


Software

Students and working professionals engaged in quality improvement work will find these

software packages useful. All these packages have a limited time free trial use.

StatToolsan Excel add-in for statistical analyses.

Minitabfairly inexpensive spreadsheet-based statistical software that can perform more

powerful statistical analyses than Excel

Microsoft Visio process mapping software

Tools

Specific Six Sigma tools are also available online:

Plan-Do-Study-Act (PDSA) Worksheet (IHI Tool)

SERVQUALdescription of the SERVQUAL instrument for measuring perceptions of service

quality and links to other resources

Tutorials

To use Six Sigma tools effectively, some practice is necessary. These tutorials provide exercises

to build skills.

Microsoft Visiofrom Microsoft

Seven Basic Quality Toolsan overview of the seven tools; from ASQ

PDCAIn this interactive exercise, students organize process improvement steps following the

PDCA model.

Six Sigma DMAIC Roadmapan extensive discussion with links to tools and other resources

related to each step

Statistical Process Control (SPC) Resource Centeran overview with links to other resources

Statistical Process Control (SPC)A Wayworld Tutorialan introduction to the basics of SPC

http://www.palisade.com/stattools/default.asphttp://www.minitab.com/http://office.microsoft.com/en-us/visio/default.aspxhttp://www.ihi.org/IHI/Topics/Improvement/ImprovementMethods/Tools/Plan-Do-Study-Act+%28PDSA%29+Worksheet.htmhttp://www.istheory.yorku.ca/SERVQUAL.htmhttp://office.microsoft.com/en-us/training/CR061832751033.aspxhttp://www.asq.org/learn-about-quality/seven-basic-quality-tools/overview/overview.htmlhttp://www.wisc-online.com/objects/index_tj.asp?objID=MFQ202http://www.isixsigma.com/library/content/c020617a.asphttp://www.qualitytrainingportal.com/resources/spc/index.htmhttp://www.wayworld.com/spc/spc.cfm


What is Process Capability?a fairly technical tutorial from NIST

Process Capability Analysisanswers to common questions about process capability

Yield the Right Wayexplanation of rolled throughput yield

Benchmarkingoverview from ASQ

Demonstrations

Process Capability Index applet demonstrating Cp and Cpk

Taguchi Loss Functionapplet demonstrating the Taguchi Loss Function

Data Sources

A key element in quality improvement is benchmarking. Here is a useful resource.

Association for Benchmarking Health Care

Philosophies/Programs/People

Philosophies/Programs

The quality movement has a number of programs committed to advancing this science. Here are

selected links:

Malcolm Baldrige National Quality Awardoverview from ASQ

ISO 9000 and Other Standardsoverview from ASQ

Six Sigmaoverview from ASQ

iSixSigma.comLinks to a variety of resources to help business professionals successfully

implement quality in their organizations. Resources include short descriptive articles,

checklists, forms, and links to other useful websites.

http://www.itl.nist.gov/div898/handbook/pmc/section1/pmc16.htmhttp://qualityadvisor.com/library/capability_menu.phphttp://www.qualitydigest.com/mar00/html/sixsigma.htmlhttp://www.asq.org/learn-about-quality/benchmarking/overview/overview.htmlhttp://elsmar.com/Cp_vs_Cpk.htmlhttp://elsmar.com/Taguchi.htmlhttp://www.abhc.org/http://www.asq.org/learn-about-quality/malcolm-baldrige-award/overview/overview.htmlhttp://www.asq.org/learn-about-quality/iso-9000/overview/overview.htmlhttp://www.asq.org/learn-about-quality/six-sigma/overview/overview.htmlhttp://www.isixsigma.com/


People

Many individuals have advanced the quality movement in the United States and Japan. Here are

links to related websites.

Crosby, Phillip B., more on Crosby

Garvin, David A.

Ishikawa, Kaoru, more on Ishikawa

Juran, Joseph, more on Juran

Taguchi, Genichi

List of Organizations

Organizations

The following organizations have made quality improvement their primary mission.

American Productivity and Quality Center (APQC)

American Society for Quality (ASQ)

Baldrige National Quality Program (BNQP)

Institute for Healthcare Improvement (IHI)

Institute of Medicine (IOM)

International Organization for Standardization (ISO)

Midwest Business Group on Health (MBGH)

Additional Readings

Case Studies (available online)

Healthcare Related Quality Case Studiesfrom ASQ

Six Sigma Practicesfrom Healthcare Informatics online

The Promise of Six Sigmafrom Creative Healthcare USA

http://en.wikipedia.org/wiki/Phil_Crosbyhttp://www.skymark.com/resources/leaders/crosby.asphttp://dor.hbs.edu/fi_redirect.jhtml?facInfo=bio&[email protected]&loc=extnhttp://en.wikipedia.org/wiki/Kaoru_Ishikawahttp://www.skymark.com/resources/leaders/ishikawa.asphttp://www.skymark.com/resources/leaders/juran.asphttp://www.asq.org/about-asq/who-we-are/bio_juran.htmlhttp://en.wikipedia.org/wiki/Genichi_Taguchihttp://www.apqc.org/portal/apqc/sitehttp://www.asq.org/index.htmlhttp://www.quality.nist.gov/index.htmlhttp://www.ihi.org/ihihttp://www.iom.edu/http://www.iso.org/iso/en/ISOOnline.frontpagehttp://www.mbgh.org/http://www.asq.org/economic-case/markets/http://www.healthcare-informatics.com/article/six-sigma-practiceshttp://www.creative-healthcare.com/resources/promise1.php


Improving Communication and Documentation Concerning Preliminary and Final Radiology

Reportsfrom NAHQ

Articles

Cost of Quality (COQ)overview from ASQ

Revamping Healthcare Using DMAIC and DFSSarticle contrasting DMAIC with DFSS

The Essential Six Sigmaoverview of Six Sigma and a discussion of how successful

implementation can improve the bottom line

Six Sigma Program Success FactorsSix Sigma philosophy says it is necessary to determine

key input variables in a process to manage and optimize the process output. This article

outlines key factors for successful Six Sigma program implementation.

Process Capability: Understanding the Conceptanalogy to aid in understanding this concept

Road Map for Quality Improvement: A Guide for Doctorsshort article presenting the essentials

of quality improvement that every physician should know

Mistake-Proofing the Design of Health Care Processes is a synthesis of practical examples from

the real world of healthcare on the use of process or design features to prevent medical errors

or the negative impact of errors. It contains over 150 examples of mistake-proofing that can

be applied in health careand in many cases relatively inexpensively. From AHRQ.

Reports

Crossing the Quality Chasm Slide SetPowerPoint slides related to the Institute of Medicines

report Crossing the Quality Chasm: Health Care for the 21st Century

http://www.nahq.org/journal/ce/article.html?article_id=276http://www.nahq.org/journal/ce/article.html?article_id=276http://www.asq.org/learn-about-quality/cost-of-quality/overview/overview.htmlhttp://www.isixsigma.com/industries/healthcare/revamping-healthcare-using-dmaic-and-dfss/http://www.asq.org/pub/qualityprogress/past/0102/qp0102lucas.pdfhttp://www.asq.org/pub/sixsigma/past/vol1_issue1/ssfmv1i1goldstein.pdfhttp://www.asq.org/pub/qualityprogress/past/2000/0100/136one_good_idea_jan2000.htmlhttp://www.mjainmd.com/medicine/roadmap_for_quality_improvement.pdfhttp://www.ahrq.gov/qual/mistakeproof/https://www.peacehealth.org/apps/Quality/References/ChasmSlides.pdf


Books (available online)

Barry, R. 2003. Nan: A Six Sigma Mystery. Milwaukee, WI: ASQ Quality Press.

George, M. L. 2003. Lean Six Sigma for Service: How to Use Lean Speed and Six Sigma Quality

to Improve Services and Transactions. New York: McGraw-Hill.

Pyzdek, T. 2003. The Six Sigma Handbook: The Complete Guide for Greenbelts, Blackbelts, and

Managers at All Levels, Revised and Expanded Edition. New York: McGraw-Hill.

Tennant, G. 2001. Six Sigma: SPC and TQM in Manufacturing and Services. London: Ashgate

Publishing.

Books (links to sources referenced in Chapter 8)

Berwick, D. M., A. B. Godfrey, and J. Roessner. 2002. Curing Healthcare: New Strategies for

Quality Improvement. San Francisco: Jossey-Bass.

http://books.google.com/books?vid=ISBN0873896122&id=wLqbfn85eMoC&pg=RA1-PA1-IA2&lpg=RA1-PA1-IA2&dq=Nan:+A+Six+Sigma+Mystery.&sig=vLVvCj5HCp8DH4kMvEg7WtbJsughttp://books.google.com/books?hl=en&lr=&id=PpS931Oc5V8C&oi=fnd&pg=PR1&sig=supoaboCxBdTUK0xbVZKRex4D9k&dq=six+sigma+healthcare&prev=http://scholar.google.com/scholar%3Fq%3Dsix%2Bsigma%2Bhealthcare%26start%3D10%26hl%3Den%26lr%3D%26sa%3DNhttp://books.google.com/books?hl=en&lr=&id=PpS931Oc5V8C&oi=fnd&pg=PR1&sig=supoaboCxBdTUK0xbVZKRex4D9k&dq=six+sigma+healthcare&prev=http://scholar.google.com/scholar%3Fq%3Dsix%2Bsigma%2Bhealthcare%26start%3D10%26hl%3Den%26lr%3D%26sa%3DNhttp://books.google.com/books?hl=en&lr=&id=ly7iBGNVGe8C&oi=fnd&pg=PR13&sig=7jQjO_K3ho8G_xMwIDCpdvrXurg&dq=six+sigma&prev=http://scholar.google.com/scholar%3Fq%3Dsix%2Bsigma%26start%3D10%26hl%3Den%26lr%3D%26sa%3DN#PPA5,M1http://books.google.com/books?hl=en&lr=&id=ly7iBGNVGe8C&oi=fnd&pg=PR13&sig=7jQjO_K3ho8G_xMwIDCpdvrXurg&dq=six+sigma&prev=http://scholar.google.com/scholar%3Fq%3Dsix%2Bsigma%26start%3D10%26hl%3Den%26lr%3D%26sa%3DN#PPA5,M1http://books.google.com/books?hl=en&lr=&id=O6276jidG3IC&oi=fnd&pg=PA1&sig=eD_VuIt1vYdM-nYSXKVRHTCtwt0&dq=six+sigma+healthcare&prev=http://scholar.google.com/scholar%3Fq%3Dsix%2Bsigma%2Bhealthcare%26start%3D10%26hl%3Den%26lr%3D%26sa%3DNhttp://www.amazon.com/Curing-Health-Care-Strategies-Improvement/dp/0787964522/sr=8-1/qid=1163597743/ref=sr_1_1/104-4261184-7360724?ie=UTF8&s=bookshttp://www.amazon.com/Curing-Health-Care-Strategies-Improvement/dp/0787964522/sr=8-1/qid=1163597743/ref=sr_1_1/104-4261184-7360724?ie=UTF8&s=books

Instructions

Notes on using the QFD (Quality Function Deployment or House of Quality) template:

All turquoise cells are to be entered by the user. Other values are automatically calculated, and do not need to be altered.

Cells with red marks in the upper right corner have comments, let the mouse hover over these cells to read the comments to further explain the contents of a cell.

This spreadsheet is locked/protected in order to keep the cells with equations from being changed. If a cell need to be altered, the spreadsheet first needs to be unlocked/unprotected. Do this by selecting the workbook you wish to unlock, click on "Tools", highlight "Protection", and select "Unprotect Sheet". The sheet will then be unlocked so alterations can be made.

Begin with the Customer and Design Requirement worksheet. 1. Input a name for your House of Quality 2. Input each customer requirement, an importance rating for that requirement, an evaluation of how well your current process/product meets that requirement, and an evaluation of how well the competition meets that requirement.This information should be gathered from the customer and is often referred to as the Voice of the Customer (VOC). 3. Input the technical requirements of the process/product. These are measureable technical requirements related to the customer requirements. Units for each requirement should be input along with an evaluation of the current process/product and the competition.

Next, go to the HOQ 1A worksheet. 4. Input the strength of the relationship between each customer requirement and each technical requirement (1=weak, 3=medium, 5=strong) in the center of the HOQ. 5. Input the direction of the relationship among technical requirements in the roof of the HOQ.

Finally, go to the HOQ 1B worksheet. 6. Input the desired target specification values.

The final House of Quality can be found on the HOQ 1Final worksheet.

Often this first House of Quality is followed by other matrices. For products, the product target values are translated into parts targets, process targets and production settings. For processes, the process target values are translated into functional requirements, design requirements and key process variables. We have included the process type in this workbook.

Customer and Tech Requirement

House of Quality for ________

Competitive Evaluations (1-5)

Customer Requirement #Customer RequirementsRelative Importance (1-5)Our Product/ServiceCompetitor ACompetitor B

1Customer requirement5511







Technical Requirement #Customer Requirement #Technical RequirementsMeasurement UnitOur Product/ServiceCompetitor ACompetitor B

1Technical requirementMeasurement Unit532

















HOQ 1A


+Positive or Reinforcing Relationship

-Negative or Tradeoff Relationship

Relative Importance or Weight

Design RequirementsTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirement

Our Service/ProductCompeting Service/Product ACompeting Service/Product B

Customer RequirementsX manufacturerY manufacturer

Customer requirement5511







calculations00000000000000000000000000000000000000000000000000000000000000000000

00000000000000000000000000000000000000000000000000000000000000000000

00000000000000000000000000000000000000000000000000000000000000000000

00000000000000000000000000000000000000000000000000000000000000000000

00000000000000000000000000000000000000000000000000000000000000000000

00000000000000000000000000000000000000000000000000000000000000000000

00000000000000000000000000000000000000000000000000000000000000000000

Importance Weighting00000000000000000

Measurement UnitsMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement UnitMeasurement Unit

Our Value55511111111111111

Competitor A33311111111111111

Competitor B22211111111111111

Target Specification Values

From the customer and design requirements worksheet

From customer and design requirements worksheet



The triangular roof of the house of quality is used to indicate where technical requirements trade-off or reinforce each other. Does improving one requirement result in a decrease (-) of an increase (+) in another requirement? This information can help the design team by making explicit where tradeoffs are needed.

From the customer and design requirements worksheet.

Calculated on this worksheet





Input the strength of the relationship between each customer requirement and each technical requirement.

1 = weak, 3=medium, 5=strong.

HOQ 1A

Our Service/Product

Competing Service/Product A

Competing Service/Product B

HOQ 1B


0

00

000

0000

00000


000000


0000000

00000000

000000000

0000000000

00000000000

Relative Importance or Weight000000000000

0000000000000

00000000000000

000000000000000

0000000000000000

Design RequirementsTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirement











Relative Importance Weighting00000000000000000


Our Value55511111111111111

Competitor A33311111111111111

Competitor B22211111111111111


Our Value

Competitor A

Competitor B






From HOQ template-1


From HOQ template-1 worksheet.





From HOQ template -1

HOQ 1B

Our Service/Product



HOQ 1Final


0

00

000

0000

00000


000000


0000000

00000000

000000000

0000000000

00000000000

Relative Importance or Weight000000000000

0000000000000

00000000000000

000000000000000

0000000000000000

Technical RequirementsTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirementTechnical requirement











Relative Importance Weighting00000000000000000


Our Value55511111111111111

Competitor A33311111111111111

Competitor B22211111111111111

Target Specification Values00000000000000000

Our Value

Competitor A

Competitor B







From HOQ template-1







From HOQ template -1

HOQ 1Final

Our Service/Product



Matrix 2

Matrix 2

Functional Requirements

Technical RequirementsRelative WeightFunctional RequirementFunctional RequirementFunctional RequirementFunctional RequirementFunctional RequirementFunctional RequirementFunctional RequirementFunctional RequirementFunctional RequirementFunctional RequirementTotal

Technical requirement00

















Total0000000000

Relative Weight (Priority)0000000000

Our Value

Competitor A

Competitor B


From HOQ template-2 worksheet


From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

Matrix 3

Matrix 3

Design Requirements

Functional RequirementsRelative WeightDesign RequirementDesign RequirementDesign RequirementDesign RequirementDesign RequirementDesign RequirementDesign RequirementDesign RequirementDesign RequirementDesign RequirementTotal

Functional Requirement00.00










Total0000000000


From Matrix 2

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

Matrix 4

Matrix 4

Key Process Variables

Design RequirementsRelative WeightKey Process VariableKey Process VariableKey Process VariableKey Process VariableKey Process VariableKey Process VariableKey Process VariableKey Process VariableKey Process VariableKey Process VariableTotal

Design Requirement00.00










Total0000000000


From Matrix 3

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

From HOQ 1 Final

Instructions

Notes on using the quality control template:

All values in blue cells are to be entered by the user, lighter blue cells can be entered but will be calculated if not entered, all other values are automatically calculated, and do not need to be altered. Each quality control model has a sample value set entered, make sure to clear all values before proceeding with your own data.

Cells with red marks in the upper right corner have comments, let the mouse hover over these cells to read the comments to further explain the quality control model.

This spreadsheet is locked/protected in order to keep the cells with equations from being changed. If a model does need to be altered, the spreadsheet first needs to be unlocked/unprotected. Do this by selecting the workbook you wish to unlock, click on "Tools", highlight "Protection", and select "Unprotect Sheet". The sheet will then be unlocked so alterations can be made.

Equations for models will be given when available.

Mean Chart ( known)

Mean Control Chart ( known)

UCL0

zLCL0

n = number of observations/ sampleMean0

SampleMeanUCLMeanLCL

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Sample Means

UCL

Mean

LCL

Period

Mean


Mean Chart ( unknown)

Mean Control Chart ( unknown)

n = number of observations/ sampleOverall Mean0

Average Range1.502

UCL0

LCL0

SampleMeanRangeUCLMeanLCL

11.38000

21.06000

31000

42.21000

51.46000

61.62000

71.03000

82.09000

90.77000

101.01000

111.1000

122.49000

131.68000

142.29000

151.34000

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n valueA2 factor

21.88

31.02

40.73

50.58

60.48

70.42

80.37

90.34

100.31

110.29

120.27

130.25

140.24

150.22

160.21

170.2

180.19

190.19

200.18

The Mean Control Chart (sigma known)plots the mean of a set number of observations (n) for each sample versus period number on the same chart as the overall mean of all observations and the upper and lower control limits based on 3 sigma. In this case sigma or s is known. A sample mean outside the upper or lower control limits is very unusual (would only happen 3 times out of 1000) and indicates assignable or non-random variation that should be investigated and the cause eliminated or corrected.

Equations:UCL = Upper Control LimitLCL = Lower Control Limit

Number of standard deviations for a specific confidence level (typically z = 3)

Sample size (number of observations per sample)

Sigma, standard deviation of the population

Upper Control Limit

Lower Control Limit

Average of sample means, population mean or overall mean

The average of the observations in each sample


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Sample Means

UCL

Mean

LCL

Period

Mean


Range Chart

Range Control Chart

nAverage Range0

UCL0

LCL0

SampleRangeUCLMeanLCL

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LCLUCL

n valueD3 factorD4 factor

203.27

302.57

402.28

502.11

602

70.081.92

80.141.86

90.181.82

100.221.78

110.261.74

120.281.72

130.311.69

140.331.67

150.351.65

160.361.64

170.381.62

180.391.61

190.41.6

200.411.59

The Mean Control Chart (sigma unknown) is similar to the previous chart, except that the control limits are based on the range found in each sample, rather than the standard deviation of the population. The assumption here is that sigma is unknown for the population and/or the range is easier to calculate. The average range of all samples is related to the standard deviation of the population and upper and lower control limits are found by multiplying the average range by a tabulated factor rather than the z value. The tabulated factors begin on A54 of this sheet. In addition to the sample mean values, the range of these values for each sample must be entered.As in the previous chart, a sample mean outside the upper or lower control limits is very unusual and indicates assignable or non-random variation.

Equation:UCL = Upper Control LimitLCL = Lower Control Limit


Average of mean values, population mean, or overall mean

Average of range values

Upper Control Limit

Lower Control Limit

The difference between the maximum and minimum values from the observations taken for each sample


Range Chart

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

15000

16000

17000

18000

19000

20000

21000

22000

23000

24000

25000

26000

27000

28000

29000

30000

31000

32000

33000

34000

35000

36000

37000

38000

39000

40000

Sample Ranges

UCL

Mean

LCL

Period

Mean

Range Control Chart

p Chart

p Chart

nAverage p0

0

zUCL0

0LCL0

0

SamplepUCLMeanLCL

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

15000

16000

17000

18000

19000

20000

21000

22000

23000

24000

25000

26000

27000

28000

29000

30000

31000

32000

33000

34000

35000

36000

37000

38000

39000

40000

The Range Control Chart plots the ranges of the samples on the same chart as the mean of the ranges and the upper and lower control limit for the range. The range is a measure of variability within the samples and sample ranges outside the control limits indicate that the variablility in the sample is very unusual and should be investigated for assingnable causes. The upper and lower control limits for the range are calculated using tabulated factors. These factors are on this sheet, starting at A54.



Upper Control Limit

Lower Control Limit



p Chart

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

15000

16000

17000

18000

19000

20000

21000

22000

23000

24000

25000

26000

27000

28000

29000

30000

31000

32000

33000

34000

35000

36000

37000

38000

39000

40000

Sample p's

UCL

Mean

LCL

Period

Mean

p Chart

c Chart

c Chart

z3Average c0

UCL0

LCL0

SamplecUCLMeanLCL

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

15000

16000

17000

18000

19000

20000

21000

22000

23000

24000

25000

26000

27000

28000

29000

30000

31000

32000

33000

34000

35000

36000

37000

38000

39000

40000

The p Chart plots values which are a proportions (typically, proportion of defects.) This chart is similar to both the Mean Chart with s known and unknown, except it is used when the data is proportional rather than absolute. It is based on the normal approximation to the binomial distribution. A sample mean proportion outside the upper or lower control limits is very unusual (would only happen 3 times out of 1000) and indicates assignable or non-random variation that should be investigated and the cause eliminated or corrected.


Average of proportions

Standard error of the proportions

Upper Control Limit

Lower Control Limit


Proportion (of defects), all values must be less than 1.


c Chart

Sample p's

UCL

Mean

LCL

Period

Mean

c Chart

Process Capability

Process Capability

StandardMachineSpecification

MachineDeviationCapabilityWidthCp

A00

B00

C00

D00

E00

Non centered

ProcessStandardLowerUpper

MachineMeanDeviationSpecificationRatioSpecificationRatioCpk

A000

B000

C000

D000

E000

The c Chart is essentially the same as the p Chart. However, In this chart, we plot the number of defectives (per batch, per day, per machine, per 100 feet of pipe, etc.) and the control limits in this chart are computed based on the Poisson distribution (distribution of rare events).


Defects per unit

Average defects per unit

Upper Control Limit

Lower Control Limit


Process Capability

Capability Index

Process Capability is a measure of how capable the process is, assuming the process is centered. A Cp of greater than or equal to 1 means the process is capable, while less than 1 means it is not capable and needs to be "fixed" or changed in order to produce the product. A capable process, with a Cp of 1, will only produce 3 defects per 1000 opportunities. A Cp of 1 means that the CLs and the SLs are the same. Six sigma has a Cp of 1.5, a very capable process. The process will only produce 3.4 defects per million opportunities (DPMO) on one tail.

Equation:Machine Capability = Standard Deviation * 6Capability = Specification Width / Machine Capability

For a non centered process (the mean of the process and the center of the specifications are not the same), the Capability Index (Cpk) needs to be calculated. As above, Cpk of greater than or equal to 1 means the process is capable, while less than 1 means it is not capable and needs to be "fixed" in order to produce the product. Note that if the process is centered Cp=Cpk. If the process is not centered, it is possible to get a Cp of greater than 1 and a Cpk of less than 1. Cpk is basically an adjustment for Cp when the process is not centered to get the right answer. Six sigma quality implies a Cpk of 1.5, a very capable process.

Equation:Lower Ratio = (Process Mean - Lower Specification) / (3 * Standard Deviation)Upper Ratio = (Upper Specification - Process Mean) / (3 * Standard Deviation)Cpk = Min[ Lower Ratio or Upper Ratio ]

Normal Distribution

Normal Distribution

Mean =20

Std Dev =2

x =29

z =4.5

P(x) =0.0000034008

Cumulative P(X)NormXZP(X)X

0.0115.3473060001-2.32634699990.013326098229

0.0215.8925049621-2.0537475190.024209137129

0.0316.2384146997-1.88079265010.03402103729

0.0416.498628866-1.7506855670.04308692529

0.0516.7102930487-1.64485347570.05156783329

0.0616.8904526186-1.55477369070.059561473729

0.0717.0484174344-1.47579128280.067133932129

0.0817.1898561931-1.40507190340.074333077329

0.0917.3184891762-1.34075541190.081195272729

0.117.4368961213-1.28155193930.087749123929

0.1117.5469430777-1.22652846110.094017744229

0.1217.6500258375-1.17498708130.100020207929

0.1317.7472172912-1.12639135440.105772527729

0.1417.8393610074-1.08031949630.111288336429

0.1517.9271330527-1.03643347360.116579377529

0.1618.0110842035-0.99445789820.121655868629

0.1718.0916695917-0.95416520420.126526775129

0.1818.1692700358-0.91536498210.131200021429

0.1918.2442077172-0.87789614140.13568265629

0.218.3167579163-0.84162104190.139980982829

0.2118.3871579444-0.80642102780.144100666529

0.2218.4556140436-0.77219297820.148046818929

0.2318.5223067859-0.7388466070.151824069229

0.2418.5873953505-0.70630232480.155436622829

0.2518.6510209487-0.67448952570.158888310429

0.2618.7133095941-0.6433452030.162182629129

0.2718.7743743638-0.61281281810.165322777529

0.2818.8343172603-0.58284136980.16831168629

0.2918.8932307558-0.55338462210.171152041929

0.318.951199084-0.5244004580.173846312129

0.3119.0082993271-0.49585033640.17639676229

0.3219.0646023376-0.46769883120.178805472129

0.3319.120173524-0.4399132380.18107435329

0.3419.1750735252-0.41246323740.183205157529

0.3519.2293587927-0.38532060360.185199492429

0.3619.2830820967-0.35845895160.187058827929

0.3719.3362929671-0.33185351650.188784506429

0.3819.3890380819-0.3054809590.190377750129

0.3919.4413616104-0.27931919480.191839667729

0.419.4933055178-0.25334724110.1931712629

0.4119.5449098382-0.22754508090.194373425629

0.4219.5962129206-0.20189353970.195446965129

0.4319.6472516522-0.17637417390.19639258529

0.4419.6980616617-0.15096916910.197210901129

0.4519.7486775074-0.12566124630.197902441729

0.4619.7991328512-0.10043357440.198467649829

0.4719.8494606225-0.07526968880.198906885329

0.4819.8996931722-0.05015341390.199220426529

0.4919.9498624204-0.02506878980.199408471829

0.520.00000000110.00000000050.199471140229

0.5120.05013757960.02506878980.199408471829

0.5220.10030682780.05015341390.199220426529

0.5320.15053937750.07526968880.198906885329

0.5420.20086714880.10043357440.198467649829

0.5520.25132249260.12566124630.197902441729

0.5620.30193833830.15096916910.197210901129

0.5720.35274834780.17637417390.19639258529

0.5820.40378707940.20189353970.195446965129

0.5920.45509016180.22754508090.194373425629

0.620.50669448220.25334724110.1931712629

0.6120.55863838960.27931919480.191839667729

0.6220.61096191810.3054809590.190377750129

0.6320.66370703290.33185351650.188784506429

0.6420.71691790330.35845895160.187058827929

0.6520.77064120730.38532060360.185199492429

0.6620.82492647480.41246323740.183205157529

0.6720.8798264760.4399132380.18107435329

0.6820.93539766240.46769883120.178805472129

0.6920.99170067290.49585033640.17639676229

0.721.0488009160.5244004580.173846312129

0.7121.10676924420.55338462210.171152041929

0.7221.16568273970.58284136980.16831168629

0.7321.22562563620.61281281810.165322777529

0.7421.28669040590.6433452030.162182629129

0.7521.34897905130.67448952570.158888310429

0.7621.41260464950.70630232480.155436622829

0.7721.47769321410.7388466070.151824069229

0.7821.54438595640.77219297820.148046818929

0.7921.61284205560.80642102780.144100666529

0.821.68324208370.84162104190.139980982829

0.8121.75579228280.87789614140.13568265629

0.8221.83072996420.91536498210.131200021429

0.8321.90833040830.95416520420.126526775129

0.8421.98891579650.99445789820.121655868629

0.8522.07286694731.03643347360.116579377529

0.8622.16063899261.08031949630.111288336429

0.8722.25278270881.12639135440.105772527729

0.8822.34997416251.17498708130.100020207929

0.8922.45305692231.22652846110.094017744229

0.922.56310387871.28155193930.087749123929

0.9122.68151082381.34075541190.081195272729

0.9222.81014380691.40507190340.074333077329

0.9322.95158256561.47579128280.067133932129

0.9423.10954738141.55477369070.059561473729

0.9523.28970695131.64485347570.05156783329

0.9623.5013711341.7506855670.04308692529

0.9723.76158530031.88079265010.03402103729

0.9824.10749503792.0537475190.024209137129

0.9924.65269399992.32634699990.013326098229

Normal Distribution

Normal

X

Normal Distribution

n

)

(z

LCL

n

)

*

(z

UCL

sample

per

ns

observatio

of

Number

n

population

of

deviation

Standard

confidence

99.7%

for

3

level

confidence

for

errrors

standard

of

Number

z

mean

overall

or

population

of

Mean

*

-

=

+

=

=

=

=

=

=

n

)(z

LCL

n

)*(z

UCL

sample per nsobservatio of Number n

population of deviation Standard

confidence 99.7% for 3 level confidence for errrors standard of Number z

mean overall or population of Mean

*

R

A

X

LCL

R

A

X

UCL

values

range

of

Average

=

R

A54)

at

(starting

level

confidence

sigma

3

for

Factor

=

A2

mean

population

or

mean,

overall

means,

sample

of

Average

=

X

2

2

*

*

-

=

+

=

RAXLCL

RAXUCL

values range of Average=R

A54)at (starting level confidence sigma 3 for Factor = A2

mean population or mean, overall means, sample of Average=X

2

2

*

*

R

*

D

UCL

R

*

D

LCL

A54)

at

(starting

for UCL

Factor

=

D4

A54)

at

(starting

LCL

for

Factor

=

D3

ranges

sample

of

Average

=

R

4

3

=

=

R*DUCL

R*DLCL

A54)at (starting for UCLFactor = D4

A54)at (starting LCLfor Factor = D3

ranges sample of Average = R

4

3

c

z

c

LCL

c

z

c

UCL

3)

(usually

level

Confidence

=

z

unit

per

defects

of

number

Average

=

c

-

=

+

=

czcLCL

czcUCL

3)(usually level Confidence = z

unit per defects of number Average= c

p

p

p

p

*

z

p

LCL

*

z

p

UCL

n

)

p

(1

*

p

confidence

99.7%

for

3

level

confidence

for

errrors

standard

of

Number

=

z

ns/sample

observatio

of

Number

=

n

s

proportion

sample

of

Average

=

p

proportion

of

error

Standard

=

-

=

+

=

-

=

=

p

p

p

p

*zpLCL

*zpUCL

n

)p(1*p

confidence 99.7% for 3 level confidence for errrors standard of Number = z

ns/sampleobservatio of Number = n

sproportion sample of Average= p

proportion of error Standard =

MBD000863BB.unknown

MBD0001552C.unknown

MBD00041595.unknown

MBD00052840.unknown

MBD0003B55B.unknown

Chapter 8

Quality Management: Focus on Six Sigma

Quality Management: Focus on Six Sigma

Defining quality

Cost of quality

Quality programs

Six Sigma

DMAIC process

Seven basic quality tools

Statistical process control (SPC)

Benchmarking

Quality function deployment (QFD)

Taguchi methods

Mistake proofing (poka-yoke)

Copyright 2012 Health Administration Press

Defining Quality

Organizations perspective

Performance (design) quality

Conformance (design) quality

Customer perspective

Garvins eight dimensions

Parasuraman, Zeithaml, and Berrys five dimensions

Institute of Medicine

Quality assurance program


Cost of (Poor) Quality

External failurecosts associated with failure after the customer receives the product or service

Internal failurecosts associated with failure before the customer receives the product or service

Appraisalcosts associated with inspecting and evaluating the quality of supplies and/or final product/service

Preventioncosts incurred to eliminate or minimize appraisal and failure costs


Quality Programs

ISO 9000

Baldrige criteria

Six Sigma


ISO 9000

International standards concerned with ensuring that organizations maintain consistently high levels of quality

Five sections:

Quality management system

Management responsibility

Resource management

Measurement, analysis, and improvement

Product realization


Baldrige Award

Established to recognize organizations for their achievements in quality and to raise awareness about the importance of quality

Seven categories:

Leadership

Strategic planning

Customer and market

focus

Measurement, analysis, and knowledge management

Human resources focus

Process management

Results


Six Sigma

Philosophy

Eliminate defects through prevention and process improvement

Methodology

Team-based approach to process improvement using the DMAIC cycle

Set of tools

Quantitative and qualitative statistically based tools

Goal

3.4 defects per million opportunities (DPMO)


Successful Six Sigma

Top-management support

Use of quantitative measures

Culture and Leadership

Extensive training

DMAIC approach

Team-based projects

Impact on organizations financials


Six Sigma Infrastructure


DMAIC Process

Define

Measure

Analyze

Improve

Control


DMAIC Process: Define

Project team chooses a project on the basis of the businesss strategic objectives and the needs or requirements of the customers of the process

Characteristics of good projects:

Save or make money for the organization

Produce measurable process outcomes

Relate clearly to organizational strategy

Are supported by the organization


DMAIC Process: Measure


DMAIC Process

Analyze

Analyze collected data to determine the root causes

Improve

Identify, evaluate, and implement the improvement solutions

Control

Put controls in place to ensure process improvement gains are maintained


Seven Basic Quality Tools

Flow Chart


Statistical Process Control (SPC)

SPC is a statistics-based methodology for determining when a process is moving out of control.

All processes have variation in output.

Some of the variation is inherent in the process (common).

Some of the variation is due to assignable (special) causes.

SPC is aimed at discovering variation due to assignable causes and correcting those causes.




SPC: Out-of-Control Situations



50% of patients wait more than 30 minutes

10% of patients wait more than 30 minutes


Process Capability

A measure of how well the process can produce output that meets desired standards or specifications

Compares process specifications (set by the customer or management) to control limits (the natural or common variability in the process)


Process Capability

Cp and Cpk


Process Capability

Cp and Cpk


Process Capability

Cp and Cpk


Rolled Throughput Yield

Step 2

95/100 error-free products

Step 3


Step 4


Step 1


Proportions

Actual


Quality Function Deployment (QFD): House of Quality

Correlation

matrix

Design

requirements

Customer

requirements

Competitive

assessment

Relationship

matrix

Specifications

or

target values

Importance

Importance weight

Importance

Importance weight


QFD Example

Customer needs

Goal: Develop a system to ensure that diabetes patients receive preventive exams


Knowledge that it is time for an office visit

Knowledge of why follow-up is needed

Convenient to schedule

Known appointment length

Appointment on time

QFD Example

Technical responses

On-time appointment

Appointment length range

Time to schedule

Information on need

Subsequent notification

Initial notification


Time knowledge

Why knowledge

Convenient

Appointment length

Appointment time

QFD Example

Importance:

Patient desire

Cost

Competitive

advantage

On-time appointment


Time to schedule

Information on need




Time knowledge5

Why knowledge3

Convenient4

Appointment length3

Appointment time4

QFD Example

On-time appointment


Time to schedule

Information on need



Relationships:

Strong = 5

Medium = 3

Weak = 1


Time knowledge5

Why knowledge3

Convenient4

Appointment length3

Appointment time4

QFD Example

On-time appointment


Time to schedule

Information on need



Replace icons with numbers

Relationships:

Strong = 5

Medium = 3

Weak = 1


Time knowledge553

Why knowledge35

Convenient43

Appointment length353

Appointment time435

QFD Example

On-time appointment


Time to schedule

Information on need



Multiply by importance and sum


Time knowledge553

Why knowledge35

Convenient45


Appointment time435

251515202729

QFD Example

On-time appointment


Time to schedule

Information on need



Technical correlations

Relationships:

+ = Strong positive

= Strong negative

+

+


Time knowledge553

Why knowledge35

Convenient45


Appointment time435

251515202729

QFD Example

On-time appointment


Time to schedule

Information on need



Target:

100 diabetics/month; 85% compliance


Time knowledge553

Why knowledge35

Convenient45


Appointment time435

251515202729

QFD Example: Outcome

To: Dan McLaughlin

From: Southview Clinic

Dear Dan,

You had an appointment with Dr. Adams about six months ago, and it is now time for another visit. We need to check your blood pressure, do some blood tests, and adjust your prescriptions if needed. We would like to review these preventive procedures in advance, so please see www.southview.com/prev22.

We have two openings available next week, on Tuesday at 8:30 am and Thursday at 2:30, to see Dr. Adams. Click on one of these days to make the appointment, or e-mail us with dates and times that work for you.

We appreciate you continuing your care with us, and Dr. Adams looks forward to seeing you.


Taguchi Methods

Taguchi loss function

A quality product is a product that causes a minimal loss (expressed in $) to society during its entire life.

Design of Experiments (DOE)

Design for Six Sigma (DFSS)


Benchmarking

Process of identifying, understanding, and adapting outstanding practices and processes to improve organizational performance

Steps in benchmarking:

Determine what to benchmark

Determine how to measure it

Gather information and data

Implement the best practice

in the organization


Mistake Proofing (Poka-Yoke)

A mechanism that either:

Prevents a mistake from being made

Makes the mistake immediately obvious

Eliminates errors


Riverview Generic Drug Project: Prescription Process


Riverview Generic Drug Project: Drug Type and Availability

Microsoft Excel screen shots reprinted with permission from Microsoft Corporation.


Riverview Generic Drug Project

Microsoft Excel screen shots reprinted with permission from Microsoft Corporation.


DMAIC Process

Seven Quality Control Tools


DMAIC Process: Other Tools


DMAIC Process: Other Tools



End of Chapter 8

OPERATIONS HEALTHCARE

MANAGEMENTs e c o n d e d i t i o n

D a n i e l B . M c L a u g h l i n a n d

J o h n R . O l s o n

6

6

6

6

6

6

20

25

30

35

40

051015202530

Day

Mean Wait Time (minutes)

+/- 1

s

+/- 2

s

+/- 3

s

Out of

Control

Sample

Observation

_

X=0

UCL=3

LCL=-3

8orMoreSamplesAbove(orBelow)Mean

Observation

_

X=0

LCL=-3

UCL=3

6orMoreSamplesIncreasing(orDecreasing)

Observation

_

X=0

UCL=3

LCL=-3

OneSampleMoreThan+/-3StandardErrorsfromMean

Observation

_

X=0

UCL=3

LCL=-3

14orMoreSamplesOscillating

One Sample More Than +/ -3 Standard

Errors from Mean

14 or More Samples Oscillating

8 or More Samples Above (or Below)

Mean

6 or More Samples Increasing (or

Decreasing)

Observation

_

X=0

UCL=3

LCL=-3

8orMoreSamplesAbove(orBelow)Mean

Observation

_

X=0

LCL=-3

UCL=3

6orMoreSamplesIncreasing(orDecreasing)

Observation

_

X=0

UCL=3

LCL=-3

OneSampleMoreThan+/-3StandardErrorsfromMean

Observation

_

X=0

UCL=3

LCL=-3

14orMoreSamplesOscillating

One Sample More Than +/ -3 Standard

Errors from Mean

14 or More Samples Oscillating

8 or More Samples Above (or Below)

Mean

6 or More Samples Increasing (or

Decreasing)

Current Wait Time

15

20

25

30

35

40

45

Wait Time (minutes)

Wait Time Goal

15

20

25

30

35

40

45

Wait Time (minutes)

-

-

=

-

-

=

-

=

-

=

s

x

USL

or

s

LSL

x

C

x

USL

or

LSL

x

C

s

LSL

USL

C

LSL

USL

C

pk

pk

p

p

3

3

min

by

estimated

is

and

3

3

min

6

by

estimated

is

and

6

s

s

s

-7-6-5-4-3-2-101234567

1.5

s

shift

LSL

USL

3.4

DPM

O

Taguchi Loss Function

Y ($)

LSLTarget

USL

Loss

2

2

2

$

whereLoss

kTYk

Best

Worst

Range

of

values

Your

organization

Best

Worst

Range

of

values

Range

of

values

Your

organization

Your

organization

Patient

needs

drug

Clinician

prescribes

drug

Information

on drugs

Type of

drug

Drug

efficacy

End

Drug

efficacy

End

Generic

Drug

works

Non-Generic

Drug

works

Drug

doesnt

work

Drug doesnt work

65%35%

15%

20%

Generic

Non-Generic,

Generic

Available

Non-Generic,

Generic Not

Available

Tools and Techniques

Define

Measure

Analyze

Improve

Control

Cause-and-Effect Diagram x

Run Chart xx

Check Sheet

Histogram xx

Pareto Chart xxx

Scatter Diagram xx

Flowchart xx

TAT Data

ObservationObservation

1234512345

DayDay

14441805125161444353276

22832584218175284556315

35483595046182820677669

45753631552192523352123

53050626842204674241047

64240504973213354624027

72617504791226455621472

85439398228235349724961

94662536457241516183578

10497134424325649514770

115364123543263621514057

127543435064272458198816

137419525559287566342771

149140661573296042205960

155932594971305228853967

RV CS Data column

DayProportion of patients who were unsatisfied

10.17

20.13

30.15

40.22

50.16

60.13

70.17

80.17

90.11

100.16

110.15

120.17

130.17

140.12

150.15

160.14

170.13

180.15

190.15

200.22

210.19

220.15

230.12

240.16

250.18

260.14

270.17

280.18

290.19

300.14

310.19

320.10

330.17

340.15

350.17

360.15

370.15

380.15

390.14

400.19

RV CS Data

DayProportion of patients who were unsatisfiedDayProportion of patients who were unsatisfiedDayProportion of patients who were unsatisfied

10.17150.15280.18

20.13160.14290.19

30.15170.13300.14

40.22180.15310.19

50.16190.15320.10

60.13200.22330.17

70.17210.19340.15

80.17220.15350.17

90.11230.12360.15

100.16240.16370.15

110.15250.18380.15

120.17260.14390.14

130.17270.17400.19

140.12

Notes

This workbook contains all the information for the Riverview Clinic SPC Example in Chapter 7 Healthcare Operations Management and utilizes the Excel Quality Control template.

The RV Wait Times contains the data both with and without the out-of-control observation.

Mean Chart (sigma known and unknown) and Range Chart are the initial control charts with the out-of-control point included. (Note that the sigma known and unknown charts are essentially the same.

Mean Chart (sigma known and unknown)(2) and Range Chart (2) are the control charts with the out-of-control point removed.

The ND charts illustrate the proportion of patients that will experience various wait times given the system mean and s.d.

The Process Capability worksheet (Machine A) illustrates the Cp and Cpk with the current system. Note that although the Cp implies that the process is capable, the process is not centered on the specification limits and, therefore, the correct measure is Cpk. The Cpk shows that the process is not capable. Machine B-E illustrate process capabilites for the other scenarios discussed in the text.

The remaining worksheets are for the text illustrations and powerpoint slides.

RV Wait Times

Riverview Clinic Wait TimeRiverview Clinic Wait Time (Remove Outlier)

Observations of Wait Times (minutes)Sample MeanSample RangeObservations of Wait Times (minutes)Sample MeanSample Range

ObservationObservation

Day123456Day123456

129292231293128.509129292231293128.509

224294026363030.8316224294026363030.8316

328332526283328.838328332526283328.838

426313830232829.3315426313830232829.3315

536292429263229.3312536292429263229.3312

626273225302928.177626273225302928.177

722333031373431.1715722333031373431.1715

840292629323031.0014840292629323031.0014

932322134282929.3313932322134282929.3313

1034263527312629.8391034263527312629.839

1135302930312730.3381135302930312730.338

1231393232303132.5091231393232303132.509

1336243029312629.33121336243029312629.3312

1425232931252326.0081425232931252326.008

1538433735383237.17111635293025283029.5010

1635293025283029.50101726292033302827.6713

1726292033302827.67131822292630362828.5014

1822292630362828.50141933333437283032.509

1933333437283032.5092026263434253630.1711

2026263434253630.1711

Standard Deviation =4.13Overall Mean =29.62

Standard Deviation =4.42Overall Mean =30.00

Instructions

Notes on using the quality control template:

All values in blue cells are to be entered by the user, lighter blue cells can be entered but will be calculated if not entered, all other values are automatically calculated, and do not need to be altered. Each quality control model has a sample value set entered, make sure to clear all values before proceeding with your own data.

Cells with red marks in the upper right corner have comments, let the mouse hover over these cells to read the comments to further explain the quality control model.

This spreadsheet is locked/protected in order to keep the cells with equations from being changed. If a model does need to be altered, the spreadsheet first needs to be unlocked/unprotected. Do this by selecting the workbook you wish to unlock, click on "Tools", highlight "Protection", and select "Unprotect Sheet". The sheet will then be unlocked so alterations can be made.

Equations for models will be given when available.

Mean Chart ( known)


4.42UCL35.4133723316

z3LCL24.5866276684

n = number of observations/ sample6Mean30


128.535.41337233163024.5866276684

230.833333333335.41337233163024.5866276684

328.833333333335.41337233163024.5866276684

429.333333333335.41337233163024.5866276684

529.333333333335.41337233163024.5866276684

628.166666666735.41337233163024.5866276684

731.166666666735.41337233163024.5866276684

83135.41337233163024.5866276684

929.333333333335.41337233163024.5866276684

1029.833333333335.41337233163024.5866276684

1130.333333333335.41337233163024.5866276684

1232.535.41337233163024.5866276684

1329.333333333335.41337233163024.5866276684

142635.41337233163024.5866276684

1537.166666666735.41337233163024.5866276684

1629.535.41337233163024.5866276684

1727.666666666735.41337233163024.5866276684

1828.535.41337233163024.5866276684

1932.535.41337233163024.5866276684

2030.166666666735.41337233163024.5866276684

2135.41337233163024.5866276684

2235.41337233163024.5866276684

2335.41337233163024.5866276684

2435.41337233163024.5866276684

2535.41337233163024.5866276684

2635.41337233163024.5866276684

2735.41337233163024.5866276684

2835.41337233163024.5866276684

2935.41337233163024.5866276684

3035.41337233163024.5866276684

3135.41337233163024.5866276684

3235.41337233163024.5866276684

3335.41337233163024.5866276684

3435.41337233163024.5866276684

3535.41337233163024.5866276684

3635.41337233163024.5866276684

3735.41337233163024.5866276684

3835.41337233163024.5866276684

3935.41337233163024.5866276684

4035.41337233163024.5866276684

35.41337233163024.5866276684

Mean Chart ( known)

Sample Means

UCL

Mean

LCL

Period

Mean




n = number of observations/ sample6Overall Mean30

Average Range11.25

UCL35.4

LCL24.6


128.5935.43024.6

230.83333333331435.43024.6

328.8333333333835.43024.6

429.33333333331535.43024.6

529.33333333331235.43024.6

628.1666666667835.43024.6

731.16666666671535.43024.6

8311435.43024.6

929.33333333331335.43024.6

1029.8333333333935.43024.6

1130.3333333333835.43024.6

1232.5935.43024.6

1329.33333333331235.43024.6

14261135.43024.6

1537.16666666671135.43024.6

1629.51035.43024.6

1727.66666666671335.43024.6

1828.51435.43024.6

1932.5935.43024.6

2030.16666666671135.43024.6

2135.43024.6

2235.43024.6

2335.43024.6

2435.43024.6

2535.43024.6

2635.43024.6

2735.43024.6

2835.43024.6

2935.43024.6

3035.43024.6

3135.43024.6

3235.43024.6

3335.43024.6

3435.43024.6

3535.43024.6

3635.43024.6

3735.43024.6

3835.43024.6

3935.43024.6

4035.43024.6

n valueA2 factor

21.88

31.02

40.73

50.58

60.48

70.42

80.37

90.34

100.31

110.29

120.27

130.25

140.24

150.22

160.21

170.2

180.19

190.19

200.18






Upper Control Limit

Lower Control Limit




Sample Means

UCL

Mean

LCL

Period

Mean


Range Chart

Range Control Chart

n6Average Range11.15

UCL22.3

LCL0


1922.311.150

21622.311.150

3822.311.150

41522.311.150

51222.311.150

6722.311.150

71522.311.150

81422.311.150

91322.311.150

10922.311.150

11822.311.150

12922.311.150

131222.311.150

14822.311.150

151122.311.150

161022.311.150

171322.311.150

181422.311.150

19922.311.150

201122.311.150

2122.311.150

2222.311.150

2322.311.150

2422.311.150

2522.311.150

2622.311.150

2722.311.150

2822.311.150

2922.311.150

3022.311.150

3122.311.150

3222.311.150

3322.311.150

3422.311.150

3522.311.150

3622.311.150

3722.311.150

3822.311.150

3922.311.150

4022.311.150

LCLUCL

n valueD3 factorD4 factor

203.27

302.57

402.28

502.11

602

70.081.92

80.141.86

90.181.82

100.221.78

110.261.74

120.281.72

130.311.69

140.331.67

150.351.65

160.361.64

170.381.62

180.391.61

190.41.6

200.411.59

The Mean Control Chart (sigma unknown) is similar to the previous chart, except that the control limits are based on the range found in each sample, rather than the standard deviation of the population. The assumption here is that sigma is unknown for the population and/or the range is easier to calculate. The average range of all samples is related to the standard deviation of the population and upper and lower control limits are found by multiplying the average range by a tabulated factor rather than the z value. The tabulated factors begin on A54 of this sheet. In addition to the sample mean values, the range of these values for each sample must be entered.As in the previous chart, a sample mean outside the upper or lower control limits is very unusual and indicates assignable or non-random variation.



Average of mean values, population mean, or overall mean


Upper Control Limit

Lower Control Limit



Range Chart

Sample Ranges

UCL

Mean

LCL

Period

Mean

Range Control Chart

Mean Chart ( known) (2)


4.13UCL34.6810033364

z3LCL24.5646106987

n = number of observations/ sample6Mean29.6228070175


128.534.681003336429.622807017524.5646106987

230.833333333334.681003336429.622807017524.5646106987

328.833333333334.681003336429.622807017524.5646106987

429.333333333334.681003336429.622807017524.5646106987

529.333333333334.681003336429.622807017524.5646106987

628.166666666734.681003336429.622807017524.5646106987

731.166666666734.681003336429.622807017524.5646106987

83134.681003336429.622807017524.5646106987

929.333333333334.681003336429.622807017524.5646106987

1029.833333333334.681003336429.622807017524.5646106987

1130.333333333334.681003336429.622807017524.5646106987

1232.534.681003336429.622807017524.5646106987

1329.333333333334.681003336429.622807017524.5646106987

142634.681003336429.622807017524.5646106987

1529.534.681003336429.622807017524.5646106987

1627.666666666734.681003336429.622807017524.5646106987

1728.534.681003336429.622807017524.5646106987

1832.534.681003336429.622807017524.5646106987

1930.166666666734.681003336429.622807017524.5646106987

2034.681003336429.622807017524.5646106987

2134.681003336429.622807017524.5646106987

2234.681003336429.622807017524.5646106987

2334.681003336429.622807017524.5646106987

2434.681003336429.622807017524.5646106987

2534.681003336429.622807017524.5646106987

2634.681003336429.622807017524.5646106987

2734.681003336429.622807017524.5646106987

2834.681003336429.622807017524.5646106987

2934.681003336429.622807017524.5646106987

3034.681003336429.622807017524.5646106987

3134.681003336429.622807017524.5646106987

3234.681003336429.622807017524.5646106987

3334.681003336429.622807017524.5646106987

3434.681003336429.622807017524.5646106987

3534.681003336429.622807017524.5646106987

3634.681003336429.622807017524.5646106987

3734.681003336429.622807017524.5646106987

3834.681003336429.622807017524.5646106987

3934.681003336429.622807017524.5646106987

4034.681003336429.622807017524.5646106987

34.681003336429.622807017524.5646106987

The Range Control Chart plots the ranges of the samples on the same chart as the mean of the ranges and the upper and lower control limit for the range. The range is a measure of variability within the samples and sample ranges outside the control limits indicate that the variablility in the sample is very unusual and should be investigated for assingnable causes. The upper and lower control limits for the range are calculated using tabulated factors. These factors are on this sheet, starting at A54.



Upper Control Limit

Lower Control Limit



Mean Chart ( known) (2)

Sample Means

UCL

Mean

LCL

Period

Mean


Mean Chart ( unknown) (2)


n = number of observations/ sample6Overall Mean29.6228070175

Average Range11.2631578947

UCL35.029122807

LCL24.2164912281


128.5935.02912280729.622807017524.2164912281

230.83333333331435.02912280729.622807017524.2164912281

328.8333333333835.02912280729.622807017524.2164912281

429.33333333331535.02912280729.622807017524.2164912281

529.33333333331235.02912280729.622807017524.2164912281

628.1666666667835.02912280729.622807017524.2164912281

731.16666666671535.02912280729.622807017524.2164912281

8311435.02912280729.622807017524.2164912281

929.33333333331335.02912280729.622807017524.2164912281

1029.8333333333935.02912280729.622807017524.2164912281

1130.3333333333835.02912280729.622807017524.2164912281

1232.5935.02912280729.622807017524.2164912281

1329.33333333331235.02912280729.622807017524.2164912281

14261135.02912280729.622807017524.2164912281

1529.51135.02912280729.622807017524.2164912281

1627.66666666671035.02912280729.622807017524.2164912281

1728.51335.02912280729.622807017524.2164912281

1832.51435.02912280729.622807017524.2164912281

1930.1666666667935.02912280729.622807017524.2164912281

2035.02912280729.622807017524.2164912281

2135.02912280729.622807017524.2164912281

2235.02912280729.622807017524.2164912281

2335.02912280729.622807017524.2164912281

2435.02912280729.622807017524.2164912281

2535.02912280729.622807017524.2164912281

2635.02912280729.622807017524.2164912281

2735.02912280729.622807017524.2164912281

2835.02912280729.622807017524.2164912281

2935.02912280729.622807017524.2164912281

3035.02912280729.622807017524.2164912281

3135.02912280729.622807017524.2164912281

3235.02912280729.622807017524.2164912281

3335.02912280729.622807017524.2164912281

3435.02912280729.622807017524.2164912281

3535.02912280729.622807017524.2164912281

3635.02912280729.622807017524.2164912281

3735.02912280729.622807017524.2164912281

3835.02912280729.622807017524.2164912281

3935.02912280729.622807017524.2164912281

4035.02912280729.622807017524.2164912281

n valueA2 factor

21.88

31.02

40.73

50.58

60.48

70.42

80.37

90.34

100.31

110.29

120.27

130.25

140.24

150.22

160.21

170.2

180.19

190.19

200.18






Upper Control Limit

Lower Control Limit



Mean Chart ( unknown) (2)

Sample Means

UCL

Mean

LCL

Period

Mean


Range Chart (2)

Range Control Chart

n6Average Range11.2631578947

UCL22.5263157895

LCL0


1922.526315789511.26315789470

21422.526315789511.26315789470

3822.526315789511.26315789470

41522.526315789511.26315789470

51222.526315789511.26315789470

6822.526315789511.26315789470

71522.526315789511.26315789470

81422.526315789511.26315789470

91322.526315789511.26315789470

10922.526315789511.26315789470

11822.526315789511.26315789470

12922.526315789511.2

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