The Basic Seven QC Tools

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The Basic Seven (B7)The Basic Seven (B7)

Tools of QualityTools of Quality

A PowerPoint TrainingA PowerPoint Training

PresentationPresentation

By Keith H. Cooper By Keith H. Cooper

"As much as 95% of quality related problems in the factory can be solved"As much as 95% of quality related problems in the factory can be solved

with seven fundamental quantitative tools." - Kaoru Ishikawawith seven fundamental quantitative tools." - Kaoru Ishikawa



What are the Basic What are the Basic

Seven Tools of Quality?Seven Tools of Quality?

• Fishbone DiagramsFishbone Diagrams

• HistogramsHistograms

• Pareto AnalysisPareto Analysis

• FlowchartsFlowcharts

• Scatter PlotsScatter Plots

• Run ChartsRun Charts• Control ChartsControl Charts



Where did the Basic Where did the Basic

Seven come from?Seven come from?

Kaoru IshikawaKaoru Ishikawa• Known for “Democratizing Statistics”Known for “Democratizing Statistics”

• The Basic Seven Tools made statisticalThe Basic Seven Tools made statistical

analysis less complicated for the averageanalysis less complicated for the average

personperson

• Good Visual Aids make statistical andGood Visual Aids make statistical and

quality control more comprehendible.quality control more comprehendible.





Fishbone DiagramsFishbone Dia

grams

• No statistics involvedNo statistics involved

• Maps out a process/problemMaps out a process/problem

• Makes improvement easier Makes improvement easier

• Looks like a “Fish Skeleton”Looks like a “Fish Skeleton”



Constructing a FishboneConstructing a Fishbone

DiagramDiagram

• Step 1 - Identify the ProblemStep 1 - Identify the Problem

• Step 2 - Draw “spine” and “bones”Step 2 - Draw “spine” and “bones”

Example:Example: High Inventory Shrinkage at local DrugHigh Inventory Shrinkage at local Drug

StoreStore

Shrinkage



Co nst ructing ao nst ructing a

Fish bone Dia gramish bone Dia gram

• Step 3 - Identify different areas whereStep 3 - Identify different areas where

problems may arise fromproblems may arise from

Ex. :Ex. : High Inventory Shrinkage at local Drug StoreHigh Inventory Shrinkage at local Drug Store

Shrinkage

employees

shoplifters



Co nst ructing ao nst ructing a

Fish bone Dia gramish bone Dia gram

• Step 4 - Identify what these specificStep 4 - Identify what these specific

causes could becauses could be

Ex. :Ex. : High Inventory Shrinkage at local Drug StoreHigh Inventory Shrinkage at local Drug Store

Shrinkage

shoplifters

Anti-theft tags poorly designedExpensive merchandise out

in the open

No security/ surveillance




DiagramDiagram

• Ex. :Ex. : High Inventory Shrinkage at local Drug StoreHigh Inventory Shrinkage at local Drug Store

Shrinkage

shoplifters

Anti-theft tags poorly designedExpensive merchandise out in the open

No security/ surveillance

employeesattitude

new trainee

training

benefits practices




DiagramDiagram

• Step 5 – Use the finished diagram toStep 5 – Use the finished diagram to

brainstorm solutions to the main problems.brainstorm solutions to the main problems.





HistogramsHistograms

• Bar chartBar chart

• Used to graphically represent groupsUsed to graphically represent groups

of dataof data



ConstructingConstructing a Histograma Histogram

From a set of data computeFrom a set of data compute

• sumsum

• mean (x)mean (x)

• MaxMax

• MinMin• Range (max-min)Range (max-min)



ConstructingConstructing a Histograma Histogram

• Use range to estimate beginningUse range to estimate beginning

and endand end

• Calculate the width of eachCalculate the width of each

column by dividing the range bycolumn by dividing the range by

the number of columnsthe number of columns

Range

# of Columns

= Width



Acme Pizza ExampleAcme Pizza Example

• Let’s say the owner wants aLet’s say the owner wants a

distribution of Acme’s Thursdaydistribution of Acme’s Thursday

Night SalesNight SalesData Set from last Thursday(slices)Data Set from last Thursday(slices)

0 2 1 2 2 4 1 3 1 2 1 2 2 4 3 4 1 4 3 2 2 3 2 1 2 2 1 2 2 1 4 20 2 1 2 2 4 1 3 1 2 1 2 2 4 3 4 1 4 3 2 2 3 2 1 2 2 1 2 2 1 4 2

2 1 2 1 2 2 1 2 1 2 1 2 1 2 1 2 1 2 2 2 1 2 1 2 1 1 2 2 2 3 1 42 1 2 1 2 2 1 2 1 2 1 2 1 2 1 2 1 2 2 2 1 2 1 2 1 1 2 2 2 3 1 4

2 2 3 2 2 2 1 2 3 2 2 4 2 2 4 4 1 2 2 2 3 2 2 1 2 2 4 2 1 2 4 22 2 3 2 2 2 1 2 3 2 2 4 2 2 4 4 1 2 2 2 3 2 2 1 2 2 4 2 1 2 4 21 7 2 1 2 2 3 1 2 1 1 2 1 2 2 2 1 2 2 1 2 1 2 2 2 4 2 41 7 2 1 2 2 3 1 2 1 1 2 1 2 2 2 1 2 2 1 2 1 2 2 2 4 2 4




Mean = 2.032258Mean = 2.032258

Max = 7Max = 7

Min = 0Min = 0

Range = 7Range = 7

QuestionQuestionFor 7 columns what would the widthFor 7 columns what would the width

be?be?Range/Columns=7/7=1 slice




33

65

812

0 0 1

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7

Slices of Pizza

# t i m

e s

o r d

e r e d

Histogram



Constructing a HistogramConstructing a Histogram

How is this helpful to Acme?How is this helpful to Acme?

• 2 slices of pizza most common order 2 slices of pizza most common order

placedplaced• Distribution of sales useful for Distribution of sales useful for

forecasting next Thursday’s lateforecasting next Thursday’s latenight demandnight demand

If you were an Acme manager howIf you were an Acme manager howcould you apply this information?could you apply this information?





Pareto AnalysisPareto Analysis

• Very similar to HistogramsVery similar to Histograms

• Use of the 80/20 ruleUse of the 80/20 rule

• Use of percentages to showUse of percentages to showimportanceimportance



Acme Pizza (part 2)Acme Pizza (part 2)

SlicesSlices FrequencyFrequency %%

00 11 .3.3

11 3333 13.0913.0922 6565 25.7925.79

33 88 3.173.17

44 1212 4.764.76

55 00 00

66 00 00

77 11 .3.3




• The completed Pareto Analysis results in theThe completed Pareto Analysis results in the

following graph:following graph:

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7

Slices of Pizza

# t i m

e s

o r d

e r e d

1 3 5 6




Critical ThinkingCritical Thinking

• How does the Pareto AnalysisHow does the Pareto Analysis

differ from the Histogram?differ from the Histogram?

• How can this be a useful tool toHow can this be a useful tool to

the Acme boss?the Acme boss?





FlowchartsFlowcharts

• A graphical picture of a PROCESSA graphical picture of a PROCESS

Process Decision

The process flow





f



How can we use the flowchart toHow can we use the flowchart to

analyze improvement ideas fromanalyze improvement ideas from

the Histogram?the Histogram?

Window Window Take Customer Take Customer Money?Money?

(start)(start) Order Order

Get PizzaGet Pizza

LockupLockup

Put More inPut More in

OvenOven 2 Pies2 Pies Available?Available?

TimeTime

to close?to close?

Take to Customer Take to Customer

no

yes

no

yesno

yes







Scatter PlotsScatter Plots

•2 Dimensional X/Y plots2 Dimensional X/Y plots

• Used to show relationshipUsed to show relationship

between independent(x) andbetween independent(x) and

dependent(y) variablesdependent(y) variables



Acme PizzaAcme Pizza

(Scatter Diagram)(Scatter Diagram)

Minutes CookingMinutes Cooking Defective PiesDefective Pies

1010 11

4545 88

3030 55

7575 2020

6060 1414

2020 44

2525 66

In this simple example, you can find the existingIn this simple example, you can find the existing

relationship without much difficulty but…relationship without much difficulty but…



Scatter DiagramsScatter Diagrams

0

5

10

15

20

25

0 20 40 60 80

•Easier to see direct

relationship

Time Cooking (minutes)

D e f e c t i v

e P i z

z a

s



Scatter DiagramsScatter Diagrams

As a quality toolAs a quality tool

• What does this tell Acme What does this tell Acme

management about their management about their processes?processes?

• Improvements?Improvements?

0

5

10

15

20

25

0 20 40 60 80

Time Cooking (minutes)

D e f e c t i v

e P

i z z a s





Run chartsRun charts

• Time-based (x-axis)Time-based (x-axis)

• CyclicalCyclical

• Look for patternsLook for patterns



Run ChartsRun Charts

8 9 10 11 12 1 2 3 4 8 9 10 11 12 1 2 3 4 8 9 10 11 12 1 2 3 4

PM- AM PM- AM PM- AMThursday

Week 1

Thursday

Week 2

Thursday

Week 3

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Slices/hour

Time





Control ChartsControl Charts

• Deviation from MeanDeviation from Mean

• Upper and Lower Spec’sUpper and Lower Spec’s

• RangeRange






Acme Pizza Management wants to get

in on the control chart action

•Average Diameter = 16 inches

•Upper Limit = 17 inches

•Lower Limit = 15 inches



Acme exampleAcme example


Upper LimitUpper Limit

17 inches17 inches

Lower LimitLower Limit

15 Inches15 Inches

Small Pie

X16 inches=



Acme example #50Acme example #50


•Pies within specifications were

acceptable

•One abnormally small pie is

“uncommon”

•Should be examined for quality control



SummarySummary

• Basic Seven Tools of QualityBasic Seven Tools of Quality

• Measuring dataMeasuring data

•Quality AnalysisQuality Analysis

• ““Democratized statistics”Democratized statistics”



BibliographyBibliography

• Foster, Thomas.Foster, Thomas. Managing Quality. An IntegrativeManaging Quality. An IntegrativeApproachApproach. Upper Saddle River : Prentice Hall,. Upper Saddle River : Prentice Hall, 2001.2001.

• Stevenson, William. “Supercharging Your ParetoStevenson, William. “Supercharging Your ParetoAnalysis.”Analysis.” Quality ProgressQuality Progress October 2000:October 2000:51-55.51-55.

• ““Dr Ka oru Is hik aw a.” In terne tr Ka oru Is hik aw a.” In terne t“http://www.dti.gov.uk/mbp/bpgt/m9ja00001/m9j“http://www.dti.gov.uk/mbp/bpgt/m9ja00001/m9j

a0000110.html.” 16 February 2001.a0000110.html.” 16 February 2001.

• ““Chemical and Process Engineering.” Internet.Chemical and Process Engineering.” Internet.“http://lorien.ncl.ac.uk/ming/spc/spc8.htm.” 17“http://lorien.ncl.ac.uk/ming/spc/spc8.htm.” 17 FebruaryFebruary2001.2001.

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