javier garcia - verdugo sanchez - six sigma training - w1 minitab - graphical methods

Introduction in Minitab:- Graphical Methods

14

12

10

Mean 6,054StDev 0,2541N 72

Histogram of Water ContentNormal

Boxplot of Water Content

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en

cy

6,66,46,26,05,85,6

8

6

4

2

0

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ter

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nt

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6,25

6,00

Time Series Plot of Water Content

Water Content5,75

5,50

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ter

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nt

6,50

6,25

6,00

5,75 ng

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eck

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190

Scatterplot of Recieving Check vs Final check

Index70635649423528211471

5,75

5,50

Final check

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cie

vin

240230220210200190180170160

180

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160 Week 1

Knorr-Bremse Group

Graphical Methods & the DMAIC Cycle

ControlMaintain

DefineMaintain

ImprovementsSPC

Control Plans

Project charter (SMART)

Business Score CardQFD VOC

D Documentation QFD + VOC

Strategic GoalsProject strategy

C M

MeasureB li A l iImprove

AIBaseline Analysis

Process MapC + E Matrix

M t S tAnalyze

ImproveAdjustment to the

OptimumFMEA Measurement System

Process CapabilityDefinition of critical

InputsFMEA

S

FMEAStatistical Tests

SimulationTolerancing Statistical Tests

Multi-Vari StudiesRegression

TolerancingAlways and Everywhere!

Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 2/34

Everywhere!

The Use of Graphs

In every phase of the DMAIC cycle during your project work you will need to answer questions In general we can findyou will need to answer questions. In general we can find answers for these questions with three methods in the following order: 1. what kind of practical relations exist, 2. how g p ,can I present that graphically and 3. which analytical methods can I use to get the proof.

Graphics are useful in every project in two ways. They are helpful to visualize the relations and to communicate them.

1. Practical1. Practical

2. GraphicalG ap ca

3. Analytical


y

About this Module

In this module you will be introduced to the use of the software „Minitab“. After a shortuse of the software „Minitab . After a short

time you will be able to create several different graphs and understand where to use these .

• Histogram

• Run Chart (Control Chart)( )

• Box Plot

• Dot Plot

• X-Y Scatter Plot

• Marginal Plot

• Matrix Plot

• Pareto Diagram


• Cause and Effect Diagram

Histogram

• For this example we need the file: WATER CONTENT.MTW

• The Variable (Y) is the water content of a mixing process.The process runs 24hrs. at 6 days a week in 3 shifts. The

water contents should be held in the range of 5,5 – 7 %. This is checked every 2 hrs

GraphThis is checked every 2 hrs.

>Histogram…

Day Time Shift Water Contenty1 6 1 5,671 8 1 61 10 1 6,271 12 1 6,33

Select a

,1 14 2 6,531 16 2 5,931 18 2 61 20 2 6,27

type of graph!

1 20 2 6,271 22 3 6,071 0 3 6,331 2 3 6,131 4 3 6 071 4 3 6,072 6 1 6,332 8 1 6,472 10 1 6


HistogramGraph

>Histogram…

>Simple

Select a graph>Simple graph

variable

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Histogram of Water Content

ncy

12

10

8

You can adjust the graph by double

clicking the item you

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qu

e

6

4

clicking the item you would like to change.

On the next page we

6,46,26,05,85,6

2

0

On the next page we will change the

number of intervals


Water Content6,46,26,05,85,6

HistogramGraph

>Histogram…

>Simple

2. Select Binning

3 Change>Simple

>Edit X Scale…

3. Change number of intervals

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12


1. Select the X axis with a n

cy

12

10

8

double click

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qu

e

6

4

6,556,406,256,105,955,805,655,50

2

0


Water Content6,556,406,256,105,955,805,655,50

HistogramGraph

>Histogram…

>With Fit and Groups>With Fit and Groups

>Multiple Graphs

>By Variables


6,66,46,26,05,85,65,4

3

1 2 3

Mean 6,133StDev 0 2292

1

gNormal

2

1

sity

StDev 0,2292N 12

Mean 6,183StDev 0,2241N 12

2

0

3

2

De

ns

4 5 6

Mean 6,161StDev 0,2386N 12

3

4

6,66,46,26,05,85,65,4

1

06,66,46,26,05,85,65,4

Water Content

Mean 5,85StDev 0,2560N 12

Mean 5,911StDev 0 1871

5


StDev 0,1871N 12

6

Panel variable: Day

HistogramGraph

>Histogram…

>With Fit and Groups

Histogram of Water ContentNormal

2,5

2,0

Day1234

qu

en

cy

2,0

1,5 Mean StDev N6,133 0,2292 126,183 0,2241 12

56

Fre

q

1,0

0 5

6,161 0,2386 125,85 0,2560 12

5,911 0,1871 126,083 0,2241 12

6, 83 0,

6,66,46,26,05,85,65,4

0,5

0,0


Water Content

Run Chart – Time Series PlotRun charts use the same set of data as histograms, but shows graphically the behavior over a certain time range. Create a run chart with the same set of data

Stat

Create a run chart with the same set of data.

>Time Series

>Time Series Plot…


Run Chart – Time Series PlotStat

>Time Series

>Ti S i Pl t>Time Series Plot…

>Simple

6,50


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6,25

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6,00

5,75

70635649423528211471

5,75

5,50


Index70635649423528211471

Run Chart – Time Series PlotStat

>Time Series

>Ti S i Pl t>Time Series Plot…

>With GroupsSelect a group

variable

6,50Shift

3

12


on

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6,25

3

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6,00

5,75

70635649423528211471

5,75

5,50


Index70635649423528211471

From a Run Chart to a Control ChartStat

>Control Charts

>V i bl Ch t f I di id l

The individual chart is the most simple graph within the statistical process control (SPC).

>Variable Charts for Individuals

>Individuals…As the output you get the mean value and the control limits based on the mean +- 3 StDev.


From a Run Chart to a Control ChartStat

>Control Charts

>Variable Charts for Indi id als>Variable Charts for Individuals

>Individuals…

6,75

UCL=6,638

I Chart of Water Content

l Va

lue

6,50

6,25

Ind

ivid

ua

l

6,00

5,75

_X=6,054

70635649423528211471

,

5,50 LCL=5,469


Observation70635649423528211471

Box PlotGraph

>Boxplot…

>One Y>One Y

Simple

6,50

Boxplot of Water Content

95%It represents 90% of the

data and there

en

t

6,25 75%

95%data and there distribution.

Very powerful if data

Wa

ter

Co

nte

6,00

25%

50%y pare split into

subgroups, see next page

5,75

25%

5 %

page


5,50

Box PlotGraph

>Boxplot…

>One Y>One Y

With Groups

6,50

Boxplot of Water Content vs Day

on

ten

t

6,25

Wa

ter

Co

6,00

5,75

654321

5,75

5,50


Day654321

Dot PlotGraph

>Dotplot…

>One Y Simple>One Y Simple

This diagram is very similarThis diagram is very similar to a histogram.

Always all the data will be shown

Dotplot of Water Contentshown.

Water Content6,446,306,166,025,885,745,60

We also have the possibility to split the data in subgroups (By variable).


Try some possibilities.

Dot PlotGraph

>Dotplot…

>One Y>One Y

With Groups

Dotplot of Water Content vs Shift

Sh

ift 1

2

Water Content6,446,306,166,025,885,745,60

3


X-Y Scatter PlotGraph

>Scatterplot…

>Simple

With scatter plots we can compare two rows of continuous data and visualize their relation.

>Simple

An example: The results shows the softening temperatures measured during the final check at the supplier and at the incoming inspection of the customer.

File: SoftenTemp mtw

pp g pThe results of two different plastic types are listed in two columns.

File: SoftenTemp.mtw


X-Y Scatter PlotGraph

>Scatterplot…

>Simple

In the menu scatter plots Minitab offers the option to add a regression line. This subject will be discussed in week 2>Simple

Graph

be discussed in week 2.

210

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Scatterplot of Recieving Check vs Final check>Scatterplot…

>With Regression

iev

ing

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Final check240230220210200190180170160

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Final check240230220210200190180170160

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Final check

X-Y Scatter Plot

With Minitab you have the possibility to adjust the

h f d

Graph

>Scatterplot…

>With Connect and Groups graphs for your needs.

We need some entries in h d di l

>With Connect and Groups

the data display.

210 Material12


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2

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240230220210200190180170160

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Final check

Marginal PlotGraph

>Marginal Plot…

>With Histogram

A further possibility for visualization is a combination of plots. Using the same data as before.

>With HistogramWe combine a scatter plot with either

histogram, box plot or dot plot.

Marginal Plot of Recieving Check vs Final check

eck

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Final check

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Matrix PlotGraph

>Matrix Plot…

>Matrix of plots Simple

This is helpful if the problem is more complex. You visualize the relations. It may serve as a

start point for further investigation>Matrix of plots Simple start point for further investigation.

Day Shift Sample time Temp Pressure Contamination %

File: Contamination.mtwDay Shift Sample time Temp Pressure Contamination %

1 1 1 91 48 21 1 2 97 52 21 1 3 88 44 21 1 4 87 43 11 2 1 109 50 6


Matrix PlotGraph

>Matrix Plot…


Matrix Plot of Contamination %; Temp; Pressure


Contamination %

5,0

2,5

11010090

2,5

0,0110

100Temp

100

90

55

5 02 50 0

50

45

Pressure

555045

? ?

5,02,50,0 555045


What can we learn here? What are the next possible steps?

Pareto Diagram

Pareto diagrams sort events vs. their frequencies, e.g. defects as a g q gfunction of their occurrence. A rule of thumb says that 20% of the

causes are liable for 80% of the effects.

Example: during an inspection process 4 different types of defects were monitored over 4 weeks. File: PARETO.CONTROL.MTW


Pareto DiagramStat

>Quality Tools

>P t Ch t>Pareto Chart

Pareto Chart of Defects

1000

800

100

80

Co

un

t

Pe

rce

nt600

00

60

P400

200

40

20

DefectsCount

12,3431 293 132 120

Percent 44,2 30,0 13,5

DeformationColorFlawsWeight0 0


Cum % 44,2 74,2 87,7 100,0

Pareto DiagramsThe same data as before. The diagrams on a weekly

lscale.

Required set up of the data has shown.

Pareto Chart of reason by W 1 to 4

DefColorFlawsWeight

300W 1 to 4 = 1 W 1 to 4 = 2 reason

WeightFlaws

Pareto Chart of reason by W 1 to 4

nt

200

100

ColorDef

Co

un

0

300

200

W 1 to 4 = 3 W 1 to 4 = 4 Defects W1 Defects W2 Defects W3 Defects W4 Reason W 1 to 4Weight Flaws Weight Weight Weight 1Flaws Flaws Weight Weight Flaws 1Weight Flaws Flaws Color Weight 1

Def Flaws Def Weight Def 1Weight Weight Color Flaws Weight 1

DefColorFlawsWeight

100

0

Flaws Weight Flaws Flaws Flaws 1Weight Flaws Flaws Weight Weight 1Flaws Weight Weight Def Flaws 1Weight Flaws Flaws Flaws Weight 1Flaws Def Weight Weight Flaws 1Weight Weight Def Flaws Weight 1


reasong

Cause and Effect Diagram

石川馨 Kaoru Ishikawa, * 1915, Tokio; † 16. April 1989

He developed the „Ishikawa Diagram“ (1943), also ll d C A d Eff t Di “called „Cause And Effect Diagram“

A graphic tool that helps identify, sort, and display possible causes of a problem or quality characteristic.

1. Identify and define the effect (objective or problem)

2 Identify the main categories like 6 M´s:2. Identify the main categories, like 6 M s: Material, Man, Machine, Measure, Method, Mother nature

3. Identify causes influencing the effect

4. Add detailed levels

5. Analyze the diagram… e.g. by help of ParetoCircle what you can measure or take action on


y


The Cause and Effect diagram is an excellent tool to present e.g. brainstorming results. It groups collected inputs with respect to g g p p pthe output. This is also named Ishikawa or Fishbone diagram.


Cause and Effect Diagram: Example

Or see Black Belt for further information or examples.


Cause and Effect DiagramStat

>Quality Tools

>Ca se and Effect>Cause and Effect

File: Fischbone.mtw

Cause-and-Effect Diagram

Measurements Material Personnel

Dust

Cutting qualityGranulate size

g

to less checks

Externl

Homogeneity

Glass distribution

Granulation temp

Surface condition

Electrical charge

Granulate size

Dust

Weight

Diameter

Length

It is also possible to QualityProblems

Nozzle plate

Cutting condition

Cutting technique

Externl

Hot material in cold pipe

Dryer temp

Electrical charge

Silo de-loading

generate sub branches for each main branch, e.g. if material split in internal

Environment Methods Machines

Conveyor design

Dust collector

Transport system

Silo de loading

Silo loading

Transport Extern

Transport Intern

if material split in internal or external causes.




Cause-and-Effect Diagram


Electrical charge

Granulate size

Dust

Cutting quality

Length

Granulate size

QualityProblems

to less checks

Externl

Homogeneity

Glass distribution

Granulation temp

Surface condition

Dust

Weight

Diameter

Nozzle plate

Cutting condition

Cutting technique

Conveyor design

D t olle to

Hot material in cold pipe

Dryer temp

Electrical charge

Silo de-loading

Silo loading

Environment Methods Machines

Dust collector

Transport system

Transport Extern

Transport Intern

FishboneFlat cat



MaterialMaschineMensch

Event ungleichSpezifikation

Zielsystem fällt ausVerantw. n. geregelt

Event fehlerhaft

Event leer

EDM Prg.fehler

Mapping n. aktuell

EDM Prg.änderung

EAISystemfällt aus

Virus

Policy geändert

DB voll

Bedienungs-fehler

Service n.gestartetReceive funct. n. gestartet

Event versehentl. gelöscht

Auf Fehler wird n. reagiert

Fehler wird n. bemerkt

Kontrollplan n. vorh./vollst.

Prg. n. getestet

Keiner/falscher Testplan

Fehlerursachen

Stromausfall

OS-FehlerUhr verstellt

Verschiedene Zonen

Event fehlerhaftNeue SWinstalliert HW ProblemStammdaten nicht oder falsch def. Falsche Prg.version installiert

ASNA lä ft i ht

wurdeungeplantgestoppt

FehlerhafteEingangsdaten

Eingabedatenzu langFehler in

Messsystem

OS Fehler

Netzwerk

Switch

Überlastung

Provider-Fehler

SZ / WZ

ASNA läuft nicht

falschkonf.

nichtgestartet

Prog.fehlerin KBMW00

Prog fehler in

RPG-Prog.

DB-Locks

Schlüsselwertenicht definiert

User sperrtDatensatz

Fehler in MW

Mitwelt

Methoden

Prog.fehler inXPPSDispatcherSchedule

Mapping

KonfigurationKomponent.

Available Fishbone tools are, e.g.Mi it b MS Vi iMS Vi i MS P i t


Minitab, MS VisioMS Visio, MS Powerpoint

Summary

The following graphical tools have been created with Minitab:with Minitab:

•Histogram•Run Chart (Control Chart)Run Chart (Control Chart)•Box Plot•Dot Plot•Dot Plot•X-Y Scatter Plot•Marginal Plot•Marginal Plot•Matrix PlotP t Di•Pareto Diagram

•Cause and Effect Diagram


javier garcia - verdugo sanchez - six sigma training - w1 minitab - graphical methods

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