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Introduction in Minitab:- Graphical Methods
14
12
10
Mean 6,054StDev 0,2541N 72
Histogram of Water ContentNormal
Boxplot of Water Content
Fre
qu
en
cy
6,66,46,26,05,85,6
8
6
4
2
0
Wa
ter
Co
nte
nt
6,50
6,25
6,00
Time Series Plot of Water Content
Water Content5,75
5,50
Wa
ter
Co
nte
nt
6,50
6,25
6,00
5,75 ng
Ch
eck
210
200
190
Scatterplot of Recieving Check vs Final check
Index70635649423528211471
5,75
5,50
Final check
Re
cie
vin
240230220210200190180170160
180
170
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 3/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 4/34
• 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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 5/34
HistogramGraph
>Histogram…
>Simple
Select a graph>Simple graph
variable
14
12
Histogram of Water Content
ncy
12
10
8
You can adjust the graph by double
clicking the item you
Fre
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 6/34
Water Content6,46,26,05,85,6
HistogramGraph
>Histogram…
>Simple
2. Select Binning
3 Change>Simple
>Edit X Scale…
3. Change number of intervals
14
12
Histogram of Water Content
1. Select the X axis with a n
cy
12
10
8
double click
Fre
qu
e
6
4
6,556,406,256,105,955,805,655,50
2
0
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 7/34
Water Content6,556,406,256,105,955,805,655,50
HistogramGraph
>Histogram…
>With Fit and Groups>With Fit and Groups
>Multiple Graphs
>By Variables
Histogram of Water Content
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 8/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 9/34
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…
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 10/34
Run Chart – Time Series PlotStat
>Time Series
>Ti S i Pl t>Time Series Plot…
>Simple
6,50
Time Series Plot of Water Content
on
ten
t
6,25
Wa
ter
Co
6,00
5,75
70635649423528211471
5,75
5,50
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 11/34
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
Time Series Plot of Water Content
on
ten
t
6,25
3
Wa
ter
Co
6,00
5,75
70635649423528211471
5,75
5,50
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 12/34
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.
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 13/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 14/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 15/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 16/34
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).
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 17/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 18/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 19/34
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
200
Scatterplot of Recieving Check vs Final check>Scatterplot…
>With Regression
iev
ing
Ch
eck
190
180
210
Scatterplot of Recieving Check vs Final check
Re
ci 180
170
Ch
eck
200
190
Final check240230220210200190180170160
160
Re
cie
vin
g C
180
170
Final check240230220210200190180170160
170
160
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 20/34
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
Scatterplot of Recieving Check vs Final check
Ch
eck
200
190
2
Re
cie
vin
g C
180
240230220210200190180170160
170
160
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 21/34
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
210
200
Re
cie
vin
g C
he
190
180
170
Final check
R
240220200180160
170
160
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 22/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 23/34
Matrix PlotGraph
>Matrix Plot…
>Matrix of plots Simple
Matrix Plot of Contamination %; Temp; Pressure
>Matrix of plots Simple
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 24/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 25/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 26/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 27/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 28/34
y
Cause and Effect Diagram
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.
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 29/34
Cause and Effect Diagram: Example
Or see Black Belt for further information or examples.
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 30/34
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.
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 31/34
Cause and Effect Diagram
Measurements Material Personnel
Cause-and-Effect Diagram
Measurements Material Personnel
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 32/34
Cause and Effect Diagram
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 33/34
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
Knorr-Bremse Group 07 BB W1 Graphics 07, D. Szemkus/H. Winkler Page 34/34
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