mod 5 04 graphical analysis april 02

8/12/2019 Mod 5 04 Graphical Analysis April 02

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2 Visteon Corporation Mod #5 Rev 1.0 3/02

Check status of request Dealer number incorrectParts not received

Change address Wrong addressName misspelled

Statement error Change addressCheck balance

Car never received Error on invoiceParts not received

Car not received Error on invoiceCheck status of request

Check status of request Check status of requestParts not received

Statement error Wrong addressParts not received

Statement error Error on invoiceParts not received

The following data is collected when customers

contact customer service

Where should you focus your improvement efforts?

Characterization ToolsGraphical Analysis


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Pareto Chart

A bar chart for non-numerical categor ies which has its bars

in decreasing height from left to r ight

Now the question is easier to answer!

40 22 19 12 5

40.8 22.4 19.4 12.2 5.1

40.8 63.3 82.7 94.9 100.0

0

10

20

30

40

50

60

70

80

90

100

0

20

40

60

80

100

Defect

CountPercent

Cum %

Percent

Cou

nt

Cumulative

line



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Why Use It ?

To focus efforts on the problems that offer the greatest potential

for improvement by showing their relative frequency or size in a

descending bar graph

What Does It Do?

Helps a team to focus on those causes that will have the

greatest impact if solved

Based on the proven Pareto principle: 20% of the sources cause80% of any problem

Displays the relative importance of problems in simple, quickly

interpreted format

Progress is measured in a highly visible format that provides

incentive to push on for more improvement

Pareto Chart



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5.31 3.84 3.36 3.33

5.57 4.16 5.39 4.27

2.70 2.80 3.46 3.31

2.91 3.09 5.84 4.12

3.06 4.64 3.38 4.671.59 5.51 3.39 4.78

6.20 4.14 3.97 3.87

4.68 4.89 3.83 3.86

5.87 3.05 4.27 2.55

2.06 3.33 4.35 2.97

Histogram

The following data represents measurements for a CTQ with upper

and lower specification limits of 2 and 6 respectively. How are you

doing at meeting your customers expectations?



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Histogram

A Histogram is

A bar chart for num erical classes and its bars need not be

in descending ord er

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

0

1

2

3

4

5

6

7

8

C1

Frequ

ency



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Histogram

What Does It Do?

Shows the relative frequency of occurrence of the various data

values

Reveals the centering, variation, and shape of the data

Provides useful information for predicting future performance of the

process

Helps to indicate if there has been a change in the processHelps answer the question, Is the process capable of meeting my

customer requirements?

Displays large amounts of data that are difficult to interpret in

tabular form



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Histogram

Why Use It?

To summarize data from a process that has been collected over a

period of time, and graphically present its frequency distribution in abar form

To give a quick estimate of the process center, spread, and shape



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Run Chart

The following data is

collected from a

critical process step.

How is the process

performing? Hasthere been a shift in

the process with

time?

Time % Defective Accounts

1 0.04

2 0.038

3 0.037

4 0.041

5 0.039

6 0.0427 0.041

8 0.043

9 0.042

10 0.044

11 0.039

12 0.045

13 0.04414 0.043

15 0.046

16 0.04

17 0.047

18 0.042

19 0.046



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Run Chart

A time ser ies plot is a chart that shows trends in data over t ime


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

0.037

0.042

0.047

Hour

%DefectiveAccounts


Used as a

primarymetric chart


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Run Chart

Why Use It ?

To allow a team to study observed data (a performance measure of

a process) for trends or patterns over a specified period of time

What Does It Do?

Monitors the performance of one or more processes over time todetect trends or patterns over a specified period of time

Allows a team to compare a performance measure before and after

implementation of a solution to measure its impact Tracks useful information for predicting trends



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Scatter Plot

Is there a relationship

between cycle time in days

and % defective

addresses? If so, how

strong is this relationship?

108 4.7%

102 4.5%

72 3.6%

68 3.6%

63 3.6%

61 3.6%60 3.8%

60 2.7%

59 3.3%

53 2.6%

47 2.5%

42 1.4%

40 2.2%

36 1.3%36 2.1%

34 1.7%

31 0.5%

31 1.4%

27 1.2%

25 1.3%

25 1.3%

% Defective

Addresses

Cycle Time

in days



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Correlation AnalysisCycle Time and % Defective Addresses

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

0 20 40 60 80 100 120

Cycle Time in days - (list pull to mail drop)

%DefectiveA

ddresses

Scatter Plot

A standard plot or sc atter diagram show s a relations hip between 2 var iables




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Scatter Plot

Why Use It ?

Produces a scatter plot that shows the relationship between two

variables graphically

Y DATA

X DATA



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Correlation vs. Causality Beware Just because two variables are correlated does not mean

that one variable causes the other

A plot of the population of Oldenburg at the end of each yearagainst the number of storks in that year, 1930-1936 Statistics

for Experimenters by Box, Hunter and Hunter

55 65 75

150

200

250

Population (in thousands)

NumberofStorks



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Customer Rep

111111111

122222222

22

# of accounts

181720191518191720

162019181518181410

1720

Customer Rep

333333333

344444444

44

# of accounts

182019191416171919

171417201619141716

2216

BoxplotThe following data represents customer service representatives

and the number of new accounts opened by them per day for 10

days. Do the customer service representatives yield the same

results?



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1 2 3 4

14

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21

22

23

24

Head Number

OAL

Boxplot

A Boxplot depic ts measures of the dis tr ibut ion that al low com par ison

of var ious dis tr ibut ions


* Outlier

75th Percentile

Minimum Observation

Maximum Observation

25th Percentile

Median (50th Percentile)



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Boxplot

Why Use It?

To give you a graphical summary of values and help you

identify extreme values

What Does It Do?

Represents roughly the middle 50% of the data in the rectangular

box

Represents the general extent of the data via lines or whiskersextending from either side

Marks outliers (observations far from the rest of the data)


mod 5 04 graphical analysis april 02

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