7qctools173-100325112603-phpapp01.ppt

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Quality
A subjective term for which each person has his or her own
definition. In technical usage, quality can have two
meanings:
1. The characteristics of a product or service that bear on its
ability to satisfy stated or implied needs.
2. A product or service free from deficiencies.

3
Can also be termed as
‘A measure of
excellence’
Quality

4
 Quality - an essential and distinguishing attribute of something.
 Attribute - an abstraction belonging to or characteristic of an entity
 Appearance, visual aspect - outward or visible aspect of a thing
 Attractiveness, attraction - the quality of arousing interest; being attractive or something
that attracts;
 Uncloudedness, clarity, clearness - the quality of clear water;
 Ease, easiness, simplicity - freedom from difficulty or hardship or effort.
 Suitability, suitableness - the quality of having the properties that are right for a specific
purpose.
 Excellence - the quality of excelling.
 Characteristic - a distinguishing quality
 Simpleness, simplicity - the quality of being simple or uncompounded
Meaning of “Quality”

5
Meaning of “Quality”
Q =
P
E
P = Performance or result
E = Expectations

6
Many people think that quality costs money and adversely
effects profits. But these costs are the costs of doing it wrong
first time .
Quality in the long run results in
increased profitability.
Quality, Cost & Profit relationship
Cost

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Cost
Quality and Profit : Traditional thinking

8
Quality and Profit : Paradigm shift
Cost

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1.Higher production due to
improved cycle time and
reduced errors and defects
2.Increased use of machine
and resources.
3.Improved material use from
reduced scrap and rejects
4.Increased use of personnel
resources
5.Lower level of asset
investments required to
support operations.
6.Lower service and support
costs for eliminated waste,
rework and non value added
activities.
QUALITY Higher productivity Increased
profitability
due to :
•Larger sales
•Lower production costs
•Faster turnover
Quality and Profit

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Quality and Profit
If the organization does not offer high
quality product or service , it will soon go
out of business . But just having high
quality will not be enough , because your
competitors will also have the high quality.
To win , companies will need to
offer high quality for a lower price
than their competitors.This requires
organizations to identify and reduce their
quality costs
High
Quality
Lower
price
C2A2C

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 Offer high quality for a lower price than their competitors.
 Reduce quality costs
 Stop producing defective thru’
 Process up-gradation
 Improving quality of analysis to identify and eliminate root causes
 Taking necessary countermeasure as when required
 Usage of right analytical tools
 Designing robust problem solving process
CHELLANGES

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PROBLEM SOLVING PROCESS
PROBLEM
SOLVING
PROCESS

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IDENTIFYING AND SELCTING PROBLEM
 Write Statement of the problem(s)
 Define Gap Between Actual & target
 Prioritize

14
ANALYSIS PROBLEM AND CAUSES
 Collect Data
 Sort symptoms & Causes (effects)
 Brain Storm
 Fishbone - cause & effect analysis
 Prioritize

15
GENERATING POTENTIAL SOLUTIONS
 Brainstorm
 Build on each other’s ideas
 Analysis potential helps & hinders

16
SELECTING AND PLANNING SOLUTION
 Prioritize solutions
 Clarify tasks / Action plan
 Resource / Costs
 Present proposals

17
IMPLEMENTING SOLUTION
 Establish controls
 Maintain Commitments
 Plan Contingencies

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EVALUATING SOLUTION
 Monitor results
 Restart Process if necessary

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7 QC TOOLS
Used to identify,analyze and resolve problems
Simple but very powerful tools to solve day to
day work related problems
Find solutions in a systematic manner
Widely used by Quality Circle members world
over

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Check sheets
 Histograms
 Pareto charts
 Cause & effect diagram (Ishikawa diagram)
 Scatter plot
 Defect concentration diagram
 Control charts
7 QC TOOLS

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Check sheets are formats used to collect and organize
data
Data can be collected easily and concisely
Data data is collected on the characteristic of interest.
The right data could be captured with all necessary facts
included e.g.
as when it happened ?
how many ?
what customer ?
CHECK SHEETS

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Check sheets for production process distribution
Defective item check sheet
Defect cause check sheet
Check sheet for work station evaluation
Check sheet for design information accuracy
Check sheet for vendor reliability
TYPES CHECK SHEETS

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CHECK SHEETS
Type of
defects
Check Sub-Total
Scratch 3
Dent 7
Flow mark 11
Short Shot 2
Total 23
Component name : ABC
Date of Production:22-Aug-03

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Histogram is the “Frequency data” obtained from
measurements displaying a peak around a certain value
and represented in form of polls
The variation of quality characteristics is called
“Distribution”
Purpose of drawing a Histogram is to understand the
“Population”
HISTOGRAM
Population
Sample

25
12
23
43
27
9
0
50
1
160-170 170-180 180-190 190-200 200-210
Histogram for distribution of Center Distance (mm)
HISTOGRAM

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HISTOGRAM A HISTORY OF PROCESS OUT PUT
0
2
4
8
10
12
14
16
6
Frequency
47 48 49 50 51 52 53 54
kg
Distribution

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Based on “80/20” rule (or ABC analysis)
Pareto(V.Pareto,an Italian economist) discovered this universal
law-80% of anything is attributed to 20% of its causes 80% of the
wealth is held by 20% of the population.
• 80% of our income goes into 20% of our needs.
• 80% of road accidents occur on 20% of the road.
• 80% of the absenteeism in a company is due to 20% of
workmen
“Significant few & in-significant many”
PARETO CHART

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PARETO CHART
Pareto analysis begins by ranking problems from highest to
lowest in order to fix priority
 The cumulative number of problems is plotted on the vertical
axis of the graph against the cause/phenomenon
Pareto by Causes e.g. Man,Machine,Method etc
Pareto by Phenomenon e.g.Quality,Cost,Delivery
Tells about the relative sizes of problems indicates an
important message about biggest few problems, if corrected,
a large % of total problems will be solved

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63.8
81.4
96.2 100.0
0
500
1000
1500
2000
2500
3000
No
of
peices
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Cum.
Percentage
DEFECT QTY 2064.0 567.0 480.0 122.0
CUM % 63.8 81.4 96.2 100.0
SHORT SHOT SILVER SINK MARK FLASH
PARETO ANALYSIS

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CAUSE n EFFECT (FISH BONE) DIAGRAM
This diagram (resembles skeleton of a fish) helps to separate out
causes from effects and to see problem in its totality
It’s a systematic arrangement of all possible causes,generated
thru’ brain storming
This can be used to :
Assist individual / group to see full picture.
 Serve as a recording device for ideas generated.
 Reveal undetected relationships between causes.
 Discover the origin/root cause of a problem
 Create a document or a map of a problem which can be posted in the work
area.

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The problem categories considered are :
Man, Machine, Method, Materials, Equipments & Environmental.
EFFECT
MACHINE METHOD ENVIRONMENT
MAN MATERIAL EQUIPMENT
CAUSE n EFFECT (FISH BONE) DIAGRAM

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SCATTER DIAGRAM
The scatter diagram is used for identifying the relationships and
performing preliminary analysis of relationship between any two
quality characteristics.
Clustering of points indicate that the two characteristics may be
related e.g.
Increasing in component weight with increase in hold time
during plastic injection molding ( + ve co-relation)
Increase in toughness components with decreasing injection
pressure (-ve co-relation) during molding

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SCATTER DIAGRAM (POSITIVE CORRELATION)
0
10
20
30
40
50
60
70
80
90
100
0 5 10
XY (Scatter) 1

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SCATTER DIAGRAM (NEGATIVE CORERLATION)
0
10
20
30
40
50
60
70
80
0 5 10
XY (Scatter) 1

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SCATTER DIAGRAM (NO CORERLATION)
0
10
20
30
40
50
60
70
80
0 5 10
XY (Scatter) 1

DEFECT CONCENTRATION DIAGRAM
 This is used to understand the potential defect prone area of
the parts produced
 The “Concentration Diagram” check sheet carries the diagram
of the problematic part,defects whenever observed to be
updated in the same using tally marks
 Based on the distribution of defects countermeasures are
taken at process/system level
 This tool is very useful to solve problems like Scratch,
Dent,Breakage thru’ handling improvement
 For plastic molded parts this tool is used to identify stress
points,weak joints,effect of gate shape/position on the quality
of parts etc.
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DEFECT CONCENTRATION DIAGRAM
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Component name : XYZ
Concentration diagram for Scratches produced ion 21-Aug-03
Total no of defective produced is 11 Nos
Area of
concern

Control Chart
Quality control charts, are graphs on which the
quality of the product is plotted as manufacturing or
servicing is actually proceeding.
It graphically, represents the output of the process
and uses statistical limits and patterns of plot, for
decision making
 Enables corrective actions to be taken at the earliest
possible moment and avoiding unnecessary
corrections.
The charts help to ensure the manufacture of uniform
product or providing consistent services which
complies with the specification.
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Elements of Typical Control Chart
1. Horizontal axis for sample number
2. Vertical axis for sample statistics e.g.
mean, range, standard deviation of sample.
3. Target Line
4. Upper control line
5. Upper warning line
6. Lower control line
7. Lower warning line
8. Plotting of sample statistics
9. Line connecting the plotted statistics
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Elements of Typical Control Chart
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1 2 3 4 5
Target
Lower control line
Upper warning line
Lower warning line
Sample Number
Upper control line
Lower control line
Sample
Statistics

Interpreting Control Chart
The control chart gets divided in three zones.
Zone - 1 If the plotted point falls in this zone, do not
make any adjustment, continue with the process.
Zone - 2 If the plotted point falls in this zone then
special cause may be present. Be careful watch for
plotting of another sample(s).
Zone - 3 If the plotted point falls in this zone then
special cause has crept into the system, and corrective
action is required.
41

Zones for Mean Control Chart
42
1 2 3 4 5 6 7
Sample Number
UCL
Target
LCL
UWL
LWL
Zone - 3
Sample
Mean
Zone - 2
Zone - 3
Zone - 2
Zone - 1
Action
Action
Warning
Warning
Continue
Continue
Zone - 1

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UCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWL
LCL
Target
LWL
Point outside the Control limit

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Control Chart Views Process in Real Time
Time Intervals
Range
Mean
LCLx
Output of the process in real time
Target
Target
UCLx
UCLr

Change in Location of Process Mean
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43 48 49 50 51 52 53
44 45 46 47
Process with
mean at Target
Process with
mean at more
than target
Process with
mean at less
than target

Case When Process Mean is at Target
46
43 48 49 50 51 52 53
44 45 46 47
Target Process
Mean
Chances of getting a reading beyond U & L is almost nil
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U
L
- 3 s +3 s
U - L = 6 s

Case - Small Shift of the Process Mean
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43 48 49 50 51 52 53
44 45 46 47
Target
Process
Mean
Chances of getting a reading outside U is small
Small shift in process
42
Shaded area
shows the
probability of
getting
a reading
beyond U
U
L
U-L = 6 s

Case - Large Shift of the Process Mean
48
Process
Mean
43 48 49 50 51 52 53
44 45 46 47
Target
Chances of getting a reading outside U is large
Large shift in process
42
Shaded area
shows the
probability of
getting
a reading
beyond U
U
L
U-L = 6 s

Change in Spread of Process
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43 48 49 50 51 52 53
44 45 46 47
Larger spread due
to special causes
Spread due
to common causes

Special cause & Common cause
 Special / Assignable cause : Causes due to negligence
in following work instructions, problem in machines
etc.This types of causes are avoidable and cannot
be neglected.
Common cause : Causes which are unavoidable and
in-evitable in a process.It is not practical to eliminate
the Chance cause technically and economically.
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Most Commonly Used Variable Control Charts
 To track the accuracy of the process
- Mean control chart or x-bar chart
 To track the precision of the process
- Range control chart
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Control Chart
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PART NAME :
GLASS RUN PART NO : MODEL : Page
THICKNESS SPECS :
MIN 1.10 TO 1.50 MAX REASON : PROCESS CAPABILITY STUDY AUDIT DATE 25/9/01
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 n d2 A2 D4
1 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.60 1.50 1.60 1.50 1.60 1.55 1.60 1.55 1.50 1.50 1 1.123 2.66 3.27
2 1.50 1.50 1.50 1.53 1.50 1.50 1.50 1.50 1.50 1.55 1.60 1.55 1.55 1.60 1.55 1.45 1.60 1.50 1.50 1.48 2 1.128 1.88 3.27
3 1.60 1.48 1.50 1.50 1.48 1.50 1.50 1.50 1.50 1.55 1.50 1.55 1.50 1.55 1.50 1.50 1.50 1.55 1.60 1.55 3 1.693 1.02 2.57
4 1.50 1.48 1.52 1.50 1.53 1.50 1.50 1.50 1.45 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.60 1.60 1.50 4 2.059 0.73 2.29
5 1.50 1.50 1.60 1.50 1.50 1.50 1.55 1.55 1.45 1.55 1.55 1.50 1.50 1.50 1.50 1.50 1.45 1.50 1.55 1.55 5 2.326 0.58 2.11
SUM X SUM X1+..+Xn 30.37
X 1.52 1.49 1.52 1.51 1.50 1.50 1.51 1.51 1.48 1.53 1.55 1.52 1.53 1.53 1.53 1.50 1.53 1.54 1.55 1.52 X SUM X1+..+Xn/n 1.519
R 0.10 0.02 0.10 0.03 0.05 0.00 0.05 0.05 0.05 0.05 0.10 0.05 0.10 0.10 0.10 0.10 0.15 0.10 0.10 0.07 R SUM R1+..+Rn/n 0.074
SIGMA R/d2 0.032
3 SIGMA 3 * R/d2 0.095
6 SIGMA 6 * R/d2 0.190
Cp = 2.11
Cpk=
MIN OF -0.20
Cpu OR
Cpl 4.41
Cpk =
USL 1.500
LSL 1.100
FOR X
UCL = X + A2.R 1.561
LCL = X - A2.R 1.476
FOR R (D3 = 0)
UCL = D4.R 0.155
LCL = D3.R 0.000
PROCESS STATAUS
CONTROLLED
NOT CONTROLLED
XYZ Ltd
O
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
R
-
CHART
R UCL LCL CL
1.400
1.420
1.440
1.460
1.480
1.500
1.520
1.540
1.560
1.580
1.600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
X
-
CHART
X UCL LCL CL
How to draw?

Summary of Effect of Process Shift
 When there is no shift in the process nearly all the
observations fall within -3 s and + 3 s.
 When there is small shift in the mean of process some
observations fall outside original -3 s and +3 s zone.
 Chances of an observation falling outside original -3 s
and + 3 s zone increases with the increase in the shift of
process mean.
86

Our Conclusion from Normal Distribution
 When an observation falls within original +3 s and -3 s
zone of mean of a process, we conclude that there is no shift
in the mean of process. This is so because falling of an
observation between these limits is a chance.
 When an observation falls beyond original +3 s and -3 s
zone of process mean, we conclude that there is shift in
location of the process
87

 Because the basis for control chart theory follows the normal
distribution, the same rules that governs the normal distribution
are used to interpret the control charts.
 These rules include:
- Randomness.
- Symmetry about the centre of the distribution.
- 99.73% of the population lies between - 3 s of and + 3 s the centre
line.
- 95.4% population lies between -2 s and + 2 s of the centre line.
88

If the process output follows these rules, the process
is said to be stable or in control with only common
causes of variation present.
If it fails to follow these rules, it may be out of
control with special causes of variation present.
 These special causes must be found and corrected.
89

90
UCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWL
LCL
LWL
One point outside
control limit

91
UCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWL
LCL
LWL
Two points out of three consecutive points
between warning limit and corresponding
control limit

92
UCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWL
LCL
LWL
Two consecutive points between warning limit and
corresponding control limit

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UCL
1 2 3 4 5 6 7 8
UWL
LCL
LWL
Seven consecutive points on one
side of the centre line
Sample Number
Statistics

94
UCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWL
LCL
LWL
Seven consecutive points having
upward trend

95
UCL
1 2 3 4 5 6 7 8
Sample Number
Statistics
UWL
LCL
LWL
Seven consecutive points having
downward trend

Learning
 Concept and definition of “Quality”
 Importance of improving Quality as a tool for cost
reduction
 Importance of proper analysis of Quality problems
 Usage of 7 QC tools to ensure “Defect free production”
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