7qc tools 173
DESCRIPTION
QC ToolTRANSCRIPT
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7 QC TOOLS
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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.
Note: ISO 9000 : 2000 version defines Quality
as “Degree to which a set of inherent characteristics fulfils requirements.
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Can also be termed as ‘A measure of excellence’
Quality
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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”
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Meaning of “Quality”
Q = PE
P = Performance or resultE = Expectations
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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
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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.
QU
AL
ITY
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
HighQuality
Lowerprice
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
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ANALYSIS PROBLEM AND CAUSES
Collect Data Sort symptoms & Causes (effects) Brain Storm Fishbone - cause & effect analysis Prioritize
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GENERATING POTENTIAL SOLUTIONS
Brainstorm Build on each other’s ideas Analysis potential helps & hinders
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SELECTING AND PLANNING SOLUTION
Prioritize solutions Clarify tasks / Action plan Resource / Costs Present proposals
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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 problemsSimple but very powerful tools to solve day to
day work related problemsFind solutions in a systematic mannerWidely 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 conciselyData 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 distributionDefective item check sheetDefect cause check sheetCheck sheet for work station evaluationCheck sheet for design information accuracyCheck 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 : ABCDate 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 pollsThe variation of quality characteristics is called
“Distribution”Purpose of drawing a Histogram is to understand the “Population”
HISTOGRAM
Population
Sample
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12
23
43
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9
0
50
1160-170 170-180 180-190 190-200 200-210
Histogram for distribution of Center Distance (mm)
HISTOGRAM
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HISTOGRAM A HISTORY OF PROCESS OUT PUT
024
810121416
6Fre
quen
cy
47 48 49 50 51 52 53 54kg
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
3000N
o o
f p
eic
es
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Cu
m.
Pe
rce
nta
ge
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)
0102030405060708090
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 DIAGRAMDEFECT 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 DIAGRAMDEFECT CONCENTRATION DIAGRAM
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Component name : XYZConcentration diagram for Scratches produced ion 21-Aug-03Total no of defective produced is 11 Nos
Area of concern
Control ChartControl 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 ChartElements 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 ChartElements 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
Sam
ple
Sta
tistic
s
Interpreting Control ChartInterpreting 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.
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Zones for Mean Control ChartZones for Mean Control Chart
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11 22 33 44 55 66 77Sample NumberSample Number
UCLUCL
TargetTarget
LCLLCL
UWLUWL
LWLLWL
Zone - 3Zone - 3
Sam
ple
Mea
nS
amp
le M
ean
Zone - 2Zone - 2
Zone - 3Zone - 3
Zone - 2Zone - 2
Zone - 1Zone - 1
ActionAction
ActionAction
WarningWarning
WarningWarning
ContinueContinue
ContinueContinueZone - 1Zone - 1
Interpreting Control ChartInterpreting Control Chart
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UCL
1 2 3 4 5 6 7 8
Sample Number
Sta
tistic
s
UWL
LCL
Target
LWL
Point outside the Control limit
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Control Chart Views Process in Real Time
Time Intervals
Ran
geM
ean
LCLx
Output of the process in real time
Target
Target
UCLx
UCLr
Change in Location of Process MeanChange in Location of Process Mean
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43 48 49 50 51 52 5344 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 TargetCase When Process Mean is at Target
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43 48 49 50 51 52 5344 45 46 47
Target ProcessMean
Chances of getting a reading beyond U & L is almost nil
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UL
- 3 s +3 sU - L = 6 s
Case - Small Shift of the Process MeanCase - Small Shift of the Process Mean
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43 48 49 50 51 52 5344 45 46 47
Target
ProcessMean
Chances of getting a reading outside U is small
Small shift in process
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Shaded area shows the
probability of getting
a reading beyond U
UL
U-L = 6 s
Case - Large Shift of the Process MeanCase - Large Shift of the Process Mean
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ProcessMean
43 48 49 50 51 52 5344 45 46 47
Target
Chances of getting a reading outside U is large
Large shift in process
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Shaded area shows the
probability of getting
a reading beyond U
UL
U-L = 6 s
Change in Spread of ProcessChange in Spread of Process
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43 48 49 50 51 52 5344 45 46 47
Larger spread dueto special causes
Spread dueto common causes
Special cause & Common causeSpecial cause & Common cause
Special 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 ChartsMost 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 ChartControl Chart
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PART NAME :GLASS RUN PART NO : MODEL : PageTHICKNESS 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 D41 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 2.11SUM 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 1.519R 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 0.0323 SIGMA 3 * R/d2 0.0956 SIGMA 6 * R/d2 0.190
Cp = 2.11 Cpk=
MIN OF -0.20Cpu OR Cpl 4.41
Cpk =USL 1.500LSL 1.100
FOR XUCL = X + A2.R 1.561LCL = X - A2.R 1.476
FOR R (D3 = 0)UCL = D4.R 0.155LCL = D3.R 0.000
PROCESS STATAUSCONTROLLEDNOT CONTROLLED
XYZ Ltd
O
-0.050.000.050.100.150.200.25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
R -
CH
AR
T
R UCL LCL CL
1.4001.4201.4401.4601.4801.5001.5201.5401.5601.5801.600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
X -
CH
AR
T
X UCL LCL CL
How to draw?
Summary of Effect of Process ShiftSummary 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.
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Our Conclusion from Normal DistributionOur 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
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Interpreting Control ChartInterpreting Control Chart
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.
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Interpreting Control ChartInterpreting Control Chart
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.
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Interpreting Control ChartInterpreting Control Chart
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UCL
1 2 3 4 5 6 7 8
Sample Number
Sta
tistic
s
UWL
LCL
LWL
One point outsidecontrol limit
Interpreting Control ChartInterpreting Control Chart
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UCLUCL
1 2 3 4 5 6 7 8Sample Number
Sta
tistic
s
UWLUWL
LCLLCL
LWLLWL
Two points out of three consecutive points between warning limit and corresponding control limit
Interpreting Control ChartInterpreting Control Chart
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UCL
1 2 3 4 5 6 7 8Sample Number
Sta
tistic
s
UWL
LCL
LWL
Two consecutive points between warning limit and corresponding control limit
Interpreting Control ChartInterpreting Control Chart
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UCLUCL
1 2 3 4 5 6 7 8
UWLUWL
LCLLCL
LWLLWL
Seven consecutive points on one side of the centre line
Sample Number
Sta
tistic
s
Interpreting Control ChartInterpreting Control Chart
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UCL
1 2 3 4 5 6 7 8
Sample Number
Sta
tistic
s
UWL
LCL
LWL
Seven consecutive points having upward trend
Interpreting Control ChartInterpreting Control Chart
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UCLUCL
1 2 3 4 5 6 7 8
Sample Number
Sta
tistic
s
UWL
LCLLCL
LWL
Seven consecutive points having downward trend
LearningLearning
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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Thank You
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