7qc tools 173

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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

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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

25

12

23

43

27

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


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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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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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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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90

UCL

1 2 3 4 5 6 7 8

Sample Number

Sta

tistic

s

UWL

LCL

LWL

One point outsidecontrol limit


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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


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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


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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


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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


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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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7qc tools 173

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