spc ppt

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Statistical Process Contol

(SPC)

Presented By:Aditya Meena

Abhishek Raj

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What is SPC?

SPC stands for

Statistical

Process

Control

Collection, analyzing and interpreting data

An activity which transforms input into output by utilizing

resources

Measuring and monitoring performance

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Statistical Process Control (SPC)

SPC is a methodology for charting the process and

quickly determining when a process is "out of control.

(e.g., a special cause variation is present because something

unusual is occurring in the process).

The process is then investigated to determine the root

cause of the "out of control" condition.

When the root cause of the problem is determined, a

strategy is identified to correct it.

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Statistical Process Control (SPC)

The management responsible to reduce common causeor system variation as well as special cause variation.

This is done through process improvement techniques,investing in new technology, or reengineering the

process to have fewer steps and therefore less variation.

Reduced variation makes the process more predictablewith process output closer to the desired or nominalvalue.

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Rationale for SPC

The rationale for SPC is to improve product

quality and simultaneously reduce costs, and

to improve product image in order to

successfully compete in world markets.

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DATA and its Types

ATTRIBUTE DATA

Counted data or attribute data answers to the questions of how

many or how often.

VARIABLE DATA

Measured data (variable data) answers to the questions like how

long, what volume, how much time and how far. This data is

generally measured with some instrument or device.

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The SPC steps

Basic approach:

Awareness that a problem exists.

Determine the specific problem to be solved. Diagnose the causes of the problem.

Determine and implement remedies.

Implement controls to hold the gains achievedby solving the problem.

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SPC requires the use of statistics

Quality improvement efforts have their foundation in

statistics.

SPC involves the

collectiontabulation

analysis

interpretationpresentation of numerical data.

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What are 7-QC ToolsGraphs Scatter Diagram

Pareto diagram

Cause & Effect

Diagram

Histograms Control Chart

Check Sheets

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SPC is comprised of 7 tools:

Pareto diagram

Histogram

Cause and Effect Diagram Check sheet

Process flow diagram

Scatter diagram Control chart

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Pareto Principle

Alfredo Pareto (1848-1923) Italian Economist:

Conducted studies of the distribution of wealth in

Europe.

20% of the population has 80% of the wealth

Joseph Juran used the term vital few & trivial

many or useful many. He noted that 20% of

the quality problems caused 80% of the dollarloss.

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Pareto

diagram

Percentfrome

achcause

Causes of poor quality

0

10

20

30

40

50

60

70(64)

(13)(10)

(6)(3) (2) (2)

A paretodiagram is agraph thatranks dataclassifications in

descendingorder from leftto right.

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Pareto diagram

Sometimes a pareto diagram has a cumulative

line.

This line represents the sum of the data as

they are added together from left to right.

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Histogram

The histogram, graphically shows the process capability and, if desired, the relationship

to the specifications and the nominal.

It also suggests the shape of the population and indicates if there are any gaps in the

data.

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Histogram

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Histogram

ata Range Frequency0-10 1

10-20 3

20-30 6

30-40 4

40-50 2

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Cause-and-Effect Diagrams

Show the relationships between a problem

and its possible causes.

Developed by Kaoru Ishikawa (1953)

Also known as

Fishbone diagrams

Ishikawa diagrams

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Cause and Effect Skeleton

Quality

Problem

Materials

EquipmentPeople

Procedures

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Quality

Problem

MachinesMeasurement Human

ProcessEnvironment Materials

Faulty testing equipment

Incorrect specifications

Improper methods

Poor supervision

Lack of concentration

Inadequate training

Out of adjustment

Tooling problems

Old / worn

Defective from vendor

Not to specifications

Material-handling problems

Deficiencies

in product design

Ineffective quality

management

Poor process design

Inaccurate

temperature

control

Dust and

Dirt

Fishbone Diagram

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Cause-and-Effect Diagrams

To construct the skeleton, remember:

For manufacturing - the 4 Ms

man, method, machine, material For service applications

equipment, policies, procedures, people

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Check SheetsCheck sheets explore what and where

an event of interest is occurring.

Attribute Check Sheet

27 15 19 20 28

Order Types 7am-9am 9am-11am 11am-1pm 1pm-3pm 3pm-5-pm

Emergency

Nonemergency

Rework

Safety Stock

Prototype Order

Other

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Flowcharts

Graphical description of how work isdone.

Used to describe processes that are tobe improved.

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Activity

DecisionYes

No

Flowcharts

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Flowcharts

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Flow Diagrams

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Process Chart Symbols

Operations

Inspection

Transportation

Delay

Storage

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Scatter Diagram

.

(a) Positive correlation (b) No correlation (c) Curvilinear relationship

The patterns described in (a) and (b) are easy tounderstand; however, those described in (c) aremore difficult.

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Process Control Charts

Establish capability of process under normal conditions

Use normal process as benchmark to statistically identifyabnormal process behavior

Correct process when signs of abnormal performance

first begin to appear

Control the process rather than inspect the product!

Statistical technique for tracking a process anddetermining if it is going out to control

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Upper Control Limit

Lower Control Limit

6

3

Target Spec


Upper Spec Limit

Lower Spec Limit

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UCL

Target

LCL

Samples

Time

In control Out of control !

Natural variation

Look for

specialcause !

Back in

control!


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When to Take Action

A single point goes beyond control limits

(above or below)

Two consecutive points are near the same limit (above or

below) A run of 5 points above or below the process mean

Five or more points trending toward either limit

A sharp change in level

Other erratic behavior

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Types of Control Charts

Attribute control charts

Monitors frequency (proportion) of defectives

p- charts

Defects control charts Monitors number (count) of defects per unit

ccharts

Variable control charts Monitors continuous variables

x-bar and Rcharts

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p-Chart

UCL =p+ zp

LCL =p- zp

where

z = the number of standard deviations from the process

average

p = the sample proportion defective; an estimate of the

process average

p = the standard deviation of the sample proportion

p=p(1 -p)

n

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Control Chart Z Values

Smaller Z values make more sensitive

chartsZ = 3.00 is standard

Compromise between sensitivity and

errors

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p-Chart Example

20 samples of 100 pairs of jeans

NUMBER OF PROPORTIONSAMPLE DEFECTIVES DEFECTIVE

1 6 .06

2 0 .00

3 4 .04

: : :

: : :20 18 .18

200

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p-Chart Example

20 samples of 100 pairs of jeans

NUMBER OF PROPORTIONSAMPLE DEFECTIVES DEFECTIVE

1 6 .06

2 0 .00

3 4 .04

: : :

: : :20 18 .18

200

Example 15.1

p =

= 200 / 20(100)

= 0.10

total defectives

total sample observations

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UCL =p+ z = 0.10 + 3p(1 -p)

n

0.10(1 - 0.10)

100

UCL = 0.190

LCL = 0.010

LCL =p- z = 0.10 - 3p(1 -p)

n0.10(1 - 0.10)

100

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0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

Proportiondefective

Sample number

2 4 6 8 10 12 14 16 18 20

UCL = 0.190

LCL = 0.010

p= 0.10

p-Chart

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Using c-charts

Find long-run proportion defective (c-bar)

when the process is in control.

Determine control limits

c

c

zcLCL

zcUCL

cc

C: count the Number of defects

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c-Chart

The number of defects in 15 sample rooms

1 12

2 83 16

: :

: :15 15

190

SAMPLE NUMBER OF DEFECTS

c= = 12.67

190

15

UCL = c+ zc

= 12.67 + 3 12.67

= 23.35

LCL = c+ zc

= 12.67 - 3 12.67

= 1.99

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c-Chart

3

6

9

12

15

18

21

24

Numbe

rofdefects

Sample number

2 4 6 8 10 12 14 16

UCL = 23.35

LCL = 1.99

c= 12.67

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Control Charts for Variables

Mean chart ( x -Chart ) Uses average of a sample

Range chart ( R-Chart ) Uses amount of dispersion in a sample

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Range ( R- ) Chart

UCL = D4R LCL = D3R

R=Rk

where

R = range of each sample

k = number of samples

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R-Chart Example

OBSERVATIONS (SLIP-RING DIAMETER, CM)

SAMPLE k 1 2 3 4 5 x R

1 5.02 5.01 4.94 4.99 4.96 4.98 0.08

2 5.01 5.03 5.07 4.95 4.96 5.00 0.12

3 4.99 5.00 4.93 4.92 4.99 4.97 0.084 5.03 4.91 5.01 4.98 4.89 4.96 0.14

5 4.95 4.92 5.03 5.05 5.01 4.99 0.13

6 4.97 5.06 5.06 4.96 5.03 5.01 0.10

7 5.05 5.01 5.10 4.96 4.99 5.02 0.14

8 5.09 5.10 5.00 4.99 5.08 5.05 0.11

9 5.14 5.10 4.99 5.08 5.09 5.08 0.15

10 5.01 4.98 5.08 5.07 4.99 5.03 0.10

50.09 1.15

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R

kR= = = 0.115

1.15

10

UCL = D4R = 2.11(0.115) = 0.243

LCL = D3R= 0(0.115) = 0

UCL = 0.243

LCL = 0

Range

Sample number

R= 0.115

|

1

|

2

|

3

|

4

|

5

|

6

|

7

|

8

|

9

|

10

0.28

0.24

0.20

0.16

0.12

0.08

0.04

0

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Mean (x-bar) Chart

Choose sample size n (same as for R-charts) Determine average of in-control sample

means (x-double-bar)

x-bar = sample mean k = number of observations of n samples

Construct x-bar-chart with limits:

kxx /

RAxLCLRAxUCL22

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x-Chart Example

OBSERVATIONS (SLIP-RING DIAMETER, CM)

SAMPLE k 1 2 3 4 5 x R

1 5.02 5.01 4.94 4.99 4.96 4.98 0.08

2 5.01 5.03 5.07 4.95 4.96 5.00 0.12

3 4.99 5.00 4.93 4.92 4.99 4.97 0.084 5.03 4.91 5.01 4.98 4.89 4.96 0.14

5 4.95 4.92 5.03 5.05 5.01 4.99 0.13

6 4.97 5.06 5.06 4.96 5.03 5.01 0.10

7 5.05 5.01 5.10 4.96 4.99 5.02 0.14

8 5.09 5.10 5.00 4.99 5.08 5.05 0.11

9 5.14 5.10 4.99 5.08 5.09 5.08 0.15

10 5.01 4.98 5.08 5.07 4.99 5.03 0.10

50.09 1.15

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x-Chart Example

Example 15.4

UCL =x+A2R= 5.01 + (0.58)(0.115) = 5.08

LCL =x-A2R= 5.01 - (0.58)(0.115) = 4.94

=

=

x= = = 5.01 cm= x

k

50.09

10

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x-Chart ExampleUCL = 5.08

LCL = 4.94

Mean

Sample number

|

1

|

2

|

3

|

4

|

5

|

6

|

7

|

8

|

9

|

10

5.10

5.08

5.06

5.04

5.02

5.00

4.98

4.96

4.94

4.92

x= 5.01=

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Benefits of SPC

Factual decision

Waste reduction

Increased monitoring

Operator involvement

COPQ reduction

Customer satisfaction

PERFORMANCE

IMPROVEMEN

T

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benefits

Provides surveillance and feedback for keepingprocesses in control

Signals when a problem with the process has occurred

Detects assignable causes of variation

Reduces need for inspection Monitors process quality

Provides mechanism to make process changes and trackeffects of those changes

Once a process is stable, provides process capabilityanalysis with comparison to the product tolerance

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SUMMARY SPC using statistical techniques to

measure and analyze the variation in processes

to monitor product quality and

maintain processes to fixed targets.

Statistical quality control using statistical techniques formeasuring and improving the quality of processes, sampling plans,

experimental design,

variation reduction,

process capability analysis,

process improvement plans.

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SUMMARY

A primary tool used for SPC is

the control chart,

a graphical representation of certain descriptive statistics for

specific quantitative measurements of the process.

These descriptive statistics are displayed in the control

chart in comparison to their "in-control" sampling

distributions.

The comparison detects any unusual variation in the

process, which could indicate a problem with the

process.

St i I l ti SPC Th

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Steps in Implementing SPCThe

Preparation Phase The three phases in implementing SPC are preparation, planning and execution.

The preparation phase has 3 steps:

1. Commit to SPCtop management must be committed. It requires spendingmoney, utilizing human resources, changing the organizations culture, hiringemployees with new skills, or retaining consultants.

2. Form a SPC CommitteeSPC can be delegated to a cross functional team that istasked to oversee implementation and execution. A typical team will be composedof representatives from manufacturing, quality assurance, engineering, finance,and statistics. In a manufacturing plant, the manufacturing member should be theteam leader. The function of the team will be to plan and organize theimplementation for its unique application, to provide training for the operators,and to monitor and guide the execution phase. Forming the committee is top

managements responsibility. 3. Train the SPC Committee: The training must be done by an expert. The

members will then know enough to set objectives and to determine whichprocess should be targeted first. Continued help from a statistics expert remainscritical.

St i I l ti SPC Th

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Planning Phase The planning phase includes the next 5 steps:

4. Set SPC Objectives: How will we measure success (balance sheet, customer feedback,

reduction in scrap, lower cost of quality). Objectives may be added, eliminated, or changed,

but they must be in place and understood by all.

5. Identify Target Processes: Select a few processes for pilot implementation. With some

initial successes under its belt, the organization can go with confidence to the processes that

are the most critical. Start implementation at the front of a series of processes. 6. Train Appropriate Operators and Teams: The operators and teams who will be directly

involved with the collection, plotting, and interpretation of SPC data, and those who will be

involved in getting the targeted processes under control will require training in the use of

quality tools.

7. Ensure Repeatability and Reproducibility of Gauges and Methods: All measuring

instruments from simple calipers and micrometers to coordinate measuring machines mustbe calibrated and certified for acceptable performance.

8. Delegate Responsibility for Operators to Play a Key Role : Operators need to be delegated

the responsibility for collecting and plotting the data, maintaining the SPC control charts, and

taking appropriate action.

St i I l ti SPC Th

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Execution Phase The execution phase includes 9 steps:

9. Flowchart the Process: Flowcharting will reveal process features or factors that were not

known to everyone. The development of the process flowcharts should be the responsibility

of special teams composed of the process operators, their internal suppliers and consumers,

and appropriate support members.

10. Eliminate the Causes of Special Variation: The cause and effect diagram is then used to

list all the factors (causes) that might impact the output (effect). Then by applying othertools such as Pareto Charts, histograms, and stratification, the special causes can be

identified and eliminated. Elimination of special causes should be a team effort.

11. Develop Control Charts: The statistics expert or consultant can help develop the

appropriate control charts and calculate valid upper and lower limits and process averages.

12. Collect and Plot SPC Data & Monitor: The process operator takes the sample data and

plots it on the control chart at regular intervals. The operator carefully observes the locationof the plots, knowing they should be inside the control limits.

13. Determine Process Capability: When a process is in control and is still not capable of

meeting the customer specifications, it is up to management to upgrade the process

capability, which may require the purchase of new equipment.

Steps in Implementing SPC The

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Execution Phase

14. Respond to Trends and Out of Limits Data: With experience, operators may

be able to handle many of these situations on their own, but if they cannot, it is

important they summon help immediately. The process should be stopped till the

cause is identified and removed. Prevent the production of defective products

that must be scrapped or reworked.

15. Track SPC Data: The SPC committee and management should see where they

should concentrate resources for improvement.

16. Eliminate the Root Cause of Any New Special Cause of Variation: For

example, it is possible that the material from a new vendor for raw material may

cause the process to shift the process average one way or the other. Eliminating

the root cause may require management approved procedure mandating the use

of preferred suppliers.

17. Narrow the Limits for Continual Improvement: Narrowing the limits will

result in fewer parts failing to meet the specifications. Quality will improve, and

costs will decrease. The key is finding ways to improve the process.

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Inhibitors of SPC

The most common inhibitor of SPC is lack of resources.

Capability in Statistics: Many organizations do not have the in house expertise in statistics

that is necessary for SPC.

Misdirected Responsibility for SPC: The process operators will require help from the

statistician and others from time to time, but they are the appropriate owners of SPC for

their processes.

Failure to Understand the Target Process: A good SPC system cannot be designed for aprocess that is not fully understood.

Failure to Have Process Under Control: Before SPC can be effective, any special cause of

variation must be removed.

Inadequate Training and Discipline: Everyone who will be involved in the SPC program must

be trained.

Measurement Repeatability and Reproducibility: Before a gauge is used for SPC it should becalibrated and its repeatability certified.

Low Production Rates: Low rates of production offers an opportunity for taking a 100%

sample.

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