lecture 23 - continuous improvement.ppt

8/10/2019 Lecture 23 - Continuous Improvement.ppt

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Continuous Improvement

Bill Pedersen, PE


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There are 3 ways to get better

numbers: Distort the System

Downsizing example

Reduced inventoryexample

Distort the figures

End of Quarter

Example

Improve the System!!!!!!


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Continuous Improvement isnt

just about improving, it is about

improving an organizationsability to improve.


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Speed is everything. It is the

indispensable ingredient in

competitiveness.

Jack Welch, GE CEO


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There is always one basic goal. Any ideas

what that might be?

SIMPLIFY!!!!!!!!!!!!!!

Repeat - forever.

Start with the basic building blocks.

Improve on that.


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Seven Step Procedure

Define the Opportunity Study the Current Situation - Key measures,

measurement system, R&R.

Cause Analysis - Pareto and Fishbone Diagrams,

Run Charts, Control Charts.

Experiment with the Process - Cpk, EZs, DOE

Check Results - new Cpk.

Standardize - SOPs, TPDs.

Communicate the gain - Improvement Record.


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A Few Continuous Improvement

Tools

It isnt the tools of improvement that

are so important. It is the principles

behind those tools that make thedifference.


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Applications of Continuous

Improvement

Product Redesign Example

Plant Operation and SchedulingExample

Machine Utilization Example


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LEADERSHIP!

Sir Ernest Shackleton

Johnsonville Sausage

Merck & Co., Inc.


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What is SPC?

My Definition: The use of statistical tools

to promote continuous improvement and

consistently produce high quality parts.

Management by Data


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Motivation for Using SPC

SPC is one of the most importantcontinuous improvement tools.

Gets people involved with process.

Provides hard numbers by which to judge

performance.

Excellent source of statistical data to

incorporate into designs.

Variation is the enemy. SPC allows you to

identify and eliminate causes.


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Data Types

Use Continuous Data

whenever possible -more information.

As Quality improves,

sample size increasesdramatically.

Sample size such that

n x p >= 5

Continuous Data

- Time

- Temperature- Dimensions

AttributeData

- Go/No Go gage

- Pass/Fail- Number of defects

- Above/Below Setpoint


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Basis of SPC

68.26%

95.46%

99.73%


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Central Limit Theorem

Justification of assuming a normal distribution:

The averages of independently distributed random

variables is approximately normal, regardless ofthe distributions of the individual samples.


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SPC System Tools

Many times SPC is thought of as control

charts. They are only one component of

SPC. It it the continuous improvement system

and methodology that makes this work.


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Variation

Common Causes of

Variation

Are always present.

Several small

contributors add up to

the whole common

cause variation.

Examples - weather,machine rigidity,

incoming material

variation.

Special Causes of

Variation

NOT always present -

sporadic in nature.

Typically have a

larger effect on

variation than any

single common cause. Examples - broken air

conditioner, worn

bearing, incorrect

material.


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Sources of Variation - 5 Ms

Machines

Method

Man

Materials (Incorporates environment

usually)

Measurement


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Measurement System

Must verify the measurement system is

precise and accurate.

Precise Accurate


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Repeatability and Reproducibility

or Gage Capability

VarianceTotal= Varianceproduct+ Variancemeasurement

Variancemeasurement= Variancerepeatability+ Variancereproducibility

Repeatability - Can the same operator get

consistent results?

Reproducibility - Can two operators get the same

results?


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Bad Application of a good tool


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Control Charts

Many Types, the most common-

Xbar and R, average and range

Xbar and mR, average and moving range

Xbar and S, average and variation of sample

p chart, proportion defective

np chart, number of defects/samplec chart, number of defectives/sample

u chart, u=c/n => average defects/unit


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Primary Goal of Control Charts

Elimination of

Variation


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Primary Problem with ControlCharts

Charting for the

Sake of Charting


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Control Limits

Control limits are generally set at +/- 3

sigma from the mean, (both estimated from

samples) example - xBar, R chart

UCL = Xdoublebar + A2*R

LCL = Xdoublebar - A2*R

Central Limit Theorem - The averages of independently

distributed random variables is approximately normal,

regardless of the distributions of the individual samples.


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Out of Control Conditions

A process that is in control only has common causevariation present.

An out of control process has special cause variation

present as well. Common cause variation is random in nature. If

there is a pattern present, it is assumed to be due to a

special cause.


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Out of Control Conditions - ctd Any point outside of control limits.

Eight points in a row on one side of centerline. Seven points in a row steadily increasing or decreasing.

Fourteen points in a row alternating up and down.

Two of Three in a row outside of 2 sigma, (same side of

centerline).

Four of Five in a row outside of 1 sigma, (same side of

centerline).

NOTE: You will see variations in theserules. The difference is based on statistical

confidence desired.

Out of Control Examples

Zone A

Zone B

Zone C

Zone C

Zone B

Zone A

1. 2. 3. 4. 5. 6.


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Xbar, R chart example - ctd

Sample sizes determined to be subgroups of

three, with sampling frequency every order.

Will hopefully be able to reduce this later on.

Need to determine control limits.

Set up a run chart to gather data.

After 25 samples, use that to calculate control

limits.

Xdoublebar +/- A2*R

Range Chart:

UCL = D4*Rbar

Centerline = Rbar

LCL = D3*Rbar


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V i bl Titl /P D i ti


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Variables Title/Process Description:

art

Date Formulas:

Model

Size X = ( x1+x2+x3 ) / 3

1

2 D = X - Target

3

AVG. (X)Target

Deviation

Plotted

Limits

UCL

0.016

X

0.000

LCL

-0.016

UCL0.011

R

0.004

NOTES

Range

Range =Greatest Data -

Least

Tail Bend Hei ht

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.000

0.002

0.004

0.006

0.008

0.0100.012

0.014

-0.026

-0.016

-0.006

0.004

0.014

0.024

1

AVERAGE

(X)

RANGE(

R)

Data

Part Len th


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Xbar, R chart example - ctd

Action plan for out of control conditions iswell documented and operators have been

trained.

Try it and see what happens. After you fix the bugs, then try it again.

Be sure to check the chart personally every

shift, (multiple times at first). Take actionwhen necessary. This is extremely

important to the operators, not to mention

the process.


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What to do with all this data?

Impress your friends. Pareto - 80/20 rule. Work on the most

significant special cause.

Pareto of Causes

0

5

10

15

20

Improper

Fixturing

Worn Tool Worn Bit Material Defect Slippage

Defects

Nu

mber

0%

20%

40%

60%

80%

100%

Perc

entage


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Cpk - ctd

Cp = (USL - centerline)/(UCL - centerline)

or centerline minus lower limit.

Cpk = min(Cp)

In other words if the Spec Limits just

happened to be equal to the Control Limits,

then Cpk = 1.0

Z-distribution Defects vs. Cpk (centered)

2700

16395

318

27

1.6

1.80

0.002

63

6.8

0

0

0

1

10

100

1000

10000

100000

0.80 1.00 1.20 1.40 1.60 1.80 2.00 2.20

Cpk (centered)

log(Defects(pp

m))

1.33 1.50


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Six Sigma

Cp = 2.0

Cpk = 1.5 due to shifting of the process by

1.5 standard deviations over time.

Result - one sided distribution failure of 3.4

ppm.

Application - electronics.


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Seven Step Procedure - revisited

Define the Opportunity

Study the Current Situation - Key measures,

measurement system, R&R. Cause Analysis - Pareto and Fishbone Diagrams,

Run Charts, Control Charts.

Experiment with the Process - Cpk, EZs, DOE

Check Results - new Cpk.

Standardize - SOPs, TPDs.

Communicate the gain - Improvement Record.

lecture 23 - continuous improvement.ppt

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