Continuous Improvement
Bill Pedersen, PE
There are 3 ways to get better
numbers:
• Distort the System
– Downsizing example
– Reduced inventory
example
• Distort the figures
– End of Quarter
Example
• Improve the System!!!!!!
Continuous Improvement isn’t
just about improving, it is about
improving an organization’s
ability to improve.
“Speed is everything. It is the
indispensable ingredient in
competitiveness.”
• Jack Welch, GE CEO
• There is always one basic goal. Any ideas
what that might be?
• SIMPLIFY!!!!!!!!!!!!!!
• Start with the basic building blocks.
• Improve on that.
• Repeat - forever.
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.
A Few Continuous Improvement
Tools

It isn’t the tools of improvement that
are so important. It is the principles
behind those tools that make the
difference.
Applications of Continuous
Improvement
• Product Redesign Example
• Plant Operation and Scheduling
Example
• Machine Utilization Example
LEADERSHIP!
• Sir Ernest Shackleton
• Johnsonville Sausage
• Merck & Co., Inc.
What is SPC?
• My Definition: The use of statistical tools
to promote continuous improvement and
consistently produce high quality parts.
• Management by Data
Motivation for Using SPC
• SPC is one of the most important
continuous improvement tools.
• Variation is the enemy. SPC allows you to
identify and eliminate causes.
• Gets people involved with process.
• Provides hard numbers by which to judge
performance.
• Excellent source of statistical data to
incorporate into designs.
Data Types
• Continuous Data
- Time
- Temperature
- Dimensions
• Use Continuous Data
whenever possible more information.
• Attribute Data
-
Go/No Go gage
Pass/Fail
Number of defects
Above/Below Setpoint
• As Quality improves,
sample size increases
dramatically.
• Sample size such that
n x p >= 5
Basis of SPC
68.26%
95.46%
99.73%
Central Limit Theorem
• Justification of assuming a normal distribution:
– The averages of independently distributed random
variables is approximately normal, regardless of
the distributions of the individual samples.
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.
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.
Sources of Variation - 5 M’s
•
•
•
•
Machines
Method
Man
Materials (Incorporates environment
usually)
• Measurement
Measurement System
• Must verify the measurement system is
precise and accurate.
Precise
Accurate
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?
Bad Application of a good tool
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/sample
c chart, number of defectives/sample
u chart, u=c/n => average defects/unit
Primary Goal of Control Charts
Elimination of
Variation
Primary Problem with Control
Charts
Charting for the
Sake of Charting
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.
Out of Control Conditions
• A process that is in control only has common cause
variation 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.
Out of Control Conditions - ctd
1.
Zone C
Zone B
Zone A
• Any point outside of
Outcontrol
of Controllimits.
Examples
• Eight points in a row on one side of centerline.
4.
5.
6.
2.
3.
• Seven points in a row steadily increasing or decreasing.
Zone A
• Fourteen points in a row alternating up and down.
Zone B
• Two of Three in a row outside of 2 sigma, (same side of
Zone C centerline).
• Four of Five in a row outside of 1 sigma, (same side of
centerline).
• NOTE: You will see variations in these
rules. The difference is based on statistical
confidence desired.
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
Title/Process Description:
Variables
Chart
Tail
PartBend
Length
Height
Date
Formulas:
Model
X = ( x1+x2+x3 ) / 3
Size
Data
1
2
3
AVG. (X)
Target
Deviation
Range
D = X - Target
Range =
Greatest Data Least
1
RANGE (R)
AVERAGE (X)
0.024
NOTES
0.014
Plotted
Limits
UCL
0.016
0.004
-0.006
-0.016
0.030
-0.026
0.025
0.014
0.020
0.012
0.015
0.010
0.010
0.008
0.005
0.006
0.000
0.004
0.002
0.000
X
0.000
LCL
-0.016
UCL
0.011
R
0.004
Xbar, R chart example - ctd
• Action plan for out of control conditions is
well 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 action
when necessary. This is extremely
important to the operators, not to mention
the process.
What to do with all this data?
• Impress your friends.
• Pareto - 80/20 rule. Work on the most
significant special cause.
Pareto of Causes
100%
80%
15
60%
10
40%
5
20%
0
0%
Improper
Fixturing
Worn Tool
Worn Bit
Defects
Material Defect
Slippage
Percentage
Number
20
Overadjustment
• This is usually the result of a lack of
understanding that variation is random in
nature.
• Perfect example - Budgets.
– Spend it or lose it.
– Matching next years budget to this years
spending.
– Result is a steadily increasing trend away from
centerline. The rich get richer, the poor get
poorer.
Capability Index - Cpk
• Control charts have control limits. What
were they based on?
– The actual variation from the process.
• Specification limits determine which
product is acceptable. They have nothing to
do with the control limits whatsoever.
• Cpk is used to predict the percentage out of
specification based on the estimated process
average and variation.
Cpk - ctd
Z-distribution Defects vs. Cpk (centered)
100000
16395 - centerline)/(UCL - centerline)
• Cp
= (USL
10000
log (Defects (ppm))
2700
– 1000
or centerline minus lower limit.
318
• Cpk100= min(Cp)
63
27
10
• In other
words if the Spec6.8Limits just
1.6
1
happened to be equal to the Control Limits,
1.80
then 0Cpk = 1.0
0
0.002
0
0.80
1.00
1.20
1.40
1.60
1.80
1.33 1.50
Cpk (centered)
2.00
2.20
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.
Improving a Process that is in
Control
• Design of Experiments.
–
–
–
–
–
Machine modifications.
Method Changes, (SOPs).
Material Changes.
Manpower Changes, (training plan).
Measurement changes are not generally
advised, but don’t always rule it out, especially
if the process has been running in control for
quite some time.
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.