Transcript MMPC Chapter 4

Chapter 6: Quality Management
© Holmes Miller 1999
Quality Specifications
 Design quality: Inherent value of the product in the
marketplace
 Dimensions include:
 Performance
 Features
 Reliability/Durability
 Serviceability
 Aesthetics
 Perceived Quality.
 Conformance quality: Degree to which the product or
service design specifications are met
Importance of Quality
Costs & market share
Market Gains
Reputation
Volume
Price
Improved
Quality
Increased
Profits
Lower Costs
Productivity
Rework/Scrap
Warranty
The Concept of Consistency:
Who is the Better Target Shooter?
Not just the mean is important, but also the variance
Need to look at the distribution function
Funnel Experiment (Deming)
 Tampering with a stable system only increases the
production of faulty items and mistakes.
 Tampering is taking action based on the belief that a common cause
is a special cause.
 Improvement of a stable system nearly always means
reduction of variation.
 One necessary qualification of anyone in management is --
-- stop asking people to explain ups and downs that come
from random variation.
Basic Forms of Variation
Assignable variation is
caused by factors that
can be clearly
identified and possibly
managed
Common variation is
inherent in the
production process
Example: A poorly trained
employee that creates
variation in finished product
output.
Example: A molding process
that always leaves “burrs” or
flaws on a molded item.
Two Types of Causes for Variation
Common Cause Variation (low level)
Common Cause Variation (high level)
Assignable Cause Variation
• Need to measure and reduce common cause variation
• Identify assignable cause variation as soon as possible
Taguchi’s View of Variation
Traditional view is that quality within the LS and US is good and that
the cost of quality outside this range is constant, where Taguchi views
costs as increasing as variability increases, so seek to achieve zero
defects and that will truly minimize quality costs.
High
High
Incremental
Cost of
Variability
Incremental
Cost of
Variability
Zero
Zero
Lower Target Upper
Spec Spec
Spec
Traditional View
Lower Target Upper
Spec Spec
Spec
Taguchi’s View
Costs of Quality
Appraisal Costs
External Failure
Costs
Costs of
Quality
Internal Failure
Costs
Prevention Costs
Six Sigma Quality
 A philosophy and set of
methods companies use to
eliminate defects in their
products and processes
 Seeks to reduce variation
in the processes that
lead to product defects
 The name, “six sigma”
refers to the variation
that exists within plus or
minus six standard
deviations of the process
outputs
The Statistical Meaning of Six Sigma
Upper
Specification
Limit (USL)
Lower
Specification
Limit (LSL)
Process A
(with st. dev sA)
X-3sA X-2sA X-1sA
X
X+1sA
X+2s
X+3sA
3
Process B
(with st. dev sB)
X-6sB
X
Process capability measure
Cp 
USL  LSL
6ˆ
x
Cp
P{defect}
ppm
1
0.33
0.317
317,000
2
0.67
0.0455
45,500
3
1.00
0.0027
2,700
4
1.33
0.0001
63
5
1.67
0.0000006
0.6
6
2.00
2x10-9
0.00
X+6sB
•Don’t confuse control limits with specification limits: a process can be in control,
yet be incapable of meeting customer specs
Six Sigma Quality (Continued)
 Six Sigma allows managers to readily
describe process performance using a
common metric: Defects Per Million
Opportunities (DPMO)
DPMO 
Number of defects
 Number of 
 opportunit ies 
 for error per  x No. of units
 unit



x 1,000,000
Six Sigma Quality (Continued)
Example of Defects Per Million Opportunities
(DPMO) calculation. Suppose we observe
200 letters delivered incorrectly to the
wrong addresses in a small city during a
single day when a total of 200,000 letters
were delivered. What is the DPMO in this
situation?
DPMO 
200
 1  x 200,000
So, for every one
million letters
delivered this
city’s postal
managers can
expect to have
1,000 letters
incorrectly sent to
the wrong address.
x 1,000,000  1, 000
Cost of Quality: What might that DPMO mean in terms of overtime employment to correct the errors? Other costs?
Six Sigma Quality: DMAIC Cycle
 Define, Measure, Analyze, Improve,
and Control (DMAIC)
 Developed by General Electric as a
means of focusing effort on quality
using a methodological approach
 Overall focus of the methodology is
to understand and achieve what the
customer wants
 A 6-sigma program seeks to reduce
the variation in the processes that
lead to these defects
 DMAIC consists of five steps….
Six Sigma Quality: DMAIC Cycle (Continued)
1. Define (D)
Customers and their priorities
2. Measure (M)
Process and its performance
3. Analyze (A)
Causes of defects
4. Improve (I)
Remove causes of defects
5. Control (C)
Maintain quality
Example to illustrate the process…
 We are the maker of this cereal.
Consumer Reports has just published an
article that shows that we frequently
have less than 15 ounces of cereal in a
box.
 Exercise: What should we do?
 Define -- What is the critical-to-quality
characteristic?
 Measure -- How would we measure to evaluate the
extent of the problem?
 Analyze -- How can we improve the capability of our
cereal box filling process?
 Improve -- How good is good enough?
Motorola’s “Six Sigma”
 Control -- Statistical Process Control (SPC)
Analytical Tools for Six Sigma and
Continuous Improvement: Flow Chart
Material
Received
from
Supplier
No,
Continue…
Inspect
Material for
Defects
Defects
found?
Yes
Can be used to
find quality
problems
Return to
Supplier
for Credit
Diameter
Analytical Tools for Six Sigma and
Continuous Improvement: Run Chart
Can be used to identify
when equipment or
processes are not behaving
according to specifications
0.58
0.56
0.54
0.52
0.5
0.48
0.46
0.44
1
2
3
4
5
6
7
8
Time (Hours)
9
10 11 12
Analytical Tools for Six Sigma and Continuous
Improvement: Pareto Analysis
80%
Frequency
Can be used to
find when 80%
of the
problems may
be attributed
to 20% of the
causes
Design
Assy.
Instruct.
Purch.
Training
Analytical Tools for Six Sigma and
Continuous Improvement: Checksheet
Monday
Billing Errors
Wrong Account
Wrong Amount
A/R Errors
Wrong Account
Wrong Amount
Can be used to keep track of
defects or used to make sure
people collect data in a correct
manner
Number of Lots
Analytical Tools for Six Sigma and
Continuous Improvement: Histogram
Can be used to identify the frequency of quality
defect occurrence and display quality
performance
0
1
2
Data Ranges
3
4
Defects
in lot
Analytical Tools: Cause and Effect Diagrams
(Fishbone Diagrams)
Specifications /
information
Machines
Cutting
tool worn
Dimensions incorrectly
specified in drawing
Vise position
set incorrectly
Clamping force too
high or too low
Machine tool
coordinates set incorrectly
Part incorrectly
positioned in clamp
Dimension incorrectly coded
In machine tool program
Vice position shifted
during production
Part clamping
surfaces corrupted
Steer support
height deviates
from specification
Extrusion temperature
too high
Error in
measuring height
Extrusion stock
undersized
Extrusion die
undersized
People
Extrusion
rate
too high
Material
too soft
Materials
Can be used to systematically track backwards to find
a possible cause of a quality problem (or effect)
Types of Statistical Sampling
 Attribute (Go or no-go information)



Defectives refers to the acceptability of product
across a range of characteristics.
Defects refers to the number of defects per unit
which may be higher than the number of
defectives.
p-chart application
 Variable (Continuous)


Usually measured by the mean and the standard
deviation.
X-bar and R chart applications
Analytical Tools for Six Sigma and Continuous
Improvement: Control Charts
Can be used to monitor ongoing production process
quality and quality conformance to stated standards of
quality
1020
UCL
1010
1000
990
LCL
980
970
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15
Statistical
Process
Normal Behavior
Control
(SPC) Charts
UCL
LCL
1
2
3
4
5
6
Samples
over time
UCL
Possible problem, investigate
LCL
1
2
3
4
5
6
Samples
over time
UCL
Possible problem, investigate
LCL
1
2
3
4
5
6
Samples
over time
Process Capability
 Process limits
 Specification limits
 How do the limits relate to one another?
Process Capability Index, Cpk
Capability Index shows how
well parts being produced
fit into design limit
specifications.
 X  LTL UTL - X 

C pk = min 
or
3 
 3
As a production process
produces items small
shifts in equipment or
systems can cause
differences in
production performance
from differing samples.
Shifts in Process Mean
The Cereal Box Example
 We are the maker of this cereal. Consumer reports has
just published an article that shows that we frequently
have less than 15 ounces of cereal in a box. We would like
to have 16 ounces in each box.
 Let’s assume that the government says that we must be
within ± 5 percent of the weight advertised on the box.
 Upper Tolerance Limit = 16 + .05(16) = 16.8 ounces
 Lower Tolerance Limit = 16 – .05(16) = 15.2 ounces
 We go out and buy 1,000 boxes of cereal and find that
they weight an average of 15.875 ounces with a standard
deviation of .529 ounces.
Cereal Box Process Capability
 Specification or
Tolerance Limits
 X  LTL UTL  X 
C pk  Min
;

3

3



 Upper Spec = 16.8 oz
 Lower Spec = 15.2 oz
 Observed Weight
 Mean = 15.875 oz
 Std Dev = .529 oz
15.875 15.2 16.8  15.875
C pk  Min
;

3
(.
529
)
3(.529)


C pk  Min.4253; .5829
C pk  .4253
What does a Cpk of .4253 mean? This is a process that will
produce a relatively high number of defects. Many companies
look for a Cpk of 1.3 or better… 6-Sigma company wants 2.0!
Basic Forms of Statistical Sampling
for Quality Control
Acceptance Sampling is sampling to
accept or reject the immediate lot
of product at hand
Statistical Process Control is
sampling to determine if the process
is within acceptable limits