Product Manufacturing
CHEN 4470 – Process Design Practice
Dr. Mario Richard Eden
Department of Chemical Engineering
Auburn University
Lecture No. 22 – Introduction to Six Sigma in Product Manufacturing
March 28, 2013
Contains Material Developed by Dr. Daniel R. Lewin, Technion, Israel
Instructional Objectives
 Be able to define the
manufacturing process
Sigma
Level
of
a
 Know the steps followed in product design and
manufacture (DMAIC)
 Be able to qualitatively analyze a process for the
manufacture of a product and know how to
identify the CTQ step using DMAIC
Product Development
•
Example: The Electronics Food Chain
Electronic Equipment and Systems
$988 B
Semiconductors
$170 B
Semiconductor
Materials and
Equipment
$33 B
Source:
Dataquest
1999 data
Product Development
IC Production Capability
–
Moore’s Law
100000000
10000000
Transistors per chip
•
1000000
100000
10000
1000
100
10
1
1959
1964
1969
1974
1979
Year
1984
1989
1994
1999
Product Development
•
Device Complexity Trends
Device
8086
80286
80386
486
Pentium
Pentium Pro
Year
1978
1981
1985
1990
1993
1995
Transistors
30K
120K
400K
2M
3.5M
5.5M
Chip Area
per Chip
(cm2)
0.34
0.77
1.0
1.8
2.9
2.9
Product Development
•
Technology vs. Economics
Physical
Limit
Cost
Economic Limit
Capability
The budget
always runs out
before the
physical limits are
reached.
Product Development
•
Technology vs. Economics (Continued)
Physical
Limit
Cost
New Physical
Limit
Economic Limit
Innovation!!
Capability
Product Development
Implications of Blind Faith in Moore’s Law
–
Fear is that exponential growth is only the first half of
an “S” shaped curve
Revenue
•
Time
Product Development
•
Industry Drivers (Push vs. Pull)
–
Market requires (push):
•
•
•
–
Smaller feature sizes desired
Larger chip area desired
Improved IC designs lead to innovations
IC industry delivers (pull):
•
•
•
Lower cost per function (higher performance per cost)
New applications are enabled to use chips with new capabilities
Higher volumes produced
Six Sigma 1:15
•
Definition
–
–
•
6 = “Six Sigma”
SSLW: Chapter 25
SSL: Chapter 19
Description
–
Structured methodology for eliminating defects, and
hence, improving product quality in manufacturing and
services.
–
Aims at identifying and reducing the variance in product
quality, and involves a combination of statistical quality
control, data analysis methods, and the training of
personnel.
Six Sigma 2:15
•
Statistical Background
–
 is the standard deviation (SD) of the value of a quality
variable, x, a measure of its variance, assumed to be
normally distributed:
1
 1 x   
f x  
exp   

 2
 2   
Average
2
Standard Deviation
–
Assume Lower Control Limit LCL =  - 3, and Upper
Control Limit UCL =  + 3 :
 + 3

 - 3
Six Sigma 3:15
•
Statistical Background (Continued)
–
At SD = , the number of Defects Per Million
Opportunities (DPMO) below the LCL in a normal
sample is:
DPMO  10
6


 3 


f  x dx  10 1   f  x dx  1,350
In a normal sample, the
DPMO will be the same
above the UCL. The plot
shows f(x) for  = 2.
1
2
6
 3 
 3 
Six Sigma 4:15
•
Methodology
–
In accepted six-sigma methodology, a worst-case shift
of 1.5 in the distribution of quality is assumed, to a
new average value of  + 1.5
In this case, the
DPMO above the
UCL = 66,807, with
only DPMO = 3
below the LCL
( = 2).
Six Sigma 5:15
•
Methodology (Continued)
–
However, if  is reduced by ½ ( = 1), so that the new
LCL =  - 6, and UCL =  + 6, the DPMO for normal
and abnormal operation are now much lower:
Six Sigma 6:15
•
Sigma Level vs. DPMO
Sigma Level
DPMO
1.0 697,672
2.0 308,770
3.0
66,810
3.5
22,750
4.0
6,210
4.5
1,350
5.0
233
5.5
32
6.0
3.4
Six Sigma 7:15
•
Simple Example: Computing the Sigma Level
–
•
On average, the primary product from a specific
distillation column fails to meet its specifications
during five hours per month of production.
Compute its sigma level.
Solution
5
DPMO  10 
 6, 944
30  24
6
–
The chart on slide 15 gives the Sigma level as 3.8
Six Sigma 8:15
•
Computing Throughput Yield
–
For n steps, where the number of expected defects in
step i is DPMOi, the defect-free throughput yield (TY)
is:
DPMOi 

TY    1 

6
10

i 1 
n
–
If the number of expected defects in each step is
identical, then TY is:
n
DPMO 

TY   1 

6
10


Six Sigma 9:15
•
Simple Example: Computing Throughput Yield
–
In the manufacture of a device involving 40 steps, each
step is operating at 4 (DPMO=6,210):
TY  1  0.00621
40
 0.779
–
This means that 22% of production is lost to defects!
–
Corresponding to approximately 220,000 units per
million produced (DPMO  220,000)
–
The chart on slide 15 gives the Overall Sigma level
as 2.3
Six Sigma 10:15
•
Monitoring and Reducing Variance
–
•
A five-step procedure is followed - Define, Measure,
Analyze, Improve, and Control - DMAIC:
Define
–
–
–
A clear statement is made defining the intended
improvement.
Next, the project team is selected, and the
responsibilities of each team member assigned.
To assist in project management, a map is prepared
showing the Suppliers, Inputs, Process, Outputs and
Customers (referred to by the acronym, SIPOC).
Six Sigma 11:15
•
Define (Continued)
–
Example: A company producing PVC tubing by
extrusion needs to improve quality. A SIPOC describing
its activities might look like this:
Six Sigma 12:15
•
Measure
–
–
–
–
The Critical To Quality (CTQ) variables are monitored
to check their compliance with the UCLs and LCLs.
Most commonly, univariate statistical process control
(SPC) techniques, such as the Shewart chart, are
utilized.
The data for the critical quality variables are analyzed
and used to compute the DPMO and the sigma level.
Example: Continuing the PVC extrusion example,
suppose this analysis indicates operation at 3, with a
target to attain 5 performance.
Six Sigma 13:15
•
Analyze
–
–
–
To increase the sigma level, the most significant causes
of variability are identified, assisted by a systematic
analysis of the sequence of manufacturing steps.
This identifies the common root cause of the
variance.
Example: In the PVC extrusion example, a list of
possible causes for product variance includes:
•
•
•
Variance in quality of PVC pellets
Variance in volatiles in pellets
Variance in steam heater operating temperature
Six Sigma 14:15
•
Improve
–
Having identified the common root cause of variance, it
is eliminated or attenuated by redesign of the
manufacturing process or by employing process control.
–
Example: Continuing the PVC tubing example,
suggestions to how the variance in product quality can
be reduced include:
•
•
•
Redesign the steam heater.
Install a feedback controller to manipulate the steam valve to
enable tighter control of the operating temperature.
Combination of the above.
Six Sigma 15:15
•
Control
–
After implementing steps to reduce the variance in the
CTQ variable, this is evaluated and maintained.
–
Thus, steps M, A, I and C in the DMAIC procedure are
repeated to continuously improve process quality.
–
Note that achieving 6 performance is rarely the goal,
and seldom achieved.
Six Sigma for Design 1:3
•
Methodology
–
The DMAIC procedure is combined with ideas specific to
product design to create a methodology that assists in
applying the six-sigma approach to product design.
–
A five-step procedure is recommended:
1.
2.
3.
4.
5.
Define project
Identify requirements
Select concept
Develop design
Implement design
Six Sigma for Design 2:3
•
Step 1: Define Project
–
–
–
•
Step 2: Identify Requirements
–
•
The market opportunities are identified.
A design team is assigned and resources are allocated.
Often, project timeline is summarized in a Gantt chart.
As in DMAIC, the requirements of the product are
defined in terms of the needs of customers.
Step 3: Select Concept
–
–
Innovative concepts for the new design are generated,
first by “brainstorming.”
The best are selected for further development.
Six Sigma for Design 3:3
•
Step 4: Develop Design
–
–
•
Often several teams work in parallel to develop and test
competing designs, making modifications as necessary.
The goal is to prepare a detailed design, together with a
plan for its management, manufacture, and quality
assurance.
Step 5: Implement Design
–
–
The detailed designs in Step 4 are critically tested.
The most promising design is pilot-tested and if
successful, proceeds to full-scale implementation.
Summary – Six Sigma
On completion of this part, you should:
 Define the Sigma Level of a manufacturing
process (Increased losses [DPMO] means
decreased sigma level).
 Apply DMAIC in product design and manufacture.
 Qualitatively analyze a process for the
manufacture of a product and know how to
identify the CTQ step using DMAIC.
Other Business
•
Next Lecture – April 9
–
Final reports and oral presentations