Product Manufacturing

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Product Manufacturing
CHEN 4470 Process Design Practice Dr. Mario
Richard EdenDepartment of Chemical
EngineeringAuburn University Lecture No. 11
Introduction to Six Sigma in Product
Manufacturing March 6, 2007 Contains Material
Developed by Dr. Daniel R. Lewin, Technion, Israel
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Instructional Objectives

• Be able to define the Sigma Level of a
  manufacturing process
• 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

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Product Development

• Example The Electronics Food Chain

Source Dataquest 1999 data
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Product Development

• IC Production Capability
• Moores Law

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Product Development

• Device Complexity Trends

Chip Area Device Year Transistors
per Chip
(cm2) 8086 1978 30K
0.34 80286 1981 120K 0.77 80386 1985 400K 1.0 486
1990 2M 1.8 Pentium 1993 3.5M 2.9 Pentium
Pro 1995 5.5M 2.9
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Product Development

• Technology vs. Economics

Physical Limit
The budget always runs out before the physical
limits are reached.
Cost
Economic Limit
Capability
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Product Development

• Technology vs. Economics (Continued)

New Physical Limit
Physical Limit
Cost
Economic Limit
Innovation!!
Capability
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Product Development

• Implications of Blind Faith in Moores Law
• Fear is that exponential growth is only the first
  half of an S shaped curve

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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

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Six Sigma 115

• Definition
• 6? Six Sigma
• 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.

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Six Sigma 215

• 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
• Assume Lower Control Limit LCL ? - 3?, and
  Upper Control Limit UCL ? 3?

Average
Standard Deviation
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Six Sigma 315

• Statistical Background (Continued)
• At SD ?, the number of Defects Per Million
  Opportunities (DPMO) below the LCL in a normal
  sample is

In a normal sample, the DPMO will be the same
above the UCL. The plot shows f(x) for ? 2.
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Six Sigma 415

• 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).
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Six Sigma 515

• 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

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Six Sigma 615

• Sigma Level vs. DPMO

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Six Sigma 715

• 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
• The chart on slide 15 gives the Sigma level as
  3.8?

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Six Sigma 815

• Computing Throughput Yield
• For n steps, where the number of expected defects
  in step i is DPMOi, the defect-free throughput
  yield (TY) is
• If the number of expected defects in each step is
  identical, then TY is

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Six Sigma 915

• Simple Example Computing Throughput Yield
• In the manufacture of a device involving 40
  steps, each step is operating at 4? (DPMO6,210)
• 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?

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Six Sigma 1015

• 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).

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Six Sigma 1115

• Define (Continued)
• Example A company producing PVC tubing by
  extrusion needs to improve quality. A SIPOC
  describing its activities might look like this

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Six Sigma 1215

• 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.

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Six Sigma 1315

• 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

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Six Sigma 1415

• 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.

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Six Sigma 1515

• 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.

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Six Sigma for Design 13

• 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
• Define project
• Identify requirements
• Select concept
• Develop design
• Implement design

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Six Sigma for Design 23

• Step 1 Define Project
• The market opportunities are identified.
• A design team is assigned and resources are
  allocated.
• Often, project timeline is summarized in a Gantt
  chart.
• Step 2 Identify Requirements
• 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.

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Six Sigma for Design 33

• 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.

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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.

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Other Business

• Guest Lecture March 8
• Latest Acrolein research results
• Individual Team Assignments
• Will be assigned over next two week period
• Progress Report No. 2
• Can be turned in Friday March 16 before 500 PM
• External Evaluators Confirmed
• Lee Daniel, Lead Product Developer, Civil Systems
  Inc.
• Robert DAlessandro, Director of Process Eng.,
  Degussa
• Resumes will be posted on website soon

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Other Business

• Field Trip to Degussa March 13
• More details will be provided at lecture on
  Thursday
• Suggested driver and passenger assignment