Quality Management, Statistical Process Control, and Six-Sigma Capability

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Quality Management, Statistical Process Control,
and Six-Sigma Capability
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Manufacturing Example
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The Concept of ConsistencyWho is the Better
Target Shooter?
Not just the mean is important, but also the
variance Need to look at the distribution
function
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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

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The Statistical Meaning of Six Sigma
Process capability measure
Upper Specification Limit (USL)
Lower Specification Limit (LSL)
Process A (with st. dev sA)
x? Cp Pdefect 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 0.00
01 63 5? 1.67 0.0000006 0,6 6? 2.00 2x10-9 0,00
3?
Process B (with st. dev sB)

• Estimate standard deviation using Excel
• Look at standard deviation relative to
  specification limits
• Dont confuse control limits with specification
  limits a process can be out of control, yet
  be incapable

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Statistical Process Control
Capability Analysis
Conformance Analysis
Investigate for Assignable Cause
Eliminate Assignable Cause

• Capability analysis
• What is the currently "inherent" capability of
  my process when it is "in control"?
• Conformance analysis
• SPC charts identify when control has likely been
  lost and assignable cause variation has
  occurred
• Investigate for assignable cause
• Find Root Cause(s) of Potential Loss of
  Statistical Control
• Eliminate or replicate assignable cause
• Need Corrective Action To Move Forward

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How do you get to a Six Sigma Process? Step 1
Do Things Consistently (ISO 9000)
1. Management Responsibility 2. Quality System 3.
Contract review 4. Design control 5. Document
control 6. Purchasing / Supplier evaluation 7.
Handling of customer supplied material 8.
Products must be traceable 9. Process control 10.
Inspection and testing
11. Inspection, Measuring, Test Equipment 12.
Records of inspections and tests 13. Control of
nonconforming products 14. Corrective action 15.
Handling, storage, packaging, delivery 16.
Quality records 17. Internal quality audits 18.
Training 19. Servicing 20. Statistical techniques
Examples The design process shall be planned,
production processes shall be defined and
planned
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Step 2 Reduce Variability in the
ProcessTaguchi Even Small Deviations are
Quality Losses
Quality
Quality Loss
Loss C(x-T)2
Performance Metric, x
Good
Performance Metric
Bad
Maximum acceptable value
Minimum acceptable value
Target value
Target value

• It is not enough to look at Good vs Bad
  Outcomes
• Only looking at good vs bad wastes opportunities
  for learning especially as failures become rare
  (closer to six sigma) you need to learn from the
  near misses
• Catapult Land in the box opposed to perfect
  on target

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Step 3 Accommodate Residual Variability Through
Robust Design
Chewiness of BrownieF1(Bake Time) F2(Oven
Temperature)
F2
F1
Bake Time
Oven Temperature
25 min.
30 min.
350 F
375 F
Design A
Design B

• Double-checking (see Toshiba)
• Fool-proofing, Poka yoke (see Toyota)
• Process recipe (see Brownie)

Pictures from www.qmt.co.uk
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Why Having a Process is so ImportantTwo
Examples of Rare-Event Failures

• Case 1 Process does not matter in most cases
• Airport security
• Safety elements (e.g. seat-belts)

Bad outcome only happens Every 10 Mio units
1 problem every 10,000 units
99 correct

• Case 2 Process has built-in rework loops
• Double-checking
• Jesicas case

99
Good
99
99
1
Bad
1
1
Bad outcome only happens with probability
(1-0.99)3
Learning should be driven by process deviations,
not by defects