Chapter 15 Control Methods

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Chapter 15Control Methods
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Control is the heart of Six Sigma

• Customers are demanding higher levels of product
  quality at a lower cost, improved responsiveness,
  and added value.
• Producers must struggle to satisfy technical,
  performance, schedule, and cost expectations of
  the customer.
• Drive the need for control methods used in Six
  Sigma TQM
• Do it right the first time
• Eliminate product variation
• ?
• Delivery of offerings, which are defect free at a
  min. cycle time

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Six Sigma initiatives to reduce variation

• In the past, the pursuit of quality was more a
  philosophy than an art or science these tools
  can change that.
• Design
• Design to standard parts
• Design to standard materials
• Robust design
• Design for assembly
• Design for reliability
• Design for simplicity

• Process
• Short-cycle manufacturing
• Process characterization
• Process standardization
• Statistical process control
• Material and Components
• Part standardization
• Transaction(s) standardization
• Supplier statistical process control (SPC)
• Supplier certification
• Material requirements planning

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Poka-Yoke

• Japanese for mistake proofing
• Poka (inadvertent error)
• Yokeru (avoidance)
• Design and implementation of actions to prevent
  errors, mistakes, or defects in our everyday
  activities and processes.
• Errors should not be considered inevitable. Any
  error type can be reduced considerably, if not
  eliminated altogether.
• http//www.youtube.com/watch?vNrnloZ12KGs

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Common types of mistakes

• Incorrect processing
• Work pieces placed incorrectly
• Missing parts
• Wrong parts
• Wrong blue print or instructions
• Wrong piece processed
• Operation skipped or omitted
• Improper adjustment
• Equipment not set up properly
• Process improperly supersized
• Use of the wrong tool

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How are these examples of daily Poka-Yoke?
Parking garages have low clearance. To insure
that cars entering the garage will fit, garages
are fitted with a go/no-go gauge at the entrance.
Hitting the swinging sign or pipe will not damage
the vehicle as much as driving into a concrete
beam.
Filling pipe insert keeps larger, leaded-fuel
nozzle from being inserted Gas cap tether does
not allow the motorist to drive off without the
cap Gas cap is fitted with ratchet to signal
proper tightness and prevent over-tightening
This iron turns off automatically when it is left
unattended or when it is returned to its holder
Examples from http//facultyweb.berry.edu/jgrou
t/everyday.html
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Keys to implementing Poka-Yoke

• Utilize Failure Mode-Effects Analysis (FMEA) to
  identify opportunities.
• Use the highest principle possible.
• Elimination
• Replacement
• Facilitation
• Detection
• Mitigation

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Statistical Process Control (SPC)

• SPC is a method of analyzing data over time and
  using the result of the analysis to solve
  manufacturing and processing problems
• Can be applied to almost anything that can be
  expressed with numbers of data.
• Control to keep something within boundaries
• Process any set of conditions or causes, which
  work together to produce an output or result.
• Process is a sequence of activities characterized
  by
• Measureable inputs
• Value-added (VA) activities
• Measureable Outputs
• Repeatability

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Statistical Control

• A process is within statistical control when the
  process contains only natural, chance variation.
• Only when a process is statistically stable can
  it be treated as a population with constant mean,
  standard deviation, and distribution.
• A process control system is a feedback four
  element system
• The Process
• Information about Performance
• Action on the Process
• Actions on the Output

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Prevention vs. Detection

• Every process contains several sources of
  variation
• Two product characteristics are not equal
• Differences among products, transactions, or
  services may range from very large to very small.
• No matter how small, variation is always present
• Time period and conditions under which
  measurements are made affect the total process
  variation visible to the user
• Strategy of Prevention - It is always more
  effective to avoid waste by not producing it
  (vs. trying to detect).
• Minimum Requirements If specification limits
  can be determined then anything within those
  limits is acceptable and everything outside them
  is unacceptable.

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Causes of Variation

• Common Causes

• Special Causes

• Only natural variation (no patterns, cycles or
  unusual points.)
• Process in statistical control when the only
  source of variation is common cause.
• Values will tend to forma pattern that can be
  described by a probability distribution.

• Assignable causes
• Unnatural patterns
• Out of control process
• Can be detected by simple statistical techniques
  ? such as Control Charts.

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Continuous Statistical Process Control (SPC) Tools

• Purposes of Control Charts
• Control a CTP characteristic (statistical process
  control - SPC)
• Used to monitor a CTQ,CTC or CTD characteristic
  (Statistical process monitoring-SPM)
• Used as diagnostic tools for any CT
  Characteristic.

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Development of Control Charts

1. Based on in-control data
2. If non-random causes present, discard data
3. Correct control chart limits
4. Combine location and variation charts
5. Charts must be reviewed and adjusted throughout
   usage and after acting on information.

Continuous Improvement
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Control Charts

• Commonly based on ? ? 3?
• Sample mean x-bar-charts ? x
• Sample range R-charts
• Sample std. deviation s-charts
• Fraction defective p-charts
• Number of defects c-charts
• Consider sample size, desired sensitivity,
  allowable complexity level of charts and
  attribute vs. variable data.

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What type of Control Chart depends on what kind
of data you have

• Attribute data
• Product characteristic evaluated with a discrete
  choice
• Good/bad, yes/no
• Variable data
• Product characteristic that can be measured
• Length, size, weight, height, time, velocity

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Z Values in Control Charts

• Smaller Z values make more sensitive charts (Type
  I error)
• Z 3.00 is standard
• Compromise between sensitivity and Type II errors

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Process Control Chart
Upper control limit
Central Line
Lower control limit
Sample number
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Is your process in Control?

• No evidence of out-of-control, if
• No sample points outside limits
• Most points near process average
• About equal number of points above below
  centerline
• Points appear randomly distributed

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Is your process out-of-control?

• Sample data fall outside control limits
• Theory of runs
• 2 out of 3 beyond the warning limits
• 4 out of 5 beyond the 1? limits
• 8 consecutive on one side
• Patterns

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Zones For Pattern Tests
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Control Chart Patterns

• 8 consecutive points on one side of the center
  line.
• 8 consecutive points up or down across zones.
• 14 points alternating up or down.
• 2 out of 3 consecutive points in zone A but still
  inside the control limits.
• 4 out of 5 consecutive points in zone A or B.

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Control Chart Patterns
Sample observations consistently below the center
line
Sample observations consistently above the center
line
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Control Chart Patterns
Sample observations consistently increasing
Sample observations consistently decreasing
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Control Charts For Variables

• Each measures process differently
• Process average and variability must be in
  control
• X Bar (Mean chart ) Measure the central tendency
  of a process over time
• Dispersion charts
• R (Range) Measure the gain or loss of
  uniformity or variability of a process across
  time
• S Chart
• X-bar and R Charts often used together and
  jointly interpreted.

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X-bar Chart Calculations
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Example X-bar Chart
X-bar
Sample
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Range (R) ChartStd. Dev. (s) Chart
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R-Chart Example

• Slip-ring diameter (cm) (sample size 5)

4.98
0.08
1
5.00
0.12
2
4.97
0.08
3
0.10
5.03
10
50.09
1.15
?
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3? Control Chart Factors
n A2 D3 D4 B3 B4
2 1.880 0 3.267 0 3.267
3 1.023 0 2.575 0 2.568
4 0.729 0 2.282 0 2.266
5 0.577 0 2.115 0 2.089
6 0.483 0 2.004 0.03 1.970
7 0.419 0.076 1.924 0.118 1.882
8 0.373 0.136 1.864 0.185 1.815
9 0.337 0.184 1.816 0.239 1.761
10 0.308 0.223 1.777 0.284 1.716
11 0.285 0.256 1.744 0.321 1.679
12 0.266 0.284 1.716 0.354 1.646
13 0.249 0.308 1.692 0.382 1.618
14 0.235 0.329 1.671 0.406 1.594
15 0.223 0.348 1.652 0.428 1.572
20 0.180 0.414 1.586 0.510 1.490
25 0.153 0.459 1.541 0.565 1.435
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Example R-Chart

Sample
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Other Variable Charts

• MR (Moving Range) Chart
• Average of a series of moving ranges
• Use difference between successive pairs of
  numbers in a series
• IM (Individual Measurement) Chart

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MR (Moving Range) Chart

• Moving Range (MR)
• Average Moving Range
• Estimation of ?
• Control Limits

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IM (Individual Measurement) Chart

• Average Value
• Control Limits

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Control Charts For Attributes

• p Charts
• Chart percent defectives in sample
• c Charts
• Display the number of defects per sample
• np Charts
• u Charts

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p-Chart
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p-Chart Example

• 20 samples of 100 pairs of jeans

Sample Defects Proportion Defective
1 6 0.06
2 0 0.00
3 4 0.04
. . .
20 18 0.18
200 0.10
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p-Chart Calculations
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Example p-Chart
Sample number
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np Chart

• np chart
• If samples are the same size it is simpler to
  plot the number of defective in each sample
  instead of calculating the defective.

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c-Chart
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c-Chart Example

• Count of defects in 15 rolls of denim fabric

Sample Defects
1 12
2 8
3 16
.
15 15
Total 190
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c-Chart Calculations
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Example c-Chart
Sample number
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U Chart

• Variation of the c chart.
• Each point is the average number of defects per
  unit in a sample of k units
• The number of units at are averaged need not be
  the same for all samples.

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Pre-Control Not waiting for failure to adjust
the process
Upper control limit
Central Line
Lower control limit
Sample number

• Establish Green zone ?1.5 s, and yellow zone ? 3
  s. Everything outside of 3s would be red.

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Using Pre-Control

• Qualify the process by taking 5 consecutive
  samples in green zone.
• The probability of 2 units falling outside of
  green should prompt a process adjustment or stop.
  
• After adjustment or stop, it will need to
  requalify process

State of 2 successive samples Action
Both A B inside Green No action
A is Green, B is yellow No Action
A is Yellow, B is Green No Action
Both A B are yellow on same side (high or low) Adjust Process
Both A B are yellow on opposite sides (high and low) Stop Process