Transcript Nestlereliability

Six-Sigma and Reliability
Dave Stewardson - ISRU with
Froydis Berke - Matforsk Norway
Soren Bisgaard - USA
Poul Thyregod - Denmark
Bo Bergman - Sweden
Pro-Enbis
All joint authors - presenters- are members of:
Pro-Enbis and ENBIS.
This presentation is supported by Pro-Enbis a
Thematic Network funded under the ‘Growth’
programme of the European Commission’s 5th
Framework research programme - contract
number G6RT-CT-2001-05059
Presenters:
Introduction
Dave Stewardson - ISRU
and
Froydis Berke Matforsk
Rational for Six-Sigma
Improve processes
Team - project based improvement
Properly costed benefits
Grow your own expertise
Visible success
Use of modern improvement tools
Rational for Modern
Maintenance
Preventative maintenance
Condition monitoring
Better planning
Less machine downtime
Operators monitor machine and
process condition
Rationales Fit!
Everyone involved
Monitoring to help operators get
better control over the process
Publicise success
Soren Bisgaard
Leading Industrial Statistician
Overview of Six-Sigma
Maintenance and Reliability
We can use six-sigma to crack
maintenance problems
Strategy is the same
What is ‘reliability’ ?
Synopsis of Reliability
Some Definitions
1) “The probability that the product continues to meet
the specification”.
2) “The probability that an item will perform as
required, under stated conditions, for a stated period of
time”.
3) “The mean lifetime of a product”.
4) “The likelihood that a product will survive stated
stresses”.
5) “The survival rate of something”.
6) “Resistance to failure”.
7) “How long we expect a thing to last”.
Related to:
•Quality
•Survival
•Product Guarantees
•Product Improvement
•Process Control
•Process Capability
•Failure Modes Analysis
•Problem Solving
•Statistical Modelling
•Quality Engineering
•Preventative maintenance
Relationship of Weibull to Statistics and modelling generally
Industrial
Statistics
Statistical
Modelling
Reliability
Weibull
Distribution
Web-page example from Quality Digest
By Thomas Pyzdek a consultant in
Six Sigma.
http://www.qualitydigest.com/june0
1/html/sixsigma.html
Web-Page Example II
•Project was initiated by a group of senior
leaders,
•After receiving numerous customer
complaints.
•Pareto analysis on customer issues raised in
the previous 12 months.
•Solder problems were the No. 1 problem for
customers.
Web-Page Example
•A program manager chosen
•Six Sigma team was formed
•A Master Black Belt provided technical leadership.
•The team began working through the design,
measure, analyze, improve and control cycle.
•Defined critical-to-quality measures,
• Pareto analysis applied to the types of solder
defects.
•A wave solder team was formed included a process
engineer, machine operator, an inspector and a
touch-up solder operator.
Web-Page Example
•A Black Belt providing training
• The team identified and assigned various tasks,
• data collection,
•creating "as is" and "should be" process maps
•Performed process audits.
Web-Page Example
Discovered:
•‘Touch-up’ was performed before any data were
collected.
•Because solder problems were routine, touch-up was
considered part of the soldering process.
•There were 24 full-time personnel and four full-time
inspectors assigned to touch-up.
•Most of the defects were touch-up defects, not wave
solder defects.
•The equipment desperately needed maintenance.
•No preventive maintenance program was in place.
Web-Page Example
Recommended several immediate changes:
1. Conduct inspection immediately after wave solder
and before touch up. (Process Change! djs)
2. Use a control chart to analyze the results.
3. Perform a complete maintenance of the process.
Web-Page Example
Defects dropped by 50 percent within a month
Began DOEs
•DOEs revealed that the majority of prior assumptions
were false
•sometimes the results were precisely the opposite of the
accepted point of view.
•Significant quality and cost savings resulted as the new
knowledge was used to modify procedures.
Web-Page Example
.
Eventually defect rate in the area dropped by 1,000
percent over a period of 10 months.
Productivity increased by 500 percent in terms of labor
hours per board.
DoE and Reliability
Example
From:
Using Designed Experiments and the analysis of
Statistical Error to determine Change Points in
Fatigue Crack Growth Rates.
1University
of Newcastle,
2Corus Group UK,
3Instituto de Engenharia Mecanica e Gestao
Industrial, Porto, Portugal,
4Centro Sviluppo Materiali, Italy,
5Voest-Alpine, Austria,
6Thyssen Krupp, Germany,
7Sogerail, France
Main Objective
Determine the effects of stress ratio and relative humidity
on the fatigue crack growth rates measured in grade 260
rail steel - Reliability
Approximately 75% of the rails currently produced for use
in Europe are 260 grade.
Started just before Hatfield crash!
The Reliability Test
Rail samples subjected to variable stress
levels under a constant cycle
Crack introduced into the sample
Growth of crack measured over time against
number of cracks
Analysis of da/dN verses the stress intensity
Experimental Design
Two stages, first considered a screening stage
involving 2 Labs only.
Design constrained by limit on material
resource.
Biggest problem - how to interpret the data?
Design Factor Settings
Factorial Points
Test Number
Rail Manufacturer
Laboratory
Relative Humidity
Stress Ratio
A1
A2
A3
A4
A6
A7
A8
A9
A11
A12
A13
A14
A16
A17
A18
A19
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
~60%
<=10%
~60%
<=10%
~60%
<=10%
~60%
<=10%
~60%
<=10%
~60%
<=10%
~60%
<=10%
~60%
<=10%
0.5
0.5
0.2
0.2
0.5
0.5
0.2
0.2
0.5
0.5
0.2
0.2
0.5
0.5
0.2
0.2
A5
A10
A15
A20
1
2
3
4
B
A
A
B
A
B
B
A
A
B
B
A
B
A
A
B
Centre Points
A
A
B
B
~35%
~35%
~35%
~35%
0.5
0.2
0.5
0.2
Plot of S for 5ptMA(2)
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
2
4
6
8
10
12
14
16
18
5ptMA of Slope(2)
15
13
11
9
7
5
3
1
0
2
4
6
8
10
12
14
16
18
Plan for second stage
1) Stress Ratio is important so fix it at a
convenient value
2) Add Cyclic Frequency as a factor
3) Just monitor Relative Humidity and
Temperature
Test
Number
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
B11
B12
B13
B14
B15
B16
B17
B18
B19
B20
A1
A2
A5
A6
A7
A11
A12
A15
A16
A17
Rail
Manufacturer
1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
1
1
1
2
2
3
3
3
4
4
Factor settings for Part 2 Tests
Laboratory
Cyclic
Temperature C
Frequency: Hz
C
15
Record
D
15
Record
E
120
Record
F
10
Record
C
70
Record
C
70
Record
D
15
Record
E
120
Record
F
10
Record
D
15
Record
C
15
Record
D
15
Record
E
120
Record
F
15
Record
E
120
Record
C
70
Record
D
15
Record
E
120
Record
F
10
Record
F
15
Record
B
10
Record
A
15
Record
A
15
Record
A
15
Record
B
10
Record
A
15
Record
B
10
Record
B
10
Record
B
10
Record
A
15
Record
Relative
Humidity, %
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
Record
60
< 10
35
60
< 10
60
< 10
35
60
< 10
Project Findings
Found most important factors
Can now set these at optimum
Found a good way to use the data
Can monitor the quality of rails
Better understanding of factors effecting
reliability of rails
Conclusions were
Experimental design helped to discover the
important factors that effect these types of
Reliability test.
It is also possible to derive quality monitoring of
the test data using charts of the Plot parameters;
slope, error and intercept.
Corus engineers now use these methods - training
by ISRU
Six-Sigma and Maintenance
Condition Monitoring
Test Equipment Condition Monitoring
Ericcson (Sweden)
Routine testing of electric components
If test kit failed (equipment not working)
Could fail a good component
Conducted designed Experiment to optimise
a monitoring scheme
Condition Monitoring II
Discovered potential problems with kit
Found an optimum scheme
Developed control charts
Discovered that the number of tests per day
was not the major influence
The worse the product quality, the more
likely the test kit would fail to work properly
Condition Monitoring III
Other examples:
1. Ohio – monitoring of large weighing
equipment (50 Tonnes)
Effected by by weather – and animals
2. Monitoring of measuring equipment used
for calibration – Electrolux
General Problems
Lack of good data
Spend time to collect this
But then USE IT
Must drive it on!
Must see benefits quickly!
Best Strategy
Involve the operators
directly
makes it ‘easier’ for the
engineers
Work as a team