statistical tools for process improvement - applications jairo muñoz, ph.d., cmfge iowa precision...
TRANSCRIPT
Statistical Tools for Process Improvement - Applications
Jairo Muñoz, Ph.D., CMfgE Iowa Precision Industries (319) 364-9181 Ext. 376 [email protected]
Fre
quen
cy
Srinkage
Contents of Today’s Meeting
• Statistical Tools for Process Improvement• The Concept of Variation• Process Capability
– Actual vs. Potential. – Example: Cut to length machinery.
• The Role of Industrial Experimentation
A METHOD FOR CONTINUOUS IMPROVEMENT
Values
Vision
Goals
Objectives
Strategies
Improvement tools and awareness
PlanningCustomer
& Supplier
Mission
Responsab.
Activities
Customers
Suppliers
Requirements
Feedback
Plan – Do – Check -- Act
Process Flow
C/S
Measures
Requirements
Baseline
Feedback
Analysis
Improve
ID the process
Define boundaries
Analysis
Measure
Solve/Test
Implement
Fool proof
Monitor variation
System change
Benchmark
Train
Lessons learned
Make sure we’re doing the right
things
Make sure we’re doing things
right
Eliminate things that we’re not
doing right
Maintain and improve what we’re doing
right
Process analysis
Problem solving
Hold the gains
What is variation?
• Variation is the inability to maintain a constant performance.– There is “natural” (NORMAL) variation.– There is “induced” (SPECIAL) variation.
• Variation means $.
Seven Deadly Sins - Waste
• Over-production & producing early• Delay• Transport(material handling)• Processing• Inventory• Motions• Defects
OTHER OPERATING COSTS CONFORMANCENON-CONFORMANCE
THE COST OF VARIATION
68%
10%
22%
Costs associated with producing a poor quality product.
Costs of assuring a good quality product
Are We Good Enough?
• 200,000 wrong drug prescriptions yearly• 15,000 newborns dropped in hospitals• Unsafe drinking water 1 hr each year• No utilities for 8.6 hrs each year• 2 short/long landings at all major airports each day• 500 incorrect surgical procedures weekly• 9 misspelled words on every page of every magazine
At 99.9%:
STATISTICAL QUALITY CONTROL
Need forC/A?
Improvedenough?
Test process options
More detailneeded?
Select condition needing to improve
Analyze current process
Define data to
be collected
Collect data
Analyze data
Implement needed changes
Recordprocesschanges
Recordunusualevents
Establish regular process monitoring
NO
Yes
Yes
Yes
NO
NO
BRAINSTORM PROCESS FLOW DIAGRAM
CAUSE / EFFECT DIAGRAM
CHECK SHEETS
PARETO, SCATTER, REGRES’n
CONTROL CHARTS
DOE, EVOP
SPC, Cp or DOE?
Eliminate SpecialCauses of Variation
Check distribution.Decrease variability.
USE FACTORIALDESIGNS
Continue Production
Is ProcessPredictable?
Is ProcessCapable?
NO
NO
Yes
Yes
“The statistical approach focuses on problem-solving by providing a rational rather than emotional basis for decision-making. It provides the basis for on going improvement.”
Dr. W.E. Deming
MEASURES
• ENGINEERING– Percent documents issued on time; ECR’s per project.
• PRODUCTION PLANNING– Actual/Planned deviation; Time lost waiting for parts.
• MIS– Average response time; Data entry errors per _____.
• PURCHASING– Purchases/sales ratio; Total dollars; Percent shortages.
Can’t Let Obstacles Slow Us
• We tried that ten years ago• We’re too busy to fix these problems• We don’t do things that way here• But those companies aren’t like us
– We have different problems• We’ll change, but let’s do it very slowly• That won’t work here
Process Capability
Process Capability
• The output of a process in control (predictable)may be compared against its specification.
• For measurement data, process capability indexes are usually expressed as a ratio of total variability and the specification range.
• Percent defective may be predicted when the process capability is known for an “in control” process.
• If the process capability is sufficiently high, end item inspection becomes a waste of time.
Process Variation Vs. Specifications
Cp = UTL – LTL = 1.33 = Ck6
Cp = 1.33; Ck = UTL – Mean = 0.53
Cp = 1.33; Ck = 0
Cp = 1.33; Ck < -1
Cp in Cut to Length Applications
• Used as a sales tool• Used as a final test tool• Used by customer as a
verification tool
• Potential process capabilities of up to 2.1 in short runs (with tolerances of +/- 0.030”)
• Actual process capabilities of up 1.7 in short runs.• Customer runs of up to 1.35 for +/- 0.005” machines
Video – Slear IV
CHARACTERISTIC
1 1
65
13
2
7
0
2
4
6
8
10
12
14
4.980 4.990 5.000 5.010 5.020 5.030
Acual Reading (inches)
Nu
mb
er o
f P
arts
Data distribution
Histogram
0
5
10
15
4.98
6
4.99
2
4.99
8
5.00
4
5.00
9
5.01
5
5.02
1
Reading
Fre
qu
ency
Frequency
Process Fallout - Centered Process
Process Capability Ratio Part Per Million Defective 0.50 133,600.00
0.75 24,400.00
1.00 2,700.00
1.10 966.00
1.20 318.00
1.30 96.00
1.40 26.00
1.50 6.80
1.60 1.60
1.70 0.34
1.80 0.06
2.00 0.0018
Six-sigma philosophy
References• Box and Draper, Evolutionary Operations.• Box, Hunter and Hunter, Statistics for Experimenters• Montgomery, Design and Analysis of Experiments• Snee, Hare and Trout, Experiments in Industry• Wheeler, Tables of Screening Designs• Wheeler, Understanding Industrial Experimentation• Software:
– www.statease.com– MatLab; Design Ease; Design Expert; SAS.