Statistical Tools for Process Improvement - Applications

Jairo Muñoz, Ph.D., CMfgE Iowa Precision Industries (319) 364-9181 Ext. 376 jmunoz@iowaprecision.com

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.