LSSG Green BeltTraining

Measure: Finding and Measuring Potential Root Causes

DMAIC Six Sigma - Measure

Objectives Identify Inputs and Outputs

Determine key inputs and outputs for the process and measures to be analyzed

Measure Process Capability Collect data and compare customer

requirements to process variation Revise Charter

Validate project opportunity and perform charter revision

Measure

Control

Analyze

Improve

Define

Agenda for Measure

1. Types of Measures/Setting Targets

2. Data Collection and Prioritization, MSA

3. SPC, Control Charts

4. Process Capability

Measures

Purpose of measurement:

Performance of a process vs. Expectations

Select Measures “SMART” Objectives Clear operational

definitionsE.g. Losing Weight

Objective Lose 13 Pounds in 3 months

Secondary Objective

Lose 1 Pound per week

Driver(s) Calories consumed less Calories burned

Critical Success Factors (Drivers

Run 4 miles/day and consume less than 1500 calories/day

Must measure both the result (Y) and the drivers (Xs). Measure daily – to determine if CSFs are met, and to make adjustments to plan.

LSS Measurement

Measurement is not control! So, what is it?

Causes/

Effects

Measurement

System

Control

System

Historical data Current data

Measurement vs. Control

Measurement Plan

Data Operational Definitions and Procedures

What data type?

How measured?

What conditions?

By who? Where measured?

What sample size?

How to ensure consistency of measurement?

What is the data collection plan?

Setting Targets

Set Targets Objective/Meaningful Management-employees collaboration Team goal compatible with value stream objective

Balanced Score Card Perspectives

Financial

Customer

Internal Process

Learning & Growth

Agenda for Measure

1. Types of Measures/Setting Targets

2. Data Collection and Prioritization, MSA

3. SPC, Control Charts

4. Process Capability

Data Collection and Prioritization

Some Collection Tools

Customer Survey

Work / Time Measurement

Check Sheet

Some Prioritization Tools

Pareto Analysis

Fishbone Diagram

Cause and Effect Matrix

Work Measurement

Goals of Work Measurement Scheduling work and allocating capacity Motivating workers / measuring performance Evaluating processes / creating a baseline Determining requirements of new processes

Time Studies

Typically using stop watches For infrequent information - estimates OK Measure person, machine, and delays independently Medium Duration - not too short; not too long Eliminate Bias - Compute Standard times from

observed times

Time Study: Calculations

Step 1: Collect Data (Observed Time) Step 2: Calculate Normal Time from Observed Time,

where:

Step 3: Calculate Standard Time from Normal Time, where:

normaln faster the orksoperator w when use

Rating) ePerformanc (1 *unit per Time Observed Time Normal

)Allowances (1 *unit per Time Normal Time Standard

Time Study: Numerical Example

A worker was observed and produced 40 units of product in 8 hours. The supervisor estimated the employee worked about 15 percent faster than normal during the observation. Allowances for the job represent 20 percent of the normal time for breaks, lunch and 5S.

Determine the Standard Time per unit.

Data Analysis Tools

02468

1012

0 10 20 30Hours of Training

De

fects

Scatter Diagram

0.46

0.5

0.54

0.58

1 2 3 4 5 6 7 8 9 10 11 12

Time

Dia

met

er

Run Chart

Can be used to identify when equipment or processes means are drifting away from specs

Can be used to illustrate the relationships between factors such us quality and training

Fre

quency

Data Ranges

Histogram

Use to identify if the process is predictable (in control)

Can be used to display the shape of variation in a set of data

400

420

440

460

480

500

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

LCL

UCL

400

420

440

460

480

500

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

LCL

UCL

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

LCLLCL

UCLUCL

Control Chart

Cause and Effect Diagram

MaterialMethod

Environmental

ManMachine

Effect

Pareto ChartsRoot Cause Analysis

Design Assy.Instruct.

Purch. Training Other

80% of theproblems may beattributed to 20%

of the causes

Continuous Improvement Process

Orlando RemanufacturingAnd

Distribution Center

Phase 1: Internal Kickbacks

Five Most Common Reasons For Returns From QA

Missing/Wrong Part

Dirt/Rust DefectivePart

Leaks PoorInsulation

Impact of Reasons for Returns from QA - Weighted Average

Weighted Avg. = % Occurring X Defect Cost (0-10, Based on Time to Repair)

Leaks Dirt/RustStainlessSteel

Missing/Wrong Part

DefectivePart

PoorInsulation

Equipment To BeRemanufactured

Tear DownAnd Wash

Remanu-facture

ReassemblyFinal

Clean-up

QA

Unit Not OKTo Customer

Why Dirt? (Fishbone)

EnvironmentDust/HumidityPoor LightingSpace Limitations

MethodsReworking Steel after

Valves are InstalledNeed to Rinse Parts off

after Sandblasting

Lack of CommunicationQA to IT

Rework

RinseTraining

Attention to Detail

Poor Lighting

Dust/Humidity

Space Limitations

Tools for $$Cleansing Compounds

Larger Wire Brushes

Environment

Dirt

Machinery Materials

Methods ManMeasurement

Materials

Cleansing Compounds

Need Larger WireBrushes

People

Need More Training

More Attention to Detail – Do it Right First Time

Machines

Best Tools for $$?

Measurement

QA Manager Fixes Some Things Without Informing the Technicians

Why Leaks? (Fishbone)

EnvironmentHigh Temperatures Poor Lighting

MethodsCheck Units for Ways

They Could LeakDoes Testing Create

Leaks?

Materials

Bad Tubing

“O” Rings Too Old (Dry)

People

Use Wrong Clamps

Don’t Crimp Properly

Forget to Connect

Machines

Need Rims That Make it Easier to Install Tubing

Measurement

No Testing for Leaks Prior to QA

Which Mfr./Model Leak the Most?

No Leak Testing Prior to QA Quality Check

Don’t Crimp Properly

Use Wrong Clamps

Poor Lighting

High Temperature

Reengineer Rims“O” Rings Old

Bad Tubing

Environment

Leaks

Machinery Materials

Methods ManMeasurement

Forget to Connect

Mishandle Units

Identify Most Occurrences

Variation Analysis

Most variation without “special” causes will be normally distributed

Variation is typically

classifiable into the 6 M’s

Variation is additive

Variation in the process inputs will generate more variation in the process output

Metho

ds

Environme

nt

Machinery

Materials

Man

Measureme

nt

Output

Variation is Present in All Processes!

Measurement System Analysis (MSA)

Goal - To identify if the measurement system can distinguish between product variation and measurement variation

222gageproductobseerved

Some key dimensions Accuracy Precision Bias

Tools: Gage R&R, DOE, Control Charts

Agenda for Measure

1. Types of Measures/Setting Targets

2. Data Collection and Prioritization, MSA

3. SPC, Control Charts

4. Process Capability

SPC vs. Acceptance Sampling

Acceptance Sampling: Used to inspect a batch prior to, or after the process

Take Sample

Receive Lot

Meet Criteria?

Accept

Reject Rework /Waste

Send to Customer

Yes

No

Statistical Process Control (SPC): Used to determine if process is within process control limits during the process and to take corrective action when out of control

400

420

440

460

480

500

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

LCL

UCL

Statistical Process Control

A Processis not in control when one or more points is/are outside the control limits

Special Causes

UCL

LCL

Process in Statistical Control

Process not in Statistical Control

Process not in Statistical ControlUCL

LCL

UCL

LCL

A Processis in control when all points are inside the control limits

Statistical process control is the use of statistics to measure the quality of an ongoing process

When to Investigate

Even if in control the process should be investigated if any non random patterns are observed OVER TIME

UCL

LCL

1 2 3 4 5 6

In Control

UCL

LCL

1 2 3 4 5

Close to Control Limit

UCL

LCL

1 2 3 4 5 6

Consecutive Points Below/Above Mean UCL

LCL

5 10 15 20

Cycles

1 2 3 4 5 6

UCL

LCL

Trend - Constant Increase/Decrease

Control Chart Development Steps

INPUTSOUTPUT

X’s Y’s

Identify Measurement1Sample

Sample Size Defective p

1 100 4 0.042 100 3 0.033 100 5 0.054 100 6 0.065 100 2 0.026 100 1 0.017 100 6 0.068 100 7 0.079 100 3 0.03

10 100 8 0.0811 100 1 0.0112 100 2 0.0213 100 1 0.0114 100 9 0.0915 100 1 0.01

Total 1500 59

Collect Data2

0

0.02

0.04

0.06

0.08

0.1

0 2 4 6 8 10 12 14 16 18

Determine Control Limits3Improve Process4

A B C D

Defects

Start

Eliminate Special Causes

Reduce Common Cause Variation Improve

Average

Frequently Used Control Charts

Attribute: Go/no-go Information, sample size of 50 to 100 Defectives

p-chart, np-chart Defects

c-chart, u-chart

Variable: Continuous data, usually measured by the mean and standard deviation, sample size of 2 to 10 X-charts for individuals (X-MR or I-MR) X-bar and R-charts X-bar and s-charts

SPC Attribute Measurements

p =Total Number of Defectives

Total Number of Observations

nS

)p-(1 p = p

p

p

Z- p = LCL

Z+ p = UCL

s

s

p-Chart Control Limits

percentage defects (mean)

Standard deviation of p

Z Number of standard deviations

n Number of observation per sample (i.e., sample size)

UCL Upper control limit

LCL Lower control limit

p

pS

-2 -1 0 1 2 3-3

Z- VALUE is the number of Standard Deviations from the mean of the Normal Curve

Normal Distribution: Z-Value

Z

p-Chart Example1. Calculate the sample proportion, p, for each sample2. Calculate the average of the sample proportions

3. Calculate the sample standard deviation

4. Calculate the control limits (where Z=3)

5. Plot the individual sample proportions, the average of the proportions, and the control limits

SampleSample

Size Defective p1 100 4 0.042 100 3 0.033 100 5 0.054 100 6 0.065 100 2 0.026 100 1 0.017 100 6 0.068 100 7 0.079 100 3 0.03

10 100 8 0.0811 100 1 0.0112 100 2 0.0213 100 1 0.0114 100 9 0.0915 100 1 0.01

Total 1500 59

0.0393=1500

59 = p

.0194= 100

.0393)-.0393(1=

)p-(1 p = p n

s

0 0.0189- = 3(.0194) - .0393 = Z- p = LCL

.0976 = 3(.0194) .0393 = Z+ p = UCL

p

p

s

s

0

0.02

0.04

0.06

0.08

0.1

0 2 4 6 8 10 12 14 16 18

SPC Continuous Measurements

n A2 D3 D4 2 1.88 0 3.27 3 1.02 0 2.57 4 0.73 0 2.28 5 0.58 0 2.11 6 0.48 0 2.00 7 0.42 0.08 1.92 8 0.37 0.14 1.86 9 0.34 0.18 1.82

10 0.31 0.22 1.78

R A- x = LCL

R A+ x = UCL

2

2

RD = LCL

RD = UCL

3

4

x Chart Limits

R Chart Limits

X-bar, R Chart Control Limits

Shewhart Table of Control Chart Constants

SPC Continuous Measurements

1 2 3 4 5

1 10.6 10.7 10.5 10.9 10.9 10.7 0.4

2 10.4 11.0 10.4 10.7 10.7 10.6 0.6

3 10.8 10.8 10.8 10.2 10.5 10.6 0.6

4 10.3 10.2 10.3 10.4 11.0 10.4 0.8

5 11.0 10.7 10.9 10.6 10.8 10.8 0.4

6 10.9 10.0 10.4 10.1 10.5 10.4 0.8

7 10.8 10.4 10.5 10.7 10.7 10.6 0.4

8 10.1 10.3 10.9 10.2 10.4 10.4 0.8

9 11.0 10.5 10.7 10.8 10.7 10.7 0.5

10 10.8 10.9 10.4 10.3 10.4 10.6 0.6

11 10.5 11.0 10.5 10.8 10.8 10.7 0.5

12 10.2 10.1 10.7 10.8 10.2 10.4 0.7

13 10.8 10.6 10.3 10.4 11.0 10.6 0.7

14 10.1 10.3 10.3 10.3 10.8 10.3 0.7

15 10.1 10.1 10.3 10.2 10.1 10.2 0.2

10.54 0.58

Sample Range

Total Average

Sample

Observation Sample Mean

R

Chart

Sample Mean

10.10

10.20

10.30

10.40

10.50

10.60

10.70

10.80

10.90

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Sample

Mean

s

LCL

UCL

Sample Range

-0.15

0.05

0.25

0.45

0.65

0.85

1.05

1.25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Sample

LCL

UCL

0

1

)58.0)(0(RD = LCL

22.)58.0)(11.2(RD = UCL

3

4

X-bar

Chart

10.19

10.87

=.58(0.58)-10.54R A- x = LCL

=.58(0.58)10.54R A+ x = UCL

2

2

Proper Assessment of Control Charts

Find special causes and eliminate If special causes treated like common causes,

opportunity to eliminate specific cause of variation is lost.

Leave common causes alone in the short term If common causes treated like special causes, you will

most likely end up increasing variation (called “tampering”)

Taking the right action improves the situation

Quarterly Audit Scores

0 1 2 3 4 5 6Quarter

Score

Did something unusual happen?

Quarterly Audit Scores

What do these lines represent?

0 1 2 3 4 5 6Quarter

Score

Quarterly Audit Scores

Now what do you think?

0 1 2 3 4 5 6

Quarter

Score

Agenda for Measure

1. Types of Measures/Setting Targets

2. Data Collection and Prioritization

3. SPC, Control Charts

4. Process Capability

Process Capability Introduction

“Voice of the Process” (The “Voice of the Data”)

Based on natural (common cause) variation

Tolerance limits (The “Voice of the Customer”) Customer requirements/Specs

Process Capability A measure of how “capable” the process is to meet customer requirements

Compares process limits to tolerance limits

Process Capability Scenarios

natural variation

specification

A

specification

natural variation

C

specification

natural variation

B

specification

natural variation

D

Process Capability Index, Cpk

Capability Index shows if the process is capable of meeting customer specifications

3

X-UTLor

3

LTLXmin=Cpk

Find the Cpk for the following:

A process has a mean of 50.50 and a variance of 2.25. The product has a specification of 50.00 ± 4.00

50.00 ± 4.00

Mean = 50.50 Stdev = 1.5

Interpreting the Cpk

Cpk > or = 0.33Capable at 1 *

Cpk > or = 0.67Capable at 2 *

Cpk > or = 1.00Capable at 3Cpk > or = 1.33Capable at 4Cpk > or = 1.67Capable at 5Cpk > or = 2.00Capable at 6

* * Processes with Cpk < 1 are traditionally called “not capable”.

However, improving from 1 to 2, for example, is extremely valuable. .

Calculating Yield

Task 1

Task 2

Task 3

Task 4

Task 5

96 units

4 rwk

98 units

2 rwk

95 units

5 rwk

90 units

10 rwk96 units100 units

Traditional Yield (TY)Started UnitsofNumber Total

Task Final theofOutput TotalTY 96.0

100

96TY

Rolled Throughput Yield (RTY):

another way to get “Sigma” level)

Started UnitsofNumber Total

work Without Re Produced Units(cumRTY

77.096.0*90.0*95.0*98.0*96.0 RTY

The Hidden Factory = TY - RTY The Hidden Factory = 0.96-0.77 =0.19

Traditional Yield assessments ignore the hidden factory!

37.099.0 90.099.0 10010

Six Sigma Quality Level

Six Sigma results in at most 3.4 DPMO - defects per million opportunities (allowing for up to 1.5 sigma shift).

1 2 3 4 5 6

1,000,000

10,000

1

100,000

1,000

100

10

DPMO

IRS Tax Advice

Doctor Prescription Writing

Airline Baggage Handling

Domestic Airline Flight Fatality Rate (0.43PMM)

93% good

99.4% good

99.98% good

Restaurant Bills

Payroll Processing

SIGMA

Is Six Sigma Quality Possible?

Source: Motorola Inc.

Six Sigma Quality

Six Sigma Shift The drift away from target mean over time

3.4 defects/million assumes an average shift of 1.5 standard deviations

With the 1.5 sigma shift, DPMO is the sum of 3.39767313373152 and 0.00000003, or 3.4. Instead of plus or minus 6 standard deviations, you must calculate defects based on 4.5 and 7.5 standard deviations from the mean! Without the shift, the number of defects is .00099*2 = .002 DPMO.

iesopportunit 1,000,000

DefectsofNumber TotalDPMO

Z 4.5 6.0 7.5

P(<Z) 0.99999660232687 0.99999999901341 0.99999999999997

1 - P(<Z) 0.00000339767313 0.00000000098659 0.00000000000003

* 1,000,000 3.39767313373152 0.00098658770042 0.00000003186340

Quality Levels and DPMO

Defects per million opportunitiesAssumes 1.5 sigma shift of the mean

Sigma LevelDPMO (Defects per

million opportunities)Reduction from previous

sigma level1.0 697672  2.0 308770 55.74%3.0 66811 78.36%4.0 6210 90.71%5.0 233 96.25%6.0 3.4 98.54%

Regardless of the current process sigma level, a very significant improvement in quality will be realized by a 1-sigma improvement!

Is Six Sigma Quality Desirable?

99% Quality means that 10,000 babies out of 1,000,000 will be given to the wrong

parents! One out of 100 flights would result in fatalities. Would you fly?

What is the quality level for Andruw Jones?