Blended Lean Six Sigma Black Belt Training – ABInBev Week One Review ©2010 ASQ. All Rights...

Blended Lean Six Sigma Black Belt Training –

ABInBev

Week One Review

©2010 ASQ. All Rights Reserved.

2012 American Society for Quality.

All Rights Reserved.Page Week One Review –ABI Black Beltv1.1

Last Time We …

• Defined our project

• Leaned our processes

• Learned statistical techniques

• Validated our measurement system

• Created standard work

• Conducted hypothesis tests

• Practiced coaching Green Belts

Let’s do a quick review before starting the new material.



Define Phase

Created the project charter – the critical first step in any LSS project

Collected Voice of the Customer

High-level process map (SIPOC)



Lean Defined

A philosophy and set of methods where waste is identified

continuously and eliminated passionately.

A complete lean system should have: Techniques/Tools

Management (mindset and accountability)

Organizational Environment (skills, culture, and behaviors)

Waste is any activity (or inactivity) that consumes resources

and do not add value to the customer through the product

or service.



Key Terms

Value-adding activities transform the product in a way the customer would be willing to pay for.

Daily Operating TimeTAKT Time=

Daily Customer Requirement

Value stream is a series of activities, both those that create value and those that do not, required to deliver a product or service.

Value stream mapping is a tool used to document (map) all activities of a value stream to clearly see waste and variations.



The Eight Wastes

DEFECTS

8Wastes

EXTRA PROCESSING

EXCESS INVENTORY

EXCESS TRANSPORTATION

OVERPRODUCTIONEXCESS MOTION

WAITING

UNUSED CREATIVITY



5 S

SORT involves sorting through the contents of the workplace and removing unnecessary items.

STRAIGHTEN involves putting the necessary items in their place and providing easy access.

SHINE involves cleaning everything, keeping it clean daily, and using cleaning to inspect the workplace and equipment for defects.

STANDARDIZE involves creating visual controls and guidelines for keeping the workplace organized, orderly, and clean.

SUSTAIN involves training and discipline to ensure that everyone follows the 5S standards.



Basic Statistics

Seven basic probability rules1. All events have a probability between 0 and 1.2. The sum of all possible probabilities of defined events is equal to 1.00.3. The probability of an event not occurring is equal to the probability of

the event occurring subtracted from 1.0.4. If events are mutually exclusive, the sum of the probability of

occurrence of these events is equal to 1.5. The probability of the joint occurrence of independent events is the

product of the probability of each event.6. The probability of occurrence of either or both non-independent

events is the sum of the probability of each independent events minus the probability of joint events.

7. The probability of observing two dependent events is the product of the probability of the first event and the conditional probability of the second event, given that the first has occurred.



Your Turn 1

Assume a sample is selected from a normally distributed population with a mean of 12 oz and a standard deviation of .01 oz.

1. What percent of the sample would be expected to have a value greater than 12.02?

2. What percent of the sample would be expected to have a value less than 11.99?

3. Ten percent of the sample would be expected to have X or less. What is X?

Find the answers and draw graphs of the results using Minitab.



Your Turn 2

Assume a fair coin is flipped 100 times.1. What is the probability of exactly 50 heads?2. What is the probability of greater than 60 heads?

Assume a process creates .01 defects per unit. One hundred units were sampled. What is the probability there will be:

3. Zero defects?4. Two defects?

Find the answers with Minitab.



Measurement Systems Analysis (Gage R&R)

Repeatability and reproducibility are typically the primary contributors to measurement error

ObservedVariation

Actual ProcessVariation

MeasuredVariation

Long-TermProcessVariation

Short-TermProcessVariation

VarianceDue toInstrument

VarianceDue toOperators

Repeatability Calibration Stability Linearity Reproducibility



Your Turn 3

A Green Belt conducted a gage R&R on a bottling line. He had three operators measure the temperature of the product at 20 locations in the line three times each. He sent the data in Temperature Measurement.mtw. What would you recommend?



Error Proofing

Error proofing is a systematic approach for anticipating and detecting potential defects and preventing them from reaching the customer (internal or external)

• Proactive identification and prevention

• Prevent first

• Do not allow defects from going further

• Intentionally designed, not by chance

• At the source—close to the origination

• Inexpensive first

• Transparent to the operator

• Absolutely reliable

2012 American Society for Quality.All Rights Reserved.

Process Capability – ABI Black Beltv1.1

Defining Process Capability

Principle: process capability is a ratio of process

variability to design tolerance.

Process width

Design width

It is measured in several ways.



Process Capability Terms

See formulae on next page.

Cp – Short-term process capability For a limited period of time (not including shifts and drifts) Does not consider process centering Also known as process entitlement

Cpk – Short-term process capability index For a limited period of time (not including shifts and drifts) Does consider process centering

Pp – Long-term process capability For an extended period of time (including shifts and drifts) Does not consider process centering

Ppk – Long-term process capability index For an extended period of time (including shifts and drifts) Does consider process centering



Specification Width(s)Short-Term Process Width

=

Specification Width(s)Long-Term Process Width

=

Lesser of: or

Lesser of: or

Capability Formulae

Cp =

Pp =

Cpk=

Ppk=

USL - LSLProcess sigma =

2 USL-LSL

6 st

USL-LSL6 lt

X-LSL3 lt

USL-X3 lt

USL-X3 st

X-LSL3 st



Your Turn 4

A Green Belt collected data on the fill volumes in cans. The specification limits are 11.995 to 12.005 with a target of 12. The data is in six pack spc. What is the capability of this process?



Standard Work

Documents layout and flow process sequenceAlso documents:

• Standard W.I.P.• Quality checks• Safety precautions

Posted in cellIncludes visual work instructions for each

operation



Hypothesis Testing

Enables us to determine if there is a statistically significant difference between the characteristics of interest of samples of data.

© 2010 ASQ. All Rights Reserved.20

Testing Method Selection Matrix

Variable Type Attribute Y Count Y Continuous Y

Discrete X

1 or 2 TreatmentsProportions

3 +TreatmentsChi Square

1 or 2 Treatments Poisson

3 + Treatments Chi Square

1 or 2 TreatmentsT tests

3 +TreatmentsANOVA

Continuous XLogistic Regression

Logistic Regression

Least Squares Regression

There are many types of hypothesis tests we will learn to use depending on the type of data available for the inputs (Xs) and outputs (Ys).


Your Turn 5

A Green Belt collected data on Gross Line Yield before and after a process change. Did the change make a statistically significant improvement (more is better) on Gross Line Yield? The data is in Gross Line Yield.mtw.


What’s Next?

• Completing our set of hypothesis testing tools• More lean tools• Designed experiments• Control plans• Design for Six Sigma

Let’s get started!

Blended Lean Six Sigma Black Belt Training –

ABInBev

Answers to Week One Review Your Turn

©2010 ASQ. All Rights Reserved.


Your Turn 1 Solution 1

Calc>Probability Distributions>Normal

This will give the probability of less than 12.02, which we will subtract from 1 to get the answer. Minitab returnsNormal with mean = 12 and standard deviation = 0.01

x P( X <= x )12.02 0.977250

So .02275 would be greater than 21.02 oz.


Your Turn 1

Assume a sample is selected from normally distributed population with a mean of 12 oz and a standard deviation of .01 oz.

1. What percent of the sample would be expected to have a value greater than 12.02?

2. What percent of the sample would be expected to have a value less than 11.99?

3. Ten percent of the sample would be expected to have X or less. What is X?

Find the answers and draw graphs of the results using Minitab.


Your Turn 1 Solution 1 Graph

Graph>Probability Distribution Plot



40

30

20

10

0X

Densi

ty

12.02

0.02275

12

Distribution PlotNormal, Mean=12, StDev=0.01



Control+e to return to the last dialog box



40

30

20

10

0X

Densi

ty

11.99

0.1587

12




40

30

20

10

0X

Densi

ty

0.1

1211.99




Your Turn 2

Assume a fair coin is flipped 100 times.1. What is the probability of exactly 50 heads?2. What is the probability of greater than 60 heads?

Assume a process creates .01 defects per unit. One hundred units were sampled. What is the probability there will be:

3. Zero defects4. Two defects

Find the answers with Minitab




Calc>Probability Distributions>Binomial

Binomial with n = 100 and p = 0.5

x P( X = x )50 0.0795892





This will return the probability of 60 or less. Subtract from 1 to get the probability of greater than 60.Binomial with n = 100 and p = 0.5

x P( X <= x )60 0.982400

The probability of greater than 60 heads is .0176.




Calc>Probability Distributions>Poisson

.01 * 100 = 1

Poisson with mean = 1

x P( X = x )0 0.367879





Poisson with mean = 1

x P( X = x )2 0.183940



Your Turn 3

A Green Belt conducted a gage R&R on a bottling line. He had three operators measure the temperature of the product at 20 locations in the line three times each. He sent the data in Temperature Measurement.mtw. What would you recommend?



Your Turn 3 Solution

Assistant>Measurement Systems Analysis




the study.variation. The process variation is estimated from the parts inThe measurement system variation equals 7.8% of the process

100%30%10%0%

NoYes

7.8%

ReprodRepeatTotal Gage

48

36

24

12

0

30

10

%Study Var

and is 2.5% of the total variation in the process.same item. This equals 31.7% of the measurement variationThe variation that occurs when different people measure the-- Operator and Operator by Part components (Reproducibility):7.4% of the total variation in the process.times. This equals 94.8% of the measurement variation and isoccurs when the same person measures the same item multiple-- Test-Retest component (Repeatability): The variation thatreproducibility to guide improvements:total gage variation is unacceptable, look at repeatability andExamine the bar chart showing the sources of variation. If the >30%: unacceptable 10% - 30%: marginal <10%: acceptableGeneral rules used to determine the capability of the system:

Number of parts in study 20Number of operators in study 3Number of replicates 3

Study Information

Variation by Source

(Replicates: Number of times each operator measured each part)

Comments

Gage R&R Study for TemperatureSummary Report

Can you adequately assess process performance?

This is a good measurement system.



Your Turn 4

A Green Belt collected data on the fill volumes in cans. The specification limits are 11.995 to 12.005 with a target of 12. The data is in six pack spc. What is the capability of this process?




Assistant>Capability Analysis




12.004

12.000

11.996

Mean

631561491421351281211141711

0.02

0.01

0.00

Range

Normality Test

Results PassP-value 0.737

(Anderson-Darling)

Xbar-R ChartConfirm that the process is stable.

Normality PlotThe points should be close to the line.

Capability Analysis for Can 1, ...Diagnostic Report




60

HighLow

Z.Bench = 1.32

> 0.50.10.050

NoYes

P = 0.433

12.01112.00812.00512.00211.99911.99611.99311.990

LSL Target USL

Actual (overall) capability is what the customer experiences. spec limits.percentage of parts from the process that are outside the-- The defect rate is 9.29%, which estimates thetarget (p > 0.05).-- The process mean does not differ significantly from theConclusions

Upper Spec 12.005Target 12Lower Spec 11.995

Customer Requirements

Mean 12.000Standard deviation 0.0029754

Actual (overall) capability Pp 0.56 Ppk 0.56 Z.Bench 1.32 % Out of spec 9.29 PPM (DPMO) 92893

Process Characterization

Does the process mean differ from 12?

Actual (overall) CapabilityAre the data inside the limits and close to the target?

Comments

Capability Analysis for Can 1, ...Summary Report

How capable is the process?

Process control in marginal and capability is poor



Your Turn 5

A Green Belt collected data on Gross Line Yield before and after a process change. Did the change make a statistically significant improvement (more is better) on Gross Line Yield? The data is in Gross Line Yield.mtw.




Assistant>Hypothesis Test




807672686460

GLY Before

GLY After

of GLY After (p < 0.05).The mean of GLY Before is significantly less than the mean

> 0.50.10.050

NoYes

P = 0.000

0-2-4-6-8

results of the test.samples. Look for unusual data before interpreting the-- Distribution of Data: Compare the location and means ofthat the true difference is between -7.9204 and -3.7796.the difference from sample data. You can be 90% confident-- CI: Quantifies the uncertainty associated with estimatingless than GLY After at the 0.05 level of significance.-- Test: You can conclude that the mean of GLY Before is

Sample size 20 20Mean 65.79 71.64 90% CI (64.41, 67.17) (70.027, 73.253)Standard deviation 3.5650 4.1725

Statistics GLY Before GLY After

-5.85(-7.9204, -3.7796)

Distribution of DataCompare the data and means of the samples.

Mean TestIs GLY Before less than GLY After?

90% CI for the DifferenceDoes the interval include zero?

Difference between means* 90% CI

* The difference is defined as GLY Before - GLY After.

Comments

2-Sample t Test for the Mean of GLY Before and GLY AfterSummary Report

He can conclude a statistically significant improvement was made.

Blended Lean Six Sigma Black Belt Training – ABInBev Week One Review ©2010 ASQ. All Rights...

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