Bb Wk1 340 Measure Phase Review

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Black Belt Training Ford Proprietary

Copyright © 2005-2006, Ford Motor Company – All Rights Reserved

Review of Measure



Measure Review

Black Belt Training Slide: 1

Questions Driving the Phase

Which Factors (Xs) seem to affect Outputs (Ys) most?

Is your ability to measure/detect "Good Enough“ (MSA)?

Is your process stable and in control?

What is your current process performance (capability)?

What actions are taken to protect the customer (containment)?

Have escape points been identified?



Measure Review


Measure Phase Outcomes

A plan for collecting data that specifies the type of data needed,key measures to provide the data, and the techniques for

collecting the data.

A validated measurement system that ensures the accuracy

and consistency of the data collected. Sufficient data for problem analysis.



Measure Review


MSA - Variable Data% Contribution

Measurement System Variation as a percentage of Total ObservedProcess Variation (using Variance)

% Study Variation

Measurement System Standard Deviation as a percentage of TotalObserved Process Standard Deviation (using Standard Deviation)

% Tolerance

Measurement Error as a percentage of Tolerance

# of Distinct Categories

Less than 5 indicates Attribute conditions

% Contribution % Study Variation

(Process Control)

% Tolerance

(Product Control)

# of Distinct Categories

It is desirable to have ALL indicators Green

R

Y

G < 1% Good

2-9% Acceptable

> 9% Unacceptable R

Y

G < 10% Good

11-30% Acceptable

> 30% Unacceptable R

Y

G < 10% Good

11-30% Acceptable

> 30% Unacceptable R

Y

G > 10 Good

5-10 Acceptable

< 5 Unacceptable



Copyright © 2001-2005

Six Sigma Academy International, LLC

All rights reserved; for use only in compliance with SSA license.

Measure Review Pg 4

Common Process Mapping

• Use Common Process Maps to

describe:

- All of the steps within the

bounded process

- The flow relationship between the

steps including loops anddecision points

- Distribution of material flow

- This is a first step in building a

Detailed Process Map






Measure Review Pg 5

Detailed Process Mapping

• Use Detailed Process Maps to

describe:

- All of the steps within the scope of

the project (frequently a subset of

what was mapped in the common

Process Map)

- The flow relationship between thesteps including loops and

decision points

- Distribution of material flow

- All inputs and outputs by step

- The classification of inputs – Noise(N), Standard Operating

Procedures (S), or Control (C)

- The Value-Add status of each step

– VA, BVA, or NVA

- Optional metrics – DPU, DPO,Cycle, or YTP






Measure Review Pg 6

Value Stream Mapping

• Value Stream Maps

facilitate understandingof the process.

• Use them to help:

- Establish baseline process

capability

- Define performance objectives

- Identify root causes and link to specific process steps

- Describe bottlenecks and queues within process

- Match specific resources and costs to each process step






Measure Review Pg 7

Cause and Effect Diagram

• Cause and effect tools help identify,

explore, and display the possiblecauses related to a problem or

condition. Use them to:

- Identify early linkages between

Potential Xs and Potential Ys – Scope

- Identify Potential Xs – Funnel

- Identify initial linkages X to Y

- Eliminate some Xs that are not vital

- Minimize the need for costly analysis






Measure Review Pg 8

FMEA

• After the Project Team has

identified possible sources of variation, they can complete

the first part of the FMEA

spreadsheet.

• Use the FMEA to:

- Identify potential Failure Modes

and Analyze risks

- Identify Potential Ys – Scope

- Identify Potential Xs – Funnel

- Identify Key Xs

- Record the Key Xs

- Record the “fixes” made

- Provide structure to ongoing

control activities






Measure Review Pg 9

FMEA Purpose

• After reviewing the existing FMEA, the Project Team should have:

- A list of potential failure modes

- A list of potential effects

- Documentation of current controls

- Documentation and prioritization of recommended improvementactions

- A historical record of improvements

In the Improve Phase, the FMEA will be used to assess potential

failures associated with proposed changes






Measure Review Pg 10

Basic Statistics

• Decisions should be based on validated data

• Prove a statistically significant difference as well as a practical

difference

• Use statistics to control improved processes (Control Charts)

• Use data types to determine how to graph and analyze

• Convert attribute to continuous whenever possible







Data Types

Numerical Data

• Measure and assign a number

-Measured data (Continuous)

• May be divided into ever

smaller increments

• Time, distance, weight

-Count data (Discrete)

• Limited to a set of numbers

that may not be divided into

smaller increments

Attribute Data

• Observe and assign a category

or name

-May be ordered (Ordinal)

• Small, medium, large

-May be unordered (Nominal)

• Red, blue, pink

- Use Continuous Data whenever

possible

- Continuous data is much richer in

information

- Discrete data is not normally

distributed




Six Sigma Academy International, LLC All rights reserved; for use only in compliance with SSA license.


Measures Of Central Location

• Mean: Average of a set

of values

• Median: Midpoint in a string

of sorted data, where 50% of

the observations, or values,

are below and 50% are

above

• Mode: The most frequently

occurring value






Measures of Spread

• Range is the difference between the largest and the

smallest observations (Range = Maximum Observation – MinimumObservation)

• Deviation is the distance between a data point and the Mean

(Deviation = X – Mean)

• The Standard Deviation represents a measure of the (average)deviation of the whole dataset

• The variance is the Standard Deviation squared

• The variance for a sum of two independent variables is found by adding

both variances






Normal Distribution

The Normal Distribution is the

concept that is the basis for moststatistical techniques/tests. The

Mean and Standard Deviation are

required to fully describe the

distribution.

Other Distributions

• t-Distribution – Continuous

• Discrete Distributions

- Binomial

- Poisson• F Distribution

• Chi-square Distribution






Normal Probability Plots

• Distributions are the basis

for inferential statistics

• Distributions are the

probability curve of an

event occurring

• Conclusions are drawn basedon the behavior of distributions

• The Normal Distribution is a

continuous distribution which

is symmetrical and extreme values are less likely than moderate values

(unimodal)

• Normal probability plots are a graphical technique to determine if a

distribution is normally distributed






Measurement System Analysis (MSA)

• Remember, total Variation in a process is equal to the sum of the

Process Variation and the Measurement System Variation.

- Minimizing measurement variation ensures that variation reflected by

the data collected mainly represents process variation.

- MSA is performed on a regular basis to ensure data is valid and

reliable.

• If the MSA fails, do not collect data. Data collection is a critical

element of success in a 6-Sigma project. If the measurement system

does not work, it must be fixed before proceeding.

- Improve the gage

- Improve the measurement process

- Train measurement takers






Purpose of the MSA

• Information gained from MSA is used to answer these questions:

- Is the measurement system capable for this study?

- How big is the measurement error?

- How much uncertainty around the interpretation of its value should

be attached to a measurement (i.e. could have “perfect”

measurement system but be measuring the wrong thing/at the wrongtime, etc)?

- Are the measurements being made with measurement units that are

small enough to properly reflect the variation present?

- What are the sources of measurement error?

- Can we detect process improvement if and when it happens?

- Is the measurement system stable over time?






Repeatability/Reproducibility

Repeatability

• Variation that occurs when repeated

measurements are made of the same

item under absolutely identical

conditions

- Same:

• Operator

• Set-up

• Units

• Environmental conditions

- Short-term

Reproducibility

• The variation that results when

different conditions are used to make

the measurements

- Different:

• Operators

• Set-ups

• Test units

• Environmental conditions

• Locations• Companies

- Long-term






Variables MSA

Examples

• Response times

• Salaries

• Pricing

• Calipers• Coordinate

measurement machine

• Labor hours

• Stopwatch

Main Sources Of Variation

• Materials

• Methods

• Machines

• People• Environment

• Measures






Attribute MSA

• Attribute Measurement Systems often involve human judgment.

Examples include:

- Data collection form

- Survey

- Customer satisfaction

- Academic grades

- Plug gages (go/no-go)

- Thread gages (go/no-go)

- Visual defect check






Purpose Of Attribute MSA

Accuracy checks

• Assess standards against customers‟ requirements

• Identify how well Measurement System conforms to a “known master”

Precision checks• To quantify the ability of inspectors (Appraisers) or gages to accurately

repeat their inspection decisions – Repeatability

• To determine if inspectors (Appraisers) across all shifts, machines,

lines, etc… use the same criteria to evaluate items – Reproducibility






Purpose Of Attribute MSA continued

• Identify how well inspectors/gages measure a known master to ensure

no misclassification occurs:

- How often operators decide to ship truly defective product

- How often operators do not ship truly acceptable product

• Determine areas where:

- Training is needed

- Procedures or Control Plans are lacking

- Standards are not clearly defined

- Gage adjustment or correlation is necessary






Statistical Process Control (SPC)

• The process which uses statistical techniques such as control charts to

analyze a process output, which serves as a basis for appropriateactions to achieve and maintain the process in a state of statistical

control, and it improve the capability of the process.

- Control Charts provide a simple means of identifying special cause

variation.






Control Chart Roadmap

Choose Appropriate

Control Chart

Attribute Data

Variable Data

Defect Data Defective Data

Constant

Sample Size

Variable

Sample Size

c Chart u Chart

Constant

Sample Size

Variable

Sample Size

np Chart p Chart

Sample

Size = 1

X and Rm

Small Sample

Size (median

value)

X and R

Large Sample

Size

(usually 10)

X and s

Small Sample

Size

(usually 3 to 5)

X and R

Source: Black Belt Memory Jogger

~






Data Collection Considerations

• As data is collected, it should be “data tagged” to facilitate grouping

• With the process knowledge gained from walking the process and

creating various types of Process Maps, select some candidate sub-

groups and the associated data tags

• The Cause and Effect tools can also help use narrow down what Xs to

collect (data tag)• Remember, the intent is to make some directionally correct decisions

regarding what Xs drive variation; thus the need to adequately capture

the variation in sampled data






Variation Component Study

• Variation component studies:

- Combine techniques from the following areas:

• Shewart Control Chart model

• Rational sub-grouping

• Measurement Systems Analysis• Graphical, Multi-Vari Charts

• Analysis Of Variance (ANOVA) methods

- Partition potential sources of variation within a process so the

researcher will know where to work first






Variance Component Road Map

• The Process Map is a key part of

identifying potentially critical inputvariables – These will be the

variables either retained or

screened out

• Draw the tree structure to aid in

understanding the hierarchy thatexists in the process

• Review those input variables to

determine an adequate sampling

plan






Control Charts At All Levels of the Hierarchy

• By utilizing Control Charts at all levels of the

hierarchy, we can understand:

- Stability

- Graphical representation of largest

variation source






Calculate Variance Components – Fully Nested ANOVA

• Multi-Vari and Control Charts have shown:

- The within sub-group variation is stable (we

can proceed!)

- The majority of the variation appears to be

Part to Part

• ANOVA separates the Total variation in thedata into components

• The data should be a balanced nested

study (equal number of observations

for each leg)

- Unbalanced data may be used

(General Linear Model), but

there is greater risk






Determine Greatest Source of Variation

• Our graphical tools (Multi-Vari,

Control Charts) have provided us aconclusion that agrees with our

analytical tool, nested ANOVA

- The majority of the variation

is Part-Part

• These are most likely the input

variables that drive the output – The

remainder of the input variables that

do not change Part-Part, can be

screened out






Process Capability

• The next step of the Measure Phase

is to collect the proper data andestablish the capability of the

process being studied. The outcome

of this step will be a statement of

the process baseline capability

expressed as a Sigma value.• It must be determined whether the process, given its natural variation,

is capable of meeting established customer requirements.

• Conducting Capability Analysis establishes:

- A current performance baseline for the process.

- A reference point to evaluate the impact of improvements.

Capability Analysis is the study

of how well a process is

performing in meeting the

expectations of customers

(CTQs).






Data Types

• Attribute Data System

- Capability is defined in terms

of PASS/FAIL or categories

• Continuous Data System

- Capability is defined in terms

of defects under the curve

and outside of the specification

limits






Z score Transformations

• If a data set is normally

distributed, we use themean and Standard

Deviation to determine

the percentage (or probability)

of observations within

a selected range• We can transform any normally distributed scale to its equivalent Z

scale or score using the formula shown

• X will often represent a Lower or Upper Specification Limit (LSL and

USL, respectively) – It is the “Point of Interest”

• Z is the measure from the mean to the Point of Interest in Standard

Deviations






Short Term vs. Long Term Data Samples

• Short Term Sample

- Free from assignable (special) causes

• Represents random (common) causes only

- Group of “like things”

- Collected across a narrow inference space• Frequently it is data from one lot of material, on one shift, on one

machine, with one operator

• Long Term Sample

- Consists of random and assignable causes

- Collected across a broad inference space

• Data from several lots, many shifts, many machines and operators






Differences in Process Capability Over Time

• The difference between short term and long term is based on the

impact of natural variation on a process over time.

• In Manufacturing, shift might be caused by a change in input materials,

while drift is a factor of tool wear. Shift and drift can be expected over

time.

Two implications of shift and drift:• In Design, shift and drift should be accounted for when setting

specification limits.

- Shift is a sudden change in process capability, while drift is a change

that occurs slowly over time.

• In problem-solving, accounting for shift and drift allows teams to

determine performance entitlement.






Calculating Capability

There are two methods of determining process capability:

• DPMO (Defects Per Million Opportunities) – which addresses

process capability using attribute data. DPMO can be converted to a

Sigma value.

• Sigma – which addresses the distribution of variable data with respect

to a target and specification limits. Sigma can be (and usually is)converted to DPMO.






Process Output Distribution

• Capability Analyses should be done with a distribution type that best fits

the data

• Most Capability Analysis assumes that the data are from a

Normal Distribution

• If it is incorrectly assumed that data follows a Normal Distribution,

performance metrics (DPMO, Z, Cp, Cpk, Pp and Ppk) will normally beincorrect and thus misleading

• Thus, the need to perform a check of normality –

Anderson-Darling test for normality






Variable Data Summary

• Process Capability is assessed by comparing the process distribution to

the customer specifications (VOP vs. VOC)

• For Continuous Data, calculate Cp, Cpk, Pp, Ppk

• Rational sub-grouping can be used to identify improvement

opportunities

• MINITAB “likes” long term data –it will estimate short term performancebased on chosen sub-groups

• MINITAB makes it optional to include short-term (within subgroup

analysis) or long-term (overall analysis) when performing capability

studies – you must interpret results based on your knowledge of the

data

• If you have only short term data, you can roughly estimate

long term capability (Z.LT = Z.ST – 1.5)






Non-Normal Capability Analysis

• Many processes do not produce data that follow a Normal Distribution

• To calculate process capability for these, one method is to transform

the data until it is sufficiently normal

• Transforming data involves performing a mathematical operation on

non-normal data (including the spec limits) so that the new data is

normal• Common transformations include Square Root, Natural Log, and

Reciprocal (1/X)

• The Box-Cox method will, in many cases, allow MINITAB to apply an

appropriate transformation

- Minitab v14 has improved ability to do Capability Analysis on non-

normal data through means other than Box-Cox transformations






Data Collection

• Data collection is the process of accumulating enough information to

calculate the current process capability (baseline) and identify thepotential causes of the problem.

• A key concept to keep in mind when collecting data is collecting the

right amount of data. The amount of data needed should be specified in

your Data Collection Plan.

- Too much data can add complexity to the data review and analysis.

- Too little data may force the team to engage in unnecessary and

costly secondary data collection efforts.






Basics Of Good Data Collection

• Have an Operational Definition

• Provide proper training

• Use collection forms

• Preserve order of data

• Take representative and meaningful samples






Training Data Collectors

• To ensure the best possible data quality:

- Properly train the data collector(s)

- Monitor the data collection process frequently

- Check whether the data looks reasonable, and challenge the data

results if it doesn‟t

- Collect several small samples over time rather than one large sample

at a single point in time

- Ensure measurements remain stable and consistent




Six Sigma Academy International, LLC All rights reserved; for use only in compliance with SSA license. Measure Review Pg 43

Sampling Considerations

• Sampling is a procedure for selecting units to estimate a characteristic

of the population.

- Result must be representative of the population

- Sufficient size given risk and process variation

- Balanced against the cost and effect on operations

- Ideally provides both short and long term profiles of process

performance

- Determine “how to sample” from the context of the specific process

• What, where, and how is it measured?

• What is the data type?

S li Pl





Sampling Plans

• When creating or evaluating a sampling plan, these four questions

should be considered:

- What is captured within a sub-group?

- What is not captured within a sub-group?

- What is captured between sub-groups?

- What is not captured between sub-groups?

S b G i d S li G id li





Sub-Grouping and Sampling Guidelines

• Sub-group/Sample Size

- Variable data: 5 if possible

- Attribute data: 30 or more depending on percentages

• Sampling Frequency

- Not too many (costly) / Not too few (unreliable information)- Generally, the more frequent, the better

• Rational Subgroups

- Attempt to minimize within sub-group variation

- True rational sub-group for an in-control process should reflect only

common cause variation

R ti l S b G





Rational Sub-Groups

• If you have data that supports rational sub-groups and you do not

sample and code accordingly, you will lose:

- The ability to accurately predict your entitlement

- The diagnostic help provided

• It is not a good practice to select arbitrary “chunks of data” that have

low variation after the fact and call them sub-groups. We define sub-groups before we collect the data in such a way that we DO collect

short term common cause variation and DO NOT collect long term

special cause (shift and drift) variation.

Measure Review



Measure Review


Progressing to the Analyze Phase

In the Define Phase:

Identify the needs and requirements of the customer

Identify the shortfall in current process performance vs.these requirements (i.e. the „gap‟)

Establish the project objectives and ensured adequateresources are in place

In the Measure Phase:

Validate the gap with measured data

Identify the types, sources, and causes of variation

This leads to the Analyze Phase, where:

Statistics are used to confirm which process inputs are thecause of variation (and hence cause the performance gap)

L T Sh t T Si





Long-Term vs. Short-Term Sigma

• Process variation has two sources:

- Normal variation inherent in the process

- Special cause variation

• Over time, normal variation will demonstrate itself after the project is

completed.

- “Shift Happens”

• This variation is reflected in a long term sigma values by subtracting a

1.5 shift from the short term sigma values:

- Processes will tend to perform better in the short term

L T Sh t T Si






ZLT = ZST – 1.5

L T Sh t T Si







ZLT = ZST – 1.5




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Bb Wk1 340 Measure Phase Review