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Data Collection




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Data Collection Pg 1

The Breakthrough Strategy® And Data Collection

1. Select Output Characteristic

2. Define Performance Standards

3. Validate Measurement System

4. Establish Baseline Process Capability

5. Define Performance Objectives

6. Identify Variation Sources

7. Screen Potential Causes

8. Discover Variable Relationships

9. Establish Operating Tolerances –

Implement Improvements

10. Validate Measurement System

11. Determine Final Process Capability

12. Implement Process Controls

Data is the basis

for all Six Sigma

decisions, it must

be properly captured.




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Module Objectives

By the end of this module, the participant will be able to:

• Explain why continuous data is of greater value than discrete data

• Describe the basics of good data collection

• Explain the importance of a well-defined “Operational Definition”

• Explain the value of maintaining “Time Order of Data”

• Identify several sampling strategies




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How Do We Gather Information?

Measure and assign a number Variable Data

Observe and assign a category or name Attribute Data

Variable Data may be further categorized into 2 subsets:

• Measured data which is Continuous

- It may be divided into ever smaller increments

- Time, Distance, Weight

• Count data which is Discrete

- The count is limited to a set of numbers that may not be divided into

smaller increments

- # of children in your family cannot be 3.34,

- # of people in this class over six ft tall cannot be 12.11




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Overview Of Data

DATA

Measure or count,apply a number

Observe and

Place into a

Category

May be ordered (Ordinal)

• Small, Medium, Large

May be unordered (Nominal)

• Red, Blue, Pink

DiscreteContinuous

Counts supplied at fixed intervals, e.g., # of defects or

# of times you observe a specific response to a survey

question (How many of 1, 1.5, 2, 2.5, etc.)

May be divided into ever

smaller measurements

• Length, PSI




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Use Continuous Data As Much As Possible

Continuous data is much richer in information

Set up a complex machine and before turning it over to the operator,

check 10 parts. The parts have a spec of 1.000” -.000”/+.010”

• There are 2 methods of measuring the inherently continuous data:

- A Go/NoGo gage that provides the count of good and bad

- A micrometer that measures to the nearest 0.001”

• Results:

- Using the Go/Nogo, 10 parts are acceptable – Tell operator to

“start making parts”

-

Using the micrometer, measure the following in the order in whichthey were made: 1.000, 1.001, 1.002, 1.003, 1.004, 1.005, 1.006,

1.007, 1.008, and 1.009.

• Do we tell the operator to start making parts?




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Continuous

Large amount of data needed due

to sparseness of information density

Small amount of data,

rich with Information

The Advantage Of Continuous Data

To obtain the same level of understanding regarding a process:

New Black Belts sometimes

convert perfectly usable

continuous data to discrete.

DO NOT DO THIS




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Issues with Data Types

Strive to collect continuous, variable data

• Discrete data

- Discrete data is not normally distributed

• Inferential statistics is less straight-forward

• Requires many more samples

• Easy to interpret graphically

• Instead of checking if call was within 10 minutes (Y/N), record the

length of the call rounded to the nearest minute




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Convert Attribute Data IntoNumeric Data

• With attribute data a single part is good or bad

- There are no numbers to analyze

• If we count the number of good and bad parts

- We have discrete-numerical data that we can graph and analyze

• If we further take the count of good and bad parts per shift and turn it intoPercent Defective

- We essentially have continuous-numerical data (with any value

between 0 and 100% – Say 23.87

DATA

DiscreteContinuous




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All Data Is Measured Discretely

• We are limited by measurement categories or significant digits

• With fine discrimination we can “see” differences between most data points

- We may analyze our data with powerful tools for continuous data

• With coarse discrimination a measurement system puts all readings into

a few categories

- We must use less powerful tools for discrete data

AttributeData

DiscreteContinuous

Count

Discrimination

between values LessMore




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Data Types Determine How WeGraph And Analyze

Y

X

Discrete

Continuous

Discrete

or

Attribute

Continuous

Discrete Y =

f(Discrete x)

Discrete Y =

f(Continuous X)

Continuous Y =

f(Discrete x)

Continuous Y =

f(Continuous x)




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Why Collect Data?

• Without data

- There is no scientific analysis

- Decisions are made by hunches and personal beliefs

- There is no proof of significant improvement

• Sources of variation can be identified, quantified, and eliminated,controlled, or reduced

• To accomplish this we need data

• We use sampling to get the data we need




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Basics Of Good Data Collection

Have An Operational Definition

Provide Proper TrainingUse Collection Forms

Preserve Order Of Data

Take Representative And Meaningful Samples




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Operational Definition

• Operational definition is the meaning of a term or activity in an

organization that is interpreted the same way by everyone each time

- Purpose: Minimize measurement ambiguity

• Example: A travel agency might measure ticket delivery as the time from

the end of a call from a customer arranging travel, to the FedEx pickup of

their tickets

- How does the customer measure delivery?

• End of the call to the inbox on their desk

• End of the call to the FedEx delivery to their shipping department

• Is the travel agency responsible for your internal mail distribution?

• Is the measurement in days or hours?

• Is the measurement continuous (days/hours) or discrete

(received the day before needed or not)?




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Operational Definition(Cont‟d) • The Operational Definition is critical to defining the defect definition

- An Operational Definition can be defined by Upper and Lower

Specification Limits provided on a blueprint/specification

- An Operational Definition can be a written definition that describes

exactly how the measurements are to be made

Example – A company wants an item to be delivered in 3 daysWhen does the clock start and when does the clock stop?

t0

– Customer calls and places order

t1 – Order is keypunched into system

t2 – Order is scheduled/promise date given

t3 – Order is made/passes inspection

t4 – Order is stocked

t0 t1 t2 t3 t4 t5 t6 t7 t8

t5

– Order is at the shipping dock

t6 – Order is picked up/title passed

t7 – Order is delivered

t8 – Order is stocked by customer




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Training

• Validate that operators have been trained and can correctly

- Apply the operational definition

- Use the required gages

- Complete the data collection form

• Consider everyone who may take data on your project- All operators, all shifts

- Backup or utility operators

- Inspectors/auditors

- Possibly supervisors




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Data Collection Forms

• Forms should be stand alone and contain

- Operational Definition (including significant digits)

- Sample size

- Sample frequency

- Sufficient space to easily record the requested information• For Continuous data:

- Collect actual measurements

• For Discrete or count data:

- Use defect tally reports with types of defects

• Have a comments section




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Sample Form: NASA Software Engineering




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Collecting Discrete Data: Caveat

• When Discrete data is collected as parts failed vs. parts submitted

• There may be many criteria (or defects) that could cause a part to

be defective

- Example: Auto Visor Manufacture – Inspect for off color, tears

and wrinkles

- As soon as we find any 1 defect, stop inspecting and scrap the visor

• We know how many visors were scrapped. Nothing is known about

the quantity of each defect.

- Now also record why it was scrapped

• We still don‟t have satisfactory information for reducing variation

• If we wish to know how many visors have off color defects, tears, and

wrinkles, we must for ALL 3 defects!




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Considerations When DesigningData Collection Forms

• Make form user friendly

• Incorporate physical visual standards

• Include pictures/sketches to clarify what defects are and where

they occur

• Self explanatory

• Easy to use

• Include instructions

• Include all significant source information

• Pilot the form and incorporate any necessary changes




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Obtaining Existing Information:Historical Data

• It is usually preferable to collect live “current” data

• However, the data you seek may be already collected as part of your

organization‟s standard operations

• Before you use it verify that

- Operational definitions were clear and consistently applied

- Operators were properly trained

- Gages specified were used, calibrated, and capable of

measuring properly

- Time order of the data is known

• Look carefully at a graphical view of the historical data, looking for

stability over time and single vs. multiple distributions

• Closely examine outliers for special causes




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Obtaining Existing Information:Information Technology

All of the considerations for use of historical data apply.

In addition:

• Be careful how you ask for the data

• Jointly determine the available files

• Request files be transferred in a form you can use• Validate that the

- Data entry system was robust

- Computer report

• Does not truncate/round improperly

• Maintains time order

IT can provide data, but you must turn it into information.




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Preserve The Order Of The Data

Why is the order important?

• If a lurking variable is present that is causing long term decay, it will not

be obvious if we don‟t have the order in which the data was collected

• Time order analysis provides simple evaluation of process stability

• Example: We run our process and take 1 sample a day for 11 days

- We plot the data in time order

- Time order not available. Data plotted as in file/record sheet.

- Plots on next slide




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Same Data – Different Stories

0 5 10

8

13

18

Da

I n d i v i d u a l V a l u

e

Plotted Correctly inTime Order

1

5 5

6

6

1

1050

20

10

0

Observation

I n d i v i d u a l V a l u e

Plot Of Order In Data File




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In The End…

• You must measure something that is

- Meaningful

- Related to the success or failure for a CTS

• Your resulting data must be

- Truly representative of the process that you are making assertionsabout, either locally or globally




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Data Collection Points

Data Collection Points should be inserted into the process to retrieve

information at key Locations.

Step 1 Step 3 Step 5 Step 6

Step 2 Step 4

Input

BoundaryOutput

Boundary= Data Collection Points



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Six Sigma Academy International, LLC

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Sampling

P l ti A d S l




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Populations And Samples

= Observations taken as “sample A” 60.07 1.44

= Observations taken as “sample B” 60.31 1.77

= Observations taken as “sample C” 59.57 1.76

A statistic‟s value is known for a specific sample,

but usually changes from sample to sample.

A sample is a portion or subset of units

taken from the population whosecharacteristics are actually measured

A statistic, any number calculated from

sample data, describes a

sample characteristic

Sample Stat ist ics

X s

P l ti S l




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Sample

Population

Population Parameters

= Population mean = Population standard deviation

Population

Sample

Sample Statistics

= Sample means = Sample standard deviationx

If we only collect samples, do we ever

know the true population parameters?

Estimate

Inference

Population vs. Sample

S li C id ti




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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

- 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?

K S li Q ti




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Key Sampling Questions

• What questions do we want to answer through this analysis?

• How can we achieve a representative sample?

• Are we only interested in collecting a baseline?

• Can we simultaneously collect additional information to help us in

our quest?

• What are some of the potential sources of variation?

• Do we need to provide traceability to those sources?

• How will we ensure accuracy and precision in our measurement system?

• What issues or barriers could we run into?

S li M th d




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Sampling Methods

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

the population; sample units should be representative of the population.

Types of Sampling

• Convenience

• Statistical

- Simple Random Sampling

- Systematic Sampling

- Stratified Sampling

R ti Y P l ti




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Representing Your Population

Convenience Sampling

• Judgement made on selecting sub-groups of readily available

products/services/customers

• Selections may suffer from bias

- We don‟t know what we don‟t know

- Statistical sampling is recommended

Statistical Sampling

• Reduces possible systematic error

Si l R d S li




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Simple Random Sampling

• Each “unit” has an equal chance of being selected

• Simple

• Unit = Individual measure

• Sub-group similar units






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Example:

To estimate the average height of the class, select 10 students at random.Calculate the average height of the sample.

Each item has equal probability of being selected.

Systematic Random Sampling





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Systematic Random Sampling

Example: Ask every 10th person their opinion on state of the economy.

Example 2: Measure 5 consecutive parts every 100 parts (or 4 hours).

Every “nth” item is sampled for study.

4003002001000

10

0

Part Number

Stratified Random Sampling





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Stratified Random Sampling

Example:

To estimate the average income of people in the US, break the populationof the US into levels of education. Then sample randomly within each

education group.

High School Associate

Degree

No High SchoolDiploma University Degree

Population is “stratified” into groups with

random selection within each group.

Graphical Representation





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Graphical RepresentationStratified Sampling Plan

• This is a stratified sampling plan

- Each individual interviewed for annual income is selected within a

specific level of education

- Individuals are unique to level of education

1 2 3 ...

No High School

1 2 3 ...

High School

1 2 3 ...

Some College

1 2 3 ...

College DegreeNo HS HS Assoc University

Rational Sampling





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Rational Sampling

• Concerned with the way the process is measured

- What is measured?

- Where is it measured?

- How is it measured?

- What is the data type?• Determine how to sample from the context of the specific process

The purpose of analysis is insight, rather than numbers

Considerations for Building a Sampling





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Considerations for Building a SamplingStrategy

• Selection of product or service characteristics

- Noise

- Factors

- Measurements

• Relevant (like the process) sampling strategy

- Dependent on process knowledge

- Dependent upon process flow

- In accordance with when and where defects occur

Linkage to Other Tools: Process Maps





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Linkage to Other Tools: Process Mapsand Graphical Techniques

• Process maps will assist in the development of sampling plans

- Sources of variation

- Short versus long term considerations

• Graphical techniques

- Used to identify sources- Employs rational sub-grouping strategy

• Minimize within subgroup variation to capture between subgroup

variation

-Within subgroup could be a measure of short term variation

Process maps are key to identifying sources of variation

Key Learning Points





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Key Learning Points

•

•

•

•

•

Objectives Review





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Objectives Review

The participant should be able to:

• Explain why continuous data is of greater value than discrete data

• Describe the basics of good data collection

• Explain the importance of a well-defined “Operational Definition”

• Explain the value of maintaining “Time Order of Data”• Identify several sampling strategies





More on Sampling

Sampling Methods





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Sampling Methods

Types of Sampling

- Census

- Judgment

- Statistical

• Simple random

• Stratified

• Cluster

Sample size and sampling error

What does your population consist of?

Recognizing Your Population





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• What and who makes up the population of your

product/process/service/customers?

• Do you need to represent the population?

• Census Sampling:

- This is the population

- Used if service or product is highly specialized

• Population is a small

- Census may represent a critical source of information

- If not a small group

• Cost prohibitive

• Data collection difficulties








• Judgment Sampling:

- Judgment made on selecting subgroups of product/services/customers

- Few cases are needed to generalize population

- Selections may suffer from bias

• We don‟t know what we don‟t know

• Statistical sampling is recommended

• Statistical Sampling:

- Removes judgment bias

- Statistical inference used to generalize the population

Statistical Approach







• Three types of sampling

- Simple Random

- Stratified

- Cluster

• Simple random sampling

- Each „unit‟ has an equal chance of being selected

- Simple

- Unit = individual measure

- Subgroup like units








• Stratified Sampling

- Researchers may elect to evaluate mutually exclusive strata

• Selecting individual units

- Stratified random sampling: random selection of individual units

within strata

- Useful when strata are expected to yield different results

- Strata are suspected sources of variation

- Unit = individual observation from each strata








• Cluster sampling:

- Selecting previously formed Subgroups of Units

- Two-stage cluster sampling: Random sampling within large

subgroups

- Ability to target specific, subgroups representing:

• Specific product lines or models

• Locations and offices within locations

• Physician specialties

• Or a priori sources of variation

- Unit = groups of units





Optional Exercises





Operational Definition Exercise








• We are making a small shaft. It has a diameter specification of .250” ±

0.002” – It is made of cold rolled steel on an automatic lathe and is 3inches long.

• Each table or team will discuss for 3 minutes and propose to the class

how they will train the operators to measure and report the diameter data

Operational Definition Exercise Discussion






Operational Definition Exercise Discussion(We Are After Consistency)

• Did your team discuss and cover the following topics:

- Do you measure max, min, or average diameter?(parts are frequently not perfectly circular, but oval)

- Do you measure at one end, the other end, the middle, the average?

(parts frequently taper)

- What did you use (and specify) for a measuring instrument:

calipers, 0-1 micrometer reading to nearest 0.001”,0-1 micrometer reading to nearest 0.0001”, other?

- How many significant digits? (.xxx or .xxxx)

- Do you round up, down, closest?

- Do you measure when the parts are hot, after they are cooled down,

or do you let the operator decide?- Are those measuring required to pass training or hold certification?

- Are the gauges being used required to be calibrated?

- Are the parts measured on the hot factory floor or in a temperature

controlled lab?





Data Collection Plan Exercise

Data Collection Plan Example






a a Co ec o a a p e

Consider the process of installing a security system in a retail store and

use this as an example to develop a data collection plan.

What to

Measure

Type of

Measure

Type

of Data

Operational

Definition

Data

Collection

Form

SamplingBaseline

Measure

Security Installation: What To Measure






y

• Sales/Installation professionalism

• Sales/Installation courteousness

• Customer wait time

• Time to process

• Time to close transaction (system installed and operable, billing initiated)

• Potential call backs and defect collection

• Number of customer generated changes

Security Installation: Type Of Measure






y yp

• Service Quality and Delivery are outputs of the process

- Type: Output/Process

• System and installation quality could be a result of 2 types of defects

- Installer defect Output/Process

- Contract defect Output/Input

• The output is highly dependent upon the verification of the input data

Resources






Six Sigma Academy and Goal/QPC,

The Black Belt Memory Jogger,(Salem, NH: Goal/QPC, 2002), pp. 19-32



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