bb wk1 270 data collection
TRANSCRIPT
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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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Data Collection Pg 2
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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Data Collection Pg 3
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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Data Collection Pg 4
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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Data Collection Pg 5
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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Data Collection Pg 6
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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Data Collection Pg 7
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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Data Collection Pg 8
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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Data Collection Pg 9
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 Collection Pg 10
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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Data Collection Pg 11
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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Data Collection Pg 13
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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Data Collection Pg 14
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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Data Collection Pg 15
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 Pg 16
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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Data Collection Pg 17
Sample Form: NASA Software Engineering
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Data Collection Pg 18
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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Data Collection Pg 19
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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Data Collection Pg 20
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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Data Collection Pg 21
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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Data Collection Pg 22
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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Data Collection Pg 23
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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Data Collection Pg 24
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 Pg 25
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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Sampling
P l ti A d S l
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Data Collection Pg 27
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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Data Collection Pg 28
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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Data Collection Pg 29
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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Data Collection Pg 30
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
Simple Random Sampling
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Simple Random Sampling
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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Data Collection Pg 36
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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Data Collection Pg 41
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
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Appendix
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More on Sampling
Sampling Methods
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Data Collection Pg 45
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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Data Collection Pg 46
Recognizing Your Population
• 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
Recognizing Your Population
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Data Collection Pg 47
Recognizing Your Population
• 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
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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
Statistical Approach
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Statistical Approach
• 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
Statistical Approach
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Statistical Approach
• 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
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Optional Exercises
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Operational Definition Exercise
Operational Definition Exercise
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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
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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?
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Data Collection Plan Exercise
Data Collection Plan Example
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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
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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
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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
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Six Sigma Academy and Goal/QPC,
The Black Belt Memory Jogger,(Salem, NH: Goal/QPC, 2002), pp. 19-32
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