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Transcript of DMAIC Version 3, Jan., 2005 Page 1 Caterpillar Confidential: Green Measure Module 2.1 Determine What...
DMAIC Version 3, Jan., 2005
Page 1
Caterpillar Confidential: Green
Measure
Module 2.1
Determine What to Measure
Green Belt – Define & Measure - Page 2DMAIC Version 3
Caterpillar Confidential: Green
Suppliers Process Inputs Business Processes Process OutputsCritical
CustomerRequirements
Important decisions based on linking
customer expectations to process
performance
Input Measures
Process Measures
Output Performance Measures
Customer Value
Performance Measures – Customer Value Achieved?
Green Belt – Define & Measure - Page 3DMAIC Version 3
Caterpillar Confidential: Green
Input, Process, and Output Indicators
Critical Customer RequirementsCritical to the Process
InputIndicators
ProcessIndicators
OutputPerformance
Indicators
Cost per transaction Time per activity Amount of rework Turnaround time Variability of an activity
Percent defective Number of errors Total response time Invoice/billing accuracy Revenue
These should all appear on your process map
# of customer inquiries Type of customer inquiries # of orders # of positions open Type of position open Accuracy of the credit analysis Timeliness of the contract
submitted for review
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Measure
Module 2.3
Manage Measurement
Green Belt – Define & Measure - Page 6DMAIC Version 3
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Data Collection
Measurement management starts with a data collection methodology.
IdentifyMeasures
Step 1Develop operational
definitions for measure
Step 2Develop measurement plan
Step 3Collect data
Step 4Display and Evaluate data
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Two Basic Types of DataBefore data collections starts, classify the data into different types: continuous or discrete. This is important because it will:
– Provide a choice of data display and analysis tools
– Dictate sample size calculation
– Provide performance or cause information
– Determine the appropriate control chart to use
– Determine the appropriate method for calculation of Sigma
Continuous or variable
Measured on a continuum
Objective• Time• Money• Weight• Length
Subjective• Satisfaction• Agreement• Extent• Type of error
Discrete, categorical, or attribute
Count or categories
Objective• Count defects• # approved• # of errors• Type of document
Subjective• Yes/No• Categories• Service
performance rating (good, poor)
• Satisfaction• Agreement
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Step 1: Develop Operational Definitions
Step 1: Operational DefinitionAn operational definition is a concept that helps guide the team’s thinking on what they need to measure as well as the key attributes of the measure: what, how, and who. It provides the foundation for the team to reach agreement and build consistency and reliability into data collection. This helps ensure any person using the agreed-on definition will be measuring the same thing.
– Provides everybody with the same meaning.
– Ensures that consistency and reliability are built in up front.
– Describes the scope of the measure (what is included and what is not included).
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Step 2: Develop a Measurement Plan
Example: Cycle time for loan application processing
Considerations for other data that should be collected at the same time:How will you display the data? What do you want to do with the data after it is collected?How do you want to stratify the data?What data might you need to identify and verify root cause?
Data collection is a balance between time, money and accuracy (getting the data you need).
Performance Measure
Operational Definition
Data Source and Location
Sample Size
Who Will Collect the
Data
When Will Data be
Collected
How Will Data be
Collected
Other Data that should
be Collected at the same
time Time to
process a loan
application
Fax date, time
Decision fax date, time
Loan applications
Representative fax center
289 Tim Smith Dave Mann
During the first week of the month, 10/1/99 to
10/7/99
Randomly selected
from September
‘99
Type of loan Amount of
loan Dealer
Time of day Day of week
Each 6 Sigma improvement team should complete a measurement plan that contains the following information:
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Step 2: Develop a Measurement Plan
Example: Industrial Pre-Treat Cost Reduction
IPT Cost Reduction - Measurement Plan
ppm dosageppm of IC -1187 per treatment
Tony's logsheets since Nov Ericweek of 5/15/06
compiled from log book
volume processedgallons discharged per treatment
Tony's logsheets since Nov Ericweek of 5/15/07
compiled from log book
IPT CostsBase chargesvariable charges
C ity of Thomasville's invoices
36+ mos Ericweek of 5/15/08
compiled from invoices
Machine Hoursmachine hours produced per month
Mfg-Pro 36+ mos Heatherweek of 5/15/09
compiled from MFG-Pro
Used Oil Volume
Used oil sent for recycling by CatUsed oil sent for recycling by IPT
Atlantic Industrial logs 18 mos Ericweek of 5/15/10
compiled from invoices
significant eventsECM Salt dumps, Central Coolant System cleanouts
Tony's logsheets Tony as needed
Sample SizeData Source & LocationWho will
collect the data?
When will data be
collected?Performance Measure Operational Definition
How will data be collected?
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Compressed Air Efficiency - Measurement Plan
Energy Usage Monthly kWH UsageC ity of Thomasville Electricity Invoice
24 months Eric NeihausJ an '04 - Dec
'05collect invoices
Energy CostElectricity Charges x Tax Rate
C ity of Thomasville Electricity invoice 24 months Eric Neihaus
J an '04 - Dec '05
collect invoices
Machine HoursTotal Monthly Machine Hours for Thomasville facility
MFG-Pro 25 monthsHeather Logan
J an '04 - Dec '05
MFG-Pro reports
Energy Usage / Mach Hr= Monthly kWH usage / Monthly Machine Hours
from above 26 monthsHeather Logan
J an '04 - Dec '05
Mini-tab
Energy Consumption per CompressorkWH Energy Meter 7 d
Air Flow Demand psig Air Flow Meter 7 d
Peak Demands - Historical City of Thomasville 12 m
Peak Demands - Calculated Energy/flow meters 7 d
Performance Measure Operational DefinitionHow will data be
collected?Sample
SizeData Source & Location
Who will collect the
data?
When will data be
collected?
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DMAIC Version 3, Jan., 2005
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Measure
Module 2.6
Gage R&R
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How might measurementvariation affect these decisions?
Verify product/process
conformity to specifications
Verify product/process
conformity to specifications
Assist incontinuous
improvement activities
Assist incontinuous
improvement activities
What if the amount of measurement variation
is unknown
?
Process
Measurement
Process
Measurement
Measurement variation can make our process capabilities appear worse than they are
Why Worry about Measurement Variation?
Consider the reasons why we measure:
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The Importance of Measurement System Analysis (MSA)
Is the importance of conducting an MSA being overlooked?– Many resources can be wasted trying to improve a process
when a major source of variability is the measurement system
– More consideration needs to be given to the measurement system before beginning process improvement work
The measurement system is only one source of variability when measuring a product or process
The purpose of a measurement system is to better understand the sources of variation that can influence the results produced by the process under investigation
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Sources of Variation
Product Variability(Actual variability)
MeasurementVariability
Total Variability(Observed variability)
Actual Product/Process Variation
Total Observed Variation
Measurement Variation
Common cause variation
Special causeVariation
Within partvariation
Variationdue to Operator
Variationdue to Gage
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T ru e V a lu es
In s trum e nt A ccu ra cy B ia s
R e p e ata b ility R e p ro d u c ib ility
G a g e R & R S ta b ility L in e a rity D isc rim in a tion
M e a sure m e nt E rro r
O b serve d M e asu re m e n ts
Gage R&R is the percentage of the total process variation related to the measurement system for repeatability and reproducibility
Gage R&R is the percentage of the total process variation related to the measurement system for repeatability and reproducibility
Generally recognized criteria for gage acceptability is when Gage R& R variability yields:
Under 10%: Acceptable gage
10% to 30%: May be acceptable
Over 30%: Gage is unacceptable and should be corrected or replaced
Generally recognized criteria for gage acceptability is when Gage R& R variability yields:
Under 10%: Acceptable gage
10% to 30%: May be acceptable
Over 30%: Gage is unacceptable and should be corrected or replaced
Components of Measurement Error
The sources of variability for the measurement system are:
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MSA for Coin Sac Depth Gage 0320-000336
TL = 14.56
Gage R&R
%ContributionSource VarComp (of VarComp)
Total Gage R&R 2.00E-06 3.38 Repeatability 1.35E-06 2.29 Reproducibility 6.48E-07 1.10 Operator 6.48E-07 1.10
Part-To-Part 5.70E-05 96.62 Total Variation 5.90E-05 100.00
StdDev Study Var %Study Var %ToleranceSource (SD) (5.15*SD) (%SV) (SV/Toler)
Total Gage R&R 1.41E-03 7.28E-03 18.40 14.56 Repeatability 1.16E-03 5.98E-03 15.12 11.97 Reproducibility 8.05E-04 4.15E-03 10.48 8.29 Operator 8.05E-04 4.15E-03 10.48 8.29
Part-To-Part 7.55E-03 3.89E-02 98.29 77.79 Total Variation 7.68E-03 3.96E-02 100.00 79.14
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Step 3: Collect Data
Follow the plan — note any deviations from the plan
Consistency — avoid bias
Observe data collection
Does anyone have any experience of data collection?
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Discrete Data - Example
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SamplingUsing a sample of data you draw conclusions about the entire population of data. This is known as “statistical inference.” Sampling saves costs and time. Sampling provides a good alternative to collecting all the data. Identifying a specific confidence level allows us to make reasonable business decisions.
Parameters:
Sampling From a Population
EntirePopulation
of Data Sample
StatisticalInference
Statistics:X, S, etc.
Analysis
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Different situations which dictate sampling techniques:
• To analyze and control a process
• To describe a large population (i.e., types of customers and buying behavior)
Systematic Process Sampling
Typical DescriptiveStatistics:
Random Samplingfrom a Population
SystematicProcess Sampling
X X XSample
X X X XSample
Average cycle time (xbar)No. of defects
proportion defectivestandard deviation (s)
X X X
X
XX
X
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Sampling Considerations Where
– Location in the process where process steps directly affect outputs (strong relationship)
– Maximize opportunity for problem identification (cause data)
Frequency– Dependent on volume of transactions and/or activity
– Unstable process—more frequently (use systematic or subgroup sampling)
– Stable process—less frequently (use sample size formula)
– Dependent on how precise the measurement must be to make a meaningful business decision
Considerations– Is the sample representative of the process or population?
– Is the process stable?
– Is the sample random?
– Is there an equal probability of selecting any data point?
– The answer to each of these questions must be yes before we can draw statistically valid conclusions.
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Step 4: Display & Evaluate Data
Has your data collection method:– given you dependable data?– provided consistent information throughout the data
collection period?– provided a reliable set of data?– given representative data?
If you repeat the data collection will you get similar results?
Does the data collected provide the information you need?
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Measure
Module 2.4
Control Charts
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Variation
Variation means that a process does not produce exactly the same result every time the product or service is delivered
Variation exists in all processes Measuring and understanding variation in our business processes helps
identify specifically what the current level of performance is and what needs to change in order to reduce the variability and therefore reduce the defects delivered to customers
Data Variation
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Common Cause Variation
There are always inherent chance causes responsible for natural variation in all processes due to “normal” variation in materials, environments, methods, etc. (common cause)
Variation within a stable pattern of chance causes is inevitable
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Special Cause Variation
Once we have an indication of a shift outside a stable pattern of variation, we must discover the reason for the shift (special cause)
We want to remove the influence of a special cause if it is adversely affecting product-process quality
If the special cause influence is improving product-process quality (e.g., 6 Sigma projects), we want to permanently capture its effect
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What Causes Variation?
Suppliers Process Inputs Business Process Process OutputsCritical
CustomerRequirements
Variation in the output of
processes causes defects
DefectsRoot cause analysis of
variation leads to permanent defect
reduction
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Charting Variation – Control Charts
14151617181920212223242526
M T W Th F M T W Th F M T W Th F M T W Th F M
Co
mm
uti
ng
Tim
e (
min
s)
Days
UCL
CL
LCL
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Control Charts - Basics
Control charts
Have the ability to indicate the presence of special causes that upset our processes
Help us to detect, diagnose, and correct process problems in a timely fashion
Provide an easy to understand visual indicator of process performance
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Reacting to Data/Tweaking
Control Charts
Recognize the extent of variation that now exists so that we do not overreact to common cause variation (tweaking)
Study the process to identify sources of variation and then act to eliminate or reduce those sources of variation or to mitigate their effects
Special causes can be identified with an effective diagnostic program
Common causes could not be removed or drastically reduced without making fundamental process changes
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Reacting to Data/Tweaking
Tweaking Tweaking
Normal
ReactingTo Data
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Measure
Module 2.7
Process Capability
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Process Capability – What is it?
Most measures have some target value and acceptable limits of variation around the target
The extent to which the “expected” values fall within these limits determines how capable the process is of meeting its requirements
Consider key measures of process performance in:– Help desk responsiveness– Laminate flatness– Pump flow rate
Quantifiable comparison of Voice of the Customer(spec limits) to Voice of the Process (control limits)Quantifiable comparison of Voice of the Customer
(spec limits) to Voice of the Process (control limits)
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Process Capability Ratio – Cp
Ratio of total variation allowed by the specification to the total variation actually measured from the process
Typical goals for Cp are greater than 1.33 (or 1.67 for safety items)
If Cp < 1, then the variability of the processis greater than the specification limits
If Cp < 1, then the variability of the processis greater than the specification limits
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Process Capability Ratio – Cpk
This index accounts for the dynamic mean shift in the process – the amount that the process is off target
σ̂σ̂ 3
LSLxor
3
xUSLMinCpk
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Uses of Capability Analysis
Performed on new equipment as part of the qualification and approval process
Performed on existing processes as a means of establishing a baseline of current operations (so it’s possible to tell when improvement has occurred)
When done periodically, is a means of monitoring wear and tear on equipment, and deterioration of a process for whatever reason (material, personnel, environment, etc.)
Can be done on any process that has a spec. established, manufacturing or transactional (spec. is needed for the values in numerator), and has a capable measuring system (needed for valid values in denominator)
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Preparing for Capability Analysis
When beginning to measure/monitor a parameter always: Calibrate measuring system (unless done recently)
– Use a standard traceable to NIST (National Institute of Standards and Technology)
– One trained technician Perform Measuring System Analysis (MSA)
– Group of actual parts (use parts from the high and low ends of the tolerance range, even rejected parts)
– Group of actual machine operators– Multiple ‘blind’ and random runs
The process should be statistically in-control
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Discrete Data – ExampleSigma Level
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Cos
t
Sigma
Optimum
Impact of Quality Level on Cost
Costs Are Always Kept in Mind
There is an optimum quality level beyond which the costs of quality improvement exceed the expected cost savings from a reduced number of defects
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Rolled Throughput Yield vs. First Pass Yield
Using Final Test “First Pass Yield” ignores the hidden factory. Final test performance is a function of inspection/test or prevention.
Using Final Test “First Pass Yield” ignores the hidden factory. Final test performance is a function of inspection/test or prevention.
66% is not 90% ... why not?
Process1 2 3
Rolled Yield 81 % 73 %
4
66 %
Final Test
=90%Yield
90%Yield
90%Yield
90%Yield
90%Yield
(Yield After Inspection or Test)
Scrap
InputsInputs
Hidden FactoryHidden Factory
NOT
OK
OperationOperation First Pass Yield
First Pass Yield
OK 90% Customer QualityInspectInspect
Rework
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Rolled Throughput Yield Example – Statement
Airtronics has a seven-step process for taking a purchase requisition and turning it into an issued Purchase Order
The seven steps have the following first pass yield rates:– Step 1: 0.981– Step 2: 0.972– Step 3: 0.943– Step 4: 0.892– Step 5: 0.943– Step 6: 0.904– Step 7: 0.963
What is the Rolled Throughput Yield for this 7 Step Process?
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Rolled Throughput Yield Example – Solution
The Rolled Throughput Yield for the seven step process is:
Step 1 x Step 2 x Step 3 x Step 4 x Step 5 x Step 6 x Step 7 = Rolled Throughput Yield
0.981 x 0.972 x 0.943 x 0.892 x 0.943 x 0.904 x 0.963 = 0.658
Conclusion: 66 percent of the purchase requisitions make it through the process with no errors the first time
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Why 6 Sigma is the Goal
Rolled Throughput Yield (RTY) is the probability that a product will pass through the entire process without rework and without any defects. If you have a complex product and/or process, 6 sigma yield will be needed at each process step or for each part in order to have RTY of greater than 90 percent.
Impact of Complexity on Rolled Throughput Yield
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000
Number of Parts/Process Steps
Ro
lled
Th
rou
gh
pu
t Y
ield
Impact of Complexity on Rolled Throughput Yield
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000
Number of Parts/Process Steps
Ro
lled
Th
rou
gh
pu
t Y
ield
4
5
6
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Measure
Module 2.5
Week 1 Financials**Note: This module is for Caterpillar internal use, so this may not be applicable to Caterpillar Suppliers and Dealers. Please check with your company Deployment Champion regarding this subject.
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CommittedLeadershipCommittedLeadership
QuantifiableMeasures& Results
Incentives &
Accountability
Full Time6 Sigma
Team Leaders
StrategyIntegration
Customer &
MarketNetwork
BusinessProcess
Framework
CommittedLeadershipCommittedLeadership
QuantifiableQuantifiableMeasuresMeasures& Results& Results
Incentives &
Accountability
Full Time6 Sigma
Team Leaders
StrategyIntegration
Customer &
MarketNetwork
ProcessFramework
CSF: Quantifiable CSF: Quantifiable Measures & Measures & ResultsResults:
6 Sigma projects should be designed to pay their way…
Quantifiable benefits must be traceable to the enterprise bottom line
Business
Why We Measure - Recipe for Success
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Why We Measure the Financial Impact
Primary reason is to ensure financial benefits from projects are real…validates success and establishes credibility
What gets measured, gets done Drives bottom-line focus Forces value-add mind set Facilitates filtering and prioritization of projects
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Establishing the Financial Baseline
Baselines reflect the current “as-is” state of the process– Current revenue or cost structure– Use budgetary approach– Not Business Plan or Prior Year Base Period
Determine the ENTERPRISE resources consumed at each step of the process– Head count, material, overtime, external charges
(contracts, etc.), expediting charges, etc.
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Establishing the Financial Baseline
Baselines should concentrate on the relevant costs – those costs that will be impacted by your project
For example, a project targeted at reducing packaging costs could ignore freight, forwarding, and duty costs if they were unaffected by the packaging
It is still important to understand all costs to be sure the project is targeted in the most appropriate area
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Module 2.8
Stakeholder Management/Communication Plan
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What is Stakeholder Management?
Identify Stakeholders
Understand Stakeholders
Influence Stakeholders
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Keep stakeholders aware and involved in your project…
Gate Reviews
Update Meetings
Gallery Walks
Communication is the Key to Overcoming Resistance
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Critical Success Factors
Enlist visible senior management sponsorship/support
Budget plenty of time up-front for stakeholder identification and communication
Get the right people involved Provide consistent and on-going communications Communicate continuously and honestly Identify issues early to resolve them
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Module 2.10
Business Risk Management Checkpoint
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Define
Measure
Analyze
Improve
Control
• BRM issues included in problem statement• All customers identified and included (e.g.
regulators, shareholders, etc.) stakeholder analysis
• ALL CCR’s appropriately prioritized
• Emphasize importance of minimizing variation
• Use BRM checkpoint process
• Broaden view of customer - prevent team from automatically eliminating activity not directly related to paying customers
• Use BRM checkpoint process
• Use risk management tools to help balance risk and control
• Assess impact of proposed change on all stakeholders
• Use BRM checkpoint process
Charter Team
Determine CCR’s
Map Process
Identify Possible Causes
Narrow to Root Causes
Quantify Opportunity
Identify and Test Solution
Refine Solution
Close Project
Pilot Solution
Implement Solution
Collect Data
Define Defect
Describe Variation
Prepare Process Control Plan • Use BRM checkpoint process
• Emphasize importance of ongoing monitoring
BRM
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BRM Checkpoints – Measure
For Critical Customer Requirements (CCRs) that have the greatest impact, have defects and related measures been defined appropriately?
When analyzing process performance, are the risk factors aligned with your organization’s risk management strategies?
How has the risk impact changed (i.e., has the updated Process Risk Impact Worksheet indicated a change in the risk profile)?