RLM & Associates LLCYour Lean Six Sigma & Project Management Trainers
Lean Six Sigma DMAIC WorkshopGreen Belt Part 3
6 σ Green Belt
04/21/23 1Kraft Foods Green Belt
04/21/23 Kraft Foods Green Belt 2
Building Effective Teams
Building Effective Teams
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Why Teams?
Improves productivity
Better results (products, processes, services)
Better able to handle complex problems
Can handle many aspects of the business
Differentiation through diversity of thoughts
Improves morale, enthusiasm and creativity
Teams succeed over the work of the lone genius!
Teams succeed over the work of the lone genius!
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High Performing Teams
Teams have a sense of PPurpose, they are EEmpowered, they practice good RRelationships & Communication, they exhibit FFlexibility, then try for OOptimal Performance, they RRecognize & Appreciate others and have high MMorale.
Blanchard’s Model
When all components are strong,location or product becomes irrelevant
When all components are strong,location or product becomes irrelevant
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Characteristics of Effective Teams
Effective teamwork encompasses:– Developing a charter – Defining team members’/leader’s roles– Establishing procedures and ground rules– Establishing relationships
Effective TeamsWhich individuals do which activities or tasks?
How do we feel about each other, talk with each other, and affect each other?
What do we do, intend to do, and why?(Purpose, Scope, Goals, Selection)
How do we do what we do? What processes and systematic approaches do we use?
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Team Growth Stages
All effective teams move though stages of growth. A team must work through four developmental stages to be successful. Team leader and members must understand the different stages and adapt their behavior to maximize team effectiveness.
Team Growth Stages
Forming
Performing Storming
Norming
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Conflict Resolution
In order for teams to move forward from Storm to Norm, teams must understand:
– How to identify conflict and how to resolve it
– How to identify blockages and how to unblock
– How team blockages affect progress and how to unblock them
When managed correctly, conflict produces new ideas, solves problems, expands capabilities, and improves creativity
When managed correctly, conflict produces new ideas, solves problems, expands capabilities, and improves creativity
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Helping Teams with Conflict
Listed below are some key steps for helping a team work through conflict. This is a suggested approach, but team leaders may use different steps. Team leaders may have to alternate between these steps as they work through conflict with the team.
Clarify and summarize different points of view Ask for points of agreement and disagreement Look at situation from customers points of view Get data Place yourself in the other person‘s position Ask how to resolve differences
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Define Phase Checklist
Is the project charter complete, accurate, and SMART?
Is the as-is process mapped appropriately?
Are Critical Customer Requirements documented?
Have any quick wins been identified?
–If so, implemented?
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Measure
Measure
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Measure Phase Process Flow
Identify Measurement and Variation
Determine Data Type
Develop Data Collection
Plan
Perform Measurement System
Analysis
Perform Data Collection
Perform CapabilityAnalysis
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Measure Phase Objectives
Define the process measures
Identify the types, sources, and causes of variation in the process
Determine data requirements
Develop a data collection plan
Perform Measurement System Analysis
Conduct data collection
Determine baseline capability of the project as SQL–(Sigma Quality Level)
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Why Measure?
Measurement allows teams to…
– Establish baseline performance
– Isolate sources of variation
– Identify areas in which improvements can be made
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Value Performance Measures –Customer Value Achieved?
SuppliersProcess Inputs
Business Processes
Process Outputs
Critical Customer
Requirements
Important Decisions Based on Linking
Customer Expectations to Process Performance
Input Measures
Process Measures Output
Performance Measures
Customer Value
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Input, Process and Output Indicators
Input Indicators
Process Indicators
OutputPerformance
Indicators
# 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
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
Critical Customer RequirementsCritical to the Process
Critical Customer RequirementsCritical to the Process
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Process Elements/Indicator Relationships
Start Boundary __________ End Boundary ____________
Suppliers Inputs Outputs Customers
Process
Input Indicators Process Indicators Output IndicatorsMeasures that evaluate the degree to which process inputs, provided by suppliers, are consistent with what the process needs to convert into customer-satisfying outputs.
Examples: # of customer inquiriesType of customer inquiries# of orders # of positions open Type of position openAccuracy of the credit analysisTimeliness of the contract
Measures that evaluate the quality of the transformation processes – the steps used to convert inputs into customer-satisfying outputs
Examples: Availability of service personnel Time required to perform credit review % of non-standard approvals required # of qualified applicants Total cost of service delivery Total overtime hours
Measures that evaluate dimensions of the output – may focus on the performance of the business as well as that associated with the delivery of services and products to customers.
Examples: # of calls/hour taken by each service rep 2nd year customer retention figures Total # of meals delivered % customer complaints
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More on Measurement Considerations
One rule holds regardless of the measures you select; try to use as few indicators as possible
There is a cost/benefit and relevance relationship to measuring. Too many measures are costly, unproductive, and may divert attention from critical areas.
Not enough or incorrect process indicators will not allow you to identify defects before they occur
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CCRs and Multiple Output Indicators
Some CCRs may be measured in terms of one specific expectation a customer has. Others may require several output indicators. The table shows how one CCR can have one or several associated output indicators.
This table lists the second CCR from the above table, but shows it as three different critical customer requirements associated with the delivery of a vehicle.
Regardless of how the CCR is stated, teams must be sure to identify all of the CCRs associated with a process production service and make sure that the CCRs are measured by the output indicator.
Output IndicatorProduct delivery cycle time from the completion of the customer order to the delivery of the product
number and type of vehicle specifications delivered correctlyactual delivery time vs. promiseddelivery time for each vehiclenumber of times vehicles were delivered to location other than what is specified on agreement.
Critical Customer Requirement
Product is delivered within three hours of order taken
Right vehicle is delivered at the right time tot he right location Output
Output IndicatorNumber and type of vehicle specificationsdelivered correctly
actual delivery time vs. promised deliverytime for each vehicle
number of times vehicles were delivered to location other than what is specified on agreement
Critical Customer Requirement
The vehicle delivered meets the vehicle specifications as described in the contract
The vehicle is delivered within the time specified in the contract
The vehicle is delivered at the location specified in the contract
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Indicators
Input Indicators
Process Indicators
Output Indicators
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Measurement Planning
Measurement Planning
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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
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Two Basic Types of Data
Attribute Data
–Go/No Go–Good/Bad–Pass/Fail
Whether something is
happening or not
Variable Data
–Time–Temperature–Size
Information about what is happening
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Two Basic Types of Data
Before 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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Obtaining the Measurements
Data collected will only be as good as the collection system itself. In order to assure timely and accurate data, the collection method should be simple to use and understand. There are several methods. The most common are:
– Checksheet - a simple log of ―tick marks‖ representing the volume and type of work – Time stamps - a recording of the time that each activity begins and ends
Example: Checksheet Applications Returned for Missing Data
All data can be collected manually (writing in the log, recording the time, etc.) or automatically. Automatic data collection assures accurate and timely data, and removes the burden of collection from the operator of the process. But, it can be very expensive to set up. It usually involves computer programming and/or hardware. For most initial efforts, a paper log is the most cost effective form of data collection
Reason Missing Incorrect
Address X x x x x X x x x x
Phone X x x x X x
Employment Info X x X x x
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Data Collection
Measurement management starts with a data collection methodology.
Identity Measures
Step 1Develop Operational
Definitions for Measure
Step 2Develop Measurement
Plan
Step 3Collect Data
Step 4Display and Evaluate
Data
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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 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
Step 2: Measurement PlanDetermining current process performance usually requires the collection of data. When developing a measurement plan ensure that:
The data collected is meaningful The data collected is valid All relevant data is collected concurrently
Questions to AnswerWhat precise data will be collected?
Performance measurement? Causes of process deficiencies?
Do we analyze all relevant data or a sample?What is the right sample size? What is the right frequency?What will be the sample selection method?
What tools are necessary? What formats will be used?What logs will be kept? Do we need a computer?
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Questions to Answer
What logistical issues are relevant?Who will collect data?Where is the data located?When will it be collected?What additional assistance is required?
What you want to do with the data?Used daily, weekly, etc.Identify trends in the process dataIdentify deficiencies in the processDemonstrate current process performanceIdentify variation in a processIdentify a cause and effect relationship
Step 2: Develop a Measurement Plan
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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).
Example: Cycle time - loan application processing
Step 2: Develop a Measurement Plan
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 application
Represent-ative fax
center
289 Tim SmithDave Man
During the first week
of the month
10/1-10/7
Randomly selected
from September
Type of loan
Amount of loan
DealerTime of
dayDay of week
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Measurement Planning
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
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Team Exercise
Complete Process Indicators Template
Complete Measurement Plan
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Measurement Planning
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
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Step 3: Collect Data
Follow the plan —note any deviations from the plan
Consistency —avoid bias
Observe data collection
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Data Collection
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Measurement System Analysis/Gage R&R
Measurement System Analysis/Gage R&R
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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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Repeatability (Precision)
The variation between successive measurements of the same part, same characteristic, by the same person using the same instrument. Also known as test/re-test error, used as an estimate of short-term variation.
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Reproducibility
Reproducibility is the variation in the average of the measurements made by different appraisers using the same measuring instrument when measuring the identical characteristics on the same part.
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Bias Effects
Average of measurements are different by a fixed amount. Bias effects include:
– Operator Bias–Different operators get detectable different averages for the same thing– Machine Bias–Different machines get detectable different averages for the same thing, etc.– Others–Day-to-day (environment), fixtures, customer and supplier (sites)
Instrument 1
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Stability
The absence of assignable causes of variation; the property of being in statistical control. The consistency of measurements over time.
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Discrimination
Discrimination is the capability of detecting even small changes in the characteristic
If unacceptable may not be appropriate to identify process variation or quantify individual part characteristic values
Cannot detect variation of process and special cause variation
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Process Capability and Sigma Quality Level (SQL)
Process Capability and Sigma Quality Level
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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
Quantifiable comparison of Voice of Customer (spec limits)
to Voice of the Process (control limits).
Quantifiable comparison of Voice of Customer (spec limits)
to Voice of the Process (control limits).
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Learning Objectives
Learn prerequisites for conducting process capability studies
Learn how Cp and C pk are calculated
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Process Capability Ratio – C p
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 process is
greater than the specification limits
If Cp< 1, then the variability of the process is
greater than the specification limits
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Process Capability Ratio – C p
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Process Capability Ratio – C pk
This index accounts for the dynamic mean shift in the process – the amount that the process is off target
Ratio of 1/2 total variation allowed by spec. to the actual variation, with only the portion closest to a spec. limit being counted
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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 with a spec established, manufacturing or transactional (spec. is needed for the values in numerator), and has a capable measuring system (needed valid denominator value)
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Capability Action Plan
Give highest priority to parameters with Cpk‘ s of less than 1.0 (center the dimension, reduce the variation or both)
– If possible, get tolerance relief. (If product/process is mature, and there have been no customer problems, what is the need for this formal spec when another ―de facto‖ spec has been used historically?)
– 100% inspect, measure and sort– Chart using the data from the measurements
Use SPC on parameters with Cpk‘ s between 1.0 and 1.33 (or 1.67 if safety related)
Consider transitioning to First Piece checks for parameters over 1.33 (or 1.67 if applicable), and SPC charting of First Piece and audit data
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Sigma Quality Level – Attribute Data
Key Terms Required
– Unit (N)
– Defect (D)
– Opportunity (O)
– DPMO
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Calculating SQL – Attribute Data
The first step is to define:
– The Unit (N)
– The Defect (D)
– The number of opportunities for generating a defect (per unit) (O)
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Calculating SQL – Attribute Data
DPMO = Total number of defects
Total units x Opportunities per unit
DPMO = (D/(NxO)) x 1,000,000
X 1,000,000
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DPMO to SQL
Convert DPMO to Sigma Quality Level (SQL) using the Z table
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Z Table to Convert DPMO to Sigma Level
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Example
500 Part Orders(N) 3 Opportunities each order (O)
o Lateo Wrong Parto Wrong Address
57 errors are made (D) 12 caught 45 observed by customer
What is the Sigma Quality Level (SQL)?
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Calculating SQL – Attribute Data
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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