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Six Sigma is a structured strategic quality management
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The Shewhart-Deming Cycle
The Shewhart-Deming
CycleThe key is a continuous cycle of improvement
Act
Plan
Do
Study
Definition Six Sigma is a structured
strategic quality management approach that: enables improved decision
making by the leadership using tools based on sound
business, statistical, and engineering principles.
Evolution of Six Sigma Originally from Motorola company focused
on manufacturing processes – mid 1980s. Six Sigma has evolved to become
applicable to non-manufacturing processes as well:
Services Medical and Insurance Procedures Call Centers Human Resources
GoalThe primary goals of Six Sigma: increase customer
satisfaction business profitability through reduction/elimination of
defects.
Known Industrial Applications Leaders in the successful
implementation of Six Sigma Motorola, GE, Allied Signal, Microsoft, Intel,
3M, Boeing, etc. WIPRO, GE India, Philips India, ONGC,
Adidas, Baxter India Manufacturing (quality improvement,
cost reduction, process optimization, measurement error improvement, safety improvement, new product development, etc.)
Known Industrial Applications Transactional: Improving customer
OTIF (On-time, In-full), call centers, supply chain excellence, human resource management, library management, payroll accuracy, etc.)
Business: Resource allocation, optimizing and prioritizing capital investments, new business development/ new product introduction, etc.
Functional Areas of Application
Improve ProductsImprove Products Design to process
capability Design to customer
needs Improve MaterialsImprove Materials
Reduce variability Improve delivery
time Reduce cost
Improve PeopleImprove People Skills and capability Problem solving
methods Improve ProcessesImprove Processes
Remove variability Improve stability Match process to
customer needs
Concepts
The Inspection Exercise
Count the number of times the 6th letter of the alphabet appears in the following text:
The necessity of training farm hands for the first class farms in the fatherly handling of farm live stock is foremost in the eyes of the farm owners. Since the forefathers of the farm owners trained the farm hands for first class farms in the fatherly handling of farm live stock, the farm owners felt they should carry on with the family tradition of training farm hands of the first class farmers in the fatherly handling of farm live stock because they believe it is the basis of good fundamental farm management.
Count the number of times the 6th letter of the alphabet appears in the following text:
The necessity in training hired hands in the strange handling of valuable live stock in premier operations is a priority in the eyes of the operations owners. Since the ancestors of the owners trained the hired hands in premier operations in the strange handling of valuable live stock, the operations owners thought they should carry on with the happy tradition of training hired hands in the premier operations in the strange handling of valuable live stock because they believe it is the basis of good basic operations management.
The Inspection Exercise
Reality of Escaping Defects
TToottaall DDeeffeeccttss//UUnniitt
EE sscc aa
pp ii nngg D
D eeff ee
cc tt ss
Escaping Defects
It doesn’t matter how good your inspection and test processes are; the more defects you create, the more defects escape to the customer
What does “Six Sigma” mean?
Six Sigma stands for Six Standard Deviations from the mean. Six Sigma allows for only 3.4 defects per million opportunities for each product or service transaction. For a process that is centered on the
target, the lower and upper specification limits are each at a distance of 6 standard deviations from the mean.
Sigma vs. Defect Levels
2 308,5373 66,8074 6,2105 2336 3.4
PPM
Sigma Level
Defects perMillion
Opportunities
The Six Sigma Measurement
What Does 6 Sigma Mean In Your Daily Life ?
Tax Advice
7
Sigma Level
1,000,000
100,000
10,000
1,000
100
10
1
PPM
Restaurant BillsPayroll Processing
Prescription Writing
Baggage Handling
AirlineSafety Rate
3 4 5 621 3 4 5 621
Practical Meaning
99% Good 99.99966% Good
Postal System
20,000 Lost Articles of Mail / Hr 7 Lost Articles / Hr
Airline System
Two Short-Long Landings / Day 1 Short-Long every 5 Years
Medical Profession
200,000 Wrong Drug Prescriptions / Year 68 Wrong Drug Prescriptions / Year
Key Benefits & Areas of Application
Data-driven decision making Quality Improvement Waste reduction Reduction of factory costs Increase in productivity and capacity Product and process optimization Alignment between “voice of customer”
and “voice of business/process” Meet business and customer schedules Supply Chain Excellence
A Statistical View Everything is a process
(manufacturing, financial operations, call centers, medical procedures, insurance procedures, etc.).
Every process has inherent variation.
A Statistical View (continued) All efforts to understand
variation and improve the process must be data driven. It is not sufficient to make
improvements based on average only; due attention must be paid to variation or spread
Two Types of Six Sigma: DMAIC
Define the opportunities (create the project charter, identify savings opportunity, project metrics, etc.)
Measure (current/baseline) performance Analyze opportunities (develop more
detailed product & process understanding)
Improve performance Control performance (“maintain the
gains”)
Two Types of Six Sigma: DMAIC
DMAIC Primarily used for existing processes and
products (not new product development) Reactive in nature Involves achieving higher quality, yield, etc.
Systematic application of statistical thinking based on trustworthy data
(Similar to PDCA approach “Plan, Do, Check, Act”)
Two Types of Six Sigma: DFSS
DFSS (Design For Six Sigma) is aimed at building
a high level of quality into the design of the product at the very beginning.
Extensive data collection in terms of customer needs, translating the knowledge into manufacturing parameters, and conducting upfront reliability and quality testing on prototypes to assure delivery on quality and cost requirements.
The Funneling Effect
Optimized Process
> 30 Inputs
8 - 10
4 - 8
3 - 6
Found Critical X’s
Controlling Critical X’s
10 - 15
All X’s
1st “Hit List”
Screened List
MMEASUREEASURE
AANALYZENALYZE
IIMPROVEMPROVE
CCONTROLONTROL
DDEFINEEFINE
Define Phase - PurposePurposeTo develop a fully-defined projectSome key steps: Drive project selection through
business strategy and the business objectives
Carefully analyze and incorporate customers needs
Define the project objectives Scope the project appropriately
Measurement Phase - PurposePurpose
To define the current process and establish metrics
Some key steps: Process mapProcess map Process outputs and inputs Process outputs and inputs (Y’sY’s and X’sX’s) Cause and Effects MatrixCause and Effects Matrix How “trustworthy” are our measurements?How “trustworthy” are our measurements? How capable is our process in meeting How capable is our process in meeting
customer requirements?customer requirements?
Analyze Phase - PurposePurpose Learn about the relationships
between the X’s and Y’s Identify potential sources of
process variabilitySome key steps: Failure Mode and Effects AnalysisFailure Mode and Effects Analysis Multi-Vari StudiesMulti-Vari Studies Plan the first improvement
activities
Improvement Phase - PurposePurpose
• Quantify relationship between critical X’s and Y’s
Some key steps: Experimentation and Data Collection Design of ExperimentsDesign of Experiments (DOE) is the
backbone of this phase
Control Phase - PurposePurpose• Maintain the gains!!!Some key steps: Document and implement a control control
planplan Establish and monitor long-term long-term
capabilitycapability Implement continuous improvementcontinuous improvement
efforts
Tools
8 Key Tools Process MapsProcess Maps: to document key process
steps, inputs, and outputs Cause and Effects MatrixCause and Effects Matrix: to prioritize key
inputs for action Measurement Systems AnalysisMeasurement Systems Analysis: to evaluate
accuracy and precision of measurement systems
Capability StudiesCapability Studies: to establish how process performance compares to customer specifications
8 Key Tools (continued) Failure Modes and Effects Analysis (FMEA)Failure Modes and Effects Analysis (FMEA): to
identify high risk inputs and improvement actions
Multi-Vari StudiesMulti-Vari Studies: to provide quantitative clues for identifying inputs to leverage
Design of Experiments (DOE)Design of Experiments (DOE): to systematically study process inputs to identify optimal process windows
SPC and Control PlansSPC and Control Plans: to monitor the process and document all actions necessary to maintain world class performance
Process Map
C&E (Cause & Effect) Matrix
Measurement Systems Analysis (MSA)
Data Integrity Validity: Is the “right” aspect of the process being measured?
The data can be from a very reliable method or source, but still not match the operational definitions established for your project
Measurement Systems Analysis (MSA)
Data Integrity Reliability: deals with the accuracy
and consistency of the data For data processing and handling
situations, use audits to assess reliability When the data comes from test
equipment, use a Gage R&R study When the data are from a qualitative
assessment, use an Attribute MSA study
Measurement Systems Analysis (MSA)
Measurement System: Is the measurement system producing good data? Resolution: Smallest unit of measurement
displayed by the system/instrument. Accuracy: Does the average of the
measurements deviate from the true value? Bias: The difference between the average of
all repeated measurements that might be made on a sample at a given time, and its true value.
Measurement Systems Analysis (MSA)
Repeatability: Variation that occurs when repeated measurementsrepeated measurements are made of the same variable under absolutely identical conditions.
Reproducibility: The difference in the average of the measurements made by:
Different people Same instrument Measuring the same characteristic Different conditions (environment, time, etc.)
Process Capability Indices
Capability Ratio - compares the capability of a process (voice of the process) to the specification limits (voice of the customer).
USLUSLLSLLSL
Voice of the Customer
Voice of The Process
USLUSLLSLLSL
Voice of the Customer
Voice of The Process
Voice of the CustomerVoice of the Process
Voice of the CustomerVoice of the Process
Process Capability Ratios, Cp
SpreadProcess Tolerance TotalCP
6sLSL-USLCP
Process of VoiceCustomer of VoiceCapability
Two Groups of Capability Indices
C - Represents process capability - what the process potential is given a stable process Standard deviation estimated from Moving Range or
pooled standard deviation – represents common cause variation
P - Represents process performance - what has happened, not necessarily what will happen Standard deviation estimated from the traditional
formula – includes both common and special causes of variation
Illustration of CpCp = 0.5
Cp = 1.0
Cp = 1.5
Cp = 2.0
Process Capability Indices - Cp, Pp
Sigma6pec - Lower SUpper Spec
Cp
Sigma is estimated from a control chart (common cause variation)
PpSigma is estimated as the traditional standard deviation (common and special cause variation)
Stable Process
Unstable Process
2dMRσ ˆ
1n)X(X
σ2
i
ˆ
Cp & Cpk for an Off-Center Process
Cp= 1.3
Cpk = 1.3
Cp= 1.3
Cpk = 0.8
Cp= 1.3
Cpk = 0.0
FMEA - Definition Primary objectivePrimary objective::
Identify ways in which the process input variables (Xs) can fail and determine what effect that has on process outputs (Ys)
A structured approach:A structured approach: Estimates the risk associated with specific causes
How X fails and the impact on Y Prioritizes the actions that should be taken to
reduce the risk How to prevent X from failing
Potential inputs into the control plan
History of FMEA Originally used in the 1960s for the
Apollo space missions.
In 1974 the US Navy developed FMEA standards.
In the late 1970s, automotive industry adapted FMEAs mainly driven by liability costs.
Types of FMEA SystemSystem - used to analyze systems and sub-
systems in the early concept and design stages
Focuses on potential failure modes associated with the functions of a system caused by the design
DesignDesign - used to analyze product designs before they are released to production
Focuses on product function ProcessProcess - used to analyze potential failures in
existing processes Focuses on process inputs
OverviewProcess
Step/Input Potential Failure Mode Potential Failure EffectsSEV
Potential CausesOCC
Current ControlsDET
RPN
Actions Recommended
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
What is the Input
What What can go can go wrong wrong
with the with the Input?Input?
What can What can be done?be done?
What is What is the Effect the Effect
on the on the Outputs?Outputs?
What are What are the the
Causes?Causes?
How are How are these found these found
or or prevented?prevented?
How How Bad?Bad?
How How Often?Often?
How How well?well?
Ways of Learning Passively Passively - Observe naturally
occurring informative events (Multi-vari Studies) If you’re lucky, an informative event
might happen while you’re watching ExperimentallyExperimentally - Create
informative events
Ways of Learning (continued)
Experimental DesignExperimental Design Proactively manipulate input
variables to study the effect on the output variables
Invites informative events to occur Experiments, if done correctly, are
Efficient Powerful
Multi-Vari Studies - Purpose
Collect data to understand which Xs affect Ys Y = f(X)
Collect data to support intuition that created C&E and FMEA
May discover inputs initially omitted from process map or removed during C&E / FMEA
Find the key Xs to advance to the Improve phase and avoid… Wasting time experimenting with unimportant Xs Implementing control plans on unimportant Xs
Multivari Analysis Before initiating DOEs, Multivari analysis is
designed to extract the maximum amount of information using either planned data collection or using existing historical data.
Concepts like ANOVA (Analysis of Variance) and other statistical methods used to identify and quantify relationships between key X’s and Y’s.
Less expensive than DOEs but this may often lead to identification of correlation rather than establish cause and effect.
Design of Experiments A systematic method for using data to
understand the cause and effect relationships in a process
Study the effects of simultaneously changing more than one X
Changes are deliberately made to input variables (Xs) to observe changes to the output response(s) (Ys)
Design of Experiments (continued)
Seek influential Xs which Center the Y on the target Minimize the variability of Y Minimize the effect of Noise Variables
Advantages of Designed Experiments Enable data-based decisions Create understanding of the process
and how to control it Take into account the inherent noise in
the system Efficient - maximum information with
minimal effort Detect variable interactions
SPC – Statistical Process Control
Used to: Quantify the “normal” variation in the process
parameter when process is in a state of control
Identify the major sources of variation Identify the correct type of control chart Identify process owners responsible for
regular monitoring and maintenance, updates to the chart, etc.
Establish clear reaction plans based on signals/alarms generated by the control chart
Control Plan How to maintain the gains? Often if the process is left to itself after
the initial optimization effort, it may revert to poor performance over time.
An effective control plan identifies monitoring schemes, owners accountable for fluctuations in process performance, and a process in place to repeat the DMAIC process if necessary.
Applications
Known Industrial Applications Leaders in the successful
implementation of Six Sigma Motorola, GE, Allied Signal, Microsoft, Intel,
3M, Boeing, etc. WIPRO, GE India, Philips India, ONGC,
Adidas, Baxter India Manufacturing (quality improvement,
cost reduction, process optimization, measurement error improvement, safety improvement, new product development, etc.)
Known Industrial Applications Transactional: Improving customer
OTIF (On-time, In-full), call centers, supply chain excellence, human resource management, library management, payroll accuracy, etc.)
Business: Resource allocation, optimizing and prioritizing capital investments, new business development/ new product introduction, etc.
Keys to Six Sigma Success Identify critical business issuescritical business issues and
strategies LinkLink efforts (projects) to resolution
of roadblocks Set clear expectationsexpectations for results MeasureMeasure the progress (Metrics) Manage for resultsresults