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GB TRG MATERIAL Slide 1 Proprietary to Wipro Ltd
D M A I C
Six SigmaGreen Belt TRAINING
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How Successful Leaders see Six Sigma
Six Sigma is the most important initiative GE hasever takenit is part of the genetic code of our future leadership.
-Jack Welch, Former CEO,General Electric
You can see the before & after of an organizationwhen Six Sigma grabs hold & takes place.
-Richard Johnson, Director of Six Sigma,Allied Signal
Successful leaders have made Six Sigma their way of conducting business.-Azim Premji, Chairman,Wipro Limited
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What is Six Sigma?
Six Sigma as a metric
At Six Sigma level, a process produces less than 3.4 defects in a million or
99.99966% Yield
Six Sigma as a methodology
Improving process capability to desired levels
Six Sigma as a philosophy
Business excellence through organizational transformation
Six Sigma : Variation Based thinking
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GB TRG MATERIAL Slide 4 Proprietary to Wipro Ltd
6 Sigma as a metric
Which process is performing best?
PROCESS PERFORMANCE
Call servicing 32 seconds ASA vs. goal of 35
Billing 98% accuracy
Accounts Receivable 33 days average aging vs. goal of 40
Customer Service 82% rated 4 or 5 on responsiveness
The Sigma Scale provides a common scale/ metric for comparison.
Sigma level DPMO
2 308,5373 66,8074 6,2105 233
6 3.4
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An efficient & effective method :
Six Sigma is a structured application of process improvement tools,
applied by dedicated resources on a project basis,
to increase customer satisfaction and reach strategic business goals.
.
Six Sigma: As Methodology
BusinessProblem StatisticalProblem StatisticalSolution BusinessSolution
Black BeltsExpertise
StatisticalTools
Black BeltsExpertise
P R O J E C T
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GB TRG MATERIAL Slide 6 Proprietary to Wipro Ltd
A philosophy :
For many companies, Six Sigma is a management philosophy.
Six Sigma: As Philosophy
Successful leaders have made Six Sigma their way of conducting business .-Azim Premji, Chairman,Wipro Limited
Six sigma is the best training that an organization can give to its employees,
its even better than sending them to Harward Business School. - Jack Welch, Former CEO, GE
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Six Sigma is Variance Based Thinking
-20 -10 0 10 20
Consider below performance on On time delivery
Target 0 days
Mean 0.70 days
On Target !!!! Good Performance ??
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Six Sigma is Variance Based Thinking
-20 -10 0 10 20
What customer sees!!
Customer always looks at , mean is meaninglessvariance
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Are We Looking at the Variation?
Dont worry,That rope is
one inch thickon average.
If I had to reduce my message to management to just a few words, Id say it all had to do with
reducing variation W. Edwards Deming
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DMAIC Overview
DEFINEThe Problem
MEASUREY ( Outcome)
Validate Measurement System
ANALYZEBaseline Y and Set Goal
Identify Xs(Variation Sources)
IMPROVEQuantify Xs
CONTROLControl Xs
DMAIC applies to an existing process
that needs improvement
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DMAIC
Establish CTQ Characteristics
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CTQ (Critical to Quality)
A CTQ is a
Product or Service
characteristic
that satisfies a
Customer Requirement
OR
Process Requirement
Business CTQ
Customer CTQ
Internal CTQ
Project CTQ
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What are Business CTQs?
Business CTQs drive the business goals & vision
Typical Business CTQs are in the area of:
Operational Excellence
Cost Reduction
Productivity Improvement
Employee Satisfaction
Customer Satisfaction
Sales Growth
Profitability
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Define your Customer
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Who is your customer?
A customer is someone who
Uses your product or service
Decides to buy your product or service
Pays for your product or service
Gets impacted by your product or service
Internal & External customers
Primary & Secondary customers
Key concepts
Different markets / segments
Different customer requirements
Product / Service chain
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Explore Customer CTQ
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Customer CTQs
Six Sigma begins with the customer
Customers find it easier to define what they do not want
Customer CTQs are defined by customers
Sources of customer CTQs
Survey results
Service reviews
Meetings
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Customer Requirement perceived by Sales Team
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Customer Requirement captured by Design Team
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Customer Requirement created by Production Team
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Customer Requirement delivered by Implementation Team
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Actual Customer Requirement
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How to Explore Customer CTQs
Often, customer requirements are hazy
Customer requirements must be understood clearly
VOC is a technique to organize, analyze & profile the customer requirements
(collection of requirements is done through surveys / meetings)
VOC Table
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VOC Data Source
REACTIVE SOURCES
InterviewsFocus groups
Surveys
Comment cards
Sales visits/calls
Direct observation
Market research/monitoring
Benchmarking
Quality scorecards
PROACTIVE SOURCES
ComplaintsProblem or service hotlines
Technical support calls
Customer service calls
Claims, credits
Sales reporting
Product return information
Warranty claims
Web page activity
NPS survey outcomes can be Reactive & Proactive source
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Define Internal CTQ / CBP
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How to Arrive at Internal CTQs / CBPs
Customer CTQs( needs) are often hazy & they must be converted into meaningfulinternal goals that are assignable to functions
More structured methods & tools are available to convert Customer CTQs /
Business CTQs into internal CTQs / CBPs
CTQ Drill-down
Quality Function Deployment (QFD / House of Quality)
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Example CTQ Drill Down
ImproveProfitability
Reducingcost
Manpower cost
Productivity Salary cost
Travel &overheads Infrastr. cost
IncreaseRealization
or Billing rate
New Geos High EndPractices
OffshoreBusiness
Model
MoreOffshore
component
Productivity (NLD) Projects were selected from the BusinessCTQ of Profitability
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GB TRG MATERIAL Slide 29 Proprietary to Wipro Ltd
One Example of A Typical CTQ Drill-down
Example of On-time delivery CTQ
Total Units 168
Orders Fully Serviced 132 Orders Not Fully Serviced 36A 36
6 Late
52 Defects 36 Defects
B 47
31 late
C 31
8 late
D 18
7 Late
A 4 B 27 C 1 D 4
With the data support, team decided to take project inProduct B, Which is contributing to more than 70% defects
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DMAIC
Define a Project
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Theme Selection Matrix
Theme selection matrix can be used to prioritize project themes coming from thebrainstorming exercise
It is a structured & data-oriented prioritization exercise to select right project
A scale of 1-9 is applied to evaluate multiple themes on four aspects. Final score isarrived by multiplying the scores of each category
Importance to the internal customer
Importance to the External customer
Financial benefits/ Annum Effort
Final score
6
4
3
CTQ type
Rating Parameters for the themes (1-3-5-7-9)
S.no. Selected Themes
1
5
2
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Scope the Project
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What is Scoping?
Its an attempt to define what will be covered in the project deliverables
Scoping sharpens the focus of the project team
Sets the expectations right
Types of Scoping
Longitudinal Scoping
Lateral Scoping
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Longitudinal Scoping
Longitudinal scoping is done on the length of the process
e.g. From the time a bug is assigned to the time it is submitted for fix
e.g. From the time of customer reporting the complaint till final satisfaction confirmation
Mostly the start & end points are baton change points
A macro as-is process map must be prepared to facilitate longitudinal scoping
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Example of a Photocopying Process
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Example - SIPOC
Suppliers Inputs Process Outputs Customers
End User Mail Receipt of Mailfrom User
AcknowledgementMail End User
Wipro MIT Helpdesk TeamCall allocation tothe HelpdeskExecutive
Client IT Dept IT infrastructure
HelpdeskExecutive Logs theTicket On E-helpline*HelpDesk sendsacknowledgementto the Customer
Process NameStarting point: User sending a mail to HelpdeskEnd point: Call logging in the Tool/Email Response to the Customer
l
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Process Mapping Nomenclature
Process Decision Data
Pre-definedProcess
Document Terminator
ManualOperation
DelayManual
Input
l
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Lateral Scoping
Lateral scoping is done on the breadth of the process
e.g. All despatches from North & South regions
e.g. Calls received during general shift
One or more of the following are covered here:
What all kinds of units the process will cover
In what situations the process is valid
What are the qualifiers for the transactions
What functional domains does the process cover
In what geographical areas the process is valid
CTQ Drill down and pareto analysis will help in effective lateral scoping
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Quantify Benefits
P j i f B fi
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Projection of Benefits
Six Sigma has a strong focus on money
Management buy-in is easier for tangible benefit projects
Tangible benefits could be of various types
Cost Reduction / Saving
Increase in Sales / Revenue
Enhanced productivity
Enhanced measurable Customer Satisfaction
Enhanced measurable Employee Satisfaction
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DEFINE THE PROBLEM STATEMENT
D fi i P bl St t t
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Defining a Problem Statement
In the last 3 months, 12% of our customers are late, by over 45 days in
paying their bills. This represents 20% of our outstanding receivables &
negatively affects our operating cash flow.
Our customers are angry with us and thus delay paying their bills.
What
When
MagnitudeConsequence
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D
M AIC
EstablishPerformanceParameters
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Map Detailed As-is Process
Wh t d h l it ?
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What do you see hereany clarity?
S th Diff N
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See the Difference Now
Process Mapping is a graphic display of steps & activities that constitute a process
Recall..Onepicture is
better than1000 words
B fit f P M i g
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Benefits of Process Mapping
Simple & visible structure for thinking through a complex process
Enables seeing the entire process as a team
Enables seeing that changes are not made in a vacuum and will carry through,
affecting the entire process down the line
Magnifies non value-added areas or steps
Identifies cycle times of each step in the process
Helps re-examine (if needed) the scope and charter of your project
Micro Process Map of a Manufacturing Process
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Micro Process Map of a Manufacturing Process
continuousuncoiling of coil
degreasingconcn-0.3-0.4temp-80 Deg
hotwater rinsingtemp=80 Deg C
cold (roomtemp)water rinsing
wetscrubbingby scothbright
chromatingconcn- 0.8 -1.2Ph-1.2-2.0
hot air dryingtemp-100Deg C
coatingon back side30 wftvisco-30 -35sec
bakingas per g.p.thicknessPMT -224
water quenching
hot air dryingtemp -90-100 Deg C
additionalcold air drying
primer coatingwft-15-18visc-30 sec
bakingas per thickness
-
water quenching
hot air dryingtemp=100Deg C
additionalcold air drying (r.t.)
topcoatingwf t=55-65visco-58-65
water quenching
hot air dryingtemp-100 -110 Deg C
additionalcold air drying
additionalcold air drying
inspection
g/f application?
G/f
yes sampling&
testing
no
recoiling ?O.k
marking for next routeas per cust.reqmt.
transfer toconcernedline
despatchas reject
finishing asper reqmnt.(coil, sheet,profile)
packing despatch/shipping
testing of ph , concn. ,temp,of water , degreasingand chromating
yesrecordingproperties
bakingas per thicknesspmt-2242 burner
storage atcustomer end
guardfilmremoval
?Paint
peel off
complaintraising bycustomer
installation
yes
end
visit tocustomer for assesment
replacement /rework end
START
POINT
squeezing byrubber roll
squeezing byrubber roll
squeezing byrubber roll
squeezing byrubber
2 stagesqueezing byrubber roll
squeezingby rubber roll & CPC
squeezing byrubber roll
squeezingby rubber roll
no
Make the process map aligned to your Projectobjective, Dont paste ISO process maps.
Another way of mapping processes CFPM
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Another way of mapping processes- CFPM
Teams can use this structure for processes that move across functions
Function 1
Function 2
Function 3
Function 4
Step 1
Step 2
Step 3
Step4
Step 5
Step 6 Step 7
Step 8 End
4.5, 4-6Hrs
1.2, 1-1.5Hrs
0.2, 0.1-0.5Hrs
See Cycle time also.Average time & Range
given
Process map is must in cycle time reduction projects
Points to Look For
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Points to Look For
The pain areas (identified at the time of project selection) must be within the
selected scope
Guard against analyzing the process at this stage, just map as-it-is
Do not map the process as you would like it to be
Take care of Parallel & Sequential activities for Cycle time reduction projects
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Define what to measure and how to measure
Why Collect Data?
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Why Collect Data?
Improvement can only occur if we understand where we are & where to go, supported
by a measurement system that validates both situations
Successful organizations have a common language to communicate-- Data
Common language- Data, promotes objectivity in decision-making process
Have you reached where you intended to? -- only data answers that question
Its rightly said If you cant Measure it, you cant improve it.
Develop Operational Definition
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Develop Operational Definition
An Operational Definition is a precise description that tells how to get a value for the
characteristic you are trying to measure. It includes what something is and how tomeasure it
Purpose:
Removes ambiguity common understanding of defect definition
Identifies what to measure
Identifies how to measure it
Aim is to have apple to apple comparison forBaseline performance and improved performance.
Operational Definition Examples
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Operational Definition - Examples
Try to Remove ambiguity from the statements
Im coming by 8 o clock.verify 8 am or pm
Im reaching Dammam by 8 o clock flight.verify 8 o clock is Arrival or Departure time
Im waiting for Doctor for so longverify 5 minute or 5 Hrs
Give me half bottle water---bottle may by 200ml to 20 Lt
Ive reduce patients waiting timeat which stage or what is the start & end point
Ive improve profitabilityin which region/ which product
Operational Definition & Data collection plan
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Operational Definition & Data collection plan
Operational DefinitionUnit of Measurement
What to Measure How to Measure
Performancemeasure (Y) Operationaldefinition Data sourceandlocation
Samplesize
Who willcollect the
data
When willdata becollected
How willdata becollected
Other data thatshould becollected at the
same time
Operation Definition - Example
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Operation Definition Example
Objective: Why we need this/ Biz case
Scope/ coverage: Reasons if not 100%
Clarify what we are measuring: Uptime/ Response/ Resolution time, etcIs it aligned with the customer reqt (SLA/ Contract)
Source of Data: IS System (Pure), Manual, Hybrid (IS System with manualintervention )
Sampling plan/ Freq of data collection
Process owner
Any stratification reqd/ recommended: SLA w.r.t. Skill calls, spare calls, EC Vs FE
Challenges in authenticity of data: Like RE Call closure may not reflect 100%
Exp.: SLA Calculation at A/c level
Types of Data
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Types of Data
Discrete data
Data that can take a limited number of values (Pass / Fail, OK / Not OK, Win / Loss)
Examples
Days in a week
Number of yes responses to a satisfaction survey
Number of countries that play cricket
Continuous Data
Data that be expressed in either fractions or whole numbers
Examples
Temperature of the room
Exchange rate of a currency
Yield of a process
Height of a person
You need to be careful while selecting thedata type and you need to follow the sameapproach in improve phase also.
Which Type of Data is Preferable?
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Which Type of Data is Preferable?
Large sample sizes are required to measure higher Sigma multiples for discrete data
Since continuous data measurements can be broken down, relatively smaller
samples are required for higher Sigma calculations
Discrete data does not allow to understand the process variation
How Good is Good and How bad is Bad.
8:30 AM
A B C D
11:00 AM
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Design Sampling Plan for Establishing Process Baseline
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Introduction to Sampling
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Introduction to Sampling
We do sampling all the time
Populations & SamplesPractical aspects Cost & Time
Sampling is done to study a representative portion of population
Any term describing the characteristics of a sample is called statistic
Any term describing the characteristics of a population is called parameter
Population
Sample
S am
pl i n
g
Tool
How Big a Sample?
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Too Big:
Requires toomany resources
Too small:
Wont do the job
g p
Take BB Assistance for calculating Sample Size
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D
M AIC
ValidateMeasurement System
for Y
Validate Measurement System for Y
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y
In transactional environment a robustOperational definition will ensure Good
measurement system
Use ITIL guidelines & Industry bestpractices for arriving at right Operation
definition ( e.g. AHT, FCR etc )
Take BB help in designing & performing GR&R
Gage Repeatability & Reproducibility
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DM
AIC
EstablishProcessBaseline
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Compute Process Sigma Multiple UsingCollected Data
Six Sigma Metrics
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g
Customer Specification / Unit / Defect Definition
Discrete Data Continuous Data
Defects
Defects per Unit(DPU)
Defects per Million Opportunities(DPMO)
Mean
Std. deviation
Process Sigma Multiple - Z
1
2a 2b
3
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Process Sigma Multiple for Discrete Data
Business Problem
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A project team wants to reduce the total waiting time taken in hospital (from reception
to discharge). Team has randomly collected the following data of last 3 weeks for thewaiting time for few patients/ customers :--
(Data given is in minutes)
USL decided by Management based on other hospitals study is decided at 60 Minutes max.
Customer Specification / Unit / Defect1
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p
Unit: Any customer/ patient coming for treatment
Customer Specifications: USL is 60 minutes
Defect Definition: Any customer whose total waiting time exceeds 60 minutes
USL: Upper Specification Limit for a Performance Standard. Anything above this is a defect.
Discrete Data2a
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Lets treat this data as discrete
That means I would count the number of patients who had to wait more than 60minutes
There are only 2 defects in 30 units
Customer 1 5 Customer 11 50 Customer 21 49Customer 2 49 Customer 12 48 Customer 22 48Customer 3 48 Customer 13 36 Customer 23 39
Customer 4 53 Customer 14 50 Customer 24 49Customer 5 58 Customer 15 50 Customer 25 34Customer 6 50 Customer 16 62 Customer 26 33Customer 7 46 Customer 17 45 Customer 27 57Customer 8 50 Customer 18 47 Customer 28 48Customer 9 49 Customer 19 51 Customer 29 47Customer 10 47 Customer 20 44 Customer 30 390
Defects per Unit2a
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So, DPU of the given data is = 2 / 30 = 0.067
DPU =Number of defects found at any check-point
Number of units processed at that check-point
Sigma Multiple Calculation Discrete Data 3
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Every DPMO value relates to a particular Sigma Multiple or Z ST value. In this case, this
process is working at 3.00 Sigma multipleThe same can be calculated by Given Excel template also.
ZST ZST
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Process Sigma Multiple for Continuous Data
Continuous Data2b
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Lets treat the data of the waiting time data as Continuous and calculate Base Line
Sigma
For calculating Baseline or Sigma Multiple ZST in continuous data, we need tounderstand: Mean, std deviation & shape (Type of Distribution)
Customer 1 5 Customer 11 50 Customer 21 49
Customer 2 49 Customer 12 48 Customer 22 48Customer 3 48 Customer 13 36 Customer 23 39Customer 4 53 Customer 14 50 Customer 24 49Customer 5 58 Customer 15 50 Customer 25 34Customer 6 50 Customer 16 62 Customer 26 33Customer 7 46 Customer 17 45 Customer 27 57Customer 8 50 Customer 18 47 Customer 28 48Customer 9 49 Customer 19 51 Customer 29 47Customer 10 47 Customer 20 44 Customer 30 390
Sigma Multiple Calculation Continuous Data 3
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In this case, Mean = 57.73 minutes
Std dev : 63.54
USL : 60 Minutes
ZST can be calculated by the Excel Template.
This process is working at 1.54 Sigma multiple ( Z ST = 1.54, or DPMO = 484750).
Can you notice the difference in Z ST & DPMO values by treating the same data in
different way. What is the message ?
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Introduction of Normal Distribution
Introduction to Normal Distribution
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Its a Probability Distribution, illustrated as N ( , )
Simply put, a probability distribution is a theoretical frequency distribution
Higher frequency of values around the mean & lesser & lesser at values away from mean
Continuous & symmetrical
Tails asymptotic to X-axis
Bell shaped
Total area under the Normal curve = 1
100 110 120 130908070
Figure 3.01
1 unitof
standarddeviation
+-
Normal Distribution withMean =100
Standard Deviation = 10
Prediction based on Normal Distribution
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Figure 3.02
+-
- 1 + 1
95.46%- 2 + 2
68.26%
- 3 + 3 99.73%
+ 4 99.9937%
- 599.99943%
+ 5
- 6 + 6 99.999998%
- 4
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DM
AIC
DefinePerformance
Goals
Improvement Paths in Process Sigma Multiple
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Mutually signed-off change in specification limits
Its not a process improvement
Shifting the mean
Reducing the variance
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Benchmarking Exercise
Benchmarking
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Benchmarking is about looking at others for superior methods
Others may include suppliers, competitors, customers or even a different industry
Benchmarking could be done both on means as well as ends
Types of benchmarking
Internal
Functional
Competitive
B en c h m
ar k i n
g
Tool
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Establish Process Improvement Goal p-value &confidence band concept
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Means & Ends of a Process
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Y = f ( X1, X2, X3X n )
Dependent
Process Output
Effect
Symptom
Monitor
Independent
Process Input / Step
Cause
Problem
Control
Y X
Generating & Prioritizing Xs
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Identifying & prioritizing Xs could be done using both non -statistical & statistical tools
Identification tools include (non-statistical basis)
Experience of process doers
Brainstorming & Multi-voting
Process mapping
Fishbone
Prioritization tools include
Pareto Diagram
Stratification
Correlation (for a single X)
Regression (for multiple Xs)
Chi-square test
ANOVA
Failure Mode & Effect Analysis (FMEA)
Focus on identifying problem areas
Focus on identifying potential root causes
Focus on prioritizing potential root causes
Identification Tools
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Brainstorming & Multi-voting is a structured methodology to collect responses from a
group & arrive at a short-listEven though it provides a short- list, its not an effective prioritization tool because short -listing
is not done on any mathematical basis
Use of Process mapping requires studying the micro as-is process mapped in step 2
earlier & walking through it
Fishbone relates an effect to its cause by drawing a tree diagram
Points to Remember in Brainstorming
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All ideas are important, dont out rightly reject any idea
Participation should be ensured from all team members
To ensure this, project teams could use the round-robin method of idea generation
Its advised to use the Black Belt as the facilitator here
Fishbone
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Also called Cause & Effect or Ishikawa diagram
Focus of Fishbone is to arrive at the root causes of the problem areas identified
through multi-voting / process mapping
It works on the principal of asking why to each cause till you reach the root cause
F i s h
b on
e
Tool
Characteristics of a Fishbone
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Characteristics of a Fishbone
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Search for the REALCAUSES of the problemsthat you deal with.
How to Construct a Fishbone
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Review the problem statement / defect definition
Brainstorm & identify possible causes
Sort causes into reasonable clusters
Choose the clusters & name them into big bones
Check the logical flow of cause and effect
Fishbone Structure
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Effect
Cause 1
Cause 11
Cause 12
Cause 21Cause 22
Root-cause221
Root-cause222
Cause 31
Big Bone Medium Bone
Backbone
Cause 2
Cause 3
Hows for 1stHouse
Fishbone Structure
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Effect
Cause 1
Cause 11
Cause 12
Cause 21Cause 22
Root-cause221
Root-cause222
Cause 31
Big Bone Medium Bone
Backbone
Cause 2
Cause 3
Hows for 2ndHouse
Fishbone Structure
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Effect
Cause 1
Cause 11
Cause 12
Cause 21Cause 22
Root-cause221
Root-cause222
Cause 31
Big Bone Medium Bone
Backbone
Cause 2
Cause 3
Hows for 3rdHouse
Fishbone Example
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Wrong CustomerDetails
CRM malfunction Error in Listening
Foul order
Printer error
CRM not saving thedata properly
Lack of trainingon listening skills Problem with
instrument
Nomaintenance
Roughhandling
No credit card verification
Root-cause 1
Backbone
No cross-check
Root-cause 2
Fishbone can be used in organizing the causes also.
Key Concepts
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Its difficult to judge how many levels one needs to explore in a fishbone
Sometimes project teams may hit the root-cause at multi-voting / process mappinglevel, but they may do a further root-cause for the sake of it
In such cases, it is always a good idea to see if there is further scope for exploring byasking why
Always remember that we are trying to identify controllable Xs , what can not
be controlled can not be a root-cause
One must be able to see the effect on Y when values / levels of root -causes are
changed
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Separate Vital Few from TrivialMany for Further Screening
Prioritization Tools
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Some teams may do a multi-voting here, at the output of root-cause analysis
Pareto diagram works on the 80:20 rule of 20% causes contributing to 80% of defects
Co-relation & Regression help in identifying the movement of continuous Y withrespect to continuous X
ANOVA & Chi-square help in identifying the movement of continuous / discrete Ywith respect to discrete X
Failure Mode & Effect Analysis (FMEA) identifies the process failure modes &
assigns a number to it that helps to prioritize the actionables
Success of all prioritization tools depends upon data collection
Pareto Diagram
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A graphical tool for ranking causes form most significant (Vital Few) to least significant (Trivial many).
Based on Pareto principle, which was first defined by J.M Juran in 1950. Pareto principle was madeafter 19th century Italian economist V. Pareto (1897). Most effects come from relatively few causes.
Also known as 80:20 Principle .
Pareto diagram indicates which area should be taken up first in eliminating defects and improvingoperations.
Example: Suppose a person identifies multiple root-causes of reaching his office late.Now he is not sure where to focus so that he reduces the occurrence of
reaching late by minimum 50%.
He has identified following root causes
Woke up late
Clothes not ready
Breakfast not readyBus not coming on time
Traffic jam
Bus waiting for other employees
He collects data on how frequent each of the root cause is & constructs a Pareto
P ar e
t o
Tool
Pareto Diagram
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B u s n o t c o m
i n g o n t i m
e
C l o t h e s n o t
r e a d y
W o k e u p
l a t e
T r a f f i c
j a m
B r e a k f a s t n o t
r e a d y
B u s w
a i t i n g
25 18 15 6 5 235.2 25.4 21.1 8.5 7.0 2.835.2 60.6 81.7 90.1 97.2 100.0
0
10
2030
40
50
60
70
0
20
40
60
80
100
CountPercentCum %
P
e r c e n t
C o u n t
Frequencies of root causes for reaching office late
Other Prioritization tools
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So far, we have not used any statistical tool to prioritize Xs.
Depending upon the data characteristics of Y & X, we can choose the appropriate tool
Correlation&
Regression ANOVA
Chi-square
Continuous
Continuous
Discrete
Discrete
Y
X
Identifyopportunities for
converting Yinto a continuousone or use FMEA
Regression
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While correlation tells us only about the direction of movement, it does not
throw much light on degree of movement in one variable with respect tomovement in another
Regression of Y on X results in a transfer function equation that can be used to
predict the value of Y for given values of X
Y can be regressed on one or more Xs simultaneously
Simple linear regression is for one X
Multiple linear regression is for more than one Xs
R e gr e
s s i on
Tool
Y = f ( X )
Multiple Linear Regression (MLR)
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Multiple regression regresses Y on more than one Xs simultaneously
It is similar to Two-way ANOVA we have discussed in step 5, except for the differencethat Xs used in ANOVA were discrete
The approach is similar & a linear multiple regression equation looks as follows:
where Y = Dependent variable / output / response
X1 = First independent variable / input / predictor
X2 = Second independent variable / input / predictor
A = Intercept of fitted line on Y axis
B1 & B2 = Regression coefficients / Slopes of the fitted plane on two axes
C = Error in the model
Y = A + B1X1 + B2X2 + + BnXn + C
Multiple Regression Example
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Suppose we are trying to predict rent of an apartment based on the size of theapartment & its distance from the main commercial area. We gather the followinginformation as below. If you are looking for a two-bedroom apartment 2 miles from themain area, what rent should you expect to pay?
X1Y X2
Multiple Regression Example
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Minitab gives the following output:
For a 2 bedroom apartment 2 miles away from main area, expected rent could be INR3614 per month
Regression Analysis
The regression equation is
Rent = 852 + 1381 * Rooms
Predictor Coef StDev T P
Constant 852 1146 0.74 0.511
Rooms 1381.0 259.5 5.32 0.013
Distance -5.4 142.1 -0.04 0.972
S = 975.3 R-Sq = 91.5% R-Sq(adj) = 85.8%
Analysis of Variance
Source DF SS MS F P
Regression 2 30665301 15332650 16.12 0.025
Residual Error 3 2853449 951150
Total 5 33518750
ANOVA
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ANOVA is used to short- list potential discrete Xs for a continuous Y
We can use one- way ANOVA & see the variation in Y for one X at a time
We can use two-way ANOVA for more than one X
A N
OV A
Tool
ANOVA Example
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A team puts great emphasis on customer satisfaction. For some weeks, the ratingsseemed to suffer & the manager tried to identify the factors that could be causing this.He chooses two of the potential discrete factors as Team that handles the call & Shiftin terms of A & B. Which factor is vital?
Team Shift Rating
Desktop A 3.4DC A 4.5
Desktop A 3.2DC A 4.8
Desktop A 3.6DC A 4.2
Desktop A 4.1DC A 4.6
Desktop A 3.1DC A 5
Desktop B 3.5
DC B 4.2Desktop B 4.6DC B 4.9
Desktop B 3.5DC B 4.1
Desktop B 4.3DC B 4.7
Desktop B 3.4DC B 3.8
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Failure Mode & Effect Analysis (FMEA)
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FMEA is a simple tool to prioritize the failure modes & actions
By understanding why and how we fail, we can plan for success
It works on the belief that proactiveness saves time
Typically, FMEA is applied on the output of root-cause analysis, & is a better tool for focus / prioritization as compared to multi-voting
We shall focus on Process FMEA (Design FMEA is used in designing products)
F ME A
Tool
FMEA Concept & Output
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Risk Priority Number (RPN) = S * O * D
Severity, Occurrence & Detectability are measured on a scale of 1-10
FailureMode
Effect
Cause
Control
Process / Product
Characteristics
S everity
Occurrence
Detectability
Risk
Priority
Number
Action
Plan
FMEA Concept & Output
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Process / Product characteristics - Purpose of the product or Process
Failure Mode - How can the product / process fail to function?
Effects - Which effects are most severe to customer?
Causes - Which causes are most likely to occur?
Controls - Ability for current controls to detect causes?
RPN - Which high risk cause we work on first?
Action Plan - Recommended actions & responsibilities
Scales of SOD - Severity
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Severity is the seriousness of the effect of the failure mode on the customer
Rating Scale:
Scales of SOD - Occurrence
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Occurrence is the probability that a specific cause will result in the particular failure mode
Rating Scale:
Scales of SOD - Detectability
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Detectability is the probability that a particular cause will be detected
Rating Scale:
FMEA Table
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I
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DMAICExplorePotentialCauses
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Explore Potential Xs for Causation
Recall Step 6: Creating a List of Xs & Prioritization
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x1x2x3x4x5x6x7x8x9x10x11
x12
x7 = 38%x6 = 27%x2 = 12%
x9 = 4%x10= 4%x5 = 2%x1x3x4x8 = 13%
x11x12
Exploration of the y-x
relationship
Vital Xs
Trivial Xs
Brainstorming
FMEA
Fishbone
Regression, FMEA
error
I
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Design Counter-Measures
DMAIC
Counter-Measure Matrix
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Counter Measure Matrix is used to arrive at action to be taken on a root cause thatwould lead to the desired result
If there are more than one counter-measure for a root cause, then the counter measure is prioritized using three parameters
Each counter-measure is given a rating on a scale of 1-7 (higher the better) & overallscore is calculated by multiplying the 3 ratings, one with the best score is chosen
C o un
t er -M
e a s ur eM
a t r i x
Tool
Counter-Measure Matrix - Example
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Cost optimisation levers CCOP
Service Desk IR
Automation
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Map the New Process
DMAIC
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Develop Control Mechanism
DMAIC
Controlling the Xs
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So far, we have identified the best settings for each of the vital X
The key now is to ensure that the Xs dont vary away from the targeted setting
Process control is a crucial tool in ensuring that this Six Sigma project delivers lastingbenefits
Maintaining Xs at their target level can be done in two ways
Detection (reactive) Why was change not detected?
Prevention (proactive) Why did / would change occur?
Correction Loop
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Prevention
Mistake-Proofing Statistical ProcessControl
Prevention&
Detection
Mistake-Proofing (Poka-Yoke)
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It is a technique to make errors difficult to happen, if not impossible
Ground Rules of Mistake Proofing
Intelligence does not depend upon hierarchy
Target tasks that require constant alert / vigilance / memory
Look for cutting unproductive time to foster creativity
Defects are not acceptable, in whatsoever small number
It is best to make it impossible to do it wrong
Mi
s t ak
e-P r o
of i n
g
Tool
Advantages of Mistake-Proofing
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Easy to accomplish without any formal training
Removes repetitive tasks
Fosters creativity & value-addition
Ensures less defects
Mistake-Proofing Techniques - Examples
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Technique Prevention Detection
SHUTDOWN Cameras that dont clickif the shutter is on Air-conditioners trip whendetect slight over-heating
CONTROL ATM does not accept acard inserted wronglyQuality check at eachpoint in the assembly
WARNING Car security systemalerting the driver that alldoors are not closed
Smoke detectors
If Mistake-Proofing is Not Possible?
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Provide guidelines
Check-lists
SOPs
Templates
Use visuals
Color-codes
Shapes
Statistical Process Control (SPC)
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SPC was developed by Walter A. Shewhart in 1924
Historically, SPC has been used to monitor & control output Y
In DMAIC, we apply SPC to control Xs (remember Y is only monitored)
However, sometimes applying SPC to Y can also be beneficial in detecting trends
About SPC
Aids visual monitoring & controlling
Depends heavily on data collection
S t a t i s t i c al P r o
c e s s C on
t r ol
Tool
Basics of Control Charts
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Control charts are useful for tracking process statistics over time and detecting thepresence of special causes
A process is in control when most of the points fall within the bounds of the controllimits, and the points do not display any nonrandom patterns
Purpose of Control Limits
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LCL
UCL
Special Cause Variation
Special Cause Variation
C o m m o n
C a u s e
V a r i a
t i o n
CL
Process Control is inherent to process characteristics as against Process Capability
which is measured as per outside targets & specifications
Purpose of Control Limits
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UCL = + 3
LCL = - 3
99.73%
0.135%
0.135%
Control Limits define a probabilistic levelof occurrence of an out of control point
Out of control point
CL
Do Not Over-react to Change
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UCL
LCL
L o a
d i n g
t i m e
Excellent Work Change the transporter??
Distinguish between Noise & Change
Top Eight Indications of an Out of Control Process
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1. A single point outside 3 control limits
2. Nine points in a row on the same side of the centerline3. Six points in a row, all increasing or all decreasing
4. Fourteen points in a row, alternating up and down
5. Two out of three successive points more than 2 on the same side of the
centerline6. Four out of five successive points beyond 1 on the same side of the centerline
7. Fifteen points in a row within 1 on either side of center line
8. Eight points in a row with none between 1 on either side of center line
Choosing An Appropriate Control Chart
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Continuous Data
Individual Data Points Subgroups
Pulling one sample at fixed frequency Taking periodic grouped data
I & MR Variability of individualcharacteristics over time X & RVariability of average
characteristics over time
Choosing An Appropriate Control Chart
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Discrete Data
Defectives Defects
P-chart U - chart
Rest of the Control Charts are in the BB training Scope
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Continuous data Control Charts
I & MR Control Chart
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Average Handle Time (AHT) by an agent is a vital X for productivity. This is monitoredperiodically to keep it within desired operating range.
Data on AHT is collected over several hoursHour AHT ( sec )
1 65
2 69
3 67
4 66
5 63
6 70
7 71
8 68
9 64
10 69
11 63
12 68
13 84
14 81
15 68
I & MR Control Chart : Minitab Output
Minitab gives the following output:
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Observation
I n
d i v i d u a
l V a
l u e
151413121110987654321
80
70
60
_ X=69.07
UC L=82.93
LC L=55.20
Observation
M o v i n g
R a n g e
151413121110987654321
16
12
8
4
0
__ MR=5.21
UC L=17.04
LCL=0
5
1
I-MR Chart of TemperatureI & MR Chart of AHT
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Discrete data Control Charts
P Control Chart
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A call center receiving calls will have to closed within specified SLA. Team hascollected data every hour to monitor process control
Defective is defined as call not closed within SLA
P Control Chart : Minitab Output
Minitab gives the following output:
P Chart of P defectives
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Sample
P r o p o r t i o n
24222018161412108642
0.4
0.3
0.2
0.1
0.0
_ P=0.0944
UCL=0.2906
LCL=0
P Chart of P defectives
Tests performed with unequal sample sizes
Points to Remember in Control Charts
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Do not apply SPC tools to processes that are known to be out on control
Do not compare control limits with specification limits
Do not ignore out -of-control signals if your Y is meeting the specifications & X ismeeting the operating limits
Ensure that observations are independent of each other
Its quite possible that X is under control, but Y is out -of-specs
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Re-compute Process Baseline & Verify AgainstTarget
Validating Process Improvements
Two Sample T Test and CI: Cost / Indent Status (Control Phase)
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Two-Sample T-Test and CI: Cost / Indent, Status (Control Phase)
Two-sample T for Cost / IndentSE
Status N Mean StDev MeanBefore 7 300.7 47.6 18Improved 7 154.1 61.6 23
Difference = mu (Before) - mu (Improved)Estimate for difference: 146.795% lower bound for difference: 93.8T-Test of difference = 0 (vs >): T-Value = 4.98 P-Value = 0.000 DF = 11
I-MR charts
( stage wise )
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How to Sustain Process Improvement
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Control Plan
Training & Communication
Project handovers with completed project dockets
Database of vital Xs
QFD / VOC
FMEA / Fishbone
Six Sigma Project Audits
Process Owners
SOPs
DMAIC SUMMARY: Problem Solving Flow
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BusinessProblem
StatisticalProblem
StatisticalSolution
BusinessSolution
MEASURE ANALYZE IMPROVE CONTROL
P R O J E C T
Six Sigma resource in Wipro Intranet
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Q City : http://qcity.wipro.com:8080/hive/index.jsp
Six Sigma resource in KM
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KM Link : http://knetsites.wipro.com/sites/1010/Pages/Default.aspx
Process Excellence Wipro Wayhttp://wiproway.wipro.com/default.asp
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WI MQ Mentor List
Name LOB Region Email id
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Sachin Agarwal [ MBB ] Services Common HO - BLR [email protected]
Jyoti Prakash [ LBB ] Services Common HO - BLR [email protected]
Priya Mahadevan [ LBB ] MIT HO - BLR [email protected]
Rashmi Salgar [ BB ] ES & SI HO - BLR [email protected]
Sumathi Rajesh [ BB ] Regional MQ S2 [email protected]
Bhakti Gokhale [ BB ] Regional MQ West [email protected]
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Improvement is a Journeyand Together We Will
Improvement is a Journeyand Together We Will
Thank You