Lean Six Sigma
Green Belt
training
programme
Lean Six
Sigma
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2
Lean Six Sigma Green Belt Training Flowchart
Module-1
Overview of Lean Six Sigma
& Digital Transformation
Stakeholders in LSS
organisation
•
Evolution, Origin & Growth of
Six Sigma
Contribution of Quality Gurus
Digital Transformation
& Significance of LSS in Digital
transformation.
•
Apex Council​
•
Champion or Sponsor​
•
Process owner​
•
Master Black Belt​
•
Black Belt​
•
LSS success story and benefits
•
Green Belt​
•
Overview of DMAIC Framework
•
Team Members​
•
Demystifying meaning of Six
Sigma
•
•
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Document Classification: KPMG Public
3
Lean Six Sigma Green Belt Training Flowchart
Module-2
Voice of Customer(VOC)
Project Charter
Process Mapping
•
Understanding Customer
•
Business Case
•
SIPOC
•
Gather VOC
•
Problem Statement
•
Sub-process mapping
•
Affinity Diagram
•
Goal Statement (SMART)
•
Sub-process examples
•
Kano Model
•
Project Scope/Milestone
•
Process Mapping Guidelines
•
VOC to CTQ
•
Roles and Responsibilities
•
SIPOC (Group Exercise)
•
Group exercise on the
identifying CTQ
•
Sample Project Charter
•
Simulation on Case study
•
Group exercise for Project
Charter
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Document Classification: KPMG Public
4
Lean Six Sigma Green Belt Training Flowchart
Module-3
Understanding
Data
Introduction to
Minitab
Measurement
System Analysis
Data Collection &
Sampling
Baseline
Performance
•
Types of Data
•
•
Gage R & R
•
•
•
Measures of
Central Tendency
•
Attribute Agreement
Analysis
Data Collection
Plan
Six Sigma
Calculation
•
•
Operational
Definition
Power BI
Introduction
•
Process Capability
[Cp, Cpk]
•
Extract Data
•
Defective/ Defects
[PPM, DPMO]
•
Data Modelling
•
Data Visualization
•
Dashboard in
Power BI
•
Measures of
Dispersion
•
Introduction to
Minitab
Demonstration of
graphical tools and
Measurement
System Analysis
on Minitab
•
Demonstration of
MSA on Minitab with
example
•
Sampling Overview
•
Sampling Methods
•
Determining
Sample Size
•
Data Distribution
•
Histogram and Box
Plot
•
Six Sigma
Interpretation
•
Simulation on Case
study.
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Data Visualisation
(Overview)
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5
Lean Six Sigma Green Belt Training Flowchart
Module-4
Process Door
Approach
• Process Analysis
• Lean (Definition &
History)
• Principles of Lean
Thinking
• Value Stream Mapping
• TIMHWOOD
Data Door Approach
• Cause Analysis
• Brainstorming –Cause
and Effect Diagram
• Segregation of Causes
• Pareto Diagram
• 5-Why Analysis
Statistical Based
Decision Making Hypothesis Testing
• Introduction to
Hypothesis
• Test of Means
(1 sample t-test/z-test, 2 sample
t-test, paired t-test, ANOVA)
• Test of Proportions
(1 proportion test, 2 proportion
test, Chi- Square test)
Statistical Based
Decision Making Correlation and
Regression
Artificial Intelligence
• Introduction of AI.
• Introduction of ML
• Scatter Plot
• Introduction to
correlation and simple
linear regression
• Demonstration on
Minitab
• Demonstration on
Minitab
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6
Lean Six Sigma Green Belt Training Flowchart
Module-5
Idea Generation
Digital
Improvements
•
Brainstorming
•
Channeling, Analogy,
Anti- solution and
Brain writing
•
IOT
•
Digital Twin
•
Robotic Process
Automation
Solution Selection
Solution with Lean
•
Payoff Matrix
•
5S
Change, Risk Proofing
& Implementation
•
Screening against
must be
•
Kanban
•
Change Management
•
SMED
•
Force Field Analysis
•
Criteria Based Matrix
•
Spaghetti chart
•
FMEA with example
•
Nominal Group
Technique
•
KAIZEN
•
Implementation Plan
•
Poka Yoke
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7
Lean Six Sigma Green Belt Training Flowchart
Module-6
Control
SPC
•
Why Control?
•
Variable Control charts
Process Management
System
•
What to Control?
•
Attribute Control charts
•
Documentation and
standardization
•
Monitoring
•
Response plan
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Project Closure
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8
Module-1
Overview of Lean Six
Sigma & Digital
Transformation
Lean Six Sigma Green Belt Training Flowchart
Module-1
Overview of Lean Six Sigma
& Digital Transformation
Stakeholders in LSS
organisation
•
Evolution, Origin & Growth of
Six Sigma
Contribution of Quality Gurus
Digital Transformation
& Significance of LSS in Digital
transformation.
•
Apex Council​
•
Champion or Sponsor​
•
Process owner​
•
Master Black Belt​
•
Black Belt​
•
LSS success story and benefits
•
Green Belt​
•
Overview of DMAIC Framework
•
Team Members​
•
Demystifying meaning of Six
Sigma
•
•
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10
Module-1 Learning Objectives
By the end of this module participants will be able to learn:
✓ To be able to describe the history and benefits of different methodologies of Lean and Six Sigma
✓ Recognize the significance of integrating Lean Six Sigma principles into digital transformation journey
of a business
✓ Recognize the importance of Lean Six Sigma in Digital Transformation to deliver business benefits
✓ To define the DMAIC Framework and its phases
✓ To know different Stakeholder of Lean Six Sigma Organization
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11
Overview of Lean
Six Sigma & Digital
Transformation
Evolution of Six Sigma
What does Quality Mean?
❖
Detecting and correcting mistakes in the product such that it meets
compliance standards.
OR
❖
Preventing defects in the first place through manufacturing controls
and product design such that it meets performance standards.
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Evolution of Six Sigma
•
Late 1970s - Motorola started experimenting with problem
solving through statistical analysis.
•
Motorola started Six Sigma approach to achieve it’s one of
the top ten corporate goal of improving the quality by ten
times within five years in 1981.
•
The term “Six Sigma” was coined by Bill Smith, an engineer
with Motorola.
•
1986 - Motorola officially launched it’s Six Sigma program
as follows:
The real problem at Motorola is that our quality stinks
……1979, Art Sundry
A product found defective and corrected during
manufacturing had high probability of failing during early use
by customer
……1985, Bill Smith
•
Improve quality 10 times by 1989.
•
Improve quality 100 times by 1991.
•
Achieve six sigma (3.4 DPMO) performance by 1992.
•
Motorola won the first Malcolm Baldridge National
Quality Award in 1988.
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Origin of
Six Sigma
Motorola gained a return
of USD 800 million in two
years
Beginning of a new era at Motorola..
•
Improve the quality.
•
Lower production cost.
•
Lower production time.
•
Focus on how the product was designed and made.
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Growth of Six Sigma – General Electric (GE)
•
Jack Welch launched Six Sigma at GE in Jan,1996.
•
1998/99 - Green Belt exam certification became the criteria
for management promotions at GE.
•
2002/03 - Green Belt certification became the criteria for
promotion to management roles at GE.
•
Scope of six sigma initiative has changed from
‘manufacturing’ to the entire business – service, product
design and innovation.
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16
Contribution of Quality Gurus
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Contribution of Quality Gurus
Dr. Walter Shewhart (1891-1967)
❑ Known for framing the problems of failures in terms of “assignable causes” and
“chance cause” variation.
❑ Known for the introduction of the SPC – control charts as a tool for distinguishing
between assignable and chance cause variation.
❑ Invented control charts which are widely used across industries to monitor
processes and to determine when there are changes in a process.
❑ Known for the introduction of the continuous improvement cycle – Plan –Do –
Check –Act (PDCA).
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Contribution of Quality Gurus
Dr. W Edward Deming (1900-1993)
Dr. Joseph M Juran (1904-2008)
❑ Made a significant contribution to Japan’s reputation for innovative, high-quality
products and for its economic power.
❑ Made a significant contribution to Japan’s reputation for innovative, high-quality
products and for its economic power.
❑ Championed the work of Walter Shewhart including statistical process control,
operational definitions and “Shewhart Cycle" which had evolved into PDSA (PlanDo-Study-Act).
❑ Known for Juran Trilogy – quality planning, quality control and quality
improvement.
❑ First to apply the work of Vilfredo Pareto to quality issues - “vital few and trivial
many”.
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Contribution of Quality Gurus
Dr. Kaoru Ishikawa (1915-1989)
Philip B Crosby(1928-2001)
❑ Considered as a key figure in the development of quality initiatives in Japan,
particularly the quality circle.
Philip Crosby is known for his four absolutes of quality management:
❑ Best known for the Ishikawa or fishbone or cause and effect diagram often used in
the industrial processes analysis.
❑ Translated, integrated and expanded the management concepts of W. Edwards
Deming and Joseph M. Juran into the Japanese system.
❑ Quality means conformance to requirements.
❑ Quality comes from prevention.
❑ Quality performance standard is zero defects.
❑ Quality measurement is the price of non-conformance
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What is Digital Transformation
Digital Transformation is a Process of implementing Digital Technologies by an organization to create a new
product or services or even modify the current way of doing Business. It can be a holistic Transformation of
Business Model through which organization works.
Examples:
•
IOT-Enabled Services.
•
Digital Twins.
•
Automation and Robotics e.g.: Robotic Process
Automation(RPA)
•
3-D Printing.
•
Cloud Computing and Digital Platforms.
•
Augmented Reality(AR) and Virtual Reality.
•
Remote Work and collaboration Tools.
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Lean Six Sigma principles and strategy provide a defined approach
to waste reduction, and continuous improvement. This makes it an
important framework for organizations focusing on digital
transformation
•
Value Stream Mapping (VSM), can be used to identify the Nonvalue-added activities and bottleneck as enabler for digital
solution.
•
KANBAN, helps visualize work, track progress and mange
workflow, it can improve efficiency and transparency for digital
work.
•
IOT/Digital Twins, can help to predict the conditions of process.
•
Robotic Process Automation (RPA), it can reduce the manual
error and improve efficiency of process
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Digital
Transformation
Lean Six Sigma
Lean Six Sigma can be used to support Digital Transformation
in a number of ways.
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22
Benefits of Lean Six Sigma for Digital Transformation
Streamlined Processes and Waste Reduction
Enhanced Quality and Reliability of Digital Solutions
Accelerated Time-to-Market
Improved Customer Experience and Satisfaction
Data-Driven Decision Making
Cultural Transformation and Change Management
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Success Story General Electric (GE)
Industry:
Diversified conglomerate, with a focus on aviation, healthcare,
power, renewable energy, and more
Objective:
In order to increase the dependability and decrease flaws in its
turbines and generators, GE set out to do both. The objective
was to use data analytics and digital transformation to optimise
operations, decrease downtime, and improve customer
happiness.
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Implementation step
1
Data Integration and Analysis: GE collected operational data from its Power plant equipment’s with help of
Internet Of Things(IOT) and sensors and used Machine learning and Analytics to analyze the data to know equipment
performance and chances of defects.
2
Predictive Maintenance: With analysis of real time data from IOT devices and Sensors on their Equipment, GE
3
Remote Monitoring and Diagnostics: GE created remote monitoring capabilities to diagnose problems and
4
5
used predictive analytics algorithm to detect failures before they appeared and can schedule the maintenance plans.
performance of the equipment from a virtual setup.
Digital Twins: GE developed Digital Twins for there Turbines, Pumps, motor and other Power generating machinery
to replicate the Physical assets and compare the performance with real time data form IOT and sensors which help it to
predict the issues and gave scope for preventive maintenance.
Collaboration and Knowledge Sharing: GE with the help of Collaboration and Knowledge sharing tools and
platforms, promoted communication, information sharing among their employees across the Globe.
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Result and Benefits
1. Improved Equipment Reliability:
By implementing predictive maintenance and remote monitoring, GE significantly
improved the reliability of their power generation equipment.
2. Enhanced Operational Efficiency:
The combination of remote monitoring, predictive maintenance, and digital twins
led to improved operational efficiency.
3. Proactive Decision-Making:
The use of digital twins provided GE with valuable insights into equipment
behavior and performance.
4. Remote Support and Expertise:
Digital collaboration tools enabled GE's engineers and experts to provide remote
support and share their expertise with field personnel and customers.
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DMAIC approach
01. Define
What are customer
expectations of the process?
02. Measure
What is the frequency of
defects?
03. Analyze
Why, when and where do
defects occur?
04. Improve
How can we fix the
process?
05. Control
How can we make the
process stay fixed?
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Multiple meanings of Sigma
Standard deviation
Process capability
Management philosophy
• View processes/ measures
completely f rom a
customer point of view.
• Continual improvement.
• Integration of quality and
daily work.
• Satisfying customer
needs profitably.
•
The Greek symbol
‘sigma’ which means
standard deviation.
• A statistical measure of
process’s ability to meet
customer requirements
(CTQs).
•
Is a measure of
variation.
• Process Sigma= 6s
equates to 3.4 defects per
million opportunities.
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Relationship between Sigma Levels and Defects Per Million Opportunity
(DPMO)
6
3.4 DPMO
5
4
230 DPMO
6,210 DPMO
2
3
3,08,500 DPMO
66,800 DPMO
1
6,91,400 DPMO
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Stakeholders in
LSS organization
Six Sigma Team
01
02
03
04
05
06
07
Apex Council
Champion or Sponsor
Process owner
Master Black Belt
Black Belt
Green Belt
Team Members
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Apex Council
Top Management:
❑ Accountable for Six Sigma business results.
❑ Develop a strong case for Six Sigma.
❑ Plan and actively participate in implementation.
❑ Create a vision and market “change”; Become a powerful advocate.
❑ Set clear , SMART (Specific , Measurable , Achievable , Realistic ,
Time bound) objectives.
❑ Hold itself and others accountable.
❑ Demand specific measures of results.
❑ Communicate results (including setbacks).
❑ Helping to quantify the impact of Six Sigma efforts on bottom line.
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Champion or Sponsor
Senior Manager:
❑ Oversees a Six Sigma project.
❑ Is accountable to the Apex Council.
❑ Sets rationale and goal for project.
❑ Be open to changes in project definitions.
❑ Find resources (time, support, money) for team.
❑ Help the team overcome roadblocks; smoothen implementation.
❑ Focus on data-driven management.
❑ Identify and recruit other key players.
❑ Assist in identifying and developing training materials.
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Process Owner
Functional Head:
❑ Implements solutions through Black Belts and project teams.
❑ Provide resources and helps resolve conflicts.
❑ Accountable to the Apex Council.
❑ Owns end-to-end process.
❑ Sets goals for projects.
❑ Project review: timeline and project is on track.
❑ Responsible for holding the gains.
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Master Black Belt
Six Sigma Coach:
❑ Advise and mentor Black Belts and teams.
❑ Communicate with champions and apex council.
❑ Establish and adhere to a schedule for projects.
❑ Deal with resistance to Six Sigma.
❑ Resolve team conflicts.
❑ Estimate, measure and validate savings.
❑ Gather and analyze data on team activities.
❑ Plan and execute training.
❑ Help teams promote and celebrate successes.
❑ Document overall progress of Six Sigma.
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Black Belt
Facilitator:
❑ Six Sigma implementation experts with the ability to develop,
coach, and lead multiple cross-functional process improvement
teams.
❑ Use tools to quickly and efficiently drive improvement.
❑ Facilitate to keep team focused on the project objective.
❑ Ensure that the Six Sigma methods are followed.
❑ Help teams learn and understand Six Sigma tools and techniques
through regular project reviews.
❑ Responsible for the ultimate success of the project.
❑ Trains and develops Green Belts.
❑ Spread Six Sigma awareness throughout the organization.
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Green Belt
Project Team Leaders:
❑ Lead and Execute Six Sigma as part of their daily jobs.
❑ Keep the project team focused on the project goal.
❑ Extract equal participation from all team members. Counsel
nonparticipating team members and motivate them to participate.
❑ Ensure discipline of Team Meetings is followed and that every
meeting starts with an Agenda. Ensure MOM is distributed the
same day.
❑ Regularly follow up with team members to ensure that assigned
tasks are completed on time.
❑ Manage conflicts and seek intervention of Process Owner /
Champion if necessary.
❑ Dual responsibility of being process experts as well as trained
resource on Six Sigma methods and quality tools.
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Team Members
Process Experts:
❑ Team members are vital for success.
❑ Good knowledge of product, process, customer.
❑ Willing to work in teams.
❑ Time to work on projects.
❑ Active in Data collection.
❑ Responsible for improvement.
❑ High Participation.
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Classroom Quiz
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Module-2
Define Phase
Lean Six Sigma Green Belt Training Flowchart
Module-2
Customer and Voice of
Customer(VOC)
Project Charter
Process Mapping
•
Business Case
•
SIPOC
•
Understanding Customer
•
Problem Statement
•
Sub-process mapping
•
Gather VOC
•
Goal Statement (SMART)
•
Sub-process examples
•
Affinity Diagram
•
Project Scope/Milestone
•
Process Mapping Guidelines
•
Kano Model
•
Roles and Responsibilities
•
SIPOC (Group Exercise)
•
VOC to CTQ
•
Sample Project Charter
•
Simulation on Case study
•
Group exercise on the
identifying CTQ
•
Group exercise for Project
Charter
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Module-2 Learning Objectives
By the end of this module participants will be able to learn:
✓ How to identify customer and capture their voice
✓ Recognize the significance of VOC & CTQ
✓ Use categorization and prioritization techniques
✓ Demonstrate how to derive CTQ, on the basis of VOC.
✓ Use Affinity model and Kano Model to categorize as well as prioritize VOCs
✓ Demonstrate how to prepare project charter
✓ Learn how to prepare process map (SIPOC)
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Customer and
Voice of
Customer(VOC)
Understanding of Customer
Customers can be Internal or External
❑ Define products or services provided to customer.
❑ Identify related process.
❑ Are your customers – External and (or) Internal ??
Process
Hiring Technical
Personnel
Deliverable Person Placed in
Customer (Internal)
the Position
Directors & Managers
Process
Accounts
Payable
Deliverable: Payment of Invoices
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Customer
(External) Suppliers
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What does your customer need?
Before you approach to a business problem, ask these questions:
❑ How does the customer view my process?
❑ What does the customer look at to measure my performance?
❑ What does the customer need from me to fulfill his process?
The approach towards any problem must be “Outside-In”, that is view the problem from customer’s perspective.
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45
Gather Voice Of Customer (VOC)
Key Considerations In Collecting Customer Data:
❑ Collector’s bias may affect what is heard.
❑ What contact/relationship do you have with the customer?
❑ What are your time constraints?
❑ What budget is available?
❑ How much certainty to do you need to move forward with the
project?
❑ Ensure customer expectations are aligned with our
intentions/actions.
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46
Gather Voice Of Customer (VOC)
Identify customer
segments that need to be
targeted
How?
Gather verbatim VOC and
determine service quality issue
What?
• List your customers
• Review existing VOC data
• Define customer segments
• Decide on what to collect
• Narrow the list of customers
• Use appropriate tools to gather VOC
– Surveys
– Interviews
– Be a customer
– Focus group
– Customer observation
– Listening posts
– Competitive comparison
• Gather VOC
• Collect data
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Tools for analyzing VOC
How do I analyze
these countless
VOCs ?
Affinity Diagram(> than 10 VOC)
Kano Model (up to 10 VOC)
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Affinity Diagram
❑ Record each VOC on a post it note in bold letters.
❑ Without talking, sort the ideas simultaneously as a team
into 5 –10 related groupings.
❑ For each grouping create summary or header cards using
consensuses.
❑ Draw the final affinity diagram connecting all finalized
header cards with their grouping.
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Affinity diagram – An Example
Flexible product
Easy process
Low interest rate
Easy application
Variable terms
Easy access to capital
All charges clearly stated
Quick decision
Availability
Personal interface
Advice/consulting
Will come to my facility
Knowledgeable reps
Knows about my
finances
Available outside normal business
hours
Professional
Knows about my
business
Available when I need to talk
Friendly
Makes finance
suggestions
Pay back when I want
Can apply over phone
Responsive to my calls
Make me feel
comfortable
Cares about my
business
No prepayment
penalties/ charges
Know status of loan during
application
Talk to one-person
Patient during
process
Has access to experts
Pre-approved credit
Know status of loan (postapproval)
Available on chat
Calm during process
Provides answers to
questions
Variable terms
Preference if bank
customer
Schedule as per availability
Professional
Calls if problems arise
OrganizeVOCinto broadcategories
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Kano Model
Purpose:
❑
To identify & prioritize the full range of the customers needs.
❑
Kano model helps to describe which needs, if fulfilled contribute to customer
dissatisfaction neutrality or delight.
❑ Kano Model Identifies:
1.
Must be needs - Critical to customer expectation.
2.
More is better – Critical to customer satisfaction.
3.
Delighter – Converting wants to needs.
How to built?
❑
Gather sorted customer needs. Classify the needs into
▪
3 Categories:
1.
Must be
2.
More the better
3.
Delighters
If there is insufficient data to enable the classification, collect addition data on VOC.
Prioritize the customer needs to develop the CTQ.
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Prioritizing VOC for CTQ Identification – Kano model
SATISFACTION
DELIGHTERS
+
MORE THE BETTER
(One-dimensional)
INNOVATION
COMPETITIVEPRIORITY
• Free upgrades
• Individual movies and games
• Special staff attention/services
•
Computer
plug- ins
(power sources)
DYSFUNCTIONAL
• Seat comfort
• Quality of refreshments
• Friendliness of staff
• Baggage speed On-time
arrival
+
-
FUNCTIONAL
MUST BE
CRITICAL PRIORITY
-
• Safe arrival
• Accurate booking
• Baggage arrives with passenger
• 99 per cent system uptime
DISSAT ISFACTION
KANO MODEL HELPS TO PRIORITIZE OUR EFFORTS TOWARDS SATISFYING CUSTOMERS
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Translating VOC into CTQs
V OC
Identify customer
segments that need to be
targeted
C T Qs
Gather verbatim VOC and
determine service quality issue
Translate to needs statement and
develop a CTQ - project Y metric
output characteristic
CTQs: Critical To Quality Characteristics
A specific measurable attribute of the output that is a key requirement for customer satisfaction
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Example: Translating Voice of Customer (VOC) to Critical To Quality (CTQ)
Verbatim
Specific need
CTQs
•
‘You take too much time in getting back to me!’
•
Quick response.
•
•
"Your bank takes forever to process the loan"
•
Quick processing time
Process turn around time not more than10
minutes.
•
Turn around time for loan processing < 72
hours
What gets measured gets managed… help ensure measurable CTQs
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Classroom Quiz
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Project Charter
What is a Project Charter?
One of the most important things necessary to get a team
started on a footing is a charter.
A Charter:
❑
Clarifies what is expected of the project.
❑
Keep the team focused.
❑
Keeps the team aligned with organizational priorities.
❑
Transfers the project from the Champion to the Improvement Team.
❑
Used as a tool by the Apex Council to review project progress.
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Elements of a project charter
Goal statement
Milestone/ project plan
What is the estimated target for improvement?
Key milestones/ timelines/detailed plan
Resources/ team members Roles
Business case/
Benefits
03
01
Does project “Y” link to
business Y’s?
02
Who are the key resources? What will be the
roles of BBs/ GBs/Sponsor/MBB’s
05
04
06
Project scope
Clearly defining project’s In scope and Out of Scope
Problem/ opportunity statement
What is the problem/opportunity of improvement?
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Business case
A Good Business case should answer these questions:
❑ Why is this project worth doing?
❑ Why is it important to do now?
❑ What are the consequences of not doing this project?
❑ What activities have higher or equal priority?
❑ How does it fit with business initiatives and targets?
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Problem Statement
The purpose of the Problem Statement is to describe what is wrong -
Description of the “pain”
❑
What is wrong or not meeting our customer’s needs?
❑
When and where does the problem occur?
❑
How big is the problem?
❑
What’s the impact of the problem?
Key Points / Potential Pitfalls
❑
Is the problem based on observation (fact).
❑
Does the problem statement prejudge a root cause?
❑
Can data be collected by the team to verify and analyze the problem?
❑
Is the problem statement too narrowly or broadly defined?
❑
Is a solution included in the statement?
❑
Is the statement blaming any person or function?
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Problem Statement - Examples
Example 1
Poor Statement
Because our customers are dissatisfied with our service, they are late
paying their bills.
Improved Statement
In the last 6 months (when) 20% of our repeat customers – not first
timers (where) – were over 60 days late (what) paying our invoices.
When surveyed, all these customers reported extreme dissatisfaction
with our service (what). The current rate of late payments is up from
10% in 1990 and represents 30% of our outstanding receivables (how
big). This negatively affects our operating cash flow (impact).
Example 2
Poor Statement
Customers are unable to access the call center half the time leading to
high revenue losses.
Improved Statement
During the year 2003, (when) 40% of our customers (extent) were
unable to access the call center at the first attempt (what). This causes
dissatisfaction to our customers and a loss of revenue opportunities to
the organization (impact).
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Goal Statement
❑
The Goal Statement defines the team’s improvement objective.
❑
Define the improvement the team is seeking to accomplish.
❑
Must not assign blame, presume cause, or prescribe solution!
❑
Goal Statement has four parts:
1.
Start with a verb (e.g., reduce, eliminate, control, increase).
2.
Focus of project (cycle time, accuracy, etc.).
3.
Has a definite Target (by 50%, by 75%).
4.
Has a definite deadline (completion time).
01
02
03
04
05
Specific
Measurable
Achievable
Realistic
Time bound
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Project Scope and Milestones / Project Plan
Project Scope
❑
What process will the team focus on?
❑
What are the boundaries of the process we are to improve? (Start
and End points of the process).
❑
What (if anything) is out of bounds for the team?
❑
What (if any) are the possible constraints?
❑
What is the time commitment expected of team members?
❑
What will happen to our ‘regular jobs’ while we are doing the project?
Milestones (Project Plan)
❑
It is a detailed project plan with key steps and target completion
dates.
❑
Tied to phases of DMAIC process, with defined tollgate reviews.
❑
Aggressive and Realistic (no ready-made solution).
❑
Documented, shared with all project team members and Champion,
and updated regularly.
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Sample Project Charter:
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Classroom Quiz
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Process Mapping
Process Mapping
Process is a collection of activities that takes one or more inputs and transforms them into outputs that are of value to
the customer.
The Business Process
Inputs
Outputs
Supplier(s)
Customer(s)
Types of Process Maps:
1.
SIPOC (Supplier-Input-Process-Output-Customer)(L1)
2.
Value Stream Mapping (L1,L2)
3.
Sub Process Map (L3,L4,L5)
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SIPOC
Supplier: The provider of inputs to your process
Input: Materials, resources or data required to execute your process
Process: A collection of activities that takes one or more kinds of input and creates output that is of value to the
customer
Output: The products or services that result f rom the process
Customer: The recipient of the process output –may be internal or external
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SIPOC
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SIPOC Example
Let us look at an example of Purchase Order (P.O.) Requisition to final Approval
SUPPLIER
INPUT
PROCESS
OUTPUT
CUSTOMER
S
I
P
O
C
User Department
Stock List
Generate Purchase Requisition
Purchase Requisition
User Department HOD
User Department HOD
1. Purchase Requisition
2. Budget
Approve Purchase Requisition
Approved Purchase
Requisition
Procurement
Procurement
1. Approved Purchase
Requisition
2. List of Approved Vendors
Raise Request for Quotation
Request for Quotation
Approved Vendors
Approved Vendors
Request for Quotation
Receive Quotations
Vendor Quotations
Procurement
1.
2.
Vendor Quotations
Techno Commercial Evaluation of
Received Quotations
Shortlisted Quotations
Procurement
User Department
Procurement
Quotation of Preferred Vendor
Procurement
Shortlisted Quotations
Select Preferred Vendor
Procurement
Quotation of Preferred
Vendor
Prepare Purchase Order
Purchase Order
Signing Authority
Signing Authority
Purchase Order
Approve Purchase Order
Approved Purchase Order
Preferred Vendor
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Procurement
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Sub-process mapping
Here are some guidelines on building a sub process
map. These are not absolute - but they should help you
avoid some of the pitfalls of process mapping:
❑
Focus on ‘As is’ – To find out why problems are occurring in a
process, you need to concentrate on how it’s working now.
❑
Clarify boundaries – If you’re working from a well-done high-level
map, this should be easy. If not, you will need to clarify start and
stop points.
❑
Brainstorm Steps – It is usually much easier to identify the steps
before you try to build the map.
❑
Starting each step description with a verb (e.g., ‘collate orders’;
‘review credit data’) helps you focus on action in the process.
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Examples: Sub-process mapping
Process flowchart
Deployment orcross-functional map/ swim-lane
flowchart
Top down flowchart
Planning for a party
1.0
Determine
partysize
1. 1
No
Decide
on budget
1. 2
Yes
Decideon
guest list
Dept 1
2.0
Dept 2
Dept
3. 0
Find
location
Invite
guests
2. 1
Decide
theme
Creates List
3. 1
Complete
invitations
2. 2
Select
location
Writes invitation
Is the guest
list Covered
3. 2
Yes
Sends out the invitation
No
Send
invitations
Completes list
Invitationtask completed
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Examples: Cross Functional Process mapping
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Process mapping guidelines
❑
Define business process to be reviewed – name it – agree on beginning and end
of process – bound it.
❑
Refer to CTQ work to identify primary outputs, the customers who receive them,
and the customers’ CTQs.
▪
Use nouns for outputs (e.g., sales call, proposal, etc.).
▪
Use adjectives for CTQs (.e.g., timely, knowledgeable, accurate).
❑
Identify the process steps using brainstorming and affinity techniques:
▪
Write large one step per card.
▪
Do not try to establish order.
▪
All steps should begin with a verb.
▪
Do not discuss process steps in detail.
❑
Use brainstorming and affinity techniques to identify critical inputs which affect
the quality of the process.
❑
For each critical input, identify the ‘supplier’ who provides it.
❑
Validate to be sure the map represents the situation as it really is today (the ‘as
is’ map) – not how you think it is, or how it should be.
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Points to Remember
Points to Remember:
• People who work on the process know it the best. Involve people who know (focus on)
the ‘as Is process’
• Decide, clarify and agree upon process boundaries
• Use group activities like brainstorming
– Use verb - noun format (e.g., Prepare contract not contracting)
– Do not aim at the person taking care of the activity
• Respect the boundaries
• Do not start ‘problem solving’
• Validate and refine before analyzing
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Classroom Quiz
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Simulation Exercise based on Case Study-1
LEAN SIX SIGMA CAPSTONE PROBLEM SCENARIOS
Scenario 1: The Problem of “Not on Time” for Delivery
ABC Ltd. is a package delivery service for homes and small businesses. ABC specializes in packages 50
pounds or less and has a full-price rebate policy for any delivery made outside the customer-designated
15-minute window. ABC advertising proudly states, "Delivery at your convenience, not ours." Consumer
can avail services by placing an order on their application, "ABC to serve" available at play store or on their
official website by entering the basic details and submitting order for making the on-time delivery.
ABC has facilities at multiple locations Downtown, Suburbia etc., each servicing customers within a 15mile radius with deliveries made by pick up or bikes based on the Size and distance in which delivery to
be made as per Annexture-1. PDI assures that they deliver the consignment post submission of order at
app. or website to deliver a package at customer designated in a 15-minute window. ABC charges
customers $5 per package plus $1 per pound (50-pound maximum).
On any given day ABC delivers and picks up approximately 50 packages having an average weight of 35
pounds at Downtown location.
Over the weeks, Sales Operators have reported that 35% customers have complained that deliveries have
not been meeting the committed timelines of 15 minutes at Downtown area. In response, Sales Operators
were instructed to remind customers of the ABC price rebate policy. Additionally, a short survey was sent
out to a small group of established customers. Survey results disclosed an appreciation of price rebates,
but a preference for deliveries within the committed timelines.
Task to do after Define phase:
•
Identify VOC.
•
Define CTQ
•
Make a Project charter
•
Develop a SIPOC or Process flow
The organization's leadership team has decided to solve the problem the Lean Six Sigma way using the
DMAIC approach to solve the problem of delayed deliveries and pickup.
Annexture-1:
Weight of consignment
Miles
Modes
20-30
<10
Bike
20-30
10-15
Truck
30-40
>2
Truck
30-40
<2
Bike
40-50
All
Truck
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Simulation Exercise based on Case Study-2
Scenario 2: The Problem of Delay in Account Opening Time in Retail Banking
Bank of XYZ, a major bank receives on an average 2000 new saving account opening customer
application forms every day. 40 operators enter the application forms in a database after cross
checking the CAF (Customer Application Form) with Identity Proof details.
The entries are rechecked against the Identify Proof details by 15 Quality Assessors and further 5%
sample is audited by 3 Quality Supervisors. The sales team promise the account opening within 48
hours from receipt of the CAF. Bank of XYZ usually achieves the account opening within average of
30 hours with a standard deviation of 6 hrs.
Recently, after a significant marketing effort, they started receiving over 3500 CAF, and the % of
defects in the CAF increased far more than the acceptable 10% of total opportunities for error and
processing time of CAF also increased, leading to account opening taking more than target of 48hrs.
The Customer Application has the following sections (Opportunities for Error):
Task to do after Define phase:
•
Identify VOC.
•
Define CTQ
•
Make a Project charter
•
Develop a SIPOC or Process flow
1. Title and Gender of the customer
2. Name of the customer
3. Address of the customer
4. Date
5. Identity Proof No
6. Product Code
7. Email Address
Any incorrect section is considered a defect and must be re-processed.
The Bank is losing $3750 every day primarily on rework and penalties. The customers are also
dissatisfied as the account opening is taking more time than promised. In the wake of the current
business situation, the management team decides to initiate a Six Sigma project to reduce defects
and achieve target account opening time.
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Inference from Define Phase
At the end of the Define phase, we should be able to
identify the problem from all the following perspectives:
✓
Define the problem statement.
✓
Identify your customers.
✓
Identify the CTQ.
✓
Create high-level process map.
✓
Identify team members and business functions required.
✓
Develop a project charter.
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Module-3
Measure Phase
Lean Six Sigma Green Belt Training Flowchart
Module-3
Understanding
Data
Introduction to
Minitab
Measurement
System Analysis
Data Collection &
Sampling
Baseline
Performance
•
Types of Data
•
•
Gage R & R
•
•
•
Measures of
Central Tendency
•
Attribute Agreement
Analysis
Data Collection
Plan
Six Sigma
Calculation
•
•
Operational
Definition
Power BI
Introduction
•
Process Capability
[Cp, Cpk]
•
Extract Data
•
Defective/ Defects
[PPM, DPMO]
•
Data Modelling
•
Data Visualization
•
Dashboard in
Power BI
•
Measures of
Dispersion
•
Data Distribution
•
Histogram and
Box Plot
•
Introduction to
Minitab
Demonstration of
graphical tools and
Measurement
System Analysis
on Minitab
•
Demonstration of
MSA on Minitab with
example
•
Sampling Overview
•
Sampling Methods
•
Determining
Sample Size
•
Six Sigma
Interpretation
•
Simulation on Case
study.
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Data Visualisation
(Overview)
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Module-3 Learning Objectives
By the end of this module participants will be able to learn:
✓ Describe the appropriate tools and techniques for analyzing each type of data.
✓ Evaluate the accuracy and reliability of the measurement system (for continuous data type)
✓ Evaluate the accuracy and reliability of the measurement system (for discrete data type)
✓ Develop and execute a plan that captures the necessary data in a timely and accurate manner with
Sampling technique and Sample size calculation.
✓ Identify the presence of outliers or anomalies in the data, which can help to identify potential
problems in a process.
✓ Determine if the data is normally distributed, which is a common assumption in many statistical
analyses.
✓ Choose appropriate statistical tests or models for analyzing the data.
✓ With Data Visualization, able to Identify areas for improvement and prioritize their Lean Six Sigma
efforts and Communicate their findings to stakeholders and make data-driven decisions.
✓ Establish the baseline of the process performance and ensure that the process is capable of meeting
customer needs.
✓ Use Power-BI to model data and build visualization (Dashboards)
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Understanding
Data
Types of Data – Discrete and Continuous
Discrete (Attribute)Data
Continuous (Variable)Data
•
Data that can be counted is termed as a Discrete or
Attribute data.
•
Data that can be measured (with a unit value) is termed
as a Continuous or Variable data.
•
Binary (Yes/No, Defect/No Defect).
•
•
Ordered categories(1-5).
Continuous data can be broken-down into increments
with infinite number of possible values.
•
Counts.
Examples
Examples
•
Number of incomplete applications.
•
Cycle time (measured in days, hours, minutes, etc.).
•
Count of response with a “5” on survey.
•
Weight (measured in tons, pounds, etc.).
•
Number of Green Belts trained.
•
Currency (Dollar, Cents, Rupees, Peso, etc.)
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Example
Percent defective parts in hourly production
Percent cream content in milk bottles (comes in four bottle container sets)
Time taken to respond to a request
Number of blemishes per square yard of cloth, where pieces of cloth may be of variable size
Daily test of water acidity
Number of accidents per month
Number of defective parts in lot of size 100
Length of screws in samples of size ten from production lot
Number of employees who took leave in the last 5 years
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Why do we need data?
Why Data Type Important ?
❑
Choice of data display and analysis tools.
❑
Amount of data required: continuous data often requires a
smaller sample size than discrete data.
❑
Information about current and historical process
performance.
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Measures of Central tendency
Placement Time for an Analyst’s Positions(in days)
22, 26, 26, 31, 33, 37, 37, 42, 52, 52, 52, 57, 59
Mean or Average
The sum of the values in a data set divided
by the number of values.
Mode
Mean = 40.46 days
Mode = 52 days
The most frequently occurring data value.
Median
The middle observation in the data set that
has been arranged in ascending or
descending order.
Median = 37 days
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Measures of Dispersion
Placement Time for an Analyst’s Positions(in days)
22, 26, 26, 31, 33, 37, 37, 42, 52, 52, 52, 57, 59
Range = Max- Min
Range
Range = 37
The largest data value minus the smallest
data value.
 2=
 2 = 162.67
The average deviation of all data values
from the mean.
The square root of variance is standard
deviation.
2
n - 1
Variance (s2)
Standard Deviation(σ)
 (x - x)
=
 (x - x)
2
n - 1
 = 12.74
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Standard Deviation
Essentially, the standard deviation is representation of the
deviation of individual data values from the sample mean.
Why Standard Deviation?
❑
Unlike the range, the standard deviation considers all the
data values in the sample.
❑
Unlike the variance, the standard deviation has the same
units of measurement as the original data.
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Types of Data Distribution (Graphical)
Bell Shape – The Normal Distribution
Right Skewed (Positively Skewed)
Left Skewed (Negatively Skewed)
Uniform Distribution
Bimodal Distribution
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Histograms
❑ A histogram is a frequency polygon in which data are grouped into classes.
❑ The height of each bar shows the frequency in each class.
Graphs
Simple/
Groups
Histogram
20
20
Frequency
Minitab
12
10
10
4
3
1
0
Days
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Histograms
❑ When creating a histogram, the data must be properly grouped to understand the shape of the data distribution.
❑ For the given sample size, the right number of classes should be used.
Number of Data Points
Number of Classes
Under 50
5-7
50 – 100
6-10
100 – 250
7-12
Over 250
10-20
.
What might you conclude from the histogram?
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What is Normal Distribution?
❑
Normal or Gaussian distribution is a descriptive model that
describes real world situations.
❑
It is defined as a continuous frequency distribution of infinite
range (can take any values not just integers as in the case of
Binomial and Poisson distribution).
❑
This is the most important probability distribution in statistics
and important tool in data analysis.
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Properties of Normal Distribution
1.
It links frequency distribution to probability distribution.
2.
Has a bell shape curve and is symmetric.
3.
It is symmetric around the mean: two halves of the curve are
the same (mirror images).
4.
In a perfectly centered Normal Distribution; mean = median
= mode.
5.
The total area under the curve is 1 (or 100%).
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Test for Normality
A normal curve originates from a histogram. A histogram is a frequency distribution chart showing the number of times a given value of
the parameter we are trying to measuring occurs.
Minitab uses the Anderson-Darling test to determine if a set of data can be treated as normal data.
❖ Interpreting the P-value: The P-value is the probability of getting the particular sample if the population is normal.
P-value < 0.05 means that the chance of getting this sample from a normal population is very small (less than 5%).
Normality Test in Minitab
Minitab
Stats
Basic
Stats
Normality
Test
.999
.99
Null Hypothesis: Data is Normal
Alternate Hypothesis: Data is not Normal
Probabilit
y
.95
.80
.50
.20
.05
.01
Since the P-value > 0.05
We conclude that the data falls a normal distribution.
.001
24.8 25.8 26.8 27.8 28.8 29.8 30.8 31.8 32.8 33.8
C1
Average: 29.9371
StDev: 1.72224
N: 90
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Anderson-D arli ng Normality Test
A-Squared: 0.366
P-Value: 0.427
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Box plot
1.
Box Plot is a graphical tool to display central tendency
(median) and dispersion (range).
2.
Box Plot enables to understand the distribution of data
(quartiles).
3.
Box Plot gives the location of data.
4.
Box Plot enables to get a quick comparison of two or more
processes.
5.
Box Plot is usually used at the initial stages of data analysis.
6.
Box Plot indicates imminent instability in the process,
through illustration of outliers.
We can calculate the Inter Quartile Range (IQR): Q3 – Q1
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Things to look for in a Box plot
1. Are the boxes about equal or different sizes?
2. Do the groups appear normal or skewed?
3. Are there any outliers?
Box Plot in Minitab
Minitab
Graphs
Box
Plot
Multiple
Y
Simple
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Box-Plot using Minitab
Interpretation:
•
Week 1 median is 4.985, and the interquartile range is
4.4525 to 5.5575.
•
Week 2 median is 5.275, and the interquartile range is 5.08
to 5.6775. An outlier appears at 7.0.
•
Week 3 median is 5.43, and the interquartile range is 4.99 to
6.975. The data are positively skewed.
•
The medians for the three weeks are similar. However,
during Week 2, an abnormally wide pipe was created, and
during Week 3, several abnormally wide pipes were created.
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Introduction to
Minitab
Introduction to Minitab
❑ Minitab is a statistical software that is widely used across businesses and industries to solve or analyze complex statistical
problems.
❑ Minitab provides convenient features that
streamline your workflow, a comprehensive
set of statistics for exploring your data, and
graphs for communicating your success.
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Data Window Elements
Project
Manager
Edit Last
Dialog Box
Cut, Copy,
Sessio n
Folder
Paste
Save
Project
Open
Redo, U n d o
Previous, Next
command
Graphs
Folder
Session
W indow
Worksh eet
Folder
Project
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Worksheet Elements
The Data Entry Arrow
There is a data entry arrow
above row 1, which
indicates the direction the
cursor will move after the
“Enter” key is pressed
Column Name Row
Remember
When data is entered into Minitab, the program
will read it as one of the three formats:
•
Numeric
•
Text
•
Date / Time
The column name row is located just
above row 1of the worksheet.
Column names can be up to 31
characters and may contain space
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Validate
Measurement
System
Validate Measurement System
Why do we need to validate Measurement System?
❑
To verify the adequacy of measurement system for Y when
establishing process baseline
❑
To verify the adequacy of measurement system when verifying
causes
❑
To verify the adequacy of measurement system when verifying
solutions.
❑
To verify the adequacy of measurement system when controlling
the X’s
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Validate Measurement System -Objective
To determine what percentage the Total Observed Variation is
due to Measurement Device and Measurement Method in
addition to the true Part to Part variation.
To statistically verify that the current measurement system provides:
❑
Unbiased results.
❑
Minimal variability within the measurement system.
❑
True representative values of the factors being measured.
Key Points
❑
There is no perfect measurement system.
❑
All measurement systems contain variation.
❑
Gage system error within a measurement system is the sum of:
▪
Bias (Accuracy)
▪
Stability
▪
Sensitivity
▪
Repeatability
▪
Reproducibility
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Types of Measurement Errors - Key Definitions
❑
Accuracy: The difference between the observed average of
measurements and the true average of the items measured.
❑
Repeatability: The variation in measurements obtained with a
gage when used several times by one operator while measuring
the identical characteristic on the same sample piece.
❑
Reproducibility: The variation in the average of measurements
taken by different operators using the same gage while
measuring the identical characteristic on the same pieces.
❑
Stability: The variation in the average of at least two sets of
measurements obtained with a gage as a result of time on the
same pieces.
❑
Sensitivity: The ability of the measuring instrument to detect the
smallest unit of change in measured value.
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Key Points
❑
Gage R&R Studies are a method to quantify the repeatability and
reproducibility of a measuring system.
❑
Gage R&R studies are conducted to evaluate a Gage's suitability
for a defined purpose.
❑
Accuracy and Stability are addressed by calibration.
❑
Sensitivity is addressed through ensuring correct Least Count of
measuring instrument.
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Breaking down overall variation
Overall
Variation
Part to Part Variation
Measurement System
Variation
Repeatability
Which variation
component do we want
to be large?
Reproducibility
Operator
Reproducibility
Operator by Parts
Interaction
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Validate Measurement System - Methods
Two methods for Validating Measurement System:
I.
Variable Gage R&R
II.
Attribute Agreement Analysis
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Variable Gage R&R
❑
At least two operators (persons doing the measuring) should
participate. Two or three operators are typical.
❑
At least 10 parts should be measured. The same characteristic is
measured on each part. These are 10 units of the same type
product that represent the full range of manufacturing variation.
❑
Each operator needs to measure each part two or three times.
Parts should be measured in random order.
❑
Parts should be masked so that operator does not realize that he
/ she is measuring the same part number of times.
NOTE:
It is very important that an operator not be aware of his or her earlier
measurement when doing a repeat measurement on the same part.
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Variable Gage R&R – Acceptability Criteria
Study Variation (SV%)
% Contribution
Acceptable Criteria
Based on SD
Based on variance
0% to 10%
Less than 1%
Unconditional Acceptance
10% to 30%
1% to 9%
Conditional Acceptance
Greater than 30%
Greater than 9%
Non-Acceptance
Number of Distinct Categories must be 5 or greater than 5 : NDC >= 5
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Number of Distinct Categories
❑
The number of distinct categories is a metric that is used in gage R&R studies to identify a measurement system's ability to detect
a difference in the measured characteristic.. The number of distinct categories also represents the number of groups within your
process data that your measurement system can discern.
❑
Usually, when the number of distinct categories is less than 2, the measurement system is of no value for controlling the process,
because it cannot distinguish between parts. When the number of distinct categories is 2, you can split the parts into only two
groups, such as high and low. When the number of distinct categories is 3, you can split the parts into 3 groups, such as low,
middle, and high.
Example:
❖
Suppose you weigh different chemicals for your batch process. Your formulation requires 4000 g of Chemical A, 75 g of Chemical
B, and 2 g of Chemical C. If you use a scale with 5-gram increments for all the measurements, the scale would be acceptable for
Chemical A, but would not be precise enough for Chemical B and Chemical C. Therefore, the scale that measures Chemical A
has an acceptable number of distinct categories because the variation in the acceptable weights (3080 g - 4020 g) is much larger
than the variation due to the scale itself.
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Variable Gage R&R using Minitab
Minitab
Stats
Quality
Tools
Gauge
Study
Gauge
R&R
(Crossed)
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Variable Gage R&R using Minitab
Gage R&R Study - ANOVA Method
Two-Way ANOVA Table With Interaction
Source
DF
SS
MS
F
Part
9 2.05871 0.228745 39.7178
Operator
2 0.04800 0.024000
4.1672
Part * Operator 18 0.10367 0.005759 4.4588
Repeatability
30 0.03875 0.001292
Total
59 2.24913
α to remove interaction term = 0.05
Gage R&R
Source
Total Gage R&R
Repeatability
Reproducibility
Operator
Operator*Part
Part-To-Part
Total Variation
VarComp
0.0044375
0.0012917
0.0031458
0.0009120
0.0022338
0.0371644
0.0416019
%Contribution
(of VarComp)
10.67
3.10
7.56
2.19
5.37
89.33
100.00
Source
Total Gage R&R
Repeatability
Reproducibility
Operator
Operator*Part
Part-To-Part
Total Variation
StdDev (SD)
0.066615
0.035940
0.056088
0.030200
0.047263
0.192781
0.203965
Study Var
(6 × SD)
0.39969
0.21564
0.33653
0.18120
0.28358
1.15668
1.22379
Number of Distinct Categories = 4
P
0.000
0.033
0.000
%Study Var
(%SV)
32.66
17.62
27.50
14.81
23.17
94.52
100.00
Measurement System does
not meet the criteria as the
SV% and Contribution %
exceed the criteria
The number of distinct
categories is less than 5,
therefore it is not acceptable.
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Variable Gage R&R using Minitab
Range Chart must be in statistical
control over all operators.
2
If special cause is present, implement counter action then redo
the test.
In this case, the range chart is acceptable
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Breaking down overall variation
Overall Variation
100% = 89.33+ 10.67
Part to Part Variation
Measurement System
Variation
89.33%
10.67% = 3.10% + 7.56%
Repeatability
3.10%
Reproducibility
7.56% = 2.19% + 5.37%
Operator
Reproducibility
Operator by
Parts Interaction
2.19%
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5.37%
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Attribute Agreement Analysis
❑
It is also important to have good repeatability and reproducibility
when obtaining attribute data.
❑
If one operator, for example, decides a unit has an “appearance”
defect and another operator concludes the same unit has no
defect, then there is a problem with the measurement system.
❑
Similarly, the measurement system is inadequate when the same
person draws different conclusions on repeat evaluations of the
same unit of product.
❑
An attribute measurement system compares each part to a
standard and accepts the part if the standard is met.
❑
The screen effectiveness is the ability of the attribute
measurement system to properly discriminate good from bad.
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Attribute Agreement Analysis – Acceptability Criteria
While 100% match is the desirable result in Attribute Agreement Analysis , the following guidelines are frequently used:
Kappa
Guideline
0.90 to 1.00
Unconditional Acceptance
0.80 to 0.90
Conditional Acceptance
Less than 0.80
Non-Acceptance
Key Points:
❑
Before collecting new data, evaluate the gage using MSA for either variable or attribute data.
❑
Before using existing data, try to estimate the trustworthiness of the data.
❑
Don’t delay a Six Sigma project due to a poor MSA. Keep the project moving with non numerical analysis wherever possible, such as
Process Analysis, Waste Identification, identification of Non-Value-Added Activities, and improve the gage as the project goes forward.
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Attribute Agreement Analysis using Minitab
Minitab
Stats
Quality
Tools
Attribute
Agreement
Analysis
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Attribute Agreement Analysis using Minitab
Agreement within Appraisers and Kappa
Values
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Attribute Agreement Analysis using Minitab
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Attribute Agreement Analysis using Minitab
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Attribute Agreement Analysis using Minitab
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Attribute Agreement Analysis using Minitab
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Classroom Quiz
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Data Collection &
Sampling
Data collection plan
❑
Data collection occurs multiple times throughout DMAIC. The
data collection plan described here can be used as the guide for
data collection. This help us ensure that we collect useful,
accurate data that is needed to answer our process questions.
❑
Data collection plan needs to be prepared / referred for data
collection on Y in Measure phase and X’s in Analyze phase.
❑
It is important to be clear about the data collection goals to
ensure the right data is collected. If your data is in the wrong
form or format, you may not be able to use it in your analysis.
❑
Operational definitions help to guide how CTQ will be measured.
The critical factor is that any two people using the operational
definition will be measuring the same thing in the same manner.
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Data collection plan
Why collect data?
• The purpose of
the data
collection
exercise
What to collect?
How to collect?
Collect data
• Identify measures
• Formulate data
• Pilot collection
• Define operational
collection plan
and validation
plan
definitions
• What data to be
collected
• Sampling
strategy
• Train data
• Pilot data
collectors
collection plan
Ensure consistency
& stability
• Develop
measurement
system analysis
• Test and validate
• Monitor and
improvise
Word of caution: Ineffective data leads to ineffective conclusions
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Operational Definition
What is an Operational Definition?
❖
An operational definition is a clear, concise description of a
measurement and the process by which it is to be collected.
Purpose of Operational Definition:
❑
To remove ambiguity: Everyone has a consistent understanding.
❑
To provide a clear way to measure the characteristic.
❑
Identifies what to measure.
❑
Identifies how to measure it.
❑
Makes sure that no matter who does the measuring, the results
are consistent.
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Data collection plan - Example
A format of an excel spread sheet that can be used for creating data collection plans for our projects. The sample data collection plan
below is for the project CTQ, in this example, number of rejections.
However, one should remember that based on C-E diagram and the SIPOC all those Xs which the team feels to have an influence over
the Y should also be included in the data collection plan.
Example of a data collection plan
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Sampling: Overview
❑
The Six Sigma team would always face a question ‘How much
data do we need to have a valid sample?’ Though an important
part of data collection is to obtain a sample of reasonable size, it
is one of many questions to be addressed during the planning
and development of a data collection strategy. Sample size is
just one aspect of a valid data collection activity.
❑
The validity of the data is impacted by many things: For example,
operational definitions, data collection procedures and recording.
❑
In process improvement, there are several questions to keep in
mind relative to sampling:
▪
Is the data representative of the situation or is bias
possible?
▪
Why am I sampling? To improve or control a process or to
describe some characteristic of a population?
▪
What are the key considerations for either a process or
population situation?
▪
What is the approach to sampling (e.g., random,
systematic, etc.) and approximately how many to sample.
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Population and Sampling
An entire set of items is called the Population.
What is Sampling?
❑
The small number of items taken from the population to make a
judgment of the population is called a Sample.
❑
The numbers of samples taken to make this judgment is called
Sample size.
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Sampling Methods
Random Sampling
❑
Samples drawn at random.
❑
That is, at any point in time, each unit in a “lot” has an equal chance of being the
next unit selected for the sample.
❑
Example: Cycle tires produced in the assembly line have a random check of 10 per
cent (QC) on daily production.
Stratified Sampling
❑
An attempt to draw the sample proportionately over the full operating range of the
process.
❑
For example: various batches of material; small and large contracts; all three shifts.
❑
Example: Tyre used with an aircraft is cut open and checked for quality randomly in
each model type
Systematic Sampling:
❑ Includes every kth unit. The formula is
k = N/n (where N is population size and n is the sample size).
Example: Suppose you want to sample 10 houses from a street of 150 houses.
150/10=15, so every 15th house is chosen.
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133
When to Sample?
When to sample?
❑
When to sample?
❑
Collecting all the data is impractical.
❑
High-cost implications due to population study.
❑
Time availability.
❑
Data collection can be a destructive process (crash testing of
cars).
❑
When measuring a high-volume process.
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134
Sampling bias
Avoiding Sampling Bias
❑
Sampling must be representative to enable solid conclusions.
❑
The data must represent the population or process.
❑
There should be no systematic (non-random) difference
between the data you collect or do not collect.
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135
Determining the Sample Size
Three metrics that determine the sample size
Level of confidence(Zc):
Error Margin ()
•
‘“How confident I am that the
result represents the true
•
•
population”
“How accurate is the result
or what are the errors or
uncertainty in my result?”
•
The team needs to be sure of
the adequate representation of
the population with the sample
chosen
•
Error margin reduces as
sample size increases
•
For higher levels of confidence,
the sample size increases
•
Higher the precision, larger
is the sample size required
Standard deviation of the
population ()
•
How much variation is in
the total data
population?
•
As standard deviation
increases, a larger
sample size is needed to
obtain reliable results
Sample size is not dependent on the ‘population size’
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Determining the Sample Size – Continuous Data
The formula for calculating the sample size for continuous data is
2
n=
Zc 

Where:
n = minimum sample size
 = estimate of standard deviation of the population
= Error Margin desired from the sample in units of measurement
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Determining the Sample Size – Example
Suppose you want to estimate the average length of incoming phone calls within 1 minute. Historical data for
the population shows a typical standard deviation of 3 minutes.
How many samples do you need. At 95% Confidence Level
2
n=
Zc 

2
2
n=
1.96 * 3
n=
1
( 5.88 )
1
Thus SampleSize = n = 35 samples
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138
Determining the Sample Size – Attribute Data
For Discrete Proportion data
2
n=
Zc

P(1-P)
Where:
n = minimum sample size
= Error Margin from the sample in units of proportion
P= Estimation of Proportion Defective of Population
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139
Determining the Sample Size – Example
Suppose you want to estimate within 2% the proportion of customers who will buy a new product. Your guess
says that 50% of them will buy. How many samples do you need at Confidence Level of 95%
2
n=
Zc

P(1-P)
2
n=
1.96
0.02
0.5 (1- 0.5)
Thus, Sample Size = n = 2401 samples
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Classroom Quiz
A qr code on a blue background
Description automatically generated
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141
Baseline
Performance
(Continuous Data)
Process Capability (Cp) & Process
Capability Index (Cpk)
What is “Out of Control "process?
❑
An unstable process...
❑
100% of the individual data values are NOT within the spread of natural
variation (+3) of the process.
❑
Few data values fall beyond the +3 limits and are referred to as
“OUTLIERS”.
…is unpredictable
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Common Cause vs. Special Cause
Type of variation
Characteristics
Inherentto the process
• Expected
COMMON CAUSE
• Predictable
• Normal
• Random
Not alwayspresent
• Unexpected
SPECIAL CAUSE
• Unpredictable
• Not normal
• Not random
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144
Process Control vs. Process Capability
Process Control = Stability over time.
Process Capability = Ability of a stable process to meet specifications.
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Control Limits vs. Specification Limits
❑
Control Limits are statistical bounds (the natural bounds of the
data) used to determine process stability.
❑
Specification Limits are applied to individual measurements.
❑
Specification limits are decided by people (voice of the
❑
Statistically Control Limits are equivalent to+3σ.
❑
Control limits are determined by the data (voice of the process).
customer).
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Check stability of data
Check all Data Points are lying within ± 3 Sigma. i.e., within UCL and LCL.
If yes Process is stable. If not, there is a special cause variation.
Checking stability of data in Minitab
Minitab
Stats
Control
Charts
Variable
charts for
individual
I-MR
Chart
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147
What is Process Capability?
❑
Process capability is a simple tool which helps us determine if a
process, given its natural variation, is capable of meeting the
customer requirements or specifications.
❑
Minimal level at which most of the customers will be satisfied
❑
Helps to determine if there has been a change in the process.
❑
Also enables the six-sigma team to determine the percent of the
product/service not meeting the customer requirement.
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148
Measuring the Capability
❑
Sigma is a statistical unit of measure, used to denote the value of
standard deviation in a set of variable data.
❑
For a given process, Sigma Level is a metric that indicates how
well a process is performing.
❑
In Six Sigma, the capability of a process to meet customer
specification is captured by the Process Sigma Level.
❑
Hence as value of Standard Deviation (σ) decreases the Process
Sigma Level increases.
Let us look at different scenarios in a process.
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149
Stable and Capable Process
❑
A stable process provides the most reliable estimates of process capability.
❑
A process is said to be capable when the ± 3σ points of the distribution of individual measurements are contained well within the
specification limits.
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150
Stable and Capable Process
What can be said about the capability of these four processes?
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How capable is the process?
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Steps for evaluating process capability for variable data
❑
Assure the data is normally distributed.
❑
Estimate the average and standard deviation of the process.
❑
Determine the process’ potential capability.
❑
Quantify process performance.
Six Sigma Team can also:
❑
Communicate base line to process owner
❑
Help process owner with data collection tools and information to
validate
❑
Sponsor and champions can be informed about the current state
of the process
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153
Process variation vs Specification
Let us assume that for a normally distributed stable process, the
average is 178.6 and the standard deviation is 3.6. The process
target is 171, USL = 182, LSL = 160.
Process Variation:
At + 3 that is 99.73% of the time, the process is
producing products that falls between167.8 and 189.4.
However, as per customer specification, we want all
product to fall between 160 and 182.
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154
Determine the potential process capability
The Cp index reflects the potential of the process if the average were perfectly centered between the specification limits.
Cp
=
USL - LSL
6
Cp
=
182 - 160
6
For the given example, the potential process capability Cp is 1.01.
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155
Quantify actual process performance (Cpk)
LSL = 160
USL = 182
To estimate the percentage of product / process that falls outside the specification limits, we compute Cp (upper) and Cp (lower).
167.8
178.6
189.4
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156
Quantify actual process performance (Cpk)
The Cp index reflects the potential of the process if the average were perfectly centered between the specification limits.
Cpk (Lower) = X – LSL
Cpk (Upper) = USL -X
3𝝈
3𝝈
Cpk (Lower) = 178.6 – 160.0
Cpk (Upper) = 182.0- 178.6
3 ∗ 𝟑. 𝟔
3 * 3.6
Cpk ( Lower) = 1.72
Cpk (upper) = 0.31
Cpk = 0.31
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Quantify actual process performance (Cpk)
Unlike the Cp, the Cpk index takes into account off-centering of the process. The larger the Cpk index, the better.
LSL
USL
LSL
USL
6
❖
6
CP = 1
CP = 1
CPK = 1
Cpk < 1
Most of the organization aim for process capability (Cpk = 2.0) with minimal acceptable capability (Cpk = 1.5)
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What is Sigma?
Upper Specification Limit
Lower Specification Limit
“Sigma” (Standard deviation) is a measure of variation.
± Standard Deviation
(“Sigma”)
Plus or minus one standard deviation around the mean is about 68% of the total process output.
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What is a Six Sigma process?
Upper Specification Limit
Lower Specification Limit
If we can squeeze six standard deviations in between our process average and the customer’s requirements…
6 5 4 3 2 1
1 2 3 4 5 6
Then,
99.99966% of our “opportunities“ meet customer requirements!
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Steps for evaluating process capability for variable data
❑
To identify the average and standard deviation of a process, we
first need to check the distribution i.e. if it is normal distribution?
❑
We check the normality of the data.
Normality test in Mintab
Minitab
Choose
Stats
Basic
Stats
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Normality
Test
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161
Checking stability for variable data in Minitab
The Anderson-Darling test's p-value at 0.892 indicates that the data follows a normal distribution.
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Cp and Cpk in Minitab
❖
Since the data follows a normal distribution, we can now check
the capability of the data.
Capability Index in Minitab
Minitab
Choose
Stats
Quality
Tools
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Capability
Analysis
Normal
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163
Cp and Cpk in Minitab
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Baseline
performance
(Discrete data)
Defective (PPM) & Defects (DPMO)
Attribute Data - DPMO
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Attribute Data – PPM and DPMO
DPU = Total Number of defectives / Total number of units inspected
Department:
Defective:
Total Defectives:
Unit:
Purchase
Unit, which has one or more Incorrect entries
56
No of units inspected - 5,000
DPU = 56 / 5000 = 0.0112
PPM = DPU * 10^6 = 11,200 = 3.8 Sigma
DPO
=
Total Numberof Defects
Total number of units inspected * Opportunities per unit
Department:
Purchase
Defect:
Incorrect entry
Total Defects:
56
Unit:
Each MIS report; No of units inspected - 5,000
No of Opportunities per report: 6
DPO = (56) / (5,000 *6) = 0.001866
DPMO = DPO*10^6 = 1,866 = 4.4 Sigma
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Sigma and DPMO table
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Sigma and DPMO table
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Sigma and DPMO table
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Classroom Quiz
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Data
Visualization
with Power BI
Data Visualization
•
Visualizations allow data to be represented in different ways, leading
to insights into data relationships that may not be easily seen.
•
Power BI allows users to create and adjust visualizations based on
their own needs as they look at data.
•
Users will be able to look at data from different perspectives and find
insights into data relationships that help them make better informed
decisions.
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Power BI overview
Power BI service
Content packs
Sharing & collaboration
Live dashboards
Visualizations
Reports
01001
10101
Datasets
Data refresh
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Navigating the
Power BI Webapp
Workspace
When you first open
Power BI, you may see
this default screen. This
is the Power BI
dashboard, but this
default page is not the
most helpful.
You will be using the
toolbar here to navigate
through the functions of
the Power BI Webapp.
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Navigating the Power BI- Workspace
❑ This is the main workspace for Power BI.
❑ This screen allows users to navigate the Power
BI workspace, pull up reports, and share
documents or dashboards with other Power BI
users.
❑ Let’s start with understanding the navigation
pane.
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What does Power BI do?
Navigating the Power BI- Workspace
Home
Favorites
Recent
Apps
Shared with me
Workspace
Get Data
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What does Power BI do?
Navigating the Power BI- Workspace
Home
Favorites
Recent
Apps
Shared with me
Workspace
Get Data
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What does Power BI do?
Navigating the Power BI- Workspace
Home
Favorites
Recent
Apps
Shared with me
Workspace
Get Data
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What does Power BI do?
Navigating the Power BI- Workspace
Home
Favorites
Recent
Apps
Shared with me
Workspace
Get Data
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180
What does Power BI do?
Navigating the Power BI- Workspace
Home
Favorites
Recent
Apps
Shared with me
Workspace
Get Data
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181
What does Power BI do?
Navigating the Power BI- Workspace
Home
Favorites
Recent
Apps
Shared with me
Workspace
Get Data
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182
Case Study
Business Problem:
You have been hired as a International
Jewelry Company as a Power BI
Developer.
Your assignment is to deliver an analysis
with area chart and dashboard which
help in showing the sales across
different demographics. Also help your
stakeholders to navigate the chart using
different filter.
What we will perform in case study
•
•
•
•
•
Line Chart
Pie Chart
Heat Map
Area Chart
Bar Graph/Histogram
•
•
•
•
Control Chart
Ease of use
Live Connections
Targets/KPIs Difference between Area
and Line Chart
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183
Simulation Exercise based on Case Study-1
Scenario 1: The Problem of “Not on Time” for Delivery of Packages
ABC Inc.is a package delivery service for homes and small businesses. ABC Inc. specializes in packages
50 pounds or less and has a full-price rebate policy for any delivery made beyond 15-minute timeline.
ABC Inc. advertising proudly states, “Delivery within 15 minutes” Consumer can avail services by
placing an order on their application, “ABC to serve” available at play store or on their official website
by entering the basic details and submitting order for making the on-time delivery.
ABC Inc. has facilities at multiple locations each servicing customers within a 15-mile radius with
deliveries made by truck or bikes based on the size and distance in which delivery to be made as per
Annexture-1. ABC Inc. assures that they deliver the consignment post submission of order at app or
website to deliver a package at customer within 15-minute window. ABC Inc. charges customers $5
per package plus $1 per pound (50-pound maximum).
On any given day ABC Inc. delivers and picks up approximately 50 packages having an average weight
of 35 pounds.
Over the 6 months, sales Operators have reported that 32% customers have complained that
deliveries have not been meeting the committed timelines of 15 minutes at Downtown area. In
response, Sales Operators were instructed to remind customers of the ABC Inc. price rebate policy.
Additionally, a short survey was sent out to a small group of established customers. Survey results
disclosed an appreciation of price rebates, but a preference for deliveries within the committed
timelines.
Task to do in Measure phase:
•
Prepare Data Collection Plan
•
Perform Descriptive Statistics
•
Using Case study data, comment on the
base line sigma level by using
appropriate tools wherever applicable
The organization’s leadership team has decided to solve the problem the Lean Six Sigma way using
the DMAIC approach to solve the problem of delayed deliveries and pickup.
Annexure 1:
Weight of Consignment
0-30
30-50
Mode
Bike
Truck
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184
Simulation Exercise based on Case Study-2
Scenario 2: The Problem of Delay in Account Opening Time in Retail Banking
Bank of XYZ, a major bank receives on an average 2000 new saving account opening customer
application forms every day. 40 operators enter the application forms in a database after cross
checking the CAF (Customer Application Form) with Identity Proof details.
The entries are rechecked against the Identify Proof details by 15 Quality Assessors and further 5%
sample is audited by 3 Quality Supervisors. The sales team promise the account opening within 48
hours from receipt of the CAF. Bank of XYZ usually achieves the account opening within average of
30 hours with a standard deviation of 6 hrs.
Recently, after a significant marketing effort, they started receiving over 3500 CAF, and the % of
defects in the CAF increased far more than the acceptable 10% of total opportunities for error and
processing time of CAF also increased, leading to account opening taking more than target of 48hrs.
The Customer Application has the following sections (Opportunities for Error):
Task to do in Measure phase:
•
Prepare Data Collection Plan
•
Perform Descriptive Statistics
•
Using Case study data, comment on the
base line sigma level by using
appropriate tools wherever applicable
1. Title and Gender of the customer
2. Name of the customer
3. Address of the customer
4. Date
5. Identity Proof No
6. Product Code
7. Email Address
Any incorrect section is considered a defect and must be re-processed.
The Bank is losing $3750 every day primarily on rework and penalties. The customers are also
dissatisfied as the account opening is taking more time than promised. In the wake of the current
business situation, the management team decides to initiate a Six Sigma project to reduce defects
and achieve target account opening time.
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185
Inference from Measure Phase
❑
The Defect (Y) and Possible Opportunities for the Defect to
Occur.
❑
Data Type – Attribute or Continuous.
❑
Types of Sampling.
❑
Data Collection & Operational Definitions
❑
Measurement Method Statements.
❑
Measurement System Analysis.
❑
Calculate Baseline (Y) – Sigma Level
❑
Process Capability (Cp and Cpk)
❑
Defects / Defective (DPMO/PPM)
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186
Module-4
Analyze Phase
Lean Six Sigma Green Belt Training Flowchart
Module-4
Process Door
Approach
• Process Analysis
• Lean (Definition &
History)
• Principles of Lean
Thinking
• Value Stream Mapping
• TIMHWOOD
Data Door Approach
• Cause Analysis
• Brainstorming –Cause
and Effect Diagram
• Segregation of Causes
• Pareto Diagram
• 5-Why Analysis
Statistical Based
Decision Making Hypothesis Testing
• Introduction to
Hypothesis
• Test of Means
(1 sample t-test/z-test, 2 sample
t-test, paired t-test, ANOVA)
• Test of Proportions
(1 proportion test, 2 proportion
test, Chi- Square test)
Statistical Based
Decision Making Correlation and
Regression
Artificial Intelligence
• Introduction of AI.
• Introduction of ML
• Scatter Plot
• Introduction to
correlation and simple
linear regression
• Demonstration on
Minitab
• Demonstration on
Minitab
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Module-4 Learning Objectives
By the end of this module participants will be able to learn:
•
Identify how to analyze process
•
Perform critical process analysis of a process through lean thinking.
•
Identify the different types of wastages.(TIMHWOOD) and Describe Value stream mapping.
•
Identify and prioritize critical X's
•
Use data to validate critical X’s (causes based on events/categories)
•
Perform the assumption-based causes analysis and validate it statistically by applying the tools and
technique taught during the session.
•
Use correlation between variables X’s and established relation between them and develop model for
future prediction
•
Understanding of basic concepts of AI & ML
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Process Door
Approach
Process Analysis – Analyzing the Process Maps
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Lean (Definition & History)
Lean, similar to Six Sigma is a process improvement that is solely based on the fundamental goal of waste elimination and flow
maximization.
In other words, meeting customer requirements faster, quicker and better, that is in a more effective and efficient way.
Lean manufacturing, as management philosophy, came mostly from the Toyota Production System (TPS). The term “lean” was
first introduced in article “Triumph of the Lean Production System” written by John Krafcik in 1988.
Kiichiro Toyoda, founder of Toyota Motor Corporation, discovered many problems in their manufacturing process. In 1936 his
processes hit new problems and he developed the “Kaizen” improvement teams. Toyota’s view is that the main method of lean
is not the tools, but the reduction of three types of waste:
1.
muda (“non-value-addingwork”)
2.
muri (“overburden”)
3.
mura (“unevenness”)
Taiichi Ohno, considered to be the father of the Toyota Production System, was instrumental in improving overall customer value
by focusing on reduction of the process wastes at Toyota.
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Principles of Lean Thinking
The Lean Enterprise Institute (LEI), founded by James P. Womack and Daniel T. Jones in 1997, introduced the five key lean
principles: Identify value, Map the value stream, Create flow, Establish pull, and Seek perfection.
1. Identify
Value
4. Establish
Pull
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Principle 1 – Identify Value
What is value?
Who defines value?
❖
According to Womack and Jones – Value is expressed in terms of a specific product or service (or both) which meets customers’ need
at a specific price and at a specific time.
The key question to be asked here is – What is the timeline? What is the price point? What are the other requirements that must be met?
Account opening takes 3 to 4
days
Introduction of instant account
opening with online
documentation
Providing value to the customers is the only reason for the existence of our business.
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Principle 2 – Map the value stream
According to Womack and Jones – Mapping the value stream is a step in
taking a specific product or service (from scratch) to its final recipient i.e.
the end customer.
What is a Value Stream?
Supplier
❑
Value stream map is a sequence of steps taken to create product /
service to the end customer.
❑
All the activities involved in creating value for the customer.
❑
The process starts with raw material or information and ends with the
end customer.
❑
The process involves functions both internal and external to the firm.
❑
Value stream mapping identifies the Value Added (VA) / Non-Value
Added (NVA) / Value Enablers (VE) steps in a process and quantifies
time spent on each step.
❑
Eliminate the NVA’s that contribute to the highest time to the process.
Value Stream
Customer
Sub Process/ Activities
START
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FINISH
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Why map the value stream?
❑
Enables to visualize the process / production flow.
❑
Allows to see waste in the system.
❑
Prevents focusing on large improvement opportunities with little
impact.
❑
Creates framework for designing complete system.
❑
Demonstrates interaction between information and material flow.
❑
Enables to know the Lead Time and Cycle Time for the process.
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Key Terms used in Value Stream Map (VSM)
❑
Value-added (VA): This step in the process adds form, function, and
value to the end product and for the customer.
❑
Non-Value-Added (NVA): This step does not add form, function, or
assist in the finished goods manufacturing of the product.
❑
Non-Value-Added-But-Necessary (Value Enabled): This step does
not add value but is a necessary step in the final value-added
product.
❑
Cycle Time (CT): Cycle time is the time taken to complete a specific
task from start to finish. It measures the time spend in one cycle of
operation.
❑
Lead Time (LT): Lead time is the time it takes for one unit to make
way through the operation from front to end.
LT = Time taken from order to dispatch (end-to-end time)
❑
❑
❑
❑
❑
Takt Time (TT): Takt time is the rate at which one needs to complete
the production process in order to meet customer demand.
TT = Net Operating Time / Customer Demand
Process Cycle Efficiency (PCE): Overall efficiency of a process, it
is the Value-added time upon the Lead time.
PCE = Value Added Time / Lead Time
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Value Stream Mapping - Symbols
Processing Box
Process Box
Worker / Operator
Customer
•C/T: 12s
•C/O: 15 min
•1 Worker
Withdrawal
Push Arrow
Supplier
Transport
Finished Goods
Manual Information Flow
Manual Observation
Document
Digital Information Flow
Database
Inventory
Problem
Idea
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Value Stream Mapping - Example
Schedule
appointment
Schedule
appointment
PT / VT 10 min
DT
120 min
Refer Physician
Patient
Admission
Prep
Procedure
Consultation
&
Prescription
20 mins
20 min
30 min
15 min
180 min
120 min
90 min
Billing &
Discharge
10 min
60 min
Process Time / Value Time = 10 min + 20 mins + 20 mins + 30 mins + 15 mins + 10 mins = 105 mins
Delay Time = 120 mins + 180 mins + 120 mins + 90 mins + 60 mins = 570 mins
Lead Time = PT + DT = 675 mins
PROCESS CYCLEEFFICIENCY % =
Total Value Time x 100
Total LeadTime
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PCE =
15.56%
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Points to Remember
❑
Start with the customer – information flow.
❑
Identify the product or service that is being worked on.
❑
Determine your process steps from cradle to grave.
❑
Identify the time it takes to perform the task without delays (starting or
within the process) or interruptions within the total cycle time.
❑
Identify and quantify the time it takes to perform the task including
delays and interruptions.
❑
Investigate the causes of the waste between processes – what are
the barriers to flow?
❑
Calculate total processing time (cycle time) versus total lead time
(throughput/turnaround).
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Principle 3 – Create Flow
What is a Flow?
It is a movement of the product or service from the supplier to the end
customer
Womack and Jones advises – “make the value creating steps occur in
tight sequence, such that the product or service flow smoothly towards
the end customer.”
❑
The rationale behind flow is that the product or service flow smoothly
in the value chain without interruptions.
❑
Waiting time and hand-offs are eliminated from the process flow.
Customer documentation
rework, applications are
waited at the branch before
sending it across to the main
processing center
Customer walks in
the bank for account
opening
Account Opening
Process
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Account
opened
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Principle 4 – Establish Pull
Pull is the movement of product or service from the supplier to the
customer only when the customer needs it.
❑
Making or processing the product only when there is a customer
demand.
❑
Create a “Just in Time” manufacturing or delivery.
❑
Reduced storage, stockpile, inventory.
Customer Pull’s from the
value stream
Order placed to the
supplier basis the
customer demand
on the value stream
Customer
places an order
online
Order
Processing
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Customer
receipt of order
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Principle 5 – Seek Perfection
Accomplishing steps 1-4 is a great start, but the fifth step is
perhaps the most important: making lean thinking and process
improvement part of your corporate culture. Everyone should be
involved in implementing lean.
As gains continue to pile up, it is important to remember that lean
is not a static system and requires constant effort and vigilance to
perfect. Lean experts often say that a process is not truly lean until
it has been through value-stream mapping at least half a dozen
times.
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Types of Waste
What is a Waste?
❑
Any element of the production, processing, service, delivery, or
distribution that adds no value to the end user or customer or
business or the final product is termed as waste.
❑
Waste adds cost, effort and time.
❑
Waste is a symptom and not a root cause of the problem.
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Identifying Waste
ValueAdded
Non-Value Added
•
Transportation
•
Inventory
•
Motion
•
Human Intellect
•
Waiting
•
Overproduction
•
Over Processing
•
Defects
Typically, 85-95% of all lead time is Non-ValueAdded across industry
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Muda Waste Examples
Waste Type
Definition
Example
Transportation
Unnecessary movement of items resulting in
wasted efforts and cost.
Conveyor Belts for transporting raw material from warehouse to plant
Inventory
Pile up of semi processedor finished goods
that block working capital and hold up cash
flow.
Loan Applications piled up on the managers table for approval.
Motion
Unnecessary movement of people to perform
an activity.
Pharmacist moving around the whole shop in order to fetch medicines from
various drawers
Human Intellect
Overutilization or underutilization of talent.
Engineer being used as operator for operating machine.
Waiting
Waiting for the next process step.
Waiting for your turn to meet the doctor at OPD in hospital
Over- Processing
Too much processing of information.
Five to six reviews / signatures on Purchase Orders before sending to
Supplier and yet wrong material gets delivered.
Over- Production
Producing more than the requirement.
Producing goods for maintaining machine utilization in spite of no customer
need or order. Over production will always lead to inventory buildup.
Defects
Any process or activity that results in rework.
Car doors rejected in paint shop due to dents or surface unevenness.
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Data Door
Approach
Cause Analysis
Why do a cause analysis?
❑
Use the process data to understand the problem and identify the
vital few causes to reduce variation that the customer
experiences.
❑
Eliminate actions based on intuition and preconceived ideas.
❑
Recalibrate project scope.
❑
Establish performance goals for the process.
❑
Allows teams to develop sustainable process improvements that
will lead to long-term benefits.
❑
Determine potential benefit of project.
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Cause Analysis
Process
Outputs
(Ys)
Input
Variables
(Xs)
Process Variables(Xs)
What Are The Vital Few Process Inputs And Variables (X’s) That Affect CTQ Performance Or Output Measures (Y’s) ?
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Cause and Effect diagram
A visual tool used by an improvement team to brainstorm and logically organize possible causes for a specific problem or effect.
Potential High Level Causes (Xs)
Measurement
Methods
Machinery
Y
(Effect)
Mother Nature
Manpower
1.
Summarize potential high-level causes.
2.
Provide visual display of potential causes.
3.
Stimulate the identification of deeper potential causes.
Materials
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Segregation of Possible Causes
1.
Non-Controllable Causes: These are causes that the team
unanimously conclude to be beyond the control of the present
process boundaries or outside the physical location of the
process execution.
2.
Direct Improvements/ Quick wins: These are causes where
solutions can be implemented directly and need no further
analysis. They are usually stated as lack of resources,
equipment, tools or training.
3.
Likely and Controllable Causes: These causes are the causes
that have passed the above two filters and need further
analysis.
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Tackling the Non-Controllable Causes/Direct Improvements
✓
The non-controllable causes need to be factored into the improved
process so that they do not affect the Process Capability.
✓
The team should launch initiatives to implement actions for direct
improvements. This can happen in parallel as the project
progresses.
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Pareto Analysis
What is a Pareto Diagram?
❑
A Pareto chart is a bar chart that graphically ranks defects from
largest to smallest, which can help prioritize quality problems and
focus improvement efforts on areas where the largest gains can be
made.
❑
Pareto analysis was conceptualized by an Italian economist –
Vilfredo Pareto.
❑
In his endeavor, Pareto tried to prove that distribution of wealth and
income in societies is not random, but there is a consistent pattern.
❑
In his study – Principle of Unequal Distribution, he proved that
approx. 80% of wealth in Italy is controlled by approx. 20% of elite.
❑
The concept was formulated in six sigma by Dr Joseph Juran.
❑
Juran extended this principle of 80-20 to quality control stating that
most defects in a production are a result of small percentage of the
cause of the defects – which he described as “vital few from trivial
many”.
❑
Therefore, Pareto analysis is based on the principle that 80% of
problems find their roots in 20% causes.
❑
In other words, it is a diagram that shows 20% of the inputs (X’s)
cause 80% of the problems with dependent process outputs (Ys).
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Pareto Analysis on Minitab
Minitab path:
Minitab
Stats
Quality
Tools
Pareto
Chart
1.
In Defects or attribute data in: Add Relevant Attribute Data
2.
In Frequencies in: enter Count (Optional).
3.
Select Combine remaining defects into one category after this
percent and enter 95.
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Pareto Analysis
In the above graph, approx. 20% of the casus are impacting approx. 80% of the total delayed delivery. The cumulative percentage for 4
causes is around 80%
1. Difficult to trace address; 2. unplanned absenteeism; 3. Training and 4. POD Device.
Thus, solving these issues can bring significant improvement in on -time deliveries.
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5 Why Analysis
❑
The immediate next step after the segregation is to attack the likely and controllable causes and ask at least 3 – 5 why’s?….. for each
cause. This is called root cause drill down.
❑
Only after we have asked why 3 - 5 times to each of the likely causes, we will be able to arrive at the possible root cause, also known
as KPIV (Key Process Input Variables).
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5 Why’s? – An Example
WHY do we have poor
improvement programs?
1.
and declining
participation in
Because people resist change .
WHY do people resist change?
2.
Because they fear making mistakes.
WHY do people fear making mistakes?
3. Because they are criticized for mistakes.
WHY are people criticized for mistakes?
No ideas, let’s move on.
Okay, then WHY else do people fear making mistakes?
4. Because they are penalized for mistakes.
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Control impact matrix
When we know the possible root causes…can we attack all?
Use change
management strategy
Impact
HIGH
The difficult piece…
Target it n o w
W hy wait…?
Just Do it
LOW
Check the effort
vis a vis the results
LOW
HIGH
Control
We prioritize the root causes obtained in our root cause analysis using Control Impact Matrix
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Control Impact Matrix
❑
Classify all the causes that the group arrives at in the brainstorming
session for the control and impact matrix.
❑
Use your team’s process knowledge and business experience to list
possible Xs in a control/impact matrix, then use process data to verify
or disprove placement of the Xs.
Prioritization steps
1.
Using the control/impact matrix, examine each X in light of two
questions:
▪
What is the impact of this X on our process?
▪
Is this X in our team’s control or out of our team’s control?
2.
With your team, place each X in the appropriate box on the matrix.
3.
Use process data to verify or disprove your assertions.
4.
The validated matrix is a guide used to addressing the Xs. Begin with
the ‘high impact/in our control’ category.
5.
‘Out of our control’ Xs may require special solutions for successful
sustainable solutions.
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Classroom Quiz
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Hypothesis
Testing
What is a Hypothesis?
A Hypothesis is a claim or statement about a property of a population
(such as mean, variance, proportion).
The American Heritage College Dictionary defines Hypothesis as –
“a tentative explanation that accounts for a set of facts and can be
tested by further investigation.”
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What is Hypothesis Testing?
Measurements are organized into statistics to provide us insights by
looking at the spread, shape, consistency and location of the process.
Hypothesis Testing is simply a statistical method of comparing reality to
an assumption and re-confirming - did things change?
In other words, Hypothesis Testing is a statistical way of checking
whether observed differences between two or more samples are due to
random chance or is there an actual difference in the sample.
We use hypothesis testing to test the assumptions established during
problem analysis and investigation.
The practical meaning of Hypothesis Testing is that decisions taken
based on the test results, with regards to implementing change will
yield true and sustainable results.
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Why Hypothesis Testing?
We use hypothesis testing in the Analyze phase of our DMAIC journey
❖
To verify that a suspected cause (X) truly impacts the CTQ (Y).
And post implementation of solutions to verify the improvement or change in CTQ (Y).
Let us look at an example here:
A team suspects that manual processing of documents (AS-IS process being followed) results in rework and delay in opening accounts for
their customers.
Is the observed
difference real?
Y = High TAT for
account opening
Manual Process
Automated
Process
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Key Terms in Hypothesis Testing
Null Hypothesis (Ho)
➢
There is “no evidence of difference”.
➢
It is assumed to be true unless proven otherwise.
➢
We never prove it; we only fail to reject it.
➢
Can contain the condition of ‘equality’.(>= or <= or =)
➢
In other words, the Null Hypothesis is the antithesis to our claim
regarding the relationship of two or more data sets.
Alternate Hypothesis(Ha)
➢
The statement that we would like to show is true.
➢
It is a claim that is being studied.
➢
Also known as research hypothesis.
➢
It usually defines the direction of desirable change, can be either < / >
/ not equal.
➢
It is the hypothesis that we are attempting to prove or test.
➢
Is the statement that must be true if Null Hypothesis is false.
The Null and Alternate Hypothesis are mutually exclusive and together complete the entire set
of probabilities.
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Practice/Exercise
The average time taken to fill open positions for an analyst is 56 days.
After implementing improvements, new data was collected.
The average is now 28 days. Workers claim the process has changed.
1.
Define the null hypothesis?
2.
Define the alternative hypothesis?
More Examples:
I.
There is a difference between the average processing time from the
two departments.
II.
A bolt with thread type A has a stronger torque, on average, than the
bolt with thread type B.
III.
There is a difference in the proportion of lost prototype seats
between the two Business Units.
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Hypothesis Testing: P- Value
Our basis of taking a decision depends on the P-value.
So what is a P-value?
The P-value is the probability of obtaining a particular sample if the null
hypothesis (Ho) is true (We fail to Reject Null).
The P-value is based on an actual or assumed reference distribution like
Normal Distribution, F-Distribution, Chi Square Distribution, etc..
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Hypothesis Testing: Level of Significance
How do we take a decision now?
•
If P-Value < (less then) , we reject the null hypothesis.
•
If P-Value > (greater then) , we fail to reject the null hypothesis.
What is the level of significance (α)?
•
The level of significance () is always set before the hypothesis test is
done.
•
The  is most often set at 0.05 at 95% confidence.
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Risk of Hypothesis
Whenever a hypothesis test is run, there is a risk associated with the
decision that is made.
There are two types of errors (risks):
Type I error: It is the probability of rejecting the null hypothesis when we
should fail to reject it. This is also known as alpha risk, denoted by α.
Type II error: The probability Fail to Reject the null hypothesis when it is
false. This also known as beta risk, denoted by β.
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Type I and Type II errors
Whenever a hypothesis test is run, there is a risk associated with the decision that is made. There are two types
of errors (risks):
Test
Fail to reject Null
Reject the Null
Correct Inference
Type 1 Error
( Alpha Error)
Type-II Error
(Beta Error)
Correct Inference
Reality
Fail to reject Null
Reject the Null
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Steps for conducting Hypothesis Testing
Let us look at the steps with some illustration.
Write Null Hypothesis
H0 : X Sample A = X Sample B
Write Alternate Hypothesis
HA : There is a difference between Samples A and B
Decide on the α value
α = 0.05
(typical for DMAIC projects at 95% confidence)
Choose the correct test, given the type of X and Y data
Choose hypothesis test
{Need to check for normality of sample (if data type is continuous) by ADTest in Minitab.}
Gather evidence and test/conduct analysis
Collect data, run analysis, get p value
Reject H0 /not reject H0 and draw conclusion
If p>0.05 conclude H0 ,then we fail to reject Null Hypothesis
If p<0.05 conclude H0 ,then we reject Null Hypothesis
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Types of Hypothesis Testing
❑
There are many types of hypothesis tests.
❑
The choice of test depends on the …
1.
Type and distribution of the data and the
2.
Kind of comparison that is being made.
Common Hypothesis Tests
•
•
•
•
•
•
•
•
One sample t-Test (n < 30) – cont – 1DS
One sample z-Test (n >= 30) – Cont – 1DS
Two sample t-Test – Independent Data
Paired t Test – dependent
One way ANOVA – More than 2 datasets
One Proportion Test – 1dataset
Two Proportion Test – 2dataset
Chi Squared Test – More than 2
Application
Comparing Population Means
Comparing Population
Proportions or Percentages
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Sample Size – Larger vs Smaller samples
When performing hypothesis test with variable data…
❑
The sample size is considered large when n >= 30.
❑
The sample size is considered small when n < 30.
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1 sample z-Test: An Overview
The 1-sample Z test is used when….
❑
Testing the equality of a population mean to a specific value, and
❑
Sample size is large (n >= 30).
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1 sample z-Test: Example
You are attempting to assess the speed of delivery when ordering a
specific commodity via two different modes.
Delivery via mode A has been traditionally assumed to generate the
best response; but we need to test that assumption against mode B.
Mode A:
Average delivery time = 6 days
Standard deviation = 2 days
A random sample of size 36 was collected from the Mode B, yielding:
Mode B:
Average delivery time = 4.7 days
Standard deviation = 2.0 days
Is there a difference in average delivery speed between Mode A
and Mode B? Define the Null and Alternate Hypothesis.
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One Sample t-Test: Overview
The 1-sample t- test is used when….
•
Testing the equality of a population mean to a specific value, and
•
Sample size is small (n < 30).
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One Sample t-Test: Example
Minitab
Stats
Basic
Stats
1 Sample
t-test
Energy $
1211
1572
1668
1250
1478
1307
1184
865
1162
1308
1188
1111
1747
1326
1142
Define the Null and Alternate Hypothesis.
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One Sample t-Test : Example
❑
Define the Hypothesis
▪
Null Hypothesis: Mean expenditure =$1080
▪
Alternate Hypothesis: Mean expenditure does not equal
$1080
❑
Compute the P-value: the probability of obtaining the observed
sample if the null hypothesis is true.
Descriptive Statistics
N
Mean
StDev
15
1301.3
231.0
μ: population mean of Energy $
SE Mean
59.6
95% CI for μ
(1173.3, 1429.2)
Test
Null hypothesis
Alternative hypothesis
T-Value
P-Value
3.71
0.002
H₀: μ = 1080
H₁: μ ≠ 1080
Since the P-value of 0.002 is less than the level of significance 0.05, we
reject the Null Hypothesis.
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1 sample z-Test: Example
❑
Define the Hypothesis.
▪
Null Hypothesis: Average delivery time using Mode B equals
to the mean of 6 days.
▪
Alternate Hypothesis: Average delivery time using Mode B
does equal to the mean of 6 days.
❑
Randomly select a representative sample of data (36 data points
were collected with average of 4.7 days and standard deviation of 2
days).
❑
Compute the P-value: the probability of obtaining the observed
sample if the null hypothesis is true. Using Minitab…
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1 sample z-Test: Example
Delivery Time
Minitab
Stats
Basic
Stats
1 Sample
z-test
6
6
7
3
3
2
6
2
7
4
8
3
5
4
3
6
2
4
5
7
3
4
9
4
5
1
5
4
4
7
6
3
4
4
5
9
We are performing a 1-sample Z test because we are testing the equality of a population mean to a specific value (6 days), and we have a
large sample ( n > 30).
Enter the data in Minitab. Standard Deviation 2, Hypothesized Mean 6.
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1 sample z-Test: Example
Descriptive Statistics
N
Mean
StDev
36
4.722
1.966
μ: population mean of Delivery
Known standard deviation = 2
SE Mean
0.333
95% CI for μ
(4.069, 5.376)
Test
Null hypothesis
Alternative hypothesis
Z-Value
P-Value
-3.83
0.000
H₀: μ = 6
H₁: μ ≠ 6
Since the P-value of 0.000 is less than the level of significance 0.05, we reject the Null Hypothesis.
The data provides sufficient evidence that the average delivery time when using Mode B does not equal to the mean value of 6 days.
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Two Sample Test: Overview
In this section we discuss about the ‘Two sample tests’ viz:
•
Two Sample z-test
•
Two Sample t-test
The Two-sample z test is used when testing the equality of two
population means and the two samples are large (n >= 30).
The Two-sample t test is used when testing the equality of two
population means and the two samples are small (n < 30).
Minitab does not offer a 2-Sample Z test. Therefore, we use the TwoSample t test instead.
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Two Sample Test: Overview
The Two-sample t test is used when the two samples are independent (not related, Unpaired data).
The conditions for using the student t distribution to conduct hypothesis tests with small samples are:
1.
The population standard deviations are unknown.
2.
The parent populations each have a distribution that is essentially normal..
Minitab
Stats
Basic
Stats
Two
Sample
t-test
A
B
81
89
77
64
75
35
74
68
86
69
90
55
62
37
73
57
91
42
98
49
59
58
65
71
67
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Two Sample t-Test : Example
❑
❑
Define the Hypothesis
▪
Null Hypothesis: Mean ratings for Hospital A and Hospital B is
the same.
▪
Alternate Hypothesis: Mean ratings for Hospital A and Hospital
B is different.
Compute the P-value: the probability of obtaining the observed
sample if the null hypothesis is true.
Descriptive Statistics
Sample
A
B
Test
N
10
15
Null hypothesis
Alternative hypothesis
T-Value
DF
4.36
22
Mean
80.7
59.0
StDev
10.6
14.2
SE Mean
3.4
3.7
H₀: μ₁ - µ₂ = 0
H₁: μ₁ - µ₂ ≠ 0
P-Value
0.000
Since the P-value of 0.000 is less than the level of significance 0.05, we
reject the Null Hypothesis. This means that there is a difference in the
mean rating for hospital A and hospital B.
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Paired t-Test: Overview
❑
If one sample is related to another, the samples are dependent
(paired).
❑
For instance, if you made a claim about a drug designed to lower
cholesterol, you would make cholesterol measurements on the same
individuals before and after the use of the drug. You would need
paired data.
❑
If you take a measurement on the same circuit boards before and after
burn in, there is a relationship or dependency between the
measurements.
❑
In these cases, you do not examine two different data sets. Instead,
you look at the difference between the before and after data to see if
the resulting differences are significant.
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Paired t-Test : Example
A Black Belt conducted a study to determine if training was effective at
reducing the time it takes to process orders. The process is not automated
and is highly dependent on the knowledge and skill level of individual
processors. The orders used throughout the study are essentially identical
in magnitude.
Processor
•
After Training
1
23
21
2
17
11
3
8
6
4
9
10
5
7
5
6
25
22
7
16
11
8
11
16
9
12
10
10
9
5
Define the Null and Alternate Hypothesis.
Minitab
•
Before Training
Stats
Basic
Stats
Paired
t-test
Interpret the P-value.
Data is processing time in hours.
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One way ANOVA: Overview
When we need to test the equality of more than two population means we
use One Way Analysis of Variance (ANOVA).
Assumptions:
❑
Independent random samples have been drawn from “r” normal
populations with means: µ1 = µ2 = µ3 = µ4 ,…, = µr.
❑
Each population has the same variance.
❑
The random samples from each population do not need to be of the
same size.
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One way ANOVA: Example
Suppose you are testing to see if there is a significant difference in average stiffness among three foam
formulations. You collected the random samples:
Formulation A
Formulation B
Formulation C
338.2
340.4
344.4
331.0
328.2
338.5
323.0
338.5
348.9
317.9
333.7
326.6
327.9
332.8
342.6
328.1
323.4
355.4
308.3
335.3
337.0
312.8
326.2
339.2
324.0
344.0
331.3
342.4
310.3
336.3
326.2
317.5
342.3
328.1
322.0
338.6
337.6
321.2
323.9
320.3
321.4
345.7
311.0
342.6
333.1
328.1
334.7
331.3
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One way ANOVA: Example
❑
Define the Null and Alternate Hypothesis.
▪
Null Hypothesis: Formulation A=B=C.
▪
Alternate Hypothesis: At least one mean is different.
❑
Random samples of 16 foam pads were tested for each formulation.
❑
Use Minitab to test the hypothesis.
Minitab
❑
Stats
ANOVA
One
Way
Interpret the P-value.
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One way ANOVA: Example
Method
Null hypothesis
All means are equal
Alternative hypothesis
Not all means are equal
Significance level
α = 0.05
Equal variances were assumed for the analysis.
Factor Information
Factor
Factor
Levels
3
Values
Formulation A, Formulation B, Formulation C
Analysis of Variance
Source
Factor
Error
Total
DF
2
45
47
Model Summary
S
9.23886
Means
R-sq
27.27%
Factor
N
Formulation A
16
Formulation B
16
Formulation C
16
Pooled StDev = 9.23886
Adj SS
1440
3841
5281
Adj MS
720.14
85.36
R-sq(adj)
24.04%
R-sq(pred)
17.25%
Mean
325.31
329.51
338.44
StDev
9.70
9.74
8.19
F-Value
8.44
P-Value
0.001
95% CI
(320.65, 329.96)
(324.86, 334.16)
(333.79, 343.10)
P-value = 0.001 <  = 0.05.
Therefore, we reject the null hypothesis. The data provides sufficient evidence that at least one formulation is
different from the others.
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One proportion test: Overview
The 1-proportion test is used when….
❑
Testing the equality of a population proportion to a specific value
❑
Calculate a range of values that is likely to include the population
proportion
Example: Purchase Orders
When purchase orders are selected out of the accounting system and
examined for whether they contain a project number, history has shown
that 18% do not. A new mistake proofing is attempted, and 40 recent
purchase orders are audited. Only 2 P.O.s now have a missing project
number.
At the 5% level of significance, has the process been improved?
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One proportion test: Example
❑
Define the Hypothesis.
▪
Null Hypothesis: The new process yields 18% or greater of
purchase orders without project numbers.
▪
Alternate Hypothesis: The new process yields less than 18% of
purchase orders without project numbers.
❑
Randomly select a representative sample of data (Sample of 40 were
randomly selected, only 2 P.O’s were found to be missing project
numbers).
❑
Compute the P-value: the probability of obtaining the observed sample
if the null hypothesis is true using Minitab…
Minitab
Stats
Basic
Stats
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One proportion test: Example
Since the P-value is less than level of significance (0.05), we reject the
null hypothesis.
This proves that the sample provides sufficient evidence that the
proportion of defective purchase orders has decreased.
The process has improved!
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Two proportion Test: Overview
The 2-proportion test is used to….
❑
Determine whether the proportions of two groups differ.
❑
Calculate a range of values that is likely to include the difference
between the population proportions.
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Two proportion Test: Example
A Black Belt is comparing two methods of processing cell phone and pager requests to determine which method
is more accurate.
Method
No. Audited
Number with Errors
Proportion Defective
Standard Paper
80
12
0.150
Intranet Form
80
5
0.063
❑
❑
Define the Hypothesis.
▪
Null Hypothesis: The methods of processing cell phone and pager requests in standard paper and intranet form is
the same.
▪
Alternate Hypothesis: The methods of processing cell phone and pager requests in standard paper and intranet
form is significantly different.
Compute the P-value: the probability of obtaining the observed sample if the null hypothesis is true. Using Minitab…
Minitab
Stats
Basic
Stats
1Proportion
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Two proportion Test: Example
Descriptive Statistics
Sample
Sample 1
Sample 2
N
80
80
Event
12
5
Sample p
0.150000
0.062500
Test
Null hypothesis
Alternative hypothesis
H₀: p₁ - p₂ = 0
H₁: p₁ - p₂ ≠ 0
Method
Normal approximation
Fisher's exact
Z-Value
1.81
0.122
P-Value
0.070
Since the P-value and Fisher’s P value is greater than level of
significance (0.05), we fail to reject the null hypothesis.
Thus, proving that the methods of processing cell phone and pager
requests in standard paper and intranet form is the same.
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Chi Square: Overview
The Chi Square test is used to….
❑
Test the equality of more than two population proportions.
❑
A contingency table, using the Chi-Square test, can be used to
determine if a difference exists among populations for proportion data.
❑
This test tests whether two variables are dependent on each other.
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Chi Square: Example
The Personnel Department wants to see if there is a link between age (old and young) and whether that person gets
hired.
Group
Hired
Not Hired
Total
Old
30
150
180
Young
45
230
275
Total
75
380
455
Define the Null and Alternate Hypothesis.
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Chi Square: Example
❑
Define the Hypothesis.
▪
Null
Hypothesis:
Age
and
Hiring
are
not
dependent
(independent).
▪
❑
Alternate Hypothesis: Age and Hiring are dependent.
Compute the P-value: the probability of obtaining the observed sample
if the null hypothesis is true. Using Minitab…
Minitab
Stats
Tables
Chi Square Test for
Association.
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Chi Square: Example
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Chi Square: Example
Rows: Group Columns: Worksheet columns
Hired
Not Hired
Total
All
Old
30
29.7
150
150.3
180
180.0
360
Young
45
45.3
230
229.7
275
275.0
550
Total
75
75.0
380
380.0
455
455.0
910
760
910
1820
DF
4
4
P-Value
1.000
1.000
All
150
Cell Contents
Count
Expected count
Chi-Square Test
Chi-Square
Pearson
0.007
Likelihood Ratio 0.007
Since the P-value is greater than level of significance (0.05), we fail to
reject the null hypothesis.
Thus, proving that Age and Hiring are dependent
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Self: Practice/Exercise
1.
Sachin wants to test the assumption that there is no difference
between the mean life of motors of two companies. For his study,
he picks up a sample of 10 motors with mean life of 4,050 hours
and standard deviation of 200 hours from the first company and a
sample of 9 motors with mean life of 4,300 hours and standard
deviation of 260 hours from the second company. You are
requested to help Sachin test the hypothesis, that there is no
difference in the mean life of both the brands of motor at 95
percent level of confidence.
2.
A team wants to see if there is relationship between ambient
temperature and the viscosity of a material.
3.
Ajay is exploring a brand of canned rasogullas to be sold in the
chain of grocery shops which he represents. For him to select a
particular brand, the drained rasogullas should not weigh less
than 260 grams (in a 500 grams can). From the 11 cans he
sampled at the distributor, the mean weight of drained rasogullas
was 247 grams with a standard deviation of 30 grams. You are
requested to help Ajay take a decision regarding procurement of
Rasogullas at 95 percent level of confidence.
4.
The Personnel Department wants to see if there is a link
between age (old and young) and whether that person
gets hired.
5.
A brand marketing firm wants to understand the television
viewing pattern during cricket matches across countries.
During a league cricket match involving players of 4
nationalities, following viewing patterns were observed in a
randomly selected sample of 50 viewers across these
countries (marked A, B, C and D). Please help the team
understand if there is a relationship between the
nationalities and the viewing patterns at 5 percent level of
significance.
A
B
C
D
Viewed
12
10
6
14
Did Not View
38
40
44
36
What type of tool would you use ?
_______________________________
Define: Ho and Ha.
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Statistical Based
Decision
Making
Correlation and Regression
What is a Scatter Plot?
A scatter diagram is the graphical representation of paired (x,y) data. This type of graph is appropriate when the values in one data set
correspond to values in another data set, and you wish to understand the relationship between the two.
Time vs. Costof Projects
Cost
($k)
80
111
76
27
55
51
150
140
80
120
150
Y: C ost ($k)
Time
Project (Days)
1.
14
2.
29
3.
26
4.
10
5.
18
6.
11
7.
34
8.
26
9.
24
10.
21
(14, 80)
100
50
10
20
X: Time(Days)
30
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What is a Scatter Plot?
Let us assume for all charts below:
Y = Participant satisfaction (scale: 1 – worst to 100 – best).
X = No of hours spent by the trainer.
Positive Correlation
Strong Positive
Perfect Positive
No Correlation
Negative Correlation
Strong Negative
Perfect Negative
Nonlinear Correlation
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What is Correlation?
When two variables show a relationship on a scatter plot, they are said to be correlated, but this does not necessarily mean they have a
cause-effect relationship.
If two variables X and Y, are related such that as Y increases / decreases with another variable X a correlation is said to exist between
them. In other words, a correlation exists between two variables when they are related to one another in some way.
Let us consider this –
Time vs. Costof Projects
Time (Days)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
14
29
26
10
18
11
34
26
24
21
Cost ($k)
80
111
76
27
55
51
150
140
80
120
150
Cost ($k)
Project
100
50
10
20
30
Time (Days)
From the above plot, we can infer that as the project time increases,
so does the cost.
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Correlation Coefficient
❑
The correlation coefficient, r, is a statistical measure of the
strength of the linear relationship between two variables.
❑
r is always between -1 and 1.
❑
r is also known as Pearson’s correlation coefficient.
❑
When r is close to zero, no linear relationship is present.
❑
When a relationship exists, the variables are said to be correlated.
▪
Perfect negative relationship:
r = –1.0
▪
No linear correlation:
r=0
▪
Perfect positive relationship:
r = +1.0
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Correlation Coefficient Examples
Note: r  0 means no linear relationship. The variables might be related, just not in a linear fashion.
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Scatter Plot and Correlation with Minitab
Let us consider the following data set for our analysis in Minitab to plot a Scatter Diagram
Project
Time (Days)
Cost ($k)
1.
14
80
2.
29
111
3.
26
76
4.
10
27
5.
18
55
6.
11
51
7.
34
150
Scatter Plot in Minitab
8.
26
140
Minitab
9.
24
80
10.
21
120
Graph
Scatter
Plot
Simple
•
Double click C2 -> Cost is put into the Y box
•
Double click C1 -> Time is put into the X box
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Scatter Plot and Correlation with Minitab
For the same set of data let us do a Correlation using Minitab.
Correlation in Minitab.
Minitab
Stats
Basic Stats
Correlation
•
Double click C1, C2 -> C1, C2 are put into the variables box.
•
Click OK.
Correlations: Time,Cost
Pearson correlation of Time and Cost (r) = 0.819
P-Value = 0.004
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Discussion
X
0.261
0.736
0.973
0.900
0.015
2.345
0.067
0.323
0.062
0.186
2.5
2.0
1.5
Y
Y
0.991
0.255
0.015
0.175
0.012
2.490
0.991
0.239
0.036
0.926
1.0
0.5
0.0
0
1
2
X
Is there a correlation between Y & X?
Why or Why not? Use Minitab to tabulate the results.
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Discussion
X
0.261
0.736
0.973
0.900
0.015
2.345
0.067
0.323
0.062
0.186
2.5
2.0
1.5
Y
Y
0.991
0.255
0.015
0.175
0.012
2.490
0.991
0.239
0.036
0.926
1.0
0.5
0.0
0
1
2
X
What is this point?
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Discussion
Y
0.991
0.255
0.015
0.175
0.012
2.490
0.991
0.239
0.036
0.926
X
0.261
0.736
0.973
0.900
0.015
2.345
0.067
0.323
0.062
0.186
Can this point be removed? What do we term them as?
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Discussion
1.0
Y1
X1
0.991
0.261
0.255
0.736
0.7
0.015
0.973
0.6
0.175
0.012
0.991
0.239
0.036
0.926
0.900
0.015
0.067
0.323
0.062
0.186
0.9
Y1
0.8
0.5
0.4
0.3
0.2
0.1
0.0
0.0
0.1
0.2 0.3
0.4
0.5
0.6
0.7
0.8 0.9
1.0
X1
Is there a Correlation between them?
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Avoid Pitfalls
Growing population of
ants
Avoid Pitfalls
✓
To avoid these problems:
✓
Always examine the x,y data.
✓
Always look at an x,y plot.
✓
If there seems to be an outlier, drop that data point and rerun
the correlation test.
Growing population of
human beings
✓
Never jump too quickly to your conclusion.
REMEMBER: Correlation does not imply causation
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What is Regression?
❑
In statistical modeling, regression analysis is a statistical
process for estimating the relationships among variables.
❑
Regression analysis is a form of predictive modelling
technique which investigates the relationship between a
dependent (target) and independent variable (s) (predictor).
❑
In simple linear regression, you obtain the graph and the
equation of the straight line that best represent the
relationship between two variables.
❑
Given a sample of pared data, the regression equation :
▪
Y = b0 + b1x
describes the relationship between two variables.
❑
The graph of the regression equation is called the regression
line (or best fit line).
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What is Regression?
❖
The graph of the regression equation is called the
regression line (or best fit line).
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Regression Equation
The regression equation:
y =  0 + 1 x
Dependent
Variable
y-intercept
slope
Independent
Variable
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Simple Linear Regression: Example
Consider the below example of a relationship between Cost and Time.
Time
14
29
26
10
18
11
34
26
24
21
Cost
80
111
76
27
55
51
150
140
80
120
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Fitting a Regression Model: Using Minitab
Minitab
Stats
Regression
Fitted Line
Plot
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Fitting a Regression Model: Using Minitab
Minitab
Stats
Regression
Fitted Line
Plot
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Simple Linear Regression: Using Minitab
Minitab
Stats
Regression
Regression
Fit
Regression
Model
Consider the below example of a relationship between Cost and Time in Minitab.
y: Cost ($k)
150
 0: y-intercept is
the value of y
where the line
crosses the y axis
100
50
b1: slope is the amount of change in y forevery 1 unit (1
day) change in x.
10
20
30
x: Time (Days)
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Simple Linear Regression: Using Minitab
Regression Analysis: Cost versus Time
Regression Equation
Cost
=
The best fitting line is y = .9 + 4.14 x
0.9 + 4.14 Time
Coefficients
Term
Constant
Time
Coef
0.9
4.14
SE Coef
23.1
1.02
T-Value
0.04
4.04
P-Value
0.971
0.004
VIF
1.00
Rejecting Null Hypothesis means
we have convincing evidence
of a linear relationship between
Time and Cost.
Model Summary
S
24.4458
R-sq
67.12%
R-sq(adj)
63.01%
P - value =.004
R-sq(pred)
54.08%
Analysis of Variance
Source
Regression
Time
Error
Lack-of-Fit
Pure Error
Total
DF
1
1
8
7
1
9
Adj SS
9761
9761
4781
2733
2048
14542
Adj MS
9761.2
9761.2
597.6
390.4
2048.0
F-Value
16.33
16.33
P-Value
0.004
0.004
0.19
0.944
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R-Squared (R-Sq)
What is R-Sq?
The coefficient of determination, R-sq is the amount of the
variation in y that is explained by the regression line.
R-Sq = Explained Variation *100
Total Variation
In the given illustration,
R-Sq = 9761/ 14542*100 = 67.1%
It means, 67.1% of the variability in cost can be explained by the
relationship to time.
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Regression Equation
Consider the below example of a relationship between Cost and Time.
150
y: Cost ($k)
y = 0.9 + 4.14 x
 0: y-intercept is
the value of y
where the line
crosses the y axis
100
50
b1: slope is the amount of change in y forevery 1 unit (1
day) change in x.
10
20
30
x: Time (Days)
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Artificial
Intelligence &
Machine Learning
What is Artificial Intelligence
❑
AI is computer systems with the use of algorithms and data
analysis, can do those activities which usually require a human
brain, such as learning, Solving problems and decision making.
❑
Purpose of AI is to create the smart machines that can simulate
cognitive abilities including learning, reasoning, problem-solving,
experience, and language understanding.
❑
AI is carried out using different of techniques, systems, and the
algorithms.
❑
AI is science of making robots that simulate human intelligence
and show characteristics we link with it, like ability to construe
natural language, identify things, take decisions, and even show
creativity.
❑
Aim of AI is to create an intelligence system that can tailor, learn
by experience, and conduct activities at its own.
❑
This will help and improve human skills.
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What is Machine Learning
❑ Machine Learning is the twig of Artificial Intelligence and computer
science which focuses on use of data and Algorithms to act like
the human action and improve itself by analyzing data.
❑ Machine Learning is key component of data science, uses
statistical method and algorithms that makes predictions or
decisions without intense programming,
❑ ML Algorithm builds a model that can self learn from the sample
data or training data and can-do prediction or take decision based
on data patterns.
❑ Machine Learning can be applied in wide range of canvas like
image and speech recognition, natural language processing,
Customer services , Fraud detection etc.
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AI/ML with Lean Six Sigma
AI/ML and Lean Six Sigma are two complementary approaches to improve process and outcome. AI/ML will be able to find patterns or
trends in data, while Lean Six Sigma can find and remove waste. In combination, these tools could lead to substantial improvements in
efficiency, quality, and profitability.
Here are some ways in which AI/ML can help:
1.
Data Analysis and Pattern Recognition: AI/ML algorithms are proficient in handling and examining vast amounts of data.
2.
Predictive Analytics: AI/ML models can be trained to predict outcomes or performance metrics based on historical data
3.
Process Optimization: AI/ML techniques, such as optimization algorithms and reinforcement learning, can be used to find
optimal process parameters and configurations.
4.
Root Cause Analysis: AI/ML can help identify the root causes of defects or variations by analyzing multiple process variables
and their interactions.
5.
Real-time Monitoring and Control: AI/ML algorithms can be deployed to monitor process parameters and performance in
real-time.
6.
Decision Support Systems: AI/ML can provide decision support tools to aid Lean Six Sigma practitioners in making
data-driven decisions.
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Regression and AI/ML
•
Regression and AI are linked in various ways.
•
Regression is methodology which uses
statistical
technique
to
establish
the
relationship between dependent variable and
one or more independent variables.
•
AI on other side has a broader approach to
technologies focused on depicting human
intelligence.
•
AI uses Regression analysis for predictive
system or modelling.
•
Application of Regression based AI e.g.,
Finance, Healthcare, Marketing, Weather
Prediction.
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Example:
For example:
Real State: let’s say we have data on house prices. We have information about variables like the size of the house, the number of
bedrooms, and the location. By applying regression in AI/ML, we can analyse this data and create a model that relates these variables to the
price of the house. Once the model is trained, we can use it to predict the price of a new house based on its characteristics.
Stock Market Prediction
Sales Forecasting
Customer Lifetime Value Prediction
AI/ML regression models can predict
stock prices by analyzing historical data.
This can help investors make informed
decisions.
Regression models can be employed to
forecast sales based on factors such as
historical sales data, marketing
expenditure, seasonality, and economic
indicators.
Regression techniques in AI/ML can be
applied to predict the lifetime value of
customers for businesses. By analyzing
historical customer data such as
purchase history, demographics, and
engagement metrics, regression models
can estimate the potential revenue a
customer is likely to generate over their
relationship with the company. This
helps businesses tailor marketing
strategies and allocate resources to
target high-value customers.
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Classroom Quiz
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Simulation Exercise based on Case Study-1
LEAN SIX SIGMA CAPSTONE PROBLEM SCENARIOS
Scenario 1: The Problem of “Not on Time” for Delivery
ABC Ltd. is a package delivery service for homes and small businesses. ABC specializes in packages 50
pounds or less and has a full-price rebate policy for any delivery made outside the customer-designated
15-minute window. ABC advertising proudly states, "Delivery at your convenience, not ours." Consumer
can avail services by placing an order on their application, "ABC to serve" available at play store or on their
official website by entering the basic details and submitting order for making the on-time delivery.
ABC has facilities at multiple locations Downtown, Suburbia etc., each servicing customers within a 15mile radius with deliveries made by pick up or bikes based on the Size and distance in which delivery to
be made as per Annexture-1. PDI assures that they deliver the consignment post submission of order at
app. or website to deliver a package at customer designated in a 15-minute window. ABC charges
customers $5 per package plus $1 per pound (50-pound maximum).
On any given day ABC delivers and picks up approximately 50 packages having an average weight of 35
pounds at Downtown location.
Over the weeks, Sales Operators have reported that 35% customers have complained that deliveries have
not been meeting the committed timelines of 15 minutes at Downtown area. In response, Sales Operators
were instructed to remind customers of the ABC price rebate policy. Additionally, a short survey was sent
out to a small group of established customers. Survey results disclosed an appreciation of price rebates,
but a preference for deliveries within the committed timelines.
Task to do in Analyze phase:
•
Generate the Factor with Root Cause
Analysis. ( C & E, Why-Why)
•
Select the factor. (Pareto graph)
•
Do Hypothesis testing.
•
Do Regression analysis.
The organization's leadership team has decided to solve the problem the Lean Six Sigma way using the
DMAIC approach to solve the problem of delayed deliveries and pickup.
Annexture-1:
Weight of consignment
Miles
Modes
20-30
<10
Bike
20-30
10-15
Truck
30-40
>2
Truck
30-40
<2
Bike
40-50
All
Truck
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Simulation Exercise based on Case Study-2
Scenario 2: The Problem of Delay in Account Opening Time in Retail Banking
Bank of XYZ, a major bank receives on an average 2000 new saving account opening customer
application forms every day. 40 operators enter the application forms in a database after cross
checking the CAF (Customer Application Form) with Identity Proof details.
The entries are rechecked against the Identify Proof details by 15 Quality Assessors and further 5%
sample is audited by 3 Quality Supervisors. The sales team promise the account opening within 48
hours from receipt of the CAF. Bank of XYZ usually achieves the account opening within average of
30 hours with a standard deviation of 6 hrs.
Recently, after a significant marketing effort, they started receiving over 3500 CAF, and the % of
defects in the CAF increased far more than the acceptable 10% of total opportunities for error and
processing time of CAF also increased, leading to account opening taking more than target of 48hrs.
The Customer Application has the following sections (Opportunities for Error):
1. Title and Gender of the customer
Task to do in Analyze phase:
•
Generate the Factor with Root Cause
Analysis. ( C & E, Why-Why)
•
Select the factor. (Pareto graph)
•
Do Hypothesis testing.
•
Do Regression analysis.
2. Name of the customer
3. Address of the customer
4. Date
5. Identity Proof No
6. Product Code
7. Email Address
Any incorrect section is considered a defect and must be re-processed.
The Bank is losing $3750 every day primarily on rework and penalties. The customers are also
dissatisfied as the account opening is taking more time than promised. In the wake of the current
business situation, the management team decides to initiate a Six Sigma project to reduce defects
and achieve target account opening time.
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Inference from Analyze Phase
✓
Perform Critical analysis of a process through lean thinking and
with value stream.
✓
Identify the different types of wastages.(TIMHWOOD) and
Describe Value stream mapping.
✓
Identify and prioritize critical X's
✓
Use data to validate critical X’s using hypothesis testing
✓
Infer whether the causes were real i.e. were the causes
statistically significant.
✓
Interpret scatter diagrams and the correlation coefficient, r, to
determine if two variables are correlated.
✓
Perform Simple Linear Regression Analysis and interpret a
regression equation.
✓
Interpret R2 and P- values to determine the adequacy of a
math model (regression equation).
✓
Make predictions using the math model.
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Module-5
Improve Phase
Lean Six Sigma Green Belt Training Flowchart
Module-5
Idea Generation
Digital
Improvements
•
Brainstorming
•
Channeling, Analogy,
Anti- solution and
Brain writing
•
IOT
•
Digital Twin
•
Robotic Process
Automation
Solution Selection
Solution with Lean
•
Payoff Matrix
•
5S
Change, Risk Proofing
& Implementation
•
Screening against
must be
•
Kanban
•
Change Management
•
SMED
•
Force Field Analysis
•
Criteria Based Matrix
•
Spaghetti chart
•
FMEA with example
•
Nominal Group
Technique
•
KAIZEN
•
Implementation Plan
•
Poka Yoke
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Module-5 Learning Objectives
By the end of this module participants will be able to learn:
✓ What is Robotic process automation (RPA), and opportunities based on process analysis.
✓ Recognize the use of brainstorm techniques to effectively organize and follow a plan.
✓ Use the key Matrix to prioritize right solutions based on improvements generated from the Improve phase in
Lean Six Sigma project
✓ Apply lean concepts in solving the problems i.e. 5S, Kanban, SMED, Spaghetti Chart, Kaizen, Poka Yoke
✓ Use FMEA tool to identify potential failures modes and able to quantify the risk involved in these failures' modes
✓ Recognize need for change and build upon implantation and acceptance strategy
✓ Use Force Field Analysis tool to analyse potential solutions identified
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Idea Generation
Brainstorming Techniques
Structured brainstorming:
This is a process in which the ideas are generated in a systematic method by moving
from one person to another and obtaining an idea. The person who does not wish to
provide an idea, passes. The process is continued until each participant passes and the
facilitator determines no fresh ideas are being generated. As with all forms of
brainstorming, the ideas are then taken up for discussion. At the end discard any ideas
that are similar and debate on each idea to come up with the finalized list of possible
solutions.
Unstructured brainstorming:
In this form of brainstorming, ideas are generated randomly and there is no need to
pass. The solutions are discussed freely and can be proposed at any time. This type of
brainstorming process is effective when the representation of each of the diverse
groups involved in the discussion is large. However, the facilitator should be careful that
even in this randomized discussion with large representations, ideas may not be
captured completely.
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Brainstorming Techniques
Structured brainstorming:
Unstructured brainstorming:
❑
Smooth flow of ideas.
❑
Smooth.
❑
Everyone has an opportunity to express opinions.
❑
Random flow of ideas.
❑
Efficient process in terms of idea generation.
❑
All opinions may not be captured.
❑
Less creative in approach.
❑
Lesser number of ideas generated.
❑
Effective process for creative ideas.
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Brainstorming principles
Must be” for all brainstorming forms
Allow free f low of ideas
Prevent biases
(group/function/subject)
Create heterogeneous
groups
Manage time
Focus on critical
Xs and CTQs
Scribe all details
Avoid large groups
(6 - 10 optimum)
Use voting techniques
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Brainstorming Techniques
Channeling
Anti solution
Structured approach to brainstorm on
specific category of solutions available to
the team.
Structured approach to brainstormon the
opposite of the objectives of the
discussion.
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Brainstorming Techniques
Analogy
Brain writing
Brainstorm on ideas on related topic to
unblock thethought process.
Build on ideas written on a sheet of paper
randomly distributed amidst thegroup.
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Brainstorming Techniques
❑ Channeling: This form of brainstorming entails channeling of thoughts of the participants similar to the discussion in fishbone diagram. The
discussion revolves on specific stream/section of solution. E.g while discussion on methods to improve the sales of soaps, potential solutions can be
channeled into: Improvements related to the packaging, fragrance, cutting of costs or distribution system for the product.
❑ Anti solution: Here the objective of the team is reversed to exactly the opposite of what is to be determined. E.g. In an effort to improve the training
programme, the team can debate about the ways in which the training programme can be worsened. The solutions are opposite of what is determined
during the discussion.
❑ Analogy: Analogy form of brainstorming encompasses a discussion on a process/product that is similar to the one which is being improved. E.g.
Instead of discussing on ‘what are ways to improve the productivity of call center associates for an insurance customer service call center, the team
can discuss opinions on productivity of associates in the customer service call center for automobile leasing’. Make associations at the end of the
discussion.
❑ Brain writing: Written ideas are exchanged in this process. Each person who receives a written idea tries to expand on it or adds a totally new idea to
it. The pieces of paper are rotated and a collections of ideas or solutions are then debated and prioritized. This is used when the team members are
unknown to each other.
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Digital
Improvements
IOT/Digital twin
Digital twins and the Internet of Things (IoT) are two associated technologies they are changing the
way we run physical systems, construct and develop machinery, buildings, and infrastructure etc.
A digital twin is a virtual replication of physical system, Machine or Process.
It takes real-data from the process with the help of IOT and sensors and make
a virtual model or twin of the machine or system that shows the working
conditions of real object, and it analyzes the data to predict the health of
system and can forecast the issues or problem on the basis of real-data.
Key features of Digital Twin:
1.
Representation of physical model or system.
2.
Real-Time Monitoring of Physical model or system.
3.
Data Integration and Analytics
4.
Simulation and Prediction Capabilities
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IOT
The IoT, Internet of things is the sensors or devices connected by equipment, machines and systems over the network or internet which
provides real time data for monitoring, analyzing, tracking the process. IoT can be attached physically to objects which will provide a data on
their efficiency or working conditions.
Key aspects of IoT:
1.Connectivity
2. Sensors and Actuators
IOT
3.Data Collection and Analysis
4. Automation and Control
For example, In manufacturing, digital twins of production lines can be used to simulate different production scenarios, such as changing
work sequences and adjusting line speeds. By collecting data from sensors and other his IoT devices on the actual production line, the
engineer can create a digital twin that accurately reflects the line's behavior. This digital twin can be used to optimize production line
performance, reduce downtime, and increase productivity.
Overall, the combination of digital twins and IoT is transforming the way we design, build, and operate physical systems, enabling us to
optimize performance, reduce costs, and improve efficiency and sustainability
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Introduction- RPA
•
Robotics Process Automation(RPA) allows organizations to automate
task just like a human being was doing them across application and
systems.
•
Robotic Process Automation is the innovative use of software to
perform repetitive rules-based knowledge work across an organization
as a substitute for, or aide to, human workers.
•
“Virtual Workers” replicate the specific actions a human would take
while working with IT systems, the decisions they make, and the logical
processes they follow, while interacting between different systems and
applications
•
RPA can be used to automate workflow, infrastructure, back-office
process which are labor intensive. These software bots can interact with
an in-house application, website, user portal, etc.
•
RPA is a software technique that enables businesses to automate
business operations by building software "bots" that can interact with
digital devices and software programmes in a manner similar to that of
people.
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The primary objectives of using RPA
Cost and Time Efficiency: By automating tedious and repetitive processes that take human workers a lot of
time, RPA attempts to cut expenses and save time.
Process Accuracy and Quality: RPA reduces the possibility of human error by carrying out activities with
extreme regularity and precision
Scalability and Agility: RPA helps businesses to increase operations without having to significantly alter their
infrastructure or resource allocation.
Enhanced Employee Experience: Employees can concentrate on more strategic and valuable tasks that
call for human judgement, creativity, and critical thinking by shifting repetitive and tedious jobs to RPA bots.
Integration and Interoperability: RPA tools can communicate with already-in-use programmes and
platforms while operating without any hiccups.
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Key Factors to consider When Distinguishing RPA Tools
User Interface
RPA tools may be
different in terms of
their usefulness and a
user interface. While
other applications
may require more
technical ability to use
and manage, some
include a more userfriendly and intuitive
user interface.
Scalability and
Performance
On scalability and
managing high
numbers of
transactions and
processes, RPA
solutions can offer a
variety of capabilities.
Support and
Vendor Reputation
Integration Capabilities
The degree of
integration that the
various RPA solutions
offer with other
networks and their
applications differs
Analyze technical
assistance, training,
and other support
services provided by
provider of the RPA
tool. Consider the
vendor's reputation
and its performance
record in the RPA
business.
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RPA Typical Business Processes
RPA can learn from people, eventually taking over the processes that humans once completed, at a much faster pace
Repetitive tasks
Carried out 50-60
times a day
Process List
and storage
Data Entry
Periodic reporting and
data analysis
Mass Email Generation
Archiving and extracting
Data Conversion
Conversion of data
format and graphics
ERP Transactions
ERP and other backoffice transactions
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Practical Case
The example will demonstrate the requisition and purchase process of materials, in RPA this is a process from end to end
(requisition to payment) where a sequence of activities are executed by multiple users interacting with multiple systems
(mail client, Excel, SAP).
In this case the bots are integrated in almost all stages of the process and through multiple functions.
Problem
The execution of manual tasks cause the process to be slow, subject to deviations, causing bottlenecks in which it is necessary to invest
several hours. The extraction of data from different applications or platforms becomes an extensive task in which it is necessary to invest
more than one resource in the execution of a simple process.
Users Involved
Area Involved
Requester, Buyer, Vendor and AP analyst
Requester, Buyer, Vendor and AP analyst
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Practical Case
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Solution Selection
Various approaches to selecting solutions
Pay-off matrix:
After the possible solutions are generated, they can be assigned to different
quadrants of the pay off matrix. The ideas which are low on benefits, are
rejected. The discussion is then focused on the high effort/high benefits and
these are rationalized for implementation. Some of the solutions in this
quadrant are so prohibitively expensive that they can be eliminated without
further analysis. Caution needs to be maintained when eliminating potential
solutions from this quadrant.
Screening against ‘Must be’ criteria:
Company policies, local laws and social considerations are extremely
important before proceeding further on any solution. Finally, the extent to
which the customer CTQs of the project are satisfied is a key consideration
for selection (use KANO model to evaluate expected extent of satisfaction
generated).
N/3 voting:
The key consideration in this form is that all rejected solutions should be
justified.
Criteria based matrix:
Solutions scoring differently under each defined criteria.
Nominal Group Technique:
Simplify application process & consolidation..
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Various approaches to selecting solutions
Screening against‘Must Be’
Pay off matrix
H
Benefits
Compliance, policies
and regulations
Customer CTQs
Business CTQs
L
H
Effort
Solutions for further discussions
N/3 voting
Criteria based matrix
Establish Criteria
Combine all similar
choices with c onsensus
G enerate/update
list of solutions
Rationalize/justify/reject
solutions
Tally votes
for each
choice
Allow members
to c hoose 1/3 of the
list as choices
Ass ign
Wt.
# of
Votes
Soln. 1
Soln. 2
• Ease of Use
• Inter site availability
Preparation of
Charts
•
• Information Real
time
•unresolved
Auto escalation of
issues
• Filters and regulated
access
Total score:
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Criteria Based Matrix (CBM)
Where to use CBM?
❑
More than a few criteria for solution selection.
❑
Solutions scoring differently under each criteria.
❑
The weight for each criteria differs.
How to use CBM?
1.
Identify criteria and assign weightage.
2.
The team members to give votes to each idea.
3.
The votes are then multiplied with the weights of the
criteria established forselection.
4.
The total scores obtained at the bottom of the solution
are used to select the same.
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N G T: Nominal Group Technique
What is the best method to reduce cost per
transaction?
Solutions
•Simplify Application process
•Automation
•Employ Six Sigma
•Consolidate sites
•Implement Web Services
•Reduce Workforce
Member 1
6
1
3
5
4
2
Member 2
4
1
3
6
5
2
Member 3 Totals
6
1
2
4
3
5
16
3
8
15
12
9
SIMPLIFYAPPLICATION PROCESS& CONSOLIDATION
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Solution
with
Lean
Lean Tools
5S
What is 5S?
▪
A systematic approach to organize and standardize the
workplace. 5S was originally developed within the Toyota
Production.
Why 5S?
▪
▪
▪
▪
To improve efficiency and productivity.
To maintain safety and cleanliness.
To maintain good control over the processes.
To maintain the good product quality.
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5S
SEIRI
Sort / Segregate
SEITON
Self Arrange
SEISO
Spic and Span
SEIKETSU
Standardize
SHITSUKE
Self Discipline
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Objectives of 5S
Objectives of 5S:
❑
Promote Safety.
❑
Improve Workflow.
❑
Better Product Quality.
❑
Reduce Inventory Waste.
❑
Give People Control of Their Workplace.
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Step 1: Sort (Seiri)
Consequences of not Sorting (Seiri):
❑
The wanted is hard to find when required.
❑
More space is demanded.
❑
Unwanted items cause misidentification.
❑
Misidentification causes errors in operation.
❑
Maintenance cost of the equipment increases.
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Step 2: Set in Order (Seiton)
Identifying places to arrange the things and placing them in
proper order for prompt usage.
“A place for everything and everything in its place.”
While arranging or setting things in order (Seiton) keep in mind:
❑
❑
❑
❑
❑
❑
❑
The right location where the things will be used.
FIFO (First in First out) arrangement.
Labeling of the area and the equipment is very important.
Keep proper gaps between two things to avoid confusion.
Good SEITON includes use of labels signs, indications, display,
cautions.
Use of labels signs, indications, display, cautions highlights
difference between normality and abnormality.
Non - users of the equipment also become aware of its use and
precautions.
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Step 2: Set in Order (Seiton)
Consequences of not arranging things in order (Seiton):
❑
Things are seldom available when needed.
❑
Items get lost.
❑
Items get mixed up.
❑
Visual control not possible.
❑
Failure to achieve targets.
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Step 3: Shine (Seiso)
❑
Sweep your workplace thoroughly so that there is no dust/dirt/scrap
anywhere.
❑
The area should say “ Who I’m” and its neatness should give you a
natural welcome.
While arranging or setting things spic and span (Seiso) keep in
mind:
❑
Cleaning should be done regularly.
❑
Use the best cleaning agent.
❑
All the nooks and corners should be cleaned.
❑
Keep all the labels intact.
❑
All the labels should correct, visible and legible to all.
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Step 3: Shine (Seiso)
Consequences of not being spic and span (Seiso):
❑
Performance of machines deteriorates.
❑
The quality / aesthetic quality deteriorates.
❑
Dirty place is unpleasant and hazardous to health.
❑
Sends uncaring and irresponsible message to the team
members and society at large.
❑
People working at dirty areas are generally found to have low
desire to excel and their motivation level is low.
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Step 4: Standardize (Seiketsu)
❑
Always aim at maintaining the standard level of cleanliness,
hygiene and visual control.
❑
Keep all the 4 M’s ( Man., Machine, Material and Method)
intact, a lapse in any one of them will make you loose the rest
of the three.
While standardizing (Seiketsu) keep in mind:
❑
The standards should be arrived at unanimously.
❑
Always keep the standards flexible to changes and
improvements.
❑
Standards should be known to all and displayed.
Essence of standardizing (Seiketsu):
❑
It is the proof that 3-S (SEIRI, SEITON, SEISO) are being
religiously carried out.
❑
It is the barometer which indicates the control level based on
the 5-S of all the workers.
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Step 4: Standardize (Seiketsu)
Consequences of not standardizing (Seiketsu):
❑
Dual standards yield multiple results.
❑
Multiple results lead to conflicts and confusions.
❑
Rework increases.
❑
Rework increases the basic cost of the finished product without
any value addition.
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Step 5: Sustain (Shitsuke)
If you are disciplined.:
❑
Rules will always be followed.
❑
Laid down targets will be achieved.
❑
Improvements will be promoted.
❑
The no. of defects will be reduced.
❑
The cost will not increase.
To ensure success:
❑
Train all team members on 4-S.
❑
Correct wrong practices on the spot.
❑
Punctuality is the backbone of 5S.
❑
Follow work instructions.
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Advantages of 5S
How to plan for 5S?:
❑
Assemble a 5S Lead team.
❑
Define the work area 5S boundaries (list them).
❑
Assign work group members to their 5S areas.
❑
Determine 5S targets, activities, and schedule.
❑
Review/finalize plans with work group and site leadership.
Advantages:
❑
Provides basis for being a world-class competitor.
❑
Starts the foundation for a more systematic approach to the
workplace.
❑
Improves productivity and morale.
❑
Empowers employees.
❑
Increases profit.
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5S Implementation
1.
Obtain existing standards for color-coding and signage.
2.
Decide on 5S color-coding and signage standards.
3.
Communicate, communicate, communicate! (e-mail, signs, one
on one).
4.
Install 5S communication board.
5.
Set acceptable timetable for completion.
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Roles for 5S implementation
TOP MANAGEMENT
•
•
•
•
Play the role of mentor
Initiate the 5Sprogramme
Provide resources
Appreciate the efforts
MIDDLE / LINE MANAGEMENT
•
•
•
•
Play the role offacilitator
Take initiative in his area of work
Train the people in 5S
Give the feedback
EMPLOYEES
•
•
•
•
•
Participate actively.
Give suggestions.
Respect the opinion of others.
Be a good team player, and
Maintain discipline
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Examples with 5S
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Examples with 5S
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Kanban
•
Kanban literally means “visual card,” “signboard, "or “billboard.”
•
Toyota originally used Kanban cards to limit the amount of
inventory tied up in “work in progress” on a manufacturing floor.
•
Not only is excess inventory waste, time spent
producing it is time that could be expended elsewhere.
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Why Kanban?
▪
Kanban is a “pull” system that involves cascading or signaling
production and delivery instructions from downstream to
upstream activities in which nothing is produced by the
upstream supplier until the downstream customer signals a
need.
▪
All production is based on consumer demand, with nothing
"pushed" downstream.
▪
Simply said, nothing is produced without a signal from the next
station in the line.
▪
Kanban acts as the means of signaling used for material &
information movement.
▪
Kanban is usually in a piece of paper in a vinyl envelope or
container; outline marking on the floor.
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Types of Kanban
❑
Production Kanban: authorizes production of goods.
❑
Withdrawal Kanban: authorizes movement of goods.
❑
Kanban square: a marked area designated to hold items.
❑
Signal Kanban: a triangular Kanban used to signal production
at the previous workstation.
❑
Material Kanban: used to order material in advance of a
process.
❑
Supplier Kanban: rotates between the factory and suppliers.
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SMED- Single Minute Exchange of Dies
• SMED, (Single Minute Exchange of Dies), is a methodology used for reducing the time it
takes to change over a machine from making one product to another. It was developed in
Shigeo Shingo a Japanese industrial engineer.
• SMED is based on the principle of dividing conversion tasks into two categories: internal
and external. Internal tasks can run when the machine is stopped. External tasks can run
while the machine is running. The purpose of
• SMED is to convert as many internal tasks as possible to external tasks. This can be
achieved by making changes to the machine or tools, or by changing the way the
changeover process is performed.
• Once all internal tasks have been converted to external tasks, the next step is to simplify
and streamline the remaining steps. This can be accomplished by removing unnecessary
steps or combining steps that can run simultaneously. By following the
• SMED methodology, manufacturers can significantly reduce the time it takes to convert
machines. This increases productivity, reduces costs and increases customer
satisfaction.
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Benefits of SMED
There are many benefits to implementing SMED, including:
Reduced changeover time: The goal of SMED is to reduce changeover time to as little as possible.
This can lead to increased productivity, as machines can be used more efficiently.
Reduced costs: Reduced changeover time can lead to reduced costs in a number of ways. For
example, it can reduce the need for overtime, it can reduce the amount of inventory that is needed,
and it can reduce the amount of waste that is produced.
Improved customer satisfaction: Reduced changeover time can lead to improved customer
satisfaction in several ways. For example, it can lead to shorter lead times, it can lead to a wider
variety of products being available, and it can lead to a more consistent quality of products.
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How to implement SMED
There are a number of steps that can be taken to implement SMED, including:
▪
Identify all the steps involved in the changeover process.
▪
Classify each step as internal or external.
▪
Convert as many internal tasks as possible to external
tasks.
▪
Simplify and streamline the remaining steps.
▪
Implement the changes and monitor the results.
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Example
One real-life example of SMED can be seen during professional car racing.
Pit crews use SMED to improve their speed by pre-positioning tools and equipment, having a standardized procedure
for each task, and reducing the number of steps involved in each task. By using SMED, pit crews can shave valuable
seconds off their pit stop times, which can give them a competitive advantage and help them win races.
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Introduction of Spaghetti charts
Shigeo Shingo a Japanese industrial engineer developed Spaghetti charts.
Spaghetti diagrams are a kind of process map that visualizes the flow of
people, products, or information through a process. They are used to find
areas for improvement in manufacturing and Service Industry
A spaghetti chart uses lines to show the flow of people, products, or
information through a process. Lines are drawn on paper or with the help
of software application.
Spaghetti charts can be used to:
❑ Identify the zone where there is lot of man or machine movements.
❑ Find fields where flow of information is transmitted backwards and
forwards unnecessarily
❑ Find the opportunities to streamline the process.
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How to create Spaghetti charts
Steps To create a spaghetti chart:
1. Identify key tasks that will take place during the session.
2. Flow of people, products, or information for each activity to
be mapped properly
3. Lines to be used to connect different tasks.
4. Mark the line or label them with task name.
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How to use Spaghetti charts
After Creating spaghetti chart, it can be used for waste identification and improve the process.
To do this, you will need to:
1.
Search for places where the lines are long and winding.
2.
See for areas where lines cross each other.
3.
Search for places where the lines are tangled.
After finding out the areas for improvement, we can start the Process improvement.
For example, you may need to:
•
Move people closer to the resources they need
•
Streamline the flow of information
•
Reduce the number of activities
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Example: Before
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Example: After
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Kaizen
❑
“Kai” means "change”.
❑
“Zen” means “good (for the better)”.
❑
Gradual, orderly, and continuous improvement.
❑
Ongoing improvement involving everyone.
What is a Kaizen?
❑
Kaizen is a Japanese word for the philosophy that defines
management’s role in continuously encouraging and
implementing small improvements involving everyone.
❑
It is the process of continuous improvement in small
increments that make the process more efficient, effective,
under control, and adaptable.
❑
It focuses on simplification by breaking down complex
processes into their sub processes and then improving
them.
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Why use Kaizen?
When to use Kaizen?
❑
To solve problems (without already knowing the solution).
❑
To eliminate waste(Muda).
❑
Transportation, Inventory, Motion, Waiting, Over-production, Overprocessing, Defects.
❑
Create ownership and empowerment.
❑
Support lean thinking.
What is a Kaizen Blitz?
❑
Total focus on a defined process to create radical improvement in
a short period of time.
❑
Dramatic improvements in productivity, quality, delivery, leadtime, set-up time, space utilization, work in process, workplace
organization.
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Kaizen Themes
Kaizen Themes:
❑
❑
❑
❑
❑
❑
❑
Red Tagging (getting rid of clutter).
Visual Control (instructions in the workplace).
Better (any small improvement).
Benchmark (adopt other industry service).
Clarity (communication without confusion).
Pit Stop (streamlining critical activity).
Service Supreme (using our best experience as our standard).
Kaizen Advantages:
❑
❑
❑
❑
❑
❑
❑
Do Right Things(effectiveness).
Do Things Right (efficiency).
Do Things Better (improve).
Do Away With Things (cut).
Do Things Others Do Well (adapt).
Do What No Other Is Doing (unbeatable).
Do What Can’t Be Done (incredible).
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Kaizen Cycle
Identify
Waste
Start
Plan
counter
measure
Document
Do it
again
Reality
Check
Kaizen
Cycle
Celebrate
Make
changes
Make this
the
standard
Verify
change
Measure
results
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Poka-Yoke
❑
Poka means “Mistake” or “Error” and Yoke means “Proofing” or
“Avoid”.
❑
In other words, Poka-Yoke means Error Proofing or Mistake
Proofing or Avoidance of Error.
❑
Poka-Yoke is a Japanese improvement strategy for mistakeproofing to prevent defects (or nonconformities) from arising during
production processes.
❑
The Poka-Yoke concept was created in the mid-1980s by Shigeo
Shingo, a Japanese manufacturing engineer.
❑
Mistake Proofing is a method for avoiding errors in a process.
❑
The simplest definition of ‘Mistake Proofing’ is that is a technique for
eliminating errors by making it impossible to make mistakes in the
process.
❑
It is often considered the best approach to process control.
❑
Poka-Yoke device is any mechanism that either prevents a mistake
from being made or makes the mistake obvious at a glance.
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Why Poka-Yoke?
❑
Error free designs or processes.
❑
Eliminate the possibility of setting the X’s beyond the limits.
❑
Warns operators before the X’s move to the outside limit so that the
preventive action can be taken.
❑
Can also be used in conjunction with the risk management or SPC.
❑
The opportunity for error needs to be minimized or eliminated.
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Poka-Yoke - Examples
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Poka-Yoke - Examples
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Classroom Quiz
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Change
Management
Why organizations need to change?
Crisis/Competition
M&A
Internal and external
changes
New technology
Performance gaps
New opportunities
Change for the sake of
change
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Understand the case for change
Emerging
technologies
New business
models
Focus on
experience creation
Big data
Changing workforce
demographics
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Shifting geopolitical &
regulatory environment
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What is change?
Verb (used without object), changed, chang·ing.
•
to become different: Overnight the
nation's mood changed.
•
to become altered or modified: Colors
change if they are exposed to the sun.
Noun
Verb (used with object),
changed, chang·ing.
•
•
to make the form, nature,
content, future course, etc.,
of (something) different
from what it is or from what
it would be if left alone
to transform or convert
(usually followed by into)
•
the act or fact of
changing; fact of being
changed: They are
pleased by the change
in their son's behavior.
•
a transformation or
modification;
alteration: They
noticed the change in
his facial expression.
Your Text Here
Bring your presentation to life.
Download this awesome
diagram. Capture your
audience’s attention.
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Internal and External Drivers of Change
Correctly identify whether a driver is an internal driver or an external driver of change?
Options
Internal
External
Capabilities
Competitors
Capabilities
Competitors
Employee Dissatisfaction
Employee Dissatisfaction
Customers
Desire
Desire
Markets
Customers
Values
Government
Values
Markets
Government
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What is change management?
“
Change management is
a structured approach
for ensuring that
changes are thoroughly
and smoothly
implemented, and that
the lasting benefits of
change are achieved.
“
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Change is Personal
Rational
Emotional
Everyone reacts to
change differently.
The response to
change is different for
different individuals.
Political
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We need to understand
personal change in order to
understand organisational
change.
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Typical responses to change
Kubler-Ross Change Curve: Change can cause a rollercoaster of responses and emotions
Happiness
At last
something’s
going to
change!
Anxiety
What is this?
Denial
Change, what
change?
Fear
What will
this mean
for me?
Threat
This is worse
than I thought!
Disillusionment
I’m off.. This isn’t
for me
Guilt
Did I really
Depression
do that?
Who am I?
What am I doing?
Moving forward
This can work
and be good
Acceptance
I can see myself
in the future
Testing
Fine, I’ll give it a go
Hostility
I’ll make this
work if it kills me!
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Facets of change
What’s in it
for me?
Why are we
doing this?
Highlighting
key benefits
Ensuring visible
leadership
What
support will I
get?
How do we
get there?
Engaging, listening
and communicating
Role-modelling the
right behaviours
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Driving change
What drives people’s motivation?
Creating a sense of
meaningfulness
INTRINSIC
MOTIVATION
There is choice about
how to change
Pay and bonuses
Role Models
Non financial
benefits &
recognition
EXTRINSIC
MOTIVATION
Success
stories
Self efficacy
and
competence
Sense of
accomplishment
and progress
Positive
reinforcement
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The Change Management Process
Kotter's 8 Steps for Leading Change
8. Institute
Change
6. Generate
Short-Term Wins
4. Enlist A
Volunteer Army
2. Build A
Guiding
Coalition
1. Create A
Sense of
Urgency
3. Form A
Strategic
Vision
5. Enable
Action By
Removing
Barriers
7. Sustain
Acceleration
Source: The 8-Step Process for Leading Change | Dr. John Kotter (kotterinc.com)
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Force Field
Analysis
What is Forced Field Analysis
Social psychologist Kurt Lewin Developed a Forced Field analysis method to identify and analyze the forces that
Drive or work as hurdle in desired changes. With the help of Visual Representation, the driving and restraining forces
can be distinguished easily. Force field analysis can help in continuous improvement projects by knowing the factors
that influence change initiatives and strategies to reinforce drivers and reduce the obstacles
Steps for Forced Field Analysis
1.
Identify the Desired Change: Define the specific change or improvement goal you want to achieve.
2.
Identify Driving Forces: Identify the factors or forces that are pushing or driving the change.
3.
Identify Restraining Forces: Identify the factors or forces that are resisting or hindering the change.
4.
Assign Scores and Weights: Assign scores or weights to each driving and restraining force to reflect their relative importance or
impact on the desired change.
5.
Analyze the Findings: Summarize and visually represent the driving and restraining forces using a force field diagram. The diagram
consists of two columns: one for driving forces and the other for restraining forces. The forces are represented as arrows with lengths
proportional to their strength or impact.
6.
Develop Strategies: Based on the analysis, identify strategies to strengthen the driving forces and weaken the restraining forces.
7.
Implement and Monitor: Implement the identified strategies and continuously monitor the progress.
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Example: Force Field Analysis - Employee Training Initiative
1.
2.
3.
4.
Identify the Desired Change: Implement a new
employee training initiative to enhance skills and
knowledge.
5.
Driving Forces:
•
Strong support from top management
•
Identified skill gaps in the workforce
•
Emphasis on continuous learning and
development
Restraining Forces:
•
Resistance to change from some employees
•
Limited budget for training programs
•
Lack of awareness about the benefits of training
Driving Forces: High
•
Restraining Forces: Medium
Driving Forces:
Restraining Forces:
•
•
•
•
•
•
6.
Scores and Weights:
•
Analyze the Findings: Visual representation of driving and
restraining forces.
6.
Top Management
Support
Identified Skill Gaps
Emphasis on Learning
and Development
Resistance to Change
Limited Training Budget
Lack of Awareness
Develop Strategies:
•
Conduct change management workshops to address employee
resistance.
•
Seek additional funding or explore cost-effective training
alternatives.
•
Implement a communication campaign to promote the benefits of
training.
Implement and Monitor: Roll out the training initiative, track
employee engagement, and evaluate the impact. Regularly review
and update the analysis based on progress and feedback
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Force Field Analysis - Diagram
Force Field Analysis
Forces for change
Forces resisting change
Top Management Support
Business Excellence Manager’s
support
Resistance to Change
New Employee Training
Initiative
Focus on learning, growth and
immediate impact
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Limited Training Budget
Lack of Awareness
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FMEA
Failure Mode Effects Analysis (FMEA)
A Failure Mode and Effects Analysis is a systemized team activity
intended to:
❑
Recognize and evaluate potential failure and its effects.
❑
Identify actions which will reduce or eliminate the chance of failure.
❑
Document analysis findings.
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FMEA
Objective of FMEA
❑
Identify the high priority failure modes and causes of defects in an
operational or transactional process.
❑
Identify high priority input variables (Xs) that impact important
output variables (Ys).
❑
Evolve a consensus on the recommended corrective actions and
procedures to follow.
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FMEA
When to use FMEA
❑
FMEA is designed to prevent failures from occurring or from getting
to internal and external customers.
❑
FMEA is essential for situations where failures might occur and the
effects of those failures occurring are potentially serious.
❑
FMEA can be used on all improvement projects.
❑
FMEA serves as an overall control document for any given process.
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FMEA Steps
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
FMEA is carried out on a new process/product or redesigned
process/product.
For each process step, list requirements for each process step.
For each requirement, list the failure mode for each requirement.
For each failure mode, list the effect of failure for each failure
mode.
For each effect of failure, estimate the severity.
For each failure mode, list causes.
For each cause of failure, estimate the likelihood of occurrence.
For each cause of failure, list the current process controls.
For each process control, estimate the detection.
For each cause of failure, calculate the Risk Priority Number by
multiplying the scores associated with severity, occurrence and
detection.
For high priority causes of failure and/or failure modes, develop
recommended actions.
For each recommended action, assign responsibility and
completion dates.
For each recommended action, implement the action and note its
effect.
For each implemented action, re-estimate the severity, occurrence
and detection rankings and recalculate the RPN.
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Severity Scale
RPN = Severity x Occurrence x Detection
High
Low
More the severity,
higheris the rating
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Occurrence Scale
RPN = Severity x Occurrence x Detection
High
High
Low
More often it occurs,
higher is the rating
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Detection Scale
RPN = Severity x Occurrence x Detection
High
Low
High
Lower the
ability to detect,
higher is the
rating
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F M E A Worksheet
Process
Step/Part
Number
Potential
Failure
Mode
Potential
Failure
Effects
S
E
V
Potential
Causes
O
C
C
Current
Controls
D
E
T
R
P
N
Actions
Recomm
ended
Resp.
Actions
Taken
S
E
V
O
C
C
D
E
T
R
P
N
0
0
0
RPN = Severity
X
Occu rrence
X
Det ection
0
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F M E A –Example
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Risk Priority Number (RPN)
❑
The RPN number is calculated from the team’s estimates of
Severity, Occurrence and Detection.
❑
RPN = S x O x D.
❑
If you are using a 1 - 10 scale for Severity, Occurrence and
Detection, the worst RPN = 1000 (10 x 10 x 10), while the best
would be RPN = 1 (1 x 1 x 1).
❑
Use RPN numbers to prioritize failure modes and/or causes of
failures in order to work on the highest priority issues.
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F M E A – Useful Tips
Suggestions for completion of the activity in time:
❑
Similar to a process map, FMEA is also a “live” document used
throughout the DMAIC journey.
❑
Make it a “team effort.”
❑
Analyze new processes to avoid problems before they happen.
❑
Address concerns from a process perspective and not business
contingency perspective.
❑
Analyze existing processes to find and fix problems.
❑
Analyze existing processes to discover the high priority (“key”)
process input variables.
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Implementation
Plan
Implementation of Lean and Six Sigma in your Team / Process
implementation Lean Six Sigma:
❑
Vision, Mission Goals (Strategic Planning)
❑
Incorporation of pilot learning (Tactical Planning)
❑
Tools, resources and time frame (Support for Tactical Plans)
❑
Culture transformation (Learning and Evolving)
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Vision & Mission
Requirements for objective quality and subjective acceptance is always dependent on knowledge and expertise for effective implementation
Empowering Vision for
Operational Excellence
Enhance efficiency and effectiveness in
all the processes
Vision
(and)
Pathway for culture of
excellence and learning
Delivery superior quality and value to our
customers
LSS
Success
Goals to support Vision &
Mission
Introduce Lean and Six Sigma to the team
to identify and launch multiple quality
improvement projects
Missions
Equip team with tools and knowledge
required to drive process improvement
initiatives
(and)
Goals
Include everyone involved in the process
to identify and solve problems proactively
(and)
Embed Lean Six Sigma Principles in daily
operations to foster culture of continuous
learning and improvement across the
organization
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Incorporate Pilot Learning
Pilot is trial over a small segment
Get Start with your Journey:
❑
Identify opportunities
❑
Validate expected results
❑
Facilitate buy-in
❑
Build a improvement team
❑
Start small and accelerate improvements
Generating Ideas
Ignite Your Project
Original
Test
Full scale
Pilot opportunity on small scale
& evaluate the results
For positive results: scale the
ideas in phased approach
Cost-Benefit Analysis
A
4
B
1
C
3
D
2
Do brainstorming, find pain
points, organize all the painpoints/opportunities where you
want to showcase your skills
Improvement Team
Perform cost-benefit to find
which opportunity likely has low
investment and may yield high
sustainable returns
Build a diverse improvement team
in terms of skills, experience,
perspective. Introduce quality
tools and techniques involved.
Project Validation
Involve all interested
stakeholder’s to share the
valuable insights and facilitate
buy-in
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Tools, Resources and Time Frame
TPC Analysis
Gantt Chart
LSS Gantt Chart
Jan Feb Mar Apr May Jun July Aug Sept Oct Nov Dec
Define
Project Charter
Process Mapping
RACI Matrix
Measure
Data Collection
Sigma Baseline
Analyze
VSM
Root Cause
Improve
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Cultural Transformation
Ensure experiments are guided with
fact based and data driven
approaches with surrounding the
problem. Encourage to identify fact
based root causes of inefficiencies
and allow them to measure the impact
of the effects to develop sense of
urgency on chronic issues.
01
02
Status Quo
Schedule meetings and session to
challenge the status Quo and set goals
towards desired future state
Dedicated Time
03
PDCA Cycles
04
Plan improvements, execute them, check
results and act on what you learn to
continuously refine the process
Learning Culture
Dedicate a specific time and give
resources to team to experiment with new
ideas and processes
Encourage team members to question
existing process and identify areas of
opportunities and improvement. Build
alliance with your leadership to foster
an environment where change is
welcomed, and innovation is
rewarded.
Explain others in most simplified way
about Lean Principles and Six sigma
approach. Encourage sharing of
knowledge vertically or horizontally.
Introduce LSS, 7QC, Lean tools
training within your team to reinforce
importance of ongoing improvements
Learning to be the guiding force in
relentless pursuit of perfection and strive
for perfection would yield continuous
improvement. “Teach & Inspire”
Plan: State the problem, measure
baseline, find the causes
Do: Develop and implement
improvement and verify change
Check: Evaluate outcomes and
measure success
Act: Find what went well, what didn’t
go well; inspect and adapt
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Simulation Exercise based on Case Study-1
LEAN SIX SIGMA CAPSTONE PROBLEM SCENARIOS
Scenario 1: The Problem of “Not on Time” for Delivery
ABC Ltd. is a package delivery service for homes and small businesses. ABC specializes in packages 50
pounds or less and has a full-price rebate policy for any delivery made outside the customer-designated
15-minute window. ABC advertising proudly states, "Delivery at your convenience, not ours." Consumer
can avail services by placing an order on their application, "ABC to serve" available at play store or on their
official website by entering the basic details and submitting order for making the on-time delivery.
ABC has facilities at multiple locations Downtown, Suburbia etc., each servicing customers within a 15mile radius with deliveries made by pick up or bikes based on the Size and distance in which delivery to
be made as per Annexture-1. PDI assures that they deliver the consignment post submission of order at
app. or website to deliver a package at customer designated in a 15-minute window. ABC charges
customers $5 per package plus $1 per pound (50-pound maximum).
On any given day ABC delivers and picks up approximately 50 packages having an average weight of 35
pounds at Downtown location.
Over the weeks, Sales Operators have reported that 35% customers have complained that deliveries have
not been meeting the committed timelines of 15 minutes at Downtown area. In response, Sales Operators
were instructed to remind customers of the ABC price rebate policy. Additionally, a short survey was sent
out to a small group of established customers. Survey results disclosed an appreciation of price rebates,
but a preference for deliveries within the committed timelines.
Task to do in Improve phase:
•
Do Brainstorming for Finding Solution.
•
Select the solution (Digital / Lean Tools)
•
Perform FMEA exercise
•
Perform forcefield analysis
The organization's leadership team has decided to solve the problem the Lean Six Sigma way using the
DMAIC approach to solve the problem of delayed deliveries and pickup.
Annexture-1:
Weight of consignment
Miles
Modes
20-30
<10
Bike
20-30
10-15
Truck
30-40
>2
Truck
30-40
<2
Bike
40-50
All
Truck
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Simulation Exercise based on Case Study-2
Scenario 2: The Problem of Delay in Account Opening Time in Retail Banking
Bank of XYZ, a major bank receives on an average 2000 new saving account opening customer
application forms every day. 40 operators enter the application forms in a database after cross
checking the CAF (Customer Application Form) with Identity Proof details.
The entries are rechecked against the Identify Proof details by 15 Quality Assessors and further 5%
sample is audited by 3 Quality Supervisors. The sales team promise the account opening within 48
hours from receipt of the CAF. Bank of XYZ usually achieves the account opening within average of
30 hours with a standard deviation of 6 hrs.
Recently, after a significant marketing effort, they started receiving over 3500 CAF, and the % of
defects in the CAF increased far more than the acceptable 10% of total opportunities for error and
processing time of CAF also increased, leading to account opening taking more than target of 48hrs.
The Customer Application has the following sections (Opportunities for Error):
Task to do in Improve phase:
•
Do Brainstorming for Finding Solution.
•
Select the solution (Digital / Lean Tools)
•
Perform FMEA exercise
•
Perform forcefield analysis
1. Title and Gender of the customer
2. Name of the customer
3. Address of the customer
4. Date
5. Identity Proof No
6. Product Code
7. Email Address
Any incorrect section is considered a defect and must be re-processed.
The Bank is losing $3750 every day primarily on rework and penalties. The customers are also
dissatisfied as the account opening is taking more time than promised. In the wake of the current
business situation, the management team decides to initiate a Six Sigma project to reduce defects
and achieve target account opening time.
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Inference from Improve Phase
✓
Identification of Digital Solutions Techniques IOT/Digital Twins/RPA
✓
Shortlist the appropriate solution Digital / Lean improvement Tools.
✓
Risk proof the solution using FMEA.
✓
Developing implementation plan/strategy
✓
Understanding and handling individual/organizational change
✓
Use of Force Field Analysis tool for potential solutions identified
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Classroom Quiz
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Module -6
Control Phase
Lean Six Sigma Green Belt Training Flowchart
Module-6
Control
SPC
•
Why Control?
•
Variable Control charts
Process Management
System
•
What to Control?
•
Attribute Control charts
•
Documentation and
standardization
•
Monitoring
•
Response plan
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Project Closure
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Module-6 Learning Objectives
By the end of this module participants will be able to learn following:
✓ Determine the stability of a process through identification of special cause variation with the help of
different types of control chart
✓ Use control charts to make correct decisions
✓ Understand Process Management System
✓ How to create and maintain documentation
✓ Explain how to make solutions sustainable
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Understand
Control
Why control?
Improvements
Process control
N
o
p
r
e s s c o n t r o
o c
l
Time
To hold the gains of the DMAIC project there by help ensuring that the efforts of the project team
are not written off.
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Why control?
Process control is essential to prevent the loss of gains over a period of
time.
Detect the out of control state of processes and determine the
appropriate actions.
Control system incorporates risk management, SPC, measurement and
audit plans, response plans, documentation and ownership.
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What to control?
Inputmeasures (Xs)
Output measures
Process measures
Monitoring points
❑ At the barest minimum, measure CTQs.
❑ Strive to measure the critical Xs to provide chances to make corrections.
❑ Install the appropriate data collection plan.
❑ Audit appropriately.
❑ Early warning system prevent $$ impact (rework reduction).
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SPC: Statistical
Process Control
Statistical Process Control (SPC): An overview
Control charts
❑ Statistical Process Control or SPC was conceptualized by Walter A Shewhart to
determine if a business process (back then manufacturing process) is in a state
of control.
❑ SPC primarily uses Control Charts.
❑ Control charts are also known as Shewhart charts, or process behavior charts.
Fundamentals of SPC
❑ All processes display variation.
❑ There are two types of variation i.e. Common Cause Variation and Special
Cause Variation.
❑ Common Cause Variation is inherent, steady, common, undiscoverable.
❑ Special Cause Variation is intermittent, special, discoverable, removable.
❑ The Control Charts are set at + 3s from the mean.
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Statistical Process Control (SPC): Why and When Control Charts?
Control charts
❑
Control Charts monitor changes in the Xs and detect changes that
are due to special causes.
❑
Used when the Xs cannot be mistake proofed or need to be
controlled for inherent variations.
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Value/Measurement Axis
Control Limits
Common
C au se/ R an d o m Va ria tio n
U p p e r C o n t r o l L imit
6000
3. UCL=5587
5000
X= 4198
4000
3000
- 3. LCL=2810
1
2000
S ubgroup
Ave r ag e
0
W eek
S p e c i a l C au se
Va ria tio n
1.
Select process, measures to be charted.
2.
Establish data collection and sampling plan.
3.
Calculate the statistics.
4.
Plot control charts.
10
20
30
40
10/ 22/ 00
12/31/ 00
3/11/ 01
5/ 20/ 01
L o w e r C o n t r o l L imit
T ime/ S eq u en ce Ax is
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SpecialCause vs. Common Cause Variation
Common causes
Special causes
True variation type…
Tampering
Common
causes
Special
causes
Mistake 1
Tampering
Focus on systematic
process change
(increases variation)
Mistake 2
Under-reacting
(missed prevention)
Investigate special
causes for possible
quick-fixes
Low
Reacting to common cause variation, as if it were due to a
special cause. System is stable and only common cause is
present, but not working at an acceptable level which
requires fundamental change. Reacting to individual data
points would be inefficient.
Under-reacting
Ignoring special cause variation, treating it as if it were
common cause. Goal is to remove special cause variation to
get stability and predictability. Try to understand the effect
of any deliberate changes.
High
In Our Control
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406
Selecting Control Charts
Type of Data
VARIABLE /
CONTINUOUS
DISCRETE /
ATTRIBUTE
Individual
Measurements or
Subgroups?
Individual
I – MR Chart
Defects or
Defectives?
Defectives
Subgroups
Defects
Subgroup < 8
Constant Sample
Size?
Constant Sample
Size?
Yes No
Yes No
Yes No
X –Bar
R chart
X - Bar
S chart
npchart
Pchart
Cchart
Uchart
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Identifying Special Cause Variations
Test 1. Pointsbeyond Control Limits:
Points beyond control limits are isolated
high or low points. 1 point more than 3
from center line.
Test 5. Points on same side:
Test 2. Points on one side of the centerline:
Test 6. Pointson same side:
9 points in a row on same side of center line.
Four out of five points more than 1s from the
center line (same side).
Test 3. Trend:
Test 7.Identifiesa patternof variation:
Trends will continue up or down without a
well defined end. Six points in a row, all
increasing or all decreasing.
Fifteen points in a row within 1s of center
line (either side).
Test 4. Variationpattern:
Test 8. MixturePattern:
A cycle produces a pattern of up and down
points, very much as if the values of the
points were time dependent. Fourteenpoints
in a row, alternating up and down.
Eight points in a row more than 1s from
center line (either side).
Two out of three points more than 2s from
the center line (same side).
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Control Charts in Minitab
Selection Guide
Variables
charts for
Individual
Continuous Data
I-MR
Individual Dataset
Continuous Data
X- Bar R
Control
Charts
Stats
Sub-Group < 8
Variables
charts for
Subgroups
Continuous Data
X- Bar S
Sub-Group > 8
Configure Test Options
Discrete Data (Defectives)
NP Charts
Constant Sample Size = Yes
Attributes
Charts
Discrete Data (Defectives)
P Charts
Constant Sample Size = No
Discrete Data (Defects)
C Charts
Constant Sample Size = Yes
Discrete Data (Defects)
U Charts
Constant Sample Size = No
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Control Charts Example: X bar S chart
Example
A canning company wants to assess whether its can-filling process is in control. The company collected 15 subgroups of 10 cans at 15minute intervals across two shifts for a single day's production. To minimize the within-subgroup (can-to-can) variation, the 10 samples for a
given subgroup were gathered in a short period of time. Minimizing this variation is important because within-subgroup variation is used to
establish the control limits for the S chartUsed when the Xs cannot be mistake proofed or need to be controlled for inherent variations.
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Control Charts Example: X bar S chart
Test Results for Xbar Chart of Weight
TEST 1. One point more than 3.00 standard deviations from center line.
Test Failed at points: 2
TEST 5. 2 out of 3 points more than 2 standard deviations from center line (on one side of CL).
Test Failed at points: 3, 9, 10
❖
Check the Data Points highlighted above and investigate reason/ possible causes
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Benefits of Control Charts
❑
Control Charts reduces defects by keeping processes centered.
❑
Improves overall quality by reducing chances of quality deviations.
❑
Aids in timely troubleshooting.
❑
Serves as a communication tool for changes in CTQs.
❑
Sustain improvements.
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Process
Management
System
Process Management System
New special cause variations identified
Documentation and
standardization
What, who, where of the process.
• Deployment flowchart
• Activity details
Monitoring
What, who, when, how of data collection and
measurements.
• Key measures
• Standards
• Measurement and process
Res ponse plan
Ongoing auditing
What, who, when, how of action upon
process failure.
• Procedure foradjustment
• Containment
• Procedure for improvement
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Process Management System
DOCUMENTATION
MONITORING
The plan for doing the work
Deployment
flowchart
Details on key
tasks
RESPONSE PLAN
Checking the work
Key process
and o u t p u t
measures
Monitoring
standards
Breakdown of Key
Add benchmarks/
tasks involved in
thresholds verses
Define the metrics
Visual diagram outlines
the process
the process
that indicate
the steps involved in
outlining their
performance for
efficiency and
deploying the process.
sequence,
measurement. A
effectiveness
Include decision points,
dependencies
baseline for
Quality indicators,
actions, and key
responsible parties
evaluating success
throughput, error
stakeholders
and expected
or to identify
rates, opportunities
timeframes
opportunities for
improvement
Method f or
recording data
The response to special causes
Containment
Procedure for
process
adjustment
Procedure f or
process
improvement
Concise protocols
Plan to mitigate
Include steps on how
Build a systematic
for collecting,
issues or failures
to adapt or modify methods for analyzing
storing and
within the process.
process wherever
process inefficiencies
analyzing data. It
Identify immediate
necessary for
and implementing
could involve
actions to avoid future
identified issues or improvements, including
software, manual occurrences. Update
failures or changes change management
logs or automated
FMEA wherever
in the requirements
system
systems
required
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Documentation
Documentation process is an activity that help ensure that the
knowledge gained by the project team is:
❑
Implemented successfully.
❑
Retained by the business.
❑
Used to standardize the business processes.
❑
Future resources can be trained easily.
❑
Improvements can be shared and translated across.
❑
Institutionalized.
D ocume nt at ion Levels
Proce ss
M anagement
Syste m
Flow charts
Proce dures
Che cklist
Increasing level of detail
C or e P r ocess
S ub P r ocess
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M icr o P r ocess
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Project Closure
Essentials of project closure:
•
Document and communicate results.
•
Recommend translation opportunities.
•
Evaluate team success-results, process and relationships.
•
Celebrate.
•
Disband project team but be kaizen!
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Classroom Quiz
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Inference from Control Phase
✓
Develop and implement control plans.
✓
Identify the appropriate KPIVs to be measured.
✓
Deploy the appropriate control chart.
✓
Develop Process Management System
✓
Transfer knowledge and responsibility for process control to
appropriate position(s).
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Simulation Exercise based on Case Study-1
LEAN SIX SIGMA CAPSTONE PROBLEM SCENARIOS
Scenario 1: The Problem of “Not on Time” for Delivery
ABC Ltd. is a package delivery service for homes and small businesses. ABC specializes in packages 50
pounds or less and has a full-price rebate policy for any delivery made outside the customer-designated
15-minute window. ABC advertising proudly states, "Delivery at your convenience, not ours." Consumer
can avail services by placing an order on their application, "ABC to serve" available at play store or on their
official website by entering the basic details and submitting order for making the on-time delivery.
ABC has facilities at multiple locations Downtown, Suburbia etc., each servicing customers within a 15mile radius with deliveries made by pick up or bikes based on the Size and distance in which delivery to
be made as per Annexture-1. PDI assures that they deliver the consignment post submission of order at
app. or website to deliver a package at customer designated in a 15-minute window. ABC charges
customers $5 per package plus $1 per pound (50-pound maximum).
On any given day ABC delivers and picks up approximately 50 packages having an average weight of 35
pounds at Downtown location.
Task to do in Control phase:
•
Check solution is in control or not.
•
Collect samples for SPC and prepare
charts
•
Make a sustainable Control Plan.
Over the weeks, Sales Operators have reported that 35% customers have complained that deliveries have
not been meeting the committed timelines of 15 minutes at Downtown area. In response, Sales Operators
were instructed to remind customers of the ABC price rebate policy. Additionally, a short survey was sent
out to a small group of established customers. Survey results disclosed an appreciation of price rebates,
but a preference for deliveries within the committed timelines.
The organization's leadership team has decided to solve the problem the Lean Six Sigma way using the
DMAIC approach to solve the problem of delayed deliveries and pickup.
Annexture-1:
Weight of consignment
Miles
Modes
20-30
<10
Bike
20-30
10-15
Truck
30-40
>2
Truck
30-40
<2
Bike
40-50
All
Truck
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Simulation Exercise based on Case Study-2
Scenario 2: The Problem of Delay in Account Opening Time in Retail Banking
Bank of XYZ, a major bank receives on an average 2000 new saving account opening customer
application forms every day. 40 operators enter the application forms in a database after cross
checking the CAF (Customer Application Form) with Identity Proof details.
The entries are rechecked against the Identify Proof details by 15 Quality Assessors and further 5%
sample is audited by 3 Quality Supervisors. The sales team promise the account opening within 48
hours from receipt of the CAF. Bank of XYZ usually achieves the account opening within average of
30 hours with a standard deviation of 6 hrs.
Recently, after a significant marketing effort, they started receiving over 3500 CAF, and the % of
defects in the CAF increased far more than the acceptable 10% of total opportunities for error and
processing time of CAF also increased, leading to account opening taking more than target of 48hrs.
Task to do in Control phase:
•
Check solution is in control or not.
•
Collect samples for SPC and prepare
charts
•
Make a sustainable Control Plan.
The Customer Application has the following sections (Opportunities for Error):
1. Title and Gender of the customer
2. Name of the customer
3. Address of the customer
4. Date
5. Identity Proof No
6. Product Code
7. Email Address
Any incorrect section is considered a defect and must be re-processed.
The Bank is losing $3750 every day primarily on rework and penalties. The customers are also
dissatisfied as the account opening is taking more time than promised. In the wake of the current
business situation, the management team decides to initiate a Six Sigma project to reduce defects
and achieve target account opening time.
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Appendix
Leading Transformation with Lean Six Sigma
Appendix
Your Success Roadmap
•
LSS Mindset
Continuous Improvement
Journey
•
Personal Improvement
•
For yourself
•
Get Certified
•
For your team and organization
•
Share your achievements
•
Inspire others
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Your Success
Roadmap
Your Success Roadmap
Lean Six Sigma Green-Belt is a project leader who use successes and failures in Lean Six Sigma as learning
opportunities towards continuous improvement or self, their teams and their organization
Start
here
Strive
# 1: Lean Six Sigma Mindset
Be passionate with self-discipline for efficient collaboration and to gain deep expertise and align your interests and skills
# 2: Personal Improvement
Continuously thrive to increase knowledge, understanding, competence, for and towards quality tools and
techniques discussed in your classroom
Validate
# 3 Get Certified
Read through the materials, case studies, understand the significance of theories and take up Lean Six
Sigma Green-Belt certification exam
Triumph
# 4 Share achievement
Certification is not the end but it’s a start i.e. steppingstone into the world of quality; Promote yourself to
create visibility using social media channels and share with others what you have learned.
Motivate
# 5: Inspire others
Tell your leadership, colleagues and other professional in your network about your vision towards
creative thinking, data driven decisions to build curiosity to challenge the status quo
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Continuous
Improvement
Journey
Continuous Improvement – For Yourself
Thriving in the current disruptive age requires sensing shifting of industry landscape. Upskilling and upgrading yourself
is pivotal is essential for any leader who is seeking continuous growth. All you need to do is set your priorities, get
access to handful resources, secure upgraded certifications and create your visibility.
Lean Six Sigma (Master Black-Belt)
Senior leaders responsible strategic initiatives
Role: Develop organizational strategies, scorecards,
ensure alignment with business goals
Skills: Mastery in Lean Six Sigma cultural
transformation, strategic planning, training and mentoring
Lean Six Sigma (Black-Belt)
Professional who lead major improvement initiatives
LSS
Master
Black
Belt
LSS
Black
Belt
LSS
Green
Belt
Role: Drive significant improvement project, mentor
Green-Belts and provide expertise on complex issues
Skills: Advance statistical analysis and project
management skills, and leadership skills
Lean Six Sigma (Green-Belt)
Professionals involved in process improvement initiatives
Role: Lead small scale project and assist black belts with
Large Projects
Skills: Basic Lean Six Sigma Tools and methods, data
collection and problem-solving techniques
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Continuous Improvement – For Your Team & Organization
For all emerging business opportunities ‘Quality’ is the cornerstone for measuring performance standards. To improve quality standards
individuals, teams, leaders need a critical enabler which are quality trainings, and workshops to measure, map, and delivery value enabled
product and services
7QC
7QC Workshop
Introduction to 7 basic
quality management tools
Basic Quality Control Tools
Lean Six Sigma Yellow-Belt
Lean Six Sigma Yellow-Belt
Introductory LSS Course
Introductory LSS Course
LSS Green-Belt
Leading Quality Projects
with LSS Framework
Introduction to LSS
Framework and Quality
Tools
Lean Six Sigma Green-Belt
Project Leadership
Lean Workshop
Lean Workshop
Hands on practice with Lean Tools
Customized
Optimize efficiency to
drive exceptional
results with KPMG’s
tailored workshops like
TPM, TQM, RCA, VSM
and many more
LSS Yellow Belt
Customized Programs
Get hands on practice for
tangible results with lean
tools to optimize
processes, reduce waste
and boost efficiency at
your workplace
Tailored workshops
SPOC / Consultants: Venu Nilgar: (venunilgar1@kpmg.com) | Vijay Gogoi (vijaygogoi@kpmg.com)
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Thank you
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