lean six sigma
DESCRIPTION
TRANSCRIPT
Lean Six Sigma An Overview
S. Zaman Khan, Ph. D.
March 28, 2009
Agenda
• History of quality
• History of Six Sigma
• Basic Statistics
• What is Six Sigma
• What is Lean Thinking
• Lean (LSS) Methodology
• LSS Application strategy
• Basic Process Improvement Tools
2
Lean Six Sigma and change
• In order to develop, sustain, and become competitive,
we have to make changes.
• Lean Six Sigma is all about:
– Changing the culture of an organization
– Changing the processes to meet new customer requirements
and to remove constraints.
Lean Six Sigma is a physical transformation of the processes, and
it is a transformation of the organizational cultural
3
“To cherish traditions, old buildings, ancient cultures and graceful
lifestyles is a worthy thing -
but in the world of technology,
to cling to outmoded methods of manufacture, old product lines,
old markets or old attitudes among management and workers,
is a prescription for suicide.”
Sir Leuan Maddock
Change is necessary
4
―In order to allow ourselves to be creative, we have to relinquish
control and overcome fear. WHY?
Because real creativity is life-alerting. It threatens the status-quo; it
makes us see things differently.
It brings about change and we are terrified of change.‖
Madeleine L’Engle
―The world hates change, yet it is the only thing that has brought
progress."
Charles F. Kettering
There is always resistance to creativity
and change
5
Evolution - Quality Gurus
• Walter A. Shewhart (1891 – 1967): The work on quality is pioneered by
Dr. Walter A. Shewhart (Bell Tel. Labs) in 1920’s. The field of quality control got its name from his book ―Economic Control of Quality of Manufactured Product‖.
• Edward Deming (1900 – 1993): Dr. Shewhart’s colleague (and student) Dr.
Deming introduced new ideas in the field of quality control. His fundamental philosophy based on defect prevention rather than detection.
• Josef Juran (1904 - 2008): Introduced new ideas in the quality improvements
and taught quality-improvement methods in Japan and the US. Deming’s fourteen points and Juran’s four points are the backbone of modern quality concepts.
• Philip B. Crosby (1926 - 2001): Diversified the quality concepts on all levels
in an organization. He insists on exclusive use of facts, not judgment or guesswork, in making decisions regarding quality.
• Kaoru Ishikawa (1915 – 1989): Known for his work in finding out the factors
contributing to quality (fishbone or cause-and-effect diagram) and also his contemporary concepts of total quality control and total quality management.
• Genichi Taguchi (1924 - ): Famous for his ideas of loss function and novel
approaches to design of experiments as applied to manufacturing quality.6
Evolution - Quality Paramid
Pre-Industrial period
Scientific management
Shewhart SPC (1920s)
Sampling methods (AQL)
Deming/Juran
SPC & QM (1950s)
1920s –
1950s
1960s
Lean principles
JIT (1950s)
1970s
Void
Quality
revolution
1980s
and
Beyond
TOC
(1970s)
TOK
If Japan can, Why can’t we? Zero Defect
movement
Qaulity is free
(70s/80s)
Throughput strategies
Quick response
methodology (1970s)
SQC
TQM
Lean Six Sigma
Six Sigma
DFSS
NPD
BenchmarkingQuality
control
ISO 9000
Baldrige National
Quality
Lean
manufacturing
Quality team
Project teamsMotivational
theories
DRIVE
MBWA
7-S framework
Taguchi
methodology
Tuckman’s
model
Sharon’s model
Quality
councils
Quality circles
TQC
QFD
Quick production strategies (1800s)
VBM
Value engineering
Work-Out
TRIZ
BPR
PDCA
Tim
e lin
e
Bu
sin
ess Im
pro
vem
en
t P
rog
ram
s
Cost & Complexity
Note: Some of the terms are trademarks of other organizations but widely used in literature
Industrial revolution & Inspection (18th century)
7
Phases of Quality Evolution • Pre-Industrial age
• Industrial revolution (17th & 18th centuries) – Manual to machine culture
– Inspection
– Does it work approach …
• Birth of quality management (19th century) – Quality control
– Variation control
– Quality assurance …
– Motivational programs • If Japanese can do, Why can’t we
• Zero defect movement
• Our goal is 0.001 defect
• Quality is free …
– Scientific, statistical and management approaches • SPC/SQC
• TOC, TRIZ, TQM
• ISO 9000
• Six Sigma …
– Common sense • Do it right the first time
• Self-inspections
• Lean principles …
– LEAN SIX SIGMA
SPC – Statistical process control SQC – Statistical quality control TOC – Theory of constraint TRIZ – Theory of inventive problem solving TQM – Total quality management 8
The birth of Six Sigma quality standard
• 1980s: Electronic industries faced challenges from
Japanese competitors.
• Motorola management selected a team of professionals
including Bill Smith and Mikel Harry to evaluate the
current quality approaches.
• The team emphasized the correlation between the
performance of a product in the market with the amount
of rework required at the point of manufacturing.
• In 1986, it was recommended to raise the Motorola
quality standard from ±3 sigma to ±6 sigma and a new
quality metric ―six sigma‖ was introduced.
9
The birth of Six Sigma quality standard
• 1993: Six Sigma tools at Asea Brown Boveri (ABB).
• 1994: Mikel Harry developed Six Sigma Breakthrough Strategy
(MAIC) and established Six Sigma Academy in Arizona, USA.
• 1994: Allied Signal (now Honeywell) implemented Six Sigma at
corporate level.
• 1994-95: GE started corporate wide six sigma initiative.
• Many companies followed Allied Signal and GE’s successful
deployment of Six Sigma.
• 1990s: Methodology improved to become DMAIC.
• 1999: Combination of Lean and Six Sigma experimented.
• 2002: Lean Six Sigma (LSS) became a standard approach in many
industries.
DMAIC – Define, Measure, Analyze, Improve, Control
10
Basic Statistical notations
11
Mean, Variance, and Standard deviation Mean (μ for population and x-bar for sample) is arithmetic average of a set of values.
Data: 17, 16, 21, 18, 13, 16, 12, 11
Variance (σ2 for population and s2 for sample)
Standard deviation (σ for population and s for sample)
12
Properties of Normal Distribution
What is the difference among these three normal distributions?
• Normal Distribution: In statistics, the normal distribution or Gaussian
distribution is a continuous probability distribution that describes
data that clusters around a mean or average.
• First property: A normal distribution can be described completely by
knowing the mean and standard deviation.
• Second Property: The area under sections of the curve can be used
to estimate the cumulative probability.
13
What is Six Sigma?
14
Six Sigma Metric
• Six Sigma as a metric
– A process running at Six
Sigma quality level
produces no more than
3.4 defective parts per
million opportunities
(DPMO).
– As the sigma quality level
increases, the DPMO
decreases and the rolled
throughput yield (RTY)
increases.
• Sigma (σ), a Greek letter, denotes standard deviation.
• Six Sigma is a metric that measures the performance of
a process.
15
Metrics and terms enforced by Six Sigma
• A defect is a shortfall that causes inadequacy or failure by not
meeting customer specification.
• An opportunity is the total quantity of chances for a defect.
• Defect per unit (DPU)
• Total opportunities (TO)
• Defects per opportunities (DPO)
• Defects per million opportunities (DPMO)
units ofnumber Total
defects ofnumber TotalDPU
iesOpportunitunits ofnumber TotalTO
iesopportunit ofnumber Total
defects ofnumber TotalDPO
1,000,000DPODPMO 16
Rolled throughput yield
• Throughput yield: The percentage of the good pieces divided by
the total pieces sent into the process.
• First pass yield (FPY): The percent of good pieces resulting from a
process step. It is the percentage of good pieces divided by total
pieces started into the process step.
• Rolled throughput yield (RTY): Rolled Throughput Yield (RTY) is
the probability that a single unit can pass through a series of
process steps free of defects. It is the product of first pass yield
(FPY) of each process step.
• Traditional throughput yield focuses on the final outcome of a
process and allows a ―hidden factory‖ flourish (percentage of good
piece/total pieces sent into process).
• RTY allows to understand what areas/steps of the process are
creating defects and how the process output is impacted by those
defects. 17
Cost of poor quality and Lean Six Sigma
• Cost of poor quality (COPQ) is directly linked to the
defects per million opportunities (DPMO) or sigma level.
• Typical three-sigma company spends about 25 percent
of each sales dollar on the COPQ.
• The COPQ exceeds the % profit margin where COPQ is
not known.
Exercise: Brainstorm the cost of quality (COQ) and COPQ.
Document at least five types in both categories.
18
Six Sigma basics
• Six Sigma quality level is derived from Gaussian curve
for normal distribution (in use since 1733) and widely
promoted by Carl Friedrich Gauss since 1794.
• In 1922, Dr. Shewhart developed statistical process
control techniques and a +/- three sigma quality
standard was adopted by industry.
• The six sigma quality standard emphasizes on shrinking
the variation in the process so that it does not produce
defects 99.99966% of the time.
19
Area under the normal curve
Large variation
• The total area under the curve
(between -∞ and +∞) is 100%.
• Area between +/- 1 standard
deviation is 68.27%.
• Area between +/- 2 standard
deviation is 95.45%.
• Area between +/- 3 standard
deviation is 99.73%.
• A process running at +/- 3 Sigma
quality level produces 66,000
PPM defective (after drifts, the in
long term).
)(
2
)(exp
2
1)(
2
2
xz
xxf
x
20
Area under the normal curve
Reduced variability
• More data points would lie
closer to the mean if
variation is reduced.
• If all the data points lie
within +/- 6 standard
deviations, than the
throughput yield of the
process is 99.9999998%.
21
Area under the normal distribution curve beyond z » 4.57 is close to zero (1-
99.99966%). This translate in to 3.4 PPM & defined as Six Sigma Quality
Level.
Six Sigma Quality level
PPB – Parts per billionPPM – Parts per million ST – Short term LT – Long Term
22
Six Sigma quality level and process capability
A process running at Six Sigma quality level produces no
more than 3.4 parts per million defectives. PPB – Parts per billionPPM – Parts per million ST – Short term LT – Long Term
23
6-Sigma99.99966% Good
• 20,000 lost articles of mail per hour.
• 5,000 incorrect surgical operations
per week.
• Two short or long landings at most
major airports each day.
• 200,000 wrong drug prescriptions
each year.
• Seven articles lost per hour.
• 1.7 incorrect operations per
week.
• One short or long landing every
five years.
• 68 wrong drug prescriptions per
year.
3.8-Sigma99% Good
.
N
3.4 defects per
million
opportunities
Based on U.S. statistics in the 1990s
Why to raise the quality standard?
24
What quality level we want to be at?
• The goal of SSQL depends on nature of the process,
business needs, cost, and customer requirements.
• Most companies use LSS for:
– Problem solving
– Cost reduction and increase in profit margins
– Process optimization
– New process/ product development
– Personnel development and leadership
– Growth.
25
What is Lean Thinking? • Lean Thinking is also known as lean, lean production, lean
manufacturing, Toyota production system (TPS), Just-in-time (JIT)
etc.
• It is a common sense approach.
• Lean ideas originally developed in the United States (Ford Motors,
1914) and than widely used by Japanese (Toyota, 1950).
• Lean is focused at eliminating the waste in the processes that in turn
increases the speed, improves the quality, and reduces the cost.
• ―Strategy that uses less of everything compared with traditional
manufacturing: half the human effort, half the space, half the
investment in tools, half the engineering hours to develop a new
product. Also it requires keeping far less than half the needed
inventory on site, results in many fewer defects and produces a
greater and ever growing variety of products.‖ Machine that changed the world by James
Womack (1990)26
Commonly used Lean Thinking tools• Value stream mapping (VSM) and process mapping
• Kaizen events
• Total productive maintenance (TPM)
• Single minute exchange of dies (SMED)
• 5 S (sort, set in order, Shine, Standardize, Sustain)
• Load balancing
• Kanban
• Pull systems
• Point-of-use inventory (as opposed to warehouses)
• Vendor managed inventory (VMI)
• Mistake-proofing
• … 27
8 Wastes
Overproduction WaitingUnwanted Transportation
Overprocessing
Over InventoryUnwanted Movement
DefectsUnused Employee Creativity
Understanding Wastes
28
Exercise
• How to remember 8 wastes
29
Variation and COPQ
A good process running at
traditional high quality has a
potential to produce defects.
Cost of poor quality (COPQ)
=2666.19*Cost per part .
Over time COPQ multiplies.
A process running at a Six Sigma
quality level has less opportunity
to produce the defects.
The process will produce less
defects even after shifts and drifts
over time.
COPQ: cost of poor quality
USL – Upper specification limit
LSL – Lower specification limit
30
Focus of Lean Six Sigma
• Every process has a target that is measured around the mean.
• Variability is inherent to the processes that makes the mean
dynamic. The measure of this variability is standard deviation.
• Every process has a constraint that directly impacts the purpose or
profit.
• Every process has a waste that makes it slow.
• Lean Six Sigma helps to
– Move the mean to the target
– Shrink the variation for consistency
– Reduce and eliminate the constraints
– Eliminate waste.
31
What does Lean Six Sigma Means to a
business
• Metric
– Produce no more than 3.4 parts per million opportunities (cost, quality,
& delivery).
• Problem solving
– Use DMAIC breakthrough methodology to reduce variation, eliminate
waste, and remove the bottlenecks (cost, quality & delivery).
Understand customer requirements and reduce variation to meet those
requirements.
• Management system, strategy and Vision
– Reduce cost, increase value, increase revenues, develop human
resources, win over competitions, new business/ product development,
…
– A high performance system for executing business strategy - Motorola
DMAIC: Define, Measure, Improve, Control
32
Lean Six Sigma defined
• Lean Six Sigma is a rigorous, disciplined, and data
driven business process optimization and problem
solving methodology which aims to reduce variability,
eliminate non-value added activities (waste), and
reduce cost.
• Lean Six Sigma is applicable to any process/activity.
• Used world-wide and is Well-proven methodology.
33
Lean Six Sigma and financial benefits
• From 1986 – 2001, Motorola saved $16 billions.
• From 1996 – 1999, GE saved $4.4 billions.
• From 1998 – 2000, Honeywell saved $ 1.8 billions.
• From 2000 - 2000 Ford saved $1 billion.
• Over the past 20 years Six Sigma saved Fortune 500
companies an estimated $427 billion.
34
Industry embraced Six Sigma
• 53 percent of Fortune 500 companies are currently using
Six Sigma-and that figure rises to 82 percent if we look
at just the Fortune 100 – 2006 survey
• True six sigma produced 40% more savings than those
with less rigorous programs – 2006 survey
• More than 55% of 418 enterprises interviewed,
implement lean six sigma – 2006 survey
35
Harvesting the Fruit of Lean Six Sigma
Difficult to Reach Fruit Design for Six Sigma (DFSS)
Middle FruitLean and Six Sigma
Low Hanging FruitLean, Basic quality tools
Ground FruitLogic and Intuition Basic tools
Degree
of
Complexity
36
Lean Six Sigma - Training strategy
• Training strategy
– Plan
– Train
– Apply
– Review
Lean Six Sigma Change Agents go through different levels
of rigorous training, coaching and mentoring
Plan
Train
Apply
Review
37
Lean
Six
Sig
ma M
eth
od
olo
gy
Characterization
Optimization
Phase 2
Measure
Phase 3
Analyze
Phase 4
Improve
Phase 5
Control
Phase 1
Define
Lean Six Sigma MethodologyApplication Strategy
Practical
Problem
Analytical
Problem
Analytical
Solution
Practical
Solution
Lean Six Sigma uses the DMAIC (Define, Measure, Analyze, Improve,
Control) process as a disciplined and methodological approach for problem
solving and process improvement 38
Lean Six Sigma Application Process
Define
• Project identification
• Value Stream Mapping
• VOC and Kano Analysis
• Project Approval Form
• COPQ analysis
• Internal Rate of Return Analysis
• Cash Flow Analysis
• RACI
• Stake holder analysis
Measure
• Process Mapping
• Data Collection plans
• Constraint Identification
• Setup Reduction
• Generic Pull
• C&E Diagrams
• C&E Matrix
• Kaizen
• TPM
• Control Charts
• MSA and Gage R&R
• Process Capability Indices
Analyze
• Brainstorming
• Basic Tools
• Components of Variation
• FMEA
• Multi-Vari
• Box Plots
• Interaction Plots
• Regression
• ANOVA
• C&E Matrices
• Hypothesis testing
Improve
• VA Improvement
• Brainstorming
• Replenishment Pull
• Process Flow
• Benchmarking
• DOE/ RSM
• Stocking, Purchasing and Sales Strategy
• Supply-chain optimization
• Batch Sizing
• Line Balancing
• Piloting and Simulation
• Training
Control & Sustain
• EWMA and CuSumControl Charts
• Pareto Charts
• Visual Process Control
• Poka-Yoke
• Process Control Plans
• Project Commissioning
• Procedures & policies
• Safety measures
• Training
• Final Control Plan
• Identify Problem
• Develop List of Customers
• Develop List of CTQ’s
• Finalize Project Focus and Key Metrics
• Financial benefits
• Complete Project approval form
• Map Business Process
• Value stream mapping
• Qualify measurement systems
• Collect Data
• Determine process stability
• Conduct process Capability analysis
• Baseline analysis
• Propose Critical X’s
• Prioritize Critical X’s
• Verify Critical X’s
• Estimate the Impact of Each X on Y
• Quantify the Opportunity
• Prioritize Root Causes
• Conduct Root Cause Analysis on Critical X’s
• Critical X’s Confirmed
• Develop Potential Solutions
• Select Solution
• Optimize Solution
• Pilot Solution
• Process capability analysis
• Implement Process Changes and Controls
• Write Control Plan
• Calculate Financial Impact
• Process Metrics
• Transition Project to Future Owners
• Identify Project
• Translation Opportunities
VOC – Voice of customer COPQ – Cost of poor quality RACI – Responsible, Accountable,
Consulted, Informed
TPM – Total productive maintenance C&E – Cause and Effect MSA – Measurement system analysis R&R – Repeatability and Reproducibility
FMEA – Failure mode and effect analysis ANOVA – analysis of variance VA – value add DOE – Design of experiment
EWMA – Exponentially Weighted Moving Average
CuSum - cumulative sum
CTQ – Critical to quality RSM – Response surface methodology
X’s – Input variables Y’s – Output variables
39
Define Phase
• Define the problem.
• Identify the customer(s).
• Organize the team and define its roles and responsibilities.
• Establish goals and milestones.
• Establish the scope of the LSS project.
• Define the metrics.
• Map the process.
• Develop data collection plan.
What is important to customers OR business goals?
40
Measure Phase
• Collect data on current process.
• Confirm the customer’s needs, and expectation.
• Validate measurement system.
• Determine input variables (X’s) that may impact output (Y’s).
• Establish baseline measurement of current process.
How is the process performing? How does it look / feel like to the customer? How good is the data?
41
Analyze Phase
• Narrow the focus to specific issues.
• Develop a mechanism to analyze data.
• Identify what is causing defects, waste and variation. Characterize the variables (X’s).
• Find improvement opportunities.
• Based on data analysis, revisit problem statement and assess the need to further scope the issues.
The shape has a bell shape.
It is symmetric.
The shape has two humps.
It is bimodal.
The shape has a long tail.
It is not symmetric.
The shape is flat. There are one or more outliers.
41Q
Histogram Interpretations
40E
Graphical Analysis Tools – Box Plot
5 6 7 8 9 1 0 1 1 1 2 1 3 1 4
Mean (8.16)
Mode
22 points of data
(Half of the distribution)
22 points of data
Median
41P
Describing the Distributions
What are the most important causes of process waste, defects & variation?
42
Improve Phase
• Validate hypothesis about the root cause of the problem
• Identify critical variables (X’s)
• Identify alternate solutions
• Determine optimal solution
• Perform cost/benefit analysis
• Design improvements
• Pilot improvements
• Implement and validate improvements
Move the mean. Shrink the variance. Eliminate the waste.
43
Control Phase
• Ensure corrective actions are taken.
• Mistake-proof the process.
• Transition the control of the new process to the process
owner.
• Provide techniques to sustain the improvements.
• Measure the final capability.
• Monitor performance. How can we maintain the process improvements?
7.5
8.5
9.5
10.5
11.5
12.5
0 10 20 30
44
Design for Six Sigma (DFSS)
• DFSS is used to determine the customer and business
needs and translating those needs into new process or
product in the most optimal way to achieve most optimal
and sustaining results results.
• DFSS is process generation (as opposed to process
improvement).
• Also called DMADV or new product or process
development (NPD).
MeasureDefine DesignAnalyze Verify
45
Sponsor
• Own the vision, direction, integration, and results
• Identify Black Belts/Green Belts, and help in project identification
• Apply Lean Six Sigma to specific projects
• Lead and direct teams to execute projects
• Support Champion in effective project scoping
• Train, coach, and develop Black Belts/Green belts
• Work on complex projects
• Approve BB certification
Champions
Black Belts
Green Belts
Master Black
Belts
Process
Owners
• Identify and assist in scoping projects
• Own the process
• Ensure changes are sustained
LSS organizational structureRoles and responsibilities
Lean Six Sigma practitioners are assisted by financial experts to estimate and verify savings
46
Training requirements
• Champions training: 2 days – one week.
– Some companies include another week of on-the-job training.
• Green Belt training: Two weeks and one project.
• Black Belt training: Four – five weeks and two projects.
• Master Black Belt training: Three – five weeks training,
coaching, mentoring and facilitation.
47
LSS Practitioner qualities
• Customer focus, self-motivated and positive personality
• Leadership skills
• Excellent communication skills
• Excellent presentation skills
• Project management skills
• Process and product knowledge is preferred
• Team player
• Result oriented
• Data mining
• Passionate
• Patience
• Learner 48
• Late Delivery
• Poor Product Reliability
• High Cost of Quality
• Incoming Product Quality
Problems
• Unpredictable Quality or Product
Performance
• Poor Process Capability
• High Incidence of ―Past Due‖
notices
• High Maintenance Costs
• Low Machine Utilization
• Transactional Defects
• Low Customer Satisfaction
• Excessive Variation
• Poor Design
• High Operating Costs
• Excessive Scrap/Rework
• High rate of rejections
• High Inventories
• Long Cycle Times
• Capacity Constraints
• Excessive Set-Up Costs
• Waste
• Low Rolled Yield Rate
Typical Lean Six Sigma project areas
49
Lean Six Sigma Project Selection Criteria
• A high value project
• A repeatable process
• Strong management sponsorship
• Strategic linkage
• Process is within your control
• Data availability
• Compelling problem statement
• Despite attempts, process owner could not solve the problem
• Workable scope
• Short completion period
• Firm defect definition 50
Common Causes of Project Failures
• Inadequate management support.
• Inadequate time for Green Belts/ Black Belts and other team
members .
• Project Scope Is Too large
• ―Boiling the ocean‖
• Scope Creep.
• Project Scope Is Too small
• Projects with little business impact.
• Solution-in-Mind
• ―Just Do It‖ projects do not require the rigors of the LSS DMAIC process.
• Data not available or not valid.
• Politics (pet projects).
• Lack of ―soft skills‖ (communication, leadership, team building, and
change management).
51
Comply
Year 1 Year 2 Year 3 Year 4 Year 5 Year 6
Commit Embed
Marginal Improvement
Encode
Incremental Improvement
Major Improvement
Lean Six Sigma deploymentModified from Lean Six Sigma by Michael L. George, 2002, pp., 227
Vision
Planning
Benchmarking
Initiating
Executive teams
….
Invest Believe and
publicize
Embrace
and spread
Part of DNA
52
Tool Time
S.M.A.R.T. Goals
• Specific: A specific goal has a greater chance of success.
• Measureable: Criteria to measure the progress.
• Attainable: Identify goals that are most important to you.
• Realistic: Set a goal that is realistic.
• Time bound: Set a timeframe to achieve the goal.
54
Project charter• Precisely and quantitatively define the problem, establish the
objectives, both in one or two sentences.
• Problem statement: On-time delivery performance of all ABC units is
only 60%. This results in customer complaints and shipment
rejections that in turn increases the inventory levels.
• Objectives: Improve on-time delivery to 95% by the end of June
2009.
• In scope/Out of scope.
• Financial and other Benefits.
• Identify customers.
• Select a cross functional team and get the support of the subject
matter experts.
• …55
Seven basic tools of quality
• Ishikawa diagram (also called cause-and-effect diagram
or fishbone diagram)
• Pareto chart
• Check sheet
• Control chart
• Flowchart (or process map)
• Histogram
• Scatter diagram
56
Make the process visible
• Helps to see the abnormalities immediately.
• Uncovers the hidden factory.
• Helps to become proactive.
• Use a simple chart to show how the process is
performing.
• Display the primary metric close to the process.
• Display the process map or flow chart of the process.
57
RS On-time Delivery Performance
RS 2005 On-time Delivery performance
0
10
20
30
40
50
60
70
80
90
100
Jan Feb M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec
Customer M etric (Performance to request), % RS M etric (Performance to schedule), % Goal, %
On-time performance:
RS metric: 60%
Customer metric: 35%
Goal (by end of June 2006): 95% by end of June 2009 58
Process map
• Identify start and end point (process boundaries).
• Document the process steps by creating the flow chart.
• Identify the inputs and outputs of each step.
• Characterize the inputs
– Controllable (C)
– Standard operating procedure (SOP)
– Noise (N).
• Document data such as cycle time of the process steps,
number of operators, etc.
59
Types of variables
• Controllable (C): These inputs can be adjusted or
controlled during the process (e.g., speed, feed,
temperature).
• Standard Operating Procedures (SOP): Common
sense items (e.g., cleaning, safety).
• Noise (N): Things that we can’t control or don’t want to
control (too expensive or too difficult; e.g., ambient
temperature, humidity, wind etc.).
Process
Input (X’s) Output (Y’s)
Characterize
Input variables
Identify
Output variables
60
Value-added and non value-added activities
• Value Added: Activity that increases the value.
• To determine if an activity is value-added ask:
– Is this something the customer is willing to pay for?
– Does the activity positively change the form, fit, or function of the
product?
• Non Value Added: Any action which consumes
resources without directly contributing to the product.
• Non value added but necessary: Activities that do not
add value but are necessary to complete the job.
• Optimize value-added, reduce non value-added but
necessary, and eliminate non value-added activities. 61
High level process map
RS Valve manufacturing process
CustomersSales &
MarketingPlanning
Purchasing
Scheduling/
WarehouseProduction
Shipping/
Receiving
- Daily orders
- 24 different sizes and Types
- No forecast
- Domestic and International
- No LT for commercials
- Dynamic LT for specials (1 to 60 days)
Suppliers
- Offices at strategic locations
- Divided by regions
- Forecast
- 15 Overseas (LT = 24 weeks)
- 25 Domestic (LT = 8 weeks)
- Bulk orders (8 weeks SS)
- MRP
- MRP
- MRP
- Overnight schedule print out
- Bulk pull from stock
- Staging processes
- 2 shifts
- Machining and sub-processes
- Assembly
- Testing
- Daily trucks
- Various shippers
Note: Compare this high-level process map with the Org chart
LT – Lead-time
SS – Safety stock
MRP – Material requirement planning
62
Detailed RS Valve assembly process map (As Is)
Enter customer
orders
Planning
review/prioritize
overnight orders
Scheduling
create, print and
distribute WO
Legend
WO – Work order
BOM – Bill of materials
Kit – Production order package
C – Controllable
N – Noise
SOP – Standard Op. Procedure
Av. - Average
VA – Value added
CT – Cycle time
NVA – Non-value added
Average CT per product in indicated
Warehouse –
pull and stage
the material
Complete
BOM ?
Incomplete kit
staging
Staging
Pick prioritized
production
orders
Match the
paperwork and
pick the parts
Take the
paperwork and
material to Shop
Deliver the kit in
the shop staging
area
Daily and weekly
production
schedules
Assembler –
pick the material
with paperwork
Place the parts
on the assembly
line
Assemble (liner,
disc, stem,
bushing, body)
Leakage test
Rework
Torque test OK ?
Rework
Place
assemblies in
the staging area
QC OK ?Take parts to
ShippingPack and ship
Assembly to
rework (or scrap)
SOP , C, 3 days Av.
Manual
Daily
5 people
VA
SOP , C, 4 days av.,
Daily
3 people
SOP , C, System
Overnight
3 people
NVA
Y
N
Y
Y
Y
N
N
N
C, NVA, 2 people,
3 days Av.
C, 5 days Av. C, Daily, 150 yards one way, 45 minutes
C, 50 yards one way
25 minutes
C, 45 days Av. waiting
SOP, C, VA ,
45 minutes Av. SOP, 5 min. Av.
C, NVA, 30 min Av.
C, NVA, 3 min Av.
C, NVA, 30 min Av.
C, NVA, 5 min Av. SOP, C, NVA, 5 min Av.
C, NVA, 30 min Av.
C, VA, 2 days Av. SOP, C, 5 min Av.
OK ?
Processing time
Transactions = 16 days
Processing = 3.74 hours per product
Waiting time = ? 63
Exercise
• Mr. Z is attending SEF2009 from Bahrain. Develop a
process map showing steps from receiving forum
information to attending the forum. Write down estimated
time for steps (where applicable) and other
characterizations and data.
64
Rolled throughput yield versus throughput yield
Manufacture
part 1
Manufacture
part 2
Inspect
Manufacture
part 3Assemble
Rework/Scrap
95%
customer
quality
Hidden Factory
Yield = 95%
Manufacture
part 1
Manufacture
part 2
Manufacture
part 3Assemble
RTY = 82 %
and not 95%
Process step yield (First pass yield)
95% 97% 94% 95%
Real yield or RTY
95% 95*97=92% 95*97*94=86% 95*97*94*95=82%
OKNot OK
RTY helps to uncover
the hidden factory and
enforces prevention
50,000 PPM wasted 30,000 PPM wasted 60,000 PPM wasted 50,000 PPM wasted
Total wasted opportunities: 190,000 PPM
Process Sigma = 2.37
65
Defects per million opportunities (DPMO)
• Assuming that a process produces 30 assemblies
– Each assembly has 5 parts
– Number of opportunities for defect in each assembly = 5 + 1 = 6 (five for each
part and a sixth for appearance).
• Total opportunities, TO = 30*6 = 180
• Number of units checked = 30
– Defects found = 5
• DPO = 5/180 = 0.0277778
• DPMO = 0.0277778*1,000,000 = 2,7777.78 or 27,778
• This means that if the process has produced 1,000,000 assemblies,
we expect to end up with 27,778 defects in those assemblies.
• Add up DPMO for each sub process to find overall PPM defective.
66
5 W and 2 H problem identification
approach
Who? Identify customers complaining about the problem
What? Define the problem accurately
When? Timing - When did the problem start?
Where? Location - Where is it occurring?
Why? Identify the causes (5 WHYs)
How? In what mode the problem occur
How many? Magnitude or frequency of the problem
67
5 Whys help to find the root cause?
1. Why did the system fail?
A: The motor burned out.
2. Why did the motor burn out?
A: The shaft seized.
3. Why did the shaft seize?
A: There was no lubrication.
4. Why was there no lubrication?
A: The line filter was clogged.
5. Why was the line filter clogged?
A: It was the wrong sized mesh!
68
TimeTime is money.
Will never wait.
You may delay, but time will not.Benjamin Franklin
There is one kind of robber whom the law does not strike at, and who
steals what is most precious to people—time.
Make use of time, let not advantage slip.
Better three hours too soon, than one minute too late.
William Shakespeare
Time and tide wait for no man.
Geoffrey Chaucer
Money, I can only gain or
lose. But time I can only
lose. So, I must spend it
carefully
Time stays long enough for those who use it.
- Leonardo Da Vinci
We must use time as a tool, not as a couch.
- John F. Kennedy
One thing you can't recycle is wasted time.
Time is the supreme Law of nature.
- Arthur Stanley Eddington
Time is the wisest counselor of all.
- Pericles
Time is the school in which we learn,
time is the fire in which we burn.
- Delmore Schwartz
Time is what we want most,
but what we use worst.
- William Penn
Time is Money
Cycle time (CT)
• Time it takes an operator to go through all of his/her
work elements before repeating them.
• It is the time measured by direct observation in which a
good item or good product is completed by the process.
Finished parts
Cycle time
Operation
1Operation
2Operation
3Operation
4
Work in process (WIP)
70
Little’s Law to approximate Process CT
• Little’s law provides a quick and reliable mean to
measure the process cycle time (PCT) also called Lead
Time (LT).
• The time from work order release into the process until
completion and measured as:
Operation
1Operation
2Operation
3Operation
4
Work in process (WIP)Finished parts
Exits/day
Process Cycle Time (PCT) or Lead Time (LT)
71
Process cycle time (PCT)
Operation
1Operation
2Operation
3Operation
4
Work in process (WIP)Finished parts
• WIP is the number of pieces or transactions being worked in the
process at any given time, WIP = 14 pieces.
• Exits is the amount of work completed over a given period of time
(Weekly, Daily, hourly etc.), Exits = 7 units/day.
• CT or LT = (WIP/Exits per day) = 14/7 = 2 days.
Exits/day
Process Cycle Time (PCT) or Lead Time (LT)
• A reduction in WIP leads to cycle time reduction.
72
Takt Time (TT)
• Takt time is the maximum time allowed to produce a
product or process a transaction in order to meet
customer demand.
• Time available – Time available per day minus break, wash-up, set up etc.
• Customer demand– Number of units on order on a given day.
• Takt Time metric helps to synchronize the pace of
production to the pace of sales.
𝑻𝒂𝒌𝒕 𝑻𝒊𝒎𝒆 =𝑻𝒊𝒎𝒆 𝒂𝒗𝒂𝒊𝒍𝒂𝒃𝒍𝒆
𝑪𝒖𝒔𝒕𝒐𝒎𝒆𝒓 𝒅𝒆𝒎𝒂𝒏𝒅
73
Understanding TT
• CT = TT, ideal situation. Customer receives on-time.
• CT >> TT, Hidden factory, too much waste.
• CT < TT, over capacity. Producing faster than customer
order pace is waste of resources.
74
Takt Time (TT), example
• Customer demand = 100 units/day.
• Assuming one shift of eight hours and lunch time is not
paid.
• Total time = 8 hours/day.
• Breaks = 2*10 minutes, wash up = 10 minutes, meetings
= 20 minutes, operator maintenance = 10 minutes.
• Time available = (8*60) – 60 = 420 minutes /day.
• TT = 420/100 = 4.20 minutes.
• In order to meet the customer demand on-time, each unit
should be completed in 4.20 minutes. 75
Cause-and-Effect diagram• Two approaches:
– Use check sheets based on data collected by team
– Brainstorming without previous preparation.
• Two way to construct:
– Use a flip chart, write down the problem (effect) on the right side of a
main line with arrow on it and draw lines with major headings and than
brainstorm causes under each topic.
– Gather team thoughts on cause in a tabular form. Each column heading
will represent the major cause and sub-causes will be recorded.
• Construct the C&E diagram
problem
agreed
Write the
Major cause 4
Major cause 3
Major cause 2
Major cause 1
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
sub-sause
Cause-and-Effect Diagram Example
76
Exercise
• Assume that the local industries and KFUPM want to
increase the interaction and cooperation to make the
research and development more effective and useful.
Develop a cause and effect diagram.
77
Determine critical causes
• Brainstorm with the cross functional team and subject
matter experts to determine the critical causes.
• Use data (if available) to determine the impact and
criticality of a cause.
• Use multi voting techniques and knowledge of subject
matter experts to narrow the list of causes.
78
Check sheet
• A simple tool to collect the real time data at the location
where it is generated.
• Typically a blank form is used to record each occurrence
of interest.
• Check sheet is an effective data collection tool.
Defects in outside diameter of a steel disc
Machine# Sat Sun Mon Tue Wed Total
Machine 1 II I I II III 9
Machine 2 III I I I II 9
Machine 3 II III II II II 11
Machine 4 III IIII III III IIIII 18
Total 10 10 7 8 12 47
79
Scatter plot
• Scatter plot is used to display the relationship between
two variables.
• We can use MS Excel to display the scatter plot.
• The graph shows that there is a positive relationship
between number of hours studied each day and exam
score.
StudentsStudy Hours
Math Score
A 3 82
B 5 92
C 2 77
D 6 82
E 7 92
F 1 52
G 2 67
H 7 87
I 1 42
J 7 102
80
Pareto chart
0
10
20
30
40
50
60
Chart of mistake categories• Based on team input and/or
data, display the impact of
critical causes on the output of
the process using a Pareto
chart or bar chart.
• Pareto analysis provides
information on what 20% of the
variables cause 80% of the
problem.
• In many cases you may need a
second and third level Pareto
analysis
81
Implementation plan
• Develop a solution to eliminate or reduce the impact of
the critical causes on the output of the process.
• Develop an implementation (or action plan). It can be a
simple Excel spread sheet.
– Action item (to reduce the impact of critical causes)
– Impact
– Responsibility
– Start date
– End date
– Status
– …
82
Specifications and process data
• Statistical software help to use various tools (such as control
charts, process capability analysis, descriptive statistics etc).
• Such tools require training and practice.
• Simple line chart can be used to see the data with reference to the
specifications (use MS Excel).
1
1.5
2
2.5
3
3.5
4
4.5
1 2 3 4 5 6 7 8 9 10
Upper specification
limit (USL)
Lower specification
limit (LSL)
Mean
83
Final thoughts
Lean Six Sigma
• Process centered and project focused.
• Focuses on customer requirements.
• Emphasizes permanent change and transformation.
• Fact based and data driven.
• Applicable to transactional as well as manufacturing processes.
• Requires planning, training, coaching and mentoring efforts.
• Helps in sustaining and consistent change across functions.
• Requires leadership involvement/ commitment and line
management buy-in.
• Top down approach is most successful
• Brings about breakthroughs. 84
85
Appendix
Thank You
Six Sigma Process
• PPM expected = 0.002
Distribution span = 12 Sigma (±6*/2)
• The variance shrunk
• The standard deviation reduced to half ( = 0.0019)
• The data distribution around mean (first quartile) is
higher
Three Sigma Process
• PPM expected = 2700
Distribution span = 6 Sigma (±3)
• Large variance
• Higher standard deviation ( = 0.0038)
• Smaller data distribution around mean (less
frequency distribution close to mean)
• Data spread along the z line
• Data points exist close to upper and lower
specs i.e., at 3 and –3 distance from the
mean.
Six Sigma and PPM
²2
)(exp
2
1)(
xxf
x
)x(scorez
.001 PPM .001 PPM
1300 PPM1300 PPM
53
6 4 3 2 1
2 1 3
1 2 3 4 5 6
1 2
Difference between ±3and ±6data distribution (no shift, short term)
0
Six Sigma (new standard) versus
Three Sigma (old standard)
87
5- Why AnalysisWhy Because
Why did some Trouble
Tickets exceed 3 days to
Resolve!?
1- Spare Parts Availability (35.10%)
2- Unclear Process! (21.22%)
3- Cost Estimation (15.08%)
4- Wrong Assignment (10.61%)
5- Customer Availability (39.10%)
6- Lack of Technical skills (28.49%)
7- Restricted/ Remote areas (23.46%)
8- Incorrect Descriptions! (21.78%)
5- Why Analysis (Cont..)
Eight wastes in a business
must be measured and eliminated or reduced
T Transportation O Over production
W Waiting I Inventory S Skills D Defect O Over processing M Movement
Another Acronym used to remember 8 waists is
TIM P WOOD
90
Mean Shift and Variance Reduction
On CenterLarge Spread
LSL USLT
On CenterSmall Spread
LSL USLTLSL USLT
Off CenterSmall Spread
Off CenterLarge Spread
LSL USLT
91
Understanding and reducing variation
Lower Specification
Limit (LSL)Upper Specification
Limit (USL)
# o
f G
oals
92
Understanding and reducing variation
Lower Specification
Limit (LSL)Upper Specification
Limit (USL)
# o
f G
oals
93
Acronyms used on slide#3
94
SPC – statistical process control
TOK – theory of knowledge
TOC – theory of constraint
TRIZ – Theory of inventive problem solving
TQC – total quality control
SQC – statistical quality control
QM – quality management
PDCA – plan-do-check-act
QFD – quality function deployment
TQM – total quality management
MBWA – management by walking about
BPR – business process re-engineering
VBM – value based management
DFSS – design for six sigma
DMADV – design, measure, improve, control, verify
DRIVE – define, review, identify, verify, execute (TQM)
AQL – Acceptable quality level
NPD – New product/process development (same as DFSS)
Tuckman’s model – forming, storming, sorming, performing model
McKinsey 7-S framework – shared Value, structure, system, style, staff, skills,
strategy
Industrial Revolution – a period in the late 18th and early 19th centuries when
major changes in agriculture, manufacturing, production, and transportation had a
profound effect on the socioeconomic and cultural conditions in Britain.