Lean Six SigmaLean Six Sigma
By
R Sawhney Ph DR. Sawhney Ph.D.Department of Industrial and Information Engineering
University of Tennessee, Knoxville406 East Stadium Hall406 East Stadium HallKnoxville, TN 37996
865 974 [email protected]
CPI-Center for Productivity &Innovations
Dr. Dr. RupyRupy SawhneySawhney Dr. Xueping LiDr. Xueping Li
Chris Chris WrightWright
Yanzhen Yanzhen LiLi
Sashi K. Sashi K. NaiduNaidu
RobertRobertKeyserKeyser
Sirisha Nukala
Joesph Joesph StainbackStainback
Gagan Gagan RajpalRajpal
Paul Paul CastoCasto
Barbara Barbara OwensOwens
Laigang Laigang SongSong
Yuerong Yuerong ChenChen
Wang Wang JiaoJiao
Dengfeng Dengfeng YangYang
Joseph Joseph AmaleshAmalesh
Arun Arun BalasundaramBalasundaram
Naveed Naveed AhmedAhmed
Ashutosh Ashutosh HengleHengle
Zeid Zeid ElEl--AkkadAkkad
Karthik Karthik SubburamanSubburaman
Comparison of Lean dand Six Sigma
What is Lean? What is Six Sigma?
THEN WHAT IS LEAN SIXTHEN WHAT IS LEAN SIX SIGMA?
A LEAN SIX SIGMA PROBLEMA LEAN SIX SIGMA PROBLEM
Effect of Variation on Flow
Simulation #1Push SystemPush System
Five workstations in a cell.The first workstation will never be starved.Every station on a line has the same level of variation. The average process time is the same for every machine centerThe average process time is the same for every machine center (10 time units). However, the individual process times are taken randomly from a normal distribution. Each machine on Line One has a coefficient of variation of 5%Each machine on Line One has a coefficient of variation of 5% and each machine on Line Two has a coefficient of variation of 50%.All other parameters besides variation are identical for both linesAll other parameters besides variation are identical for both lines. Parts are pushed through the system meaning that when a machine is finished with a part it will immediately travel to the queue for thequeue for the succeeding machine.
Effect of Variability -Push System
Lead Times for Push System
Process StandardProcess Standard
Variability 1 2 3 4 5 6 7 8 9 10 Average Deviation
5% 88.9 88.1 94.3 104.7 95.1 101.9 95.1 87.8 88.7 97.0 94.1 5.91
10% 165.6 155.3 99.4 120.3 122.6 138.9 114.6 102.8 140.4 208.4 136.8 33.05
20% 186.7 168.3 149.4 184.6 236.2 235.0 204.4 226.5 174.5 237.4 200.3 32.1120% 186.7 168.3 149.4 184.6 236.2 235.0 204.4 226.5 174.5 237.4 200.3 32.11
30% 242.4 254.1 394.3 257.9 308.6 358.6 213.6 373.8 299.5 235.6 293.8 63.48
40% 359.8 521.8 382.7 360.4 212.3 419.2 335.6 671.8 452.0 422.3 413.8 121.67
50% 464.9 431.2 366.2 381.9 568.8 525.4 490.5 242.8 277.2 486.9 423.6 105.86
AVERAGE LEAD TIMES PER COEFFICIENT OF VARIATION
30%
40%
50%
of v
aria
tion
AVERAGE LEAD TIMES PER COEFFICIENT OF VARIATION
0 100 200 300 400 500
5%
10%
20%
Co-
effic
ient
o
Series1
Flow Times (seconds)
*Note: These values represent the average results of simulations replicated ten times for a givenlevel of variation
Effect of Variability -Push System
Average Throughput for Push System
Process Variability 1 2 3 4 5 6 7 8 9 10 Average
Standard Dev
5% 358.8 359 359.1 358.6 358.4 359 358.9 358.9 358.9 358.4 358.8 0.25
10% 357.9 357.9 357.8 357.4 357.6 358 357.1 357.9 357.5 355.9 357.5 0.63
20% 356.8 354.9 354.7 353.7 353.2 355.8 354.7 354.5 356.1 354.7 354.9 1.0820% 356.8 354.9 354.7 353.7 353.2 355.8 354.7 354.5 356.1 354.7 354.9 1.08
30% 352.6 354.7 352.6 352 353.7 354.1 353.2 351.5 353.3 351.6 352.9 1.07
40% 350.8 351 348.7 347.6 345.2 350.8 352.1 349.2 347.5 349.9 349.3 2.08
50% 347.4 345.3 349 354.2 344.4 348 348.3 346.8 348 345 346.7 1.65
30.0%
40.0%
50.0%
of V
aria
tion
AVERAGE THROUGHPUT PER COEFFICIENT OF VARIATION
340 345 350 355 360
Average Throughput(pieces/hours)
5.0%
10.0%
20.0%
Coef
ficie
nt o
f
Series1
*Note: These values represent the average results of simulations replicated ten times for a givenlevel of variation
Effect of Variability -Push System
Average Overall WIP
Process StandardProcess Standard
Variability 1 2 3 4 5 6 7 8 9 10 Average Deviation
5% 8.9 8.8 9.4 10.5 9.4 10.2 9.5 8.8 8.9 9.7 9.4 0.59
10% 16.5 15.5 9.9 12.0 12.2 13.9 11.4 10.2 14.0 20.8 13.6 3.31
20% 18 5 16 7 14 8 18 3 23 5 23 5 20 5 22 6 17 4 23 5 19 9 3 2120% 18.5 16.7 14.8 18.3 23.5 23.5 20.5 22.6 17.4 23.5 19.9 3.21
30% 23.8 25.1 39.5 25.5 30.8 35.7 21.3 37.2 29.9 23.5 29.2 6.41
40% 35.6 51.5 37.8 35.6 20.5 42.4 33.1 67.7 44.8 41.9 41.1 12.44
50% 45.4 42.1 35.8 37.2 56.1 52.5 48.2 23.6 26.8 47.7 41.5 10.63
30%
40%
50%
varia
tion
AVERAGE OVERALL WIP PER COEFFICIENT OF VARIATION
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0
5%
10%
20%
30%
Coe
ffici
ent o
f v
Series1
Flow Times (seconds)
*Note: These values represent the average results of simulations replicated ten times for a givenlevel of variation
Simulation #2Pull System Kanban Pull
Five workstations in a cell
Pull System-Kanban Pull
Five workstations in a cell.
The first workstation will never be starved.
Every station on a line has the same level of variation.
The average process time is the same for every machine center (10The average process time is the same for every machine center (10 time units). However, the individual process times are taken randomly from a normal distribution.
Each machine on Line One has a coefficient of variation of 5% and each machine on Line Two has a coefficient of variation of 50%.
All th t b id i ti id ti l f b th liAll other parameters besides variation are identical for both lines.
Pull System- Kanban Pull System
A WIP level of one unit is allowed between each station. WS1 WS2 WS3
The logic used to model this system is that a machine will work only when there is zero or one WIP between itself and the succeeding station If the queue between stations is equal to 2 the machine will notstation. If the queue between stations is equal to 2 the machine will not work.
Effect of Variability -Kanban Pull SystemSystem
Lead Times For Kanban Pull System
Process Standard
Variability 1 2 3 4 5 6 7 8 9 10 Average Deviation
5% 71.2 68.9 70.8 66.8 71.8 67.9 68.3 66.4 71.4 71.9 69.5 2.12
10% 70.7 68.7 71.1 70.5 73.2 71.1 69.6 72.7 72.0 72.4 71.2 1.41
AVERAGE FLOW TIMES PER COEFFICIENT OF VARIATION
20% 72.3 71.2 73.6 73.6 73.3 73.7 72.6 73.5 73.6 73.5 73.1 0.80
30% 75.6 75.5 76.8 77.9 74.5 78.0 77.0 76.1 75.1 74.7 76.1 1.24
40% 81.1 80.7 81.4 78.8 77.8 79.1 79.6 78.7 78.7 78.9 79.5 1.19
50% 81.3 82.3 82.3 83.7 79.2 82.4 84.8 81.1 83.8 82.3 82.3 1.57
40%
50%
riat
ion
AVERAGE FLOW TIMES PER COEFFICIENT OF VARIATION
5%
10%
20%
30%
Co-
effic
ient
of v
a
Series1
0 100 200 300 400 500
Flow Times (seconds)
5%
*Note: These values represent the average results of simulations replicated ten times for a givenlevel of variation
Effect of Variability -Kanban Pull System
Throughput For Kanban Pull System
Std
System
ProcessVariatio
n 1 2 3 4 5 6 7 8 9 10 Average
Std Dev
5% 356.2 356.9 356.8 357 356.6 357 357 356.5 357.1 356.8 356.8 0.28
10% 351.8 352.6 352.2 353.4 352.8 352.8 352.6 353.1 353.1 353.4 352.8 0.52
20% 338.5 340.4 339.7 341.3 339.4 340.7 339 338.9 340.4 339.6 339.8 0.89
30% 322.2 321.7 321.8 323.2 324.6 320.9 324.2 320.2 325.6 322.3 322.7 1.7
40% 305.1 300.5 304.3 304.6 309.2 304.4 305 304 307.4 306.5 305.1 2.3
50% 285.3 283.8 289 288.4 295.5 288.5 285.5 288.6 283.8 289.1 287.7 3.45
40%
50%
aria
tion
AVERAGE FLOW TIMES PER COEFFICIENT OF VARIATION
0 5 10 15 20 25 30 35 40 45
5%
10%
20%
30%
Coe
ffici
ent o
f v
Series1
Flow Times (seconds)
*Note: These values represent the average results of simulations replicated ten times for a givenlevel of variation
Effect of Variability -Kanban Pull System
Average Overall WIP for Kanban
System
g
Process Standard
Variability 1 2 3 4 5 6 7 8 9 10 Average Deviation
5% 7.0 6.8 7.0 6.6 7.1 6.7 6.8 6.6 7.1 7.1 6.9 0.21
10% 6.9 6.7 7.0 6.9 7.2 7.0 6.8 7.1 7.1 7.1 7.0 0.14
20% 6.8 6.7 6.9 7.0 6.9 7.0 6.8 6.9 7.0 6.9 6.9 0.08
30% 6.8 6.8 6.9 7.0 6.7 7.0 6.9 6.8 6.8 6.7 6.8 0.11
40% 6.9 6.7 6.9 6.7 6.7 6.7 6.7 6.6 6.7 6.7 6.7 0.08
50% 6.4 6.5 6.6 6.7 6.5 6.6 6.7 6.5 6.6 6.6 6.6 0.10
40%
50%
aria
tion
AVERAGE FLOW TIMES PER COEFFICIENT OF VARIATION
0 0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 40 0 45 0
5%
10%
20%
30%
Coe
ffici
ent o
f va
Series1
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0
Flow Times (seconds)
*Note: These values represent the average results of simulations replicated ten times for a givenlevel of variation
Simulation #3Pull System CONWIPPull System- CONWIP
The same assumptions apply for the conwip system as did the pull system except for the following: A WIP level of seven units are allowed within the cellA WIP level of seven units are allowed within the cell.
The logic used to model CONWIP is that no part was allowed to enter the system until a part exited the system. This established the CONstant WIP. Within the system, the cell operates in the same y pmanner as a push system
Effect Of Variability- CONWIP System
Lead Time for CONWIP
Process StandardProcess Standard
Variability 1 2 3 4 5 6 7 8 9 10 Average Deviation
5% 70.3 70.3 70.5 70.4 70.3 70.4 70.4 70.4 70.3 70.3 70.4 0.07
10% 71.4 71.3 71.3 71.4 71.2 71.2 71.3 71.3 71.3 71.3 71.3 0.06
20% 74 2 74 4 74 4 74 7 74 1 74 3 74 6 74 1 74 6 74 9 74 4 0 2520% 74.2 74.4 74.4 74.7 74.1 74.3 74.6 74.1 74.6 74.9 74.4 0.25
30% 78.2 78.3 78.7 78.0 78.0 78.0 78.5 78.1 78.6 78.7 78.3 0.29
40% 82.7 82.3 83.0 82.6 83.0 82.3 82.8 82.9 83.2 82.3 82.7 0.33
50% 87.8 86.7 86.8 87.1 87.9 87.1 86.9 87.9 87.0 87.4 87.3 0.47
30%
40%
50%
varia
tion
AVERAGE LEAD TIMES PER COEFFICIENT OF VARIATION
0.0 100.0 200.0 300.0 400.0 500.0
5%
10%
20%
30%
Coe
ffici
ent o
f
Series1
0.0 100.0 200.0 300.0 400.0 500.0
Flow Times (seconds)
*Note: These values represent the average results of simulations replicated ten times for a givenlevel of variation
Effect Of Variability- CONWIP System
Throughput For CONWIP Pull System
Process 1 2 3 4 5 6 7 8 9 10 Average Standard
5% 358.4 358.5 357.5 357.7 358.4 358.1 358 357.8 358.4 358.5 358.1 0.36
10% 353 353.6 353.4 353.1 354.1 353.8 353.3 353.3 353.6 353.6 353.5 0.31
20% 339 5 338 8 338 6 337 6 340 3 338 9 338 340 1 337 8 336 6 338 6 1 1520% 339.5 338.8 338.6 337.6 340.3 338.9 338 340.1 337.8 336.6 338.6 1.15
30% 322.2 321.7 320.5 323.1 323.3 323.2 321 322.5 320.8 320.1 321.8 1.19
40% 305 305.7 303.6 305.4 303.9 306.3 304.2 304.3 303 306.5 304.8 1.19
50% 287 290.7 290.4 289.6 287 289.1 290 286.5 289.8 288.1 288.8 1.57
0.4
0.5
AVERAGE THROUGHPUT PER COEFFECIENT OF VARIATION
0 50 100 150 200 250 300 350 400
0.05
0.1
0.2
0.3
Ser ies1
Aver age T hr oughput ( pi eces/ hour )
Effect Of Variability- CONWIP System
Overall Average WIP for CONWIP
Process StandardProcess Standard
Variability 1 2 3 4 5 6 7 8 9 10 Average Deviation
5% 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 0.00
10% 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 0.00
20% 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 0.00
AVERAGE WIP PER COEFFICIENT OF VARIATION
20% 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 0.00
30% 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 0.00
40% 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 0.00
50% 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 0.00
30%
40%
50%
f var
iatio
n
5%
10%
20%
Co-e
ffici
ent o
f
Series1
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0
Flow Times (seconds)*Note: These values represent the average results of simulations replicated ten times for a givenlevel of variation
Summary
Higher levels of variation effect flow time, WIP, and throughput (capacity)(capacity)
Variation early in the push production system is more detrimental than variation late in the routing g
Variation late in the kanban pull production system is more detrimental than variation early in the routing
Pull systems establish a WIP cap, that decreases flow time, while maintaining a similar throughput level (Little's Law).
Variation of flow times is drastically reduced when using pull
Tradeoff of zero WIP is lost capacity for decreased flow times
WIP of one is more robust to variation
US Manufacturer Response to Global CompetitionGlobal Competition
•Hypothesis•Is not Lean Six Sigma•Is based on greater work performed by US workforceIs based on greater work performed by US workforce
Source: National Institute for Occupational Safety and Health (NIOSH)
US Manufacturing In The News
Plant Startups Manufacturing Income
420
440
460
480
Num
ber
Decline in real earnings in manufacturing by 9.1%
nationallyGlobal Competition380
400
2003 2004 2005
Year
nationallyp
Plant Closures Manufacturing Jobs
GM, Ford announces plant shutdowns and
60,000 layoffs
Decline in Manufacturing employment by 59% since
1998
Source: Bureau of Labor Statistics
How are Manufacturers Responding?
Manufacturing Employment Trend
Manufacturing Employment; 1995 2004 Manufacturing Employment; 1995-2004
120
100
110
t Tre
nd
USCanadaAustralia
90
100
Empl
oym
ent Australia
JapanGermanyUK
70
80
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
E
YearSource: Bureau of Labor Statistics
Manufacturing Output Trend
Manufacturing Output; 1995-2004Manufacturing Output; 1995 2004
200
160
180
ut
USCanadaAustralia
120
140Out
p JapanGermanyUK
1001995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
Source: Bureau of Labor Statistics
Less People Higher Productivity?
Strategy 1: Operational ExcellenceLean Agile Flexible Six Sigma AutomationLean, Agile, Flexible, Six Sigma, Automation
Process
4%2% 3%91%
Move Queue Set- Up
Non – Value Added Value Added
Lead Time = 20 days
Strategy 2: Lean Enterprisegy p
ProcessMove Set- Up Process
Lead Time = 1.8 days
Move p
Non – Value Added Value Added
Strategy 3: Outsourcing
Source: Economic Policy Institute
Strategy 4: Global Supplier D l tDevelopment
Source: Economic Policy Institute
Strategy 5: Rapid New Product I d iIntroduction
A 35 2 Status QuoAverage 35.2
World-Class Manufacturers 32.6
Top Performers 17.7
Status QuoDelivery
ServiceValue
Manufacturingp
Source: www.industryweek.com “Zeroing In On World Class” D. Drickhamer, 11/1/2001
Service
Strategy 6: Premium Value gy
Strategy 7: Employees Working M N t S t ?More….Not Smarter?
What Workers Say About Stress on the JobWhat Workers Say About Stress on the Job
One-fourth of employees view their jobs as the number one stressor in their lives.
--Northwestern National Life
Three-fourths of employees believe the worker has more on-the-job stress than a generation ago.
-Princeton Survey Research Associates
Problems at work are more strongly associated with health complaints than are any other life stressor-more so than even financial problems or family problems.
S P l Fi-St. Paul Fire
and Marine Innsuance Co
Source: National Institute for Occupational Safety and Health (NIOSH)
US Manufacturer Response to Global CompetitionGlobal Competition
•Hypothesis True???????•Is not Lean Six Sigma•Is based on greater work performed by US workforceIs based on greater work performed by US workforce
Source: National Institute for Occupational Safety and Health (NIOSH)
Wh A Th Di i O Whi h YWhat Are The Dimensions On Which You Design Continuous Improvement?
Do you “design” based on the following?C itCapacity
SalesSchedulingScheduling
CapabilityCp Cpk Cr Pp PpkCp, Cpk, Cr, Pp, Ppk…
Motivated and Skilled Workforce
Lean Six SigmaD fi itiDefinition
Definition of Lean Six Sigmag
Guidon Performance Solutions defines LeanSigma as the combination of LeanGuidon Performance Solutions defines LeanSigma as the combination of Lean Thinking and Six Sigma into a single, coordinated initiative, eliminating the guesswork about when and how to use these tools – and eliminating months from the time it typically takes to implement them.
http://www.guidonps.com/capabilities/lean_six_sigma.php
Why Lean Six Sigma
Neither Lean nor Six Sigma can by themselves fulfill the operational improvement demandsimprovement demandsLean and Six Sigma are required to meet the customer expectations The successful implementation of Lean will enhance the performance of Six Sigma and vice versa
Lean and Six Sigma ContributionContribution
LeanL f li i ti
Six SigmaSi Si f d iLean focuses on eliminating
non-value added steps and activities in a process Lean makes sure we are
Six Sigma focuses on reducing variation from the remaining value-added steps. Six Sigma makes sure we are doingLean makes sure we are
working on the right activities Lean establishes the value flow as pulled by the customer
Six Sigma makes sure we are doing the right things right the very first time Six Sigma makes the value flow smoothly without interruption
Source: Air Academy Associates
Integrating Lean & Six Sigma
Six Sigma will eliminate defects but it will not address the question of how to g qoptimize process flowLean principles exclude the advanced statistical tools often required to achieve the process capabilities needed to be truly 'lean‘Each approach can result in dramatic improvement, while utilizing both methods simultaneously holds the promise of being able to address all types of process problems with the most appropriate toolkit.
Note: This chart is modified from a study done by Motorola Sixdone by Motorola Six
Sigma Research Institute
Source: Lean Sigma Institute
Roadmap to Integrate L & Si SiLean & Six Sigma
Lean Six Sigma DMAIC Integration ModelDMAIC Integration Model
Source: Lean Sigma Institute
Overlap of Lean and Six Sigma Toolsp g
Cycle Time Reduction Variance Reduction
• PF
MappingLogicalPhysical
IPOCECNXPF
• Scorecard• SOP• Mistake
JITQuickChangeovers
Time CNX
TestingCorrelations
Proofing• $$$
Single PieceFlow
5S
CorrelationsHypothesisDOE
5SsVisualControls
FMEAMSALean Si SiSix Sigma
Lean Six Sigma Principles
Specif al e in the e es of the c stomerSpecify value in the eyes of the customer.
Identify the value stream and eliminate waste / variationIdentify the value stream and eliminate waste / variation.
Make value flow smoothly at the pull of the customerMake value flow smoothly at the pull of the customer.
Involve align and empower employeesInvolve, align and empower employees.
Continuously improve knowledge in pursuit of perfectionContinuously improve knowledge in pursuit of perfection
Benefits of Lean Six Sigma
Achieve total customer satisfaction and improved operationalAchieve total customer satisfaction and improved operational effectiveness and efficiency
Remove wasteful/non-value added activitiesDecrease defects and cycle time, and increase first pass yieldsDecrease defects and cycle time, and increase first pass yields
Improve communication and teamwork through a common set of tools and techniques (a disciplined, repeatable methodology)
Develop leaders in breakthrough technologies to meet stretch goals of producing better products and services delivered faster and at lower costcost
UT’s Simplified A hApproach
Goal 1: Reduce Lead Time
Current Process
Bad Lead time
Future Process
Better Lead time
Future Process
Ti t t Ti i h Ti t it thTime part enters the system
Time part exits the system
Time part exits the system
Lead Time Impacts What?p
Can I change th l d ti ?
On-time delivery, days to produce andthe lead time? days to produce and
inventory turns
Goal 2: Reduce Variation
Total Variation
Variation Impacts What?p
Can I change the variation? Change COY and
COQCOQ
Goal 3: Change Will Only Come Through PeopleThrough People
ProductionSystems.
Employee. ProcessCapability
ContinuousImprovement
& Control.
The Role of People and Teams in Lean Six SigmaSix Sigma
UT Model Componentsp
PlanningPlanningWorkplaceFlFlowConsistencySupportSupply ChainSupply ChainSustain
UT’s Lean Implementation Template
Your Performance PredictionsPredictions
Planning – lowest – P3Planning lowest P3Workplace –highest – P1,P2Fl hi h l d ti P1Flow –high – lead time - P1Consistency – lowest – P2Support – low – P1,P2Supply Chain – ok – P1 P2Supply Chain ok P1,P2Sustain – P3
UT’sUT’s Lean Six Sigmag
Tools
Research
Value Stream MappingFMEAHuman Factors/ErgonomicsERP in SCML I l t tiLean ImplementationsProduction Rate AnalysisMeasurement Systems AnalysisMeasurement Systems AnalysisPull/CONWIP systems
Assessment
A comprehensive assessment must meet the following criteria
1. Ability to link performance metrics to the operational performance and the level of Lean and Six Sigma implementation.
2. Ability to ascertain current opportunities on the shop floor, support functions and the supply chain.
3. Ability to understand the organizational concerns.
4. Ability to consider the personnel skills and availability
5. Ability to utilize the above information to develop a customized plan for implementation
Assessment LogicAssessment
Type Focus Issues Anticipated Outcome
Time In System Shop Floor Variation
Shop Floor Constraints
Operational Operational Concerns
Customized Plan
Time In System Shop floor Support Variation
Shop Support Constraints
Time In System Office Support Sigma Variation
Office Support Constraints
Time In System Supply Chain Variation
Supply Chain Performance
O Ti D liD li R CM i R
For Change
On Time DeliveryDays to Produce Inventory Levels Setup Time
Delivery
Root Causes for Delivery Concerns
Cost Of Quality First Article Yield
Metric
Quality
Schedule Deviation
Root Causes for Quality Concerns
Root Cause Of Concerns
TechnicalSkills PersonnelPersonnel PersonnelTechnicalLean and Six Sigma Facilitation
Skills
Project Management
Personnel Ability to Support Change
Personnel
Culture Personnel Stress Levels
Current Perceived Personnel Concerns
Personnel Support To Correct Concerns
Management Support Actual Organ. Company CommitmentClear Vision and Plan Clear Expectations Organization Structure Alignment with Rewards
Support for Change
Availability ofResources Actual
Structure Support To Correct Concerns
Availability of Personnel
Resources
Availability of Capital
Actual Resources for Change
UT Assessment Methodology
Assessment ToolkitObjectivesH th
Scenarios
Comprehensive Assessment
Mapping•Product Flow
•Information Flow Basic Process
Analysis
Modeling
j
Brainstorming
Hypotheses
Assessment
Prioritize Opportunities
Process Performance•Metric Analysis
Observations
Loop AnalysisSystems Issues
Simulation Analysis
Identify Quick Victories
Loop AnalysisSystems Issues Functional Issues
StressEvaluation
Lean Assessment
Final Data
Recommendations
Sample of Training Design for the I l t ti f L Si SiImplementation of Lean Six Sigma
Category Topics
ucto
r(s)
rs ialis
t
ect
ers
rvis
ors
loye
e
Inst
ru
Hou
r
Spec
i
Proj
eLe
ade
Supe
r
Empl
Technical Lean Principles M R Technical Six Sigma Principles M R Technical Developing Lean Six Sigma Strategy M M R Technical Applying Lean Six Sigma for Leaders M M M Technical Applying Lean Six Sigma for
Employees M/R
Facilitation Introduction to Teamwork M M M M Facilitation Workstyle Assessment M M M R Facilitation Interpersonal / Communication Skills M M M Facilitation Conflict Resolution M M M R Facilitation Meeting Management / Facilitation M M M RFacilitation Meeting Management / Facilitation M M M RFacilitation Presentational Skills R M R Project Mgt Project Management Fundamentals M M R Project Mgt Planning a Project M M R Project Mgt Scheduling and Budget M M R Project Mgt Controlling & Closing M M R M = Mandatory; R = Recommended;
y; ;
Example of UT’s Training MethodologyMethodology
M d l 1 M f LModule 1 : Management of LeanModule 2 : Designing WorkplaceModule 3 : Designing Flow in LeanModule 3 : Designing Flow in LeanModule 4 : Designing Support FunctionsModule 5 : Sustaining Lean gModule 6 : Overview of Six SigmaModule 7 : DefineModule 8 : MeasureModule 9 : AnalyzeModule 10: Improve & ControlModule 10: Improve & Control