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Lean Six Sigma Introduction and examples
Dr. Inez M. Zwetsloot
29th of October 2016
Dr. Inez M. Zwetsloot
Assistant Professor at the Department of Operations Management
Consultant at the Institute for Business and Industrial Statistics
Amsterdam Business School, University of Amsterdam
IBIS UvA
Established in 1994.
Young and dynamic group that combines research, teaching and consultancy.
Lean Six Sigma center of expertise in the Netherlands
Mission
To foster and stimulate the knowledge and optimal use of statistics in society.
Quantitative Methods
ABS
Part of the Amsterdam Business School of the UvA.
4
Manufacturing industry
General Electric Plastics DAF Trucks (Paccar) LG.Philips-Displays Wolters Kluwer Perlos Douwe Egberts / Sara Lee United Biscuits (Verkade) Noviant
Finance and services
ABN AMRO, Achmea Pensions, Getronics, PostNL, Heijmans (Burgers Ergon), NedTrain, AEGON, ZwitserLeven, Ziggo
Healthcare Red Cross, Deventer, Rivas, Canisius Wilhelmina, WFG, Virga Jesse Hospital, RdGG UMCG, UMCU, AMC, EMC
Lean Operations, Six Sigma
1990-now: The Netherlands
High-tech / product design
Philips Medical Systems BOSCH Security Systems Sensata Technologies NXP VDL ETG
Data analysis is used in, for example
• Businesses: data driven decision making, market
research, lean six sigma methodology, etc.
• Scientific research: finding, describing, and
understanding relations amongst variables.
• In life: understand data that comes at you.
Why are data skills important?
Data analysis is used in, for example
• Businesses: data driven decision making, market
research, lean six sigma methodology, etc.
• Scientific research: finding, describing, and
understanding relations amongst variables.
• In life: understand data that comes at you.
Why are data skills important?
Programme
• Example of data analysis: tripadvisor & baseball & Lean Six Sigma
• What is Lean Six Sigma?
• Example of LSS project
Introduction 7
Data driven decision making in baseball
Moneyball: The Art of Winning an
Unfair Game, by Michael Lewis
Nonfiction account of the Oakland
Athletics baseball team under
leadership of Billy Bean.
They introduced data-driven
evaluation of players to decide whom
to scout.
With this the Oakland A’s won the
exact same number of games as the
Yankees in the season of 2002.
The Yankees paid $1.400.000 per
win, while Oakland paid $260.000.
Two years later, the Boston Red Sox
won their first World Series, since
1918, using the same data-driven
philosophy
8
Link to Tripadvisor presentation
Introduction 9
Example 2: Lean Six Sigma project
Process: An administrative process in an insurance company. The
process handles insurance claims, and results in a reimbursement
of the customer (if the claim is accepted) or a rejection of the claim.
Project: The process should be improved in terms of efficiency
(cost) and service quality (total lead time of claim handling).
Problem: Clients complain that it takes way too long before they get
their money. Moreover, they judge the company too expensive
compared to competing insurance companies.
140 120 100 80 60 40 20 0
90 80 70 60 50 40 30 20 10 0
TT (Calendar days)
Histogram of TT Lognormal
Days 9.446 7.297 4.340 0.880 0.880 0.756
Percent 40.0 30.9 18.4 3.7 3.7 3.2
Cum % 40.0 70.9 89.3 93.1 96.8 100.0
Activity OtherWT-RecWT-OrdWT-SpecWT-ClientAWT-rework
25
20
15
10
5
0
100
80
60
40
20
0
Th
rou
gh
pu
t ti
me
(w
ork
da
ys)
Pe
rce
nt
Pareto Chart of Throughputtime per activity9
8
7
6
5
4
3
2
1
WT
-S
pe
c(d
ays)
(A) (B) (C) (D)
Lean Six Sigma
Introduction
Six Sigma
Managerial and methodological framework for organizing continuous improvement in organizations.
Improvement of routine processes:
- Manufacturing
- Service delivery
- Sales
- Transactional
Complete methodology:
- Management and organizational structures
- Methodology for projects (DMAIC method)
- Tools and techniques, such as statistical analyses
Electronics Sony Samsung Philips
Telecom Nokia Ericsson Motorola
Aircraft Bombardier Boeing KLM
Automotive Ford Paccar/DAF Volvo
Finance Citibank Bank of
America
Materials GE DuPont Shell
1987: Start of Six Sigma initiative at Motorola.
1995: General Electric adopts Six Sigma
2016: “Six Sigma”: 18.7 million hits in Google
Six Sigma
Collection of best practices from Toyota and other Japanese companies.
Based on a manufacturing system that focuses on speed, flexibility and low cost.
Mainly aimed at process flow, throughput time and inefficiencies.
Lean Thinking
“Lean thinking”
“Lean” process:
– Jobs and clients flow smoothly through the process,
no waiting queues (“just-in-time”).
– But: disruptions and variability bring the whole
process to a standstill.
– Traditional solution: buffers of WIP and slack time.
Lean 6 Sigma
–
+
Time consuming
and
difficult
Solidly structured,
integrated and
focused approach
–
+
Weak structure
and lack of
strategic focus
Standard cures
reflecting best
practices
Integrated Lean Six
Sigma approach
with solid structure
and solutions
Lean and Six Sigma balance
Task
Task
Task
Task
Task
Resources
Task
Task
Task
Task
Task
Process step
WIP queue
(“Work in Process”)
Route
Quote by W. Edwards Deming (1900-1993):
“If you can’t describe what you are doing as a process, you don’t know what you are doing”
Thinking in processes
Input Output Customer Process Waste Waste Waste Waste
7 56
121110
8 4
21
9 3
7 56
121110
8 4
21
9 3
€
The Hidden Factory
Supplier
Operations management is about ensuring
effective and efficient processes.
90% of trains are on time
0.9 × 0.9 × 0.9 × 0.9 = 0.66 (3 changeovers)
Probability to arrive in time:
66%
(0.9)8 = 0.43 (go and return, 3 changeovers)
Probability to have no delay all day:
43%
The effect of complexity
The effect of complexity
First pass yield per process:
Number of processes 93.3% 99.38% 99.977% 99.9997%
1
10
100
500
1000
2000
2955
93.32
50.09
0.1
0
0
0
0
99.379
93.96
53.64
4.44
0.2
0
0
99.9767
99.77
97.70
89.02
79.24
62.75
50.27
99.99966
99.9966
99.966
99.83
99.66
99.32
99.0
Sigma level: 3σ 4σ 5σ 6σ
Just-do-it
Who is going to
do it? And when?
Problem solution
Why did it happen?
Lean/Kaizen event
How are we going
to handle this?
Lean Six Sigma
What is the
solution?
Known Unknown
Low
H
igh
Solution
Com
ple
xity
Projects and execution
Hoerl and Snee (2013). One size does not fit all: Identifying the
right improvement methodology. Quality Progress 46(5).
Lean Six Sigma
Methodology
Black Belts are trained in solving problems
efficiently by making use of
scientific method.
Scientific approach
To control a system
by understanding how the system works.
Understanding a system
To have a theory which relates the system’s
behaviour to the effects of influence factors.
Y = f(X1, X2, …, Xn)
Scientific method
Principles of sound method
Causal modeling: understand the root causes.
Operational definitions: clear understanding of KPIs and measures.
Quantification and parameterization of problems: most problems are trade-off problems!
Data-based diagnosis: focus!!!
Creative, experimental and innovative generation of ideas: beyond the usual suspects.
Empirical testing of ideas: fine-tune your ideas to the dirty details of the real world.
Statistics
Measure
Analyze
Improve
Control
5. Establish the effect of influence factors 6. Design improvement actions
7. Improve / design process control 8. Close the project
3. Diagnose the current process 4. Identify potential influence factors
1. Define the CTQs (Critical To Quality charactics) 2. Validate measurement procedures
Define
Lean Six Sigma: DMAIC model
J. de Mast, R.J.M.M. Does, H. de Koning and J. Lokkerbol (2012), “Lean Six Sigma for Services and Healthcare”
Measure
Analyze
Improve
Control
5. Establish the effect of influence factors 6. Design improvement actions
7. Improve / design process control 8. Close the project
3. Diagnose the current process 4. Identify potential influence factors
1. Define the CTQs (Critical To Quality charactics) 2. Validate measurement procedures
Define
Lean Six Sigma: DMAIC model
Make the problem quantifiable and measurable
“You cannot improve what you cannot measure”
J. de Mast, R.J.M.M. Does, H. de Koning and J. Lokkerbol (2012), “Lean Six Sigma for Services and Healthcare”
Measure
Analyze
Improve
Control
5. Establish the effect of influence factors 6. Design improvement actions
7. Improve / design process control 8. Close the project
3. Diagnose the current process 4. Identify potential influence factors
1. Define the CTQs (Critical To Quality charactics) 2. Validate measurement procedures
Define
Lean Six Sigma: DMAIC model
Attempts at improvement should be preceded by a data-based diagnosis
“What is the nature of the main problem?”
J. de Mast, R.J.M.M. Does, H. de Koning and J. Lokkerbol (2012), “Lean Six Sigma for Services and Healthcare”
Measure
Analyze
Improve
Control
5. Establish the effect of influence factors 6. Design improvement actions
7. Improve / design process control 8. Close the project
3. Diagnose the current process 4. Identify potential influence factors
1. Define the CTQs (Critical To Quality charactics) 2. Validate measurement procedures
Define
Lean Six Sigma: DMAIC model
The effectiveness of proposed interventions must be demonstrated:
Evidence-based intervention
“In God we trust, all others must bring data”
J. de Mast, R.J.M.M. Does, H. de Koning and J. Lokkerbol (2012), “Lean Six Sigma for Services and Healthcare”
Measure
Analyze
Improve
Control
5. Establish the effect of influence factors 6. Design improvement actions
7. Improve / design process control 8. Close the project
3. Diagnose the current process 4. Identify potential influence factors
1. Define the CTQs (Critical To Quality charactics) 2. Validate measurement procedures
Define
Lean Six Sigma: DMAIC model
Structures for continued control and improvement of the process
“It takes all the running you can do to stay in the same place”
J. de Mast, R.J.M.M. Does, H. de Koning and J. Lokkerbol (2012), “Lean Six Sigma for Services and Healthcare”
Lean Six Sigma
projects
Improving a call center
Improving revenue through
website design Decreasing dispatch time
Improving a call center
Improving revenue through
website design Decreasing dispatch time
Lesson learned
Effect of project is difficult to
maintain in the long run
Lesson learned
• Implementation is not fully
completed due to politics.
• Use of data to underpin effect of
‘well known truths’ is essential
Lesson learned
Pareto principle can be very usefull
to determine focus in the
improvement actions.
Which project shall I
show you in detail?
Concluding
“Make friends with your data”
“Tell a story with your data”
Daniel Wrigth (2003) “Making fiends with your data: Improving how statistics are
conducted and reported.” British Journal of Educational Psychology, 73,123-136
Lean Six Sigma · 34
2 vacancies
http://www.uva.nl/over-de-uva/werken-bij-de-
uva/vacatures/nav/type/phd-position/keys/feb/item/16-408-two-phd-
candidates-in-statistical-process-monitoring.html
Fundaments of SPM
Fundamental methodological questions regarding statistical process monitoring such as: what is the effect of frequently updating monitoring limits, how should data be sampled in order to estimate process parameters and are the assumptions that underlie the currently available models relevant in practice.
SPM for big data
Monitoring and big data, a growing area of research addressing the use of big data sets. The use of SPM for big data sets is relatively novel and new techniques are required. For this project, you will take some real data sets as a starting point and develop suitable SPM methods.
This presentation is based on the book:
J. de Mast, R.J.M.M. Does, H. de Koning and J. Lokkerbol (2012), “Lean Six Sigma for Services and Healthcare”, Beaumont, Alphen a/d Rijn, the Netherlands.
And the articles:
• G.C. Niemeijer, R.J.M.M. Does, J. de Mast, A. Trip, J. van den Heuvel & S.
Bisgaard (2011), “Generic project definitions for improvement of healthcare
delivery: case-based reasoning research”, Quality Management in Health Care
20(2), pp. 152-164
• Lokkerbol, J., Does, R. J. M. M., De Mast, J., Schoonhoven, M. (2012).
Improving processes in financial service organizations: where to begin?
International Journal of Quality and Reliability Management, 29(9), 981-999.
• Inez Zwetsloot, Marly Buitenhuis, Bart Lameijer and Ronald Does. Quality
Quandary: Increasing the First Time Fix Rate in a Customer Contact Center.
Quality Engineering, 2015, 27(3), pp. 393-400.
• Inez Zwetsloot and Ronald Does Quality Quandary: Improving Revenue by
Attracting more Clients Online. Quality Engineering, 2015, 27(1), pp. 130-137.
• Basta, Zwetsloot, Klinkednbijl, Rohof, Monster, Fockens, Tytgat. Decreasing
the dispatch time of medical reprots sent form hospital to primary care with Lean
Six Sigma. Journal of Evaluation in Clinical Practice. 2016.
Additional information, such as papers or case studies can be found on our website:
http://ibisuva.nl/english/research.html