lean six sigma - os3 · (2012), “lean six sigma for services and healthcare ... spm for big data...

Lean Six Sigma Introduction and examples

Dr. Inez M. Zwetsloot

29th of October 2016

[email protected]

Dr. Inez M. Zwetsloot

[email protected]

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

Decisions based on data.pptx





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





Define


Make the problem quantifiable and measurable

“You cannot improve what you cannot measure”


Measure

Analyze

Improve

Control





Define


Attempts at improvement should be preceded by a data-based diagnosis

“What is the nature of the main problem?”


Measure

Analyze

Improve

Control





Define


The effectiveness of proposed interventions must be demonstrated:

Evidence-based intervention

“In God we trust, all others must bring data”


Measure

Analyze

Improve

Control





Define


Structures for continued control and improvement of the process

“It takes all the running you can do to stay in the same place”


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

lean six sigma - os3 · (2012), “lean six sigma for services and healthcare ... spm for big data...

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