KIT – Universität des Landes Baden-Württemberg und
nationales Großforschungszentrum in der Helmholtz-Gemeinschaft www.kit.edu
Institut für Fördertechnik und Logistiksysteme
Applications for stochastic models in lean management – Opportunities and missing links
GOR – AG Supply Chain Management
Synthese von Lean Management und Operations Research
Leinfelden, 27.9.2013 – Kai Furmans
© Institut für Fördertechnik und Logistiksysteme, 2009 2
IFL
Toyota Lean - Shock Lean-Revival
1950 1990 - 1995 2000 - today
Development of Lean Systems
MRP ERP SCM as APS
1960 – 1970 1970 – 1990 1995 – today
Lean
SCM
© Institut für Fördertechnik und Logistiksysteme, 2009 © Institut für Fördertechnik und Logistiksysteme, 2012 © Institut für Fördertechnik und Logistiksysteme, 2010
Why Lean Management?
3
Empirical and Anecdotal Evidence, that Lean
Management enhances Productivity more than the
average productivity gains in the base population
Leadership and
Target Setting
Design Elements
which tackle
variability and
create a direct
relation between
Design, KPI and
KPR
Associate
involvement and
Empowerment,
large base for
problem solving
See i.e. Dehdari 2013
© Institut für Fördertechnik und Logistiksysteme, 2009
Some foundations – Supply Chain Physics
Leadership and
Target Setting
Design Elements
which tackle
variability and
create a direct
relation between
Design, KPI and
KPR
Associate
involvement and
Empowerment,
large base for
problem solving
Stochastic
models can
explain, why
it works
© Institut für Fördertechnik und Logistiksysteme, 2009 5
IFL
Supply Chain Physics – Little‘s Law
stock = throughput * throughput time
Ns [units] = λ [units per time] * ts [time]
• Initial situation
• Double throughput with same throughput time
doubled stock
• Same throughput with longer throughput time
increased stock
© Institut für Fördertechnik und Logistiksysteme, 2009
Supply Chain Physics – Little‘s Law
Application in Value Stream Mapping
Response Time = Inventory ∗ Customer Takt
CT =
160 min = 1/3 d
Source: valuestreamguru
© Institut für Fördertechnik und Logistiksysteme, 2009 8
IFL
Supply Chain Physics –
Variability Generates Waiting Times
random
interarrival time
& clocked
processing
Waiting times
arise – visible by
stocks (Little)
arrivals &
processing
clocked
no waiting time
© Institut für Fördertechnik und Logistiksysteme, 2009 9
Ereignis
0,8
1
0 200 400 600 800 1000 1200 1400 1600 1800
Ereignis
0,8
1
0 500 1000 1500 2000 2500
Excursus - Variability
2
2
2
2
)(
)(
XXE
XVarc
c²=0,01
c²=1,00
9
IFL
© Institut für Fördertechnik und Logistiksysteme, 2009 10
Supply Chain Physics –
Capacity Utilization (workload) and Variability
0
1
2
3
4
5
6
7
8
9
10
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
stock
workload
0
5
10
15
20
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
stock
workload
With the workload, the influence of
variability on stocks and leadtimes
increases
Comparison of the average stock of the
system at varying variabilities
Low variability Medium variability
High variability
No variability
Medium vs. high variability at 94% workload
Low vs. medium variability at 94% workload
No vs. Low variability at 94% workload
10
IFL
21
222
ba ccN
© Institut für Fördertechnik und Logistiksysteme, 2009 11
IFL
Typical sources for Emergence and
Intensification of Volatility
Caused by the design of the material flow
Caused by downtimes
In the material flow
In the processing
As a result of scrap
Caused by forming batches in containers
Caused by inventory control
© Institut für Fördertechnik und Logistiksysteme, 2009 12
IFL
Branches in the Production Flow Generate
Variability Initial
variability
in front of
the branch
Whitt, 1983
Create Linear flow
© Institut für Fördertechnik und Logistiksysteme, 2009 13
Oversized Container Fill-Up Capacities
Generate Variability
0,00
50,00
100,00
150,00
200,00
250,00
1 5 9 13 17 21 25 29 33 37 41 45 49
dem
an
d / o
rder
qu
an
tity
period
Demand
Order
Average demand: 100 / period, = 20
13
IFL
0
20
40
60
80
100
120
0 50 100 150 200 250
sta
nd
ard
de
via
tio
n d
em
an
d
container quantity
standard deviation container calls (demand)
Standardabweichung Behälterabrufe
Standard
deviation
container calls
© Institut für Fördertechnik und Logistiksysteme, 2009 14
IFL
00,05
0,1
0,15
0,2
0,25
0,3
0,35
0,4
0,45
0,5
0,55
0,6
0,65
0,7
0,75
0,8
0,85
0,9
0,95
0
5
10
15
20
25
30
35
40
Auslastung
Ve
rw
eilze
it d
er A
ufträg
e
Effect of Downtimes
V ( t) =
Z t
0V ( u) du =
A( t)X
i = 1
³W i ¢B i + B 2
i =2´
+ ²( t )
( 1)
The increase in
leadtime caused by
downtimes is
essentially higher
than it can be
explained only by the
loss of capacity!
With
ou
t d
ow
ntim
es
dw
ell
tim
e
Incre
ase
in
ca
pa
city
utiliz
atio
n (
wo
rklo
ad)
by d
ow
ntim
es
capacity utilization
(workload)
© Institut für Fördertechnik und Logistiksysteme, 2009 15
IFL
Methods of Control Effects
Why pull?
Why leveling?
© Institut für Fördertechnik und Logistiksysteme, 2009 16
IFL
Open vs. Closed Systems
1
N
Open system:
• control of system load (z.B. MRP)
• result is stocks
Closed system:
• control of stocks
• result is throughput (performance)
The required stock (leadtime at equivalent throughput) is in closed systems
always less than/equal to the one in opened
© Institut für Fördertechnik und Logistiksysteme, 2009 17
IFL
Kanban-Systems are Closed loops with a Brake
Pre-
Products
Demand
Pre-Products
Pre- and finished products
with Kanban (WIP)
Demand
satisfied
demand
Station of
Synchronization j
© Institut für Fördertechnik und Logistiksysteme, 2009
0
5
10
15
20
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
stock
workload
Impact of the Design Elements of Lean
Systems
18
Variability Poka Yoke
Standards
Small handling
units
Milkruns
SMED
Linear flow
Levelling
Load Control
Kanban
TPM
Low Cost Automation
© Institut für Fördertechnik und Logistiksysteme, 2009
Leadership and Target Setting
Leadership and
Target Setting
Design Elements
which tackle
variability and
create a direct
relation between
Design, KPI and
KPR
Associate
involvement and
Empowerment,
large base for
problem solving
© Institut für Fördertechnik und Logistiksysteme, 2009
Leadership and Target Setting
Evolution of Management Style
21 27.09.2013
Directions
Delegation
and motivation
Pressure by
results publication
Develop
target states
Creativity
z.B. size of company
Maturity
Age of organization Inspired by Greiner, HBR, 1972
© Institut für Fördertechnik und Logistiksysteme, 2009 © Institut für Fördertechnik und Logistiksysteme, 2012 8
IFL
Approach:
Design a System, which achieves your goals!
Target Value Stream Design
Business Targets in
€
translate from
physical units
to €
translate from
€ to physical units
Implemented Value Stream
Calculate
KPI and
KPR
KPI and KPR
met?
Improvement
Set New Targets
Implement
© Institut für Fördertechnik und Logistiksysteme, 2009
Support needed by OR – some examples
Develop exact or approximate methods, which allow a fast
and simple sizing and evaluation of value streams
Sizing supermarkets (here, only number of Kanbans)
23 27.09.2013
Source: vision-lean.com
Shingo:
𝐾 =𝑄+𝛼
𝑛
K = number of kanbans;
Q = quantity of products in batch production;
α = minimum security stock level;
n = quantity of products transported on a pallet.
Monden:
K = 𝑑 𝑡𝑒 + 𝑡𝑓
𝑐(1 + 𝛽)
k = number of kanbans;
d = demand on the planned period;
te = waiting time, defined from the time
since the necessity of production is defined
until effective production starting time;
tf = time it takes to produce a container
(one kanban) of products;
β = safety factor (around 15%);
c = container capacity.
Bosch:
K = 𝑅𝐸 + 𝐿𝑂 + 𝑊𝐼 + 𝐿𝑂 + 𝑆𝐴
Source: Salgado, Varela, 2010 …
DIMASCOLO, M. ; FREIN, Y. ; DALLERY, Y. :
An Analytical Method for Performance
Evaluation of Kanban Controlled Production
Systems, 1993
© Institut für Fördertechnik und Logistiksysteme, 2009
Dynamic Aspects
When to change the supermarket size?
Determine escalation levels -> when to change operations
When to increase / decrease capacity?
When to use a second source…
How to ramp up production over several levels?
Fill the buffers first?
Try to ramp up simultaneously?
What is the impact of adjustable capacity, when capacity
adjustment is subject to:
Early announcement?
Limitations in extend?
How much does (possibly inaccurate) advance information
help in a dynamic environment?
24 27.09.2013
© Institut für Fördertechnik und Logistiksysteme, 2009 © Institut für Fördertechnik und Logistiksysteme, 2012 8
IFL
Approach:
Design a System, which achieves your goals!
Design
Description and Calculation
Layout
Try it!
Measure it!
© Institut für Fördertechnik und Logistiksysteme, 2009
Continuous Improvement at the Base
Leadership and
Target Setting
Design Elements
which tackle
variability and
create a direct
relation between
Design, KPI and
KPR
Associate
involvement and
Empowerment,
large base for
problem solving
© Institut für Fördertechnik und Logistiksysteme, 2009 © Institut für Fördertechnik und Logistiksysteme, 2012 7
IFL
The Continous Improvement Cycle
Describe
Standards
Observe
Standards -
Measure
Deviations
Pareto
Analysis of
Deviations
Solve most
Severe
Problems
first
Improve
Standards
Location,
Time,
Sequence
Right Location
?
Right Time?
Right
Sequence?
© Institut für Fördertechnik und Logistiksysteme, 2009 © Institut für Fördertechnik und Logistiksysteme, 2012 2
IFL
Development of a Lean Operation
Shop-floor
level
Project
level
Management
level
Mind-set
Lean basics
training 5S, pull,
Kanban…
Value Stream
Analysis
Face-the-Facts On the shop floor
Awareness: we are
not good enough
t
Basics
training Kaizen, KVP, CIP
Problem Solving…
Value Stream
Design Vision, First Step,
Set-up Project
Face-the-Facts On the shop floor
Willingness: we have to change
Implementation
of basics Attention to
Work Systems
i.e. MTM…
Implementation upstream, from
customer to supplier
one value stream,
no shortcuts
Face-the-Facts
Attention to
Change Project
Support: we support those
who try to improve
Continuous
Improvement
Advanced Lean
i.e. heijunka
Hand Over responsibility moves
from project to
operations
Request
Adherence to
Defined
Processes
Continuous
Improvement: we are never satisfied
Warming Storming Norming Performing
© Institut für Fördertechnik und Logistiksysteme, 2009
Problem Solving Approaches
What is better:
Put more emphasis on problem solving or
just have more inventory?
What is better:
A simple system, which is well understood and under control or
an optimal system, which is not quite understood and not under
control?
What is the impact of reward systems on
Problem solving vs.
having excess capacity / excess inventory?
30 27.09.2013
© Institut für Fördertechnik und Logistiksysteme, 2009 31 27.09.2013
Karlsruher Institut für Technologie (KIT)
Institut für Fördertechnik und Logistiksysteme (IFL)
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