1. education
2. school

Euroma Service Operations Management Forum, Tilburg, 22 Sept. 2014
Servitization in the
Capital Goods Industry
Geert-Jan van Houtum
Prof. of Maintenance, Reliability, and Quality
Eindhoven University of Technology
[email protected]
/ School of Industrial Engineering
10/15/2014
PAGE 1
Key words:
System availability
Total Cost of Ownership
/ School of Industrial Engineering
10/15/2014
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Size capital goods industry Netherlands
Import:
117 billion Euro
Export:
117 billion Euro
Source: Statistisch Jaarboek, 2009
Concerns: “Machines en apparaten, Elektrotechnische
machines en apparaten, Transportmiddelen”
/ School of Industrial Engineering
10/15/2014
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Size capital goods industry Netherlands
Import:
117 billion Euro
Export:
117 billion Euro
Potential size services:
Equally large !!!
Source: Statistisch Jaarboek, 2009
Concerns: “Machines en apparaten, Elektrotechnische
machines en apparaten, Transportmiddelen”
/ School of Industrial Engineering
10/15/2014
PAGE 4
Life Cycle
Needs and
requirements
Design
Production
Big influence on System
Availability and TCO
One may look at:
• Modular design
• More reliable components
• Building in redundancy
• Means to facilitate monitoring
and diagnosis
•…
/ School of Industrial Engineering
Exploitation
Disposal
• The maximum
system availability is
determined in the
design phase
• A large portion of
the TCO is made in
this phase
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Long term trends
• Maintenance of complex systems becomes too complicated
for users themselves
• Users require higher system availabilities (less downtime)
• Users look at TCO
• Maintenance is outsourced to third party or OEM (pooling
resources, pooling data, remote monitoring)
• More extreme: One sells function plus availability
• Feedback to design (better systems, higher sustainability)
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Challenges for the OEM\third party?
• Definition of the service portfolio
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Example of a service portfolio
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Challenges for the OEM\third party?
• Definition of the service portfolio
• Design of the service supply chain
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PAGE 9
Service supply chain: Example
Reg. repl.:
1-2 weeks
Local
Stockpoint
Central
Stockpoint
Emergency Shipm.:
1-2 days
Supply
spare parts
Direct
sales
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Customers with
contracts
Lateral
Shipments:
A few hours
Customers with
contracts
Reg. repl.:
1-2 weeks
Local
Stockpoint
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Challenges for the OEM\third party?
•
•
•
•
•
•
•
Definition of the service portfolio
Design of the service supply chain
Design of the service processes
Pricing
Organization
New business models
…
/ School of Industrial Engineering
10/15/2014
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PURPOSE OF THIS TALK
Showing how we can contribute
via OM research
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CONTENTS
1. Introduction
2. Creation of differentiation while keeping the
portfolio effect
3. Effect of product design: Redundancy decision
4. New opportunity: Remote monitoring data
5. General OM research agenda
/ School of Industrial Engineering
10/15/2014
PAGE 13
2. Creation of differentiation while
keeping the portfolio effect
(Tiemessen, Fleischmann, v.Houtum, v.Nunen, and Pratsini, EJOR, 2013)
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10/15/2014
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Spare parts network for high-tech
equipment
HUB
Legend
Local Warehouse
4hr response area
2hr response area
customer (region)
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10/15/2014
PAGE 15
Spare parts network for high-tech
equipment
Spare parts network
Legend
HUB
Local Warehouse
4hr response area
2hr response area
customer (region)
Key features:
Lateral transshipments
Multiple customer classes
/ School of Industrial Engineering
10/15/2014
PAGE 16
Setting
•
•
•
•
Central Warehouse (CW): Infinite stock
Multiple Local Warehouses (LW’s)
Poisson demand processes
Cost factors:
• Cost to fulfill a demand at point x from LW i or from the CW
• Unit replenishment costs
• Penalty costs for violating the maximum response time
constraints
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10/15/2014
PAGE 17
Planning problems
Tactical planning problem:
Decision on base stock levels: Given in this study
Operational planning problem:
We consider two allocation rules
 Static: Common in practice
 Dynamic:
 Exploits real-time stock level information
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PAGE 18
The two allocation rules
Static Allocation (SA-rule):
•
•
•
•
Fulfill demand from nearest LW with positive on-hand stock
But, fulfill from CW if cheaper
Easy to compute, easy to execute (Markov model)
Weak differentiation between customer classes
Dynamic Allocation (DA-rule):
• Estimate near-future effect when fulfilling from one of the
LW’s with positive on-hand stock (Use of Appr. Dyn. Progr.)
• Select LW or CW with lowest “direct + near-future costs”
• Requires computer support to be applied
• Better differentiation between customer classes
/ School of Industrial Engineering
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Computational experiment
Large instances:
 Relative savings of DA-rule compared to SArule: 7.9%
Conclusion:
• Savings relative to the static rule are significant
• Dynamic rule gives a better way to differentiate
between customer classes
• You get implicitly a kind of dynamic, reserved stock levels for
high-priority customers
/ School of Industrial Engineering
10/15/2014
PAGE 20
3. Effect of product design:
Redundancy decision
(Öner, Scheller-Wolf, and van Houtum, OR, 2013)
/ School of Industrial Engineering
10/15/2014
PAGE 21
Setting
Production site
Spare parts
stock
Regular
replenishments
Emergency
supply procedure
(in case of stockout)
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10/15/2014
PAGE 22
Model (cont.)
Component
1
Component
2
Component
2
Component
i
Component
m
Component
m
Per machine:
• Multiple critical components
• Serial structure
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PAGE 23
Model (cont.)
Three possible policies per component
1. No redundancy
2. No redundancy, apply emergency supply procedure
when on-hand stock drops to 1
3. Redundancy
Optimization problem
Min. TCO
s.t. system availability constraint
/ School of Industrial Engineering
10/15/2014
PAGE 24
Approach
• Generation of the efficient frontier for TCO
and system availability (via Lagr. Rel.)
• One curve for case with policy 2
• One curve for case without policy 2
• One gets an order for which components
have to be made redundant
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10/15/2014
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Analysis per component
Costs
Policy 1
Policy 2
Policy 3

Policy 1
optimal
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Policy 2
optimal
Policy 3
optimal
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PAGE 26
Efficient frontier
Without
policy 2
With policy 2
included
Availability
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10/15/2014
PAGE 27
Efficient frontier
Notice:
Without
The optimal designpolicy
depends
strongly
2
on the required availability
So:
Multiple customer classes => multiple
With policy 2
designs
included
Availability
/ School of Industrial Engineering
10/15/2014
PAGE 28
4. New opportunity:
Remote monitoring data
(based on Topan, Dekker Tan, and Van Houtum, WP, 2014)
/ School of Industrial Engineering
10/15/2014
PAGE 29
Monitoring data
Sample Data: Collected at central level,
for one critical unit
• Condition data:
parameters
which are
directly or
indirectly related
with the health
state of Module
X
• Failure data:
failure time
PAGE 30
MACHINE
NUMBER
TIME
STAMP
VALUE
MACHINE
TYPE
SITE
ID
CUSTOMER
CONTINENT
CUSTOMER CUSTOMER PARAM
COUNTRY
NUMBER
ID
M1297
M2572
M2488
17-Dec-09
22-Oct-09
30-Jul-09
-8.856 T0010
-8.9597 T0005
-3.9977 T0083
1288 Asia
665 Asia
755 Other
South Korea 188
Singapore
2046
Other
OT01
M0822
14-Jul-09
-4.0141 T0016
1284 Asia
South Korea 188
960
M1621
08-May-09
-3.8854 T0010
South Korea 1146
957
M1647
23-Oct-09
-3.9167 T0001
1294 Asia
North
277 America
USA
196
966
M0003
21-Jul-09
-3.873 T0010
1291 Asia
South Korea 188
990
M0004
M2862
M2631
21-Feb-09
27-Aug-09
06-Jan-09
-3.8264 T0010
-3.7398 T0004
-8.551 T0004
1291 Asia
629 Asia
801 Europe
South Korea 188
Taiwan
222
France
192
966
993
972
M1141
M3241
M0051
M1171
M1171
10-Aug-09
22-Apr-09
05-Sep-09
28-Feb-09
12-Aug-09
-6.8885
-8.551
-8.9597
-3.9977
-6.8885
1290
629
1178
629
629
South Korea
Taiwan
Taiwan
Taiwan
Taiwan
966
963
996
987
990
M1614
04-Dec-09
M1951
16-Jul-09
T0011
T0010
T0008
T0006
T0006
-8.551 T0007
-8.9597 T0019
Asia
Asia
Asia
Asia
Asia
1146
222
386
222
222
1284 Asia
South Korea 188
1286 Asia
/ School of
South KoreaIndustrial
188
Engineering
3756
990
981
990
960
10/15/2014
Imperfect warnings
Demand signals produced by the prediction model are
imperfect:
• Prediction model can produce false signals (false
positives)
• Exact time of the failure is uncertain
• Prediction model may also produce false negatives
− Sudden, unpredicted failures which cannot be
detected in advance by the monitoring system
PAGE 31
/ School of
Industrial
Engineering
10/15/2014
Research topic
Value of the imperfect warnings
for spare parts supply
Remark: We do not look at preventive replacements.
Setting:
• Single stockpoint (a local warehouse, gets
replenishments from a central warehouse)
• Single item
• Imperfect warnings = Imperfect Advance Demand
Information (ADI)
PAGE 32
/ School of
Industrial
Engineering
10/15/2014
Imperfect ADI
 demand lead time
Demand
[
t
]
l
u
p
L supply lead time
Supply
t
PAGE 33
p: probability that a
signal will ever
become a demand
realization = reliability
(false positives)
[l, u] : prediction
interval for the
demand lead time
(timing)
q : ratio of predicted
demand to total
demand = sensitivity
(false negatives)
/ School of
Industrial
Engineering
10/15/2014
Approach
3 scenarios:
1. Optimal cost without ADI (benchmark)
2. Optimal cost with imperfect ADI, but no returns allowed
3. Optimal cost with imperfect ADI, and returns allowed
Each scenario: Analyzed by a Markov decision model
PAGE 34
/ School of
Industrial
Engineering
10/15/2014
Case study at an OEM
• 4 parts that OEM supplies its customers all over the world.
• p, q, [l, u] : obtained from prediction model in use
• cem: transportation cost + high downtime cost
cem = 75000 Euro
• cr: transportation cost + pipeline holding cost
• L : 2 weeks
PAGE 35
/ School of
Industrial
Engineering
10/15/2014
Case study at an OEM
Part
P
T
X
W
h
[𝝉𝒍 , 𝝉𝒖 ]
𝝀
(€/unit/week) (week) (unit/week)
2720
112
152
646
[2,8]
[8,16]
[0,4]
[0,1]
0.0188
0.0600
0.0019
0.0036
𝒑
𝒒
𝒄𝒓
0.42
0.90
0.45
0.90
0.44
0.90
0.43
0.50
5500
325
400
1400
𝒈𝑵𝒐𝑨𝑫𝑰 𝒈𝑨𝑫𝑰𝑵𝒐𝑹𝒆𝒕𝒖𝒓𝒏
(€/week)
(€/week)
1406.01
248.13
145.29
273.28
1399.68
139.78
138.83
273.28
𝒈𝑨𝑫𝑰
𝑷𝑪𝑹𝑨𝑫𝑰𝑵𝒐𝑹𝒆𝒕𝒖𝒓𝒏
(€/week)
956.06
131.80
134.37
260.00
0.45%
43.67%
4.45%
0.00%
𝑷𝑪𝑹𝑨𝑫𝑰
32.00%
46.88%
7.52%
4.86%
• Part P (p and q are low and information is timely)
• value of information is high when returning excess inventory is allowed.
• benefit of returns
• Policy with ADI and no returns: Local warehouse carries no stock + a spare
part is shipped to the local warehouse only if a warning is issued
• Part T (p and q are high and information is timely),
• value of information is high even for without return case.
PAGE 36
/ School of
Industrial
Engineering
10/15/2014
Case study at an OEM
Part
P
T
X
W
h
[𝝉𝒍 , 𝝉𝒖 ]
𝝀
(€/unit/week) (week) (unit/week)
2720
112
152
646
[2,8]
[8,16]
[0,4]
[0,1]
0.0188
0.0600
0.0019
0.0036
𝒑
𝒒
𝒄𝒓
0.42
0.90
0.45
0.90
0.44
0.90
0.43
0.50
5500
325
400
1400
𝒈𝑵𝒐𝑨𝑫𝑰 𝒈𝑨𝑫𝑰𝑵𝒐𝑹𝒆𝒕𝒖𝒓𝒏
(€/week)
(€/week)
1406.01
248.13
145.29
273.28
1399.68
139.78
138.83
273.28
𝒈𝑨𝑫𝑰
𝑷𝑪𝑹𝑨𝑫𝑰𝑵𝒐𝑹𝒆𝒕𝒖𝒓𝒏
(€/week)
956.06
131.80
134.37
260.00
0.45%
43.67%
4.45%
0.00%
𝑷𝑪𝑹𝑨𝑫𝑰
32.00%
46.88%
7.52%
4.86%
• Parts X and W (L > l),
• value of information is low even when return is allowed => negative impact
of timing of information on the value of information
PAGE 37
/ School of
Industrial
Engineering
10/15/2014
5. General OM research agenda
/ School of Industrial Engineering
10/15/2014
PAGE 38
Topic: Servitization in the capital goods industry
Purpose of this talk:
Showing how can we contribute with OM research
1. Creation of differentiation while keeping the portfolio
effect
2. Effect of product design: Redundancy decision
3. New opportunity: Remote monitoring data
/ School of Industrial Engineering
10/15/2014
PAGE 39
General OM research agenda
• Design of service networks and processes
•
•
•
•
Creation of differentiation while keeping the portfolio effect
Location strategy
For spare parts, service engineers, tools, back office,…
Where to decompose: For the offered services and
between service provider and user
• Effect of product design decisions in general
•
•
•
•
•
Redundancy decision, choice of components/suppliers,…
Commonality across multiple machine types
Level of modularity
How to deal with modifications
Design per type of service contract?
/ School of Industrial Engineering
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PAGE 40
General OM research agenda
• Exploiting new technologies in general
•
•
Remote monitoring data
Use of 3D printers
• Sharing of resources/data by multiple companies
• Service costs and pricing
• Sustainability issues:
• Re-use of systems and components
• CO2 and other emissions
• …
/ School of Industrial Engineering
10/15/2014
PAGE 41
Questions/Discussion
/ School of Industrial Engineering
10/15/2014
PAGE 42