Performance based contracting (PBC) emerged as a new service model which is reshaping the acquisition, operation and maintenance of capital equipment. PBC is often referred to as performance based logistics in defense industry, or is called as power-by-the-hour in the airline industry. The focus of PBC is on the outcome of the system reliability performance, not materials and labors involved in the maintenance. This presentation introduces a novel quantitative approach to planning performance-based contracts in the presence of system usage uncertainty. We develop an analytical model to characterize the system availability by comprehending five key performance drivers: failure rate, usage variability, spare parts inventory, repair turn-around time, and system fleet population. This analytical insight into the system performance allows us to estimate the lifecycle cost by taking into account the design, manufacturing, maintenance and repair across the system lifetime. Two types of contracting schemes are examined under the cost minimization and the profit maximization. This presentation aims to provide theoretical guidance to facilitate the paradigm change as it shits from material based services to performance based contracting.

1. Managing System Reliability and
Maintenance under Performance
Maintenance nder Performance
Based Contract (面向综合表现合
同的系统可靠性和维修性管理)
同的系统可靠性和维修性管理
Dr. Tongdan Jin (金彤丹博士) ,
Assistant Professor of Industrial Engineering, Texas State
A i t tP f fI d t i lE i i T St t
University (德克萨斯州立大学工业工程系助理教授)
©2012 ASQ & Presentation Jin
Presented live on Jul 15th, 2012
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3. Managing Reliability and
Maintenance under Performance-
Based Contract
Tongdan Jin (金彤丹), Ph.D.
Ingram School of Engineering
Texas State University, TX 78666, USA
ASQ Webinar Series
July 14, 2012 (US Time)
1

4. Contents
• Introduction to Performance-Based Contract
(PBC)
• Five Overarching Performance Measures
• Multi-Criteria Approach to PBC
• Application to Wind Power Industry
• Future Study and Conclusion
2

5. Topic One
Introduction to Performance-
Based Contracting (PBC)
3

6. 4
Characteristics of Capital Equipment
• Capital-intensive
• Long service time
• Prohibitive downtime cost
• Expensive in maintenance, repair, and
overhaul (MRO)
• Integrated service and sustainment

7. 5
Overview of Maintenance/Service Business
• Representing 8-10% of GDP in the US.
• US airline industry is $45B on MRO in 2008.
• US auto industry is $190B and $73B for parts in
2010.
• US DoD maintenance budget $125B and $70B
inventory with 6,000 suppliers.
• Joint Strike Fighter (F-35): $350B for R/D/P, and
$600B for after-production O/M for 30 years.
• EU Wind turbine service revenue €3B in 2011
Reference: 1). Nowicki et al. (2010), 2). Smith and Thompson (2006), 3) http://trade.gov/static/2011Parts.pdf

8. Challenges in Material-Based Contract (MBC)
• Local or sub-optimal decision-making on
maintenance.
• Disintegration of design, manufacturing
and maintenance/sustainment.
• Lack of reliability commitment from OEM.
• Profit-driven by the OEM or 3rd party
logistics supply.
6

9. The Goal of Performance-Based Contracting
Lifecyle Cost ($)
30-40% 50-60%
10-20% 5%
Research
Manufacturing Operation and Support Retirement
Development
Performance based contracting (PBC) aims to reduce the cost
ownership while ensuring the reliability goals.
7
Ref: DoD 5000, University of Tennessee

10. PBC and The Technology Suite
PBC
PBD PBMfg PBM
Reliability Reliability, quality, warranty, maintenance
and functions and time-to-market schedule, spares,
operational availability
PBD=performance-based design
PBMfg=performance-based manufacturing
PBM=performance-based maintenance
Note: In military, PBC is also called Performance based logistics (PBL),
In airline industry, PBC is referred as Power-by-hour (PBH). 8

11. Existing Maintenance/Sustainment Strategy
• Corrective Maintenance
* Run-to-failure
• Preventive Maintenance
* Age-based maintenance
* Block replacement
• Condition-based Maintenance (Monitoring)
* Remaining useful lifetime
Ref: E. Elsayed (1996), Reliability Engineering,
H.-Z. Wang (2002) “A survey of maintenance policies of deteriorating systems,”
Si X.S., et al. “Remaining useful life estimation-A review on the statistical data driven approaches”.
9

12. Evolution of Maintenance Strategy
Evolution of Asset Management Strategy
CM=Corrective Maintenance
PM=Preventive Maintenance
Lifecycle Cost
CBM=Condition-Based Maintenance
PBM=Performance-Based Maintenance
CM
PM
CBM PBM
Source: T. Jin, Y. Ding, H. Guo,N. Nalajala “Managing wind turbine reliability and maintenance under performance based contract”
IEEE Power and Energy Society General Meeting , July 22-26, 2012 (accepted).
10

13. Topic Two
Design, Implement, and Monitor
PBC Programs
11

14. Logistics & Supply Chain (MBC vs. PBC)
New System Shipping/Installation
Replenish Repair by
inventory replacement
OEM for In-service
Repair Center Spares
design and Fleet with n
M/G/ Inventory
production Systems
OEM Customer
New System Shipping/Installation
Replenish Repair by
inventory replacement
OEM for In-service
Repair Center Spares
design and Fleet with n
M/G/ Inventory
production Systems
12
OEM Customer

15. 13
A 4-Step Process to PBC
Step 1 Step 2 Step 3 Step 4
Performance Performance Performance Performance
Outcome Measures Criteria Compensation
System System Mini Cost plus
readiness, availability, availability, incentive fee,
operational MTBF, max failure cost plus
reliability, MTTR, Mean rate, max award fee,
assurance of downtime, repair waiting linear reward,
spare parts logistics time, max cost exponential
supply response time per unit time reward

16. US DoD’s Overarching Performance Measures
• Operational availability (OA)
• System reliability/Mission reliability (MR)
• Logistics response time (LRT)
• Logistics footprint (LF)
• Cost per unit usage (CUU)
Mission
Reliability
Operational Logistics Cost Per Unit
Availability Footprint Usage
Logistics Response
Time
14

17. Operational Availability
MTBF
CM Ao 
MTBF  MDT
MTBR
PM and Ao 
CBM MTBR  MDT
MTBF=Mean time between failures
MTBR=mean time between replacements
MDT=mean down time
15

18. Performance Measures and Drivers
Inherent system
Reliability
MTBF
OEM
Maintenance
Controlled
Schedule ()
Operational
MTTR Availability (Ao)
Logistics Support
(s, ts, tp , tr)
MLDT
Customer Fleet size (n)
Controlled System usage ()
16

19. 17
Availability and Variable Fleet Size
Variable Fleet Size
• Availability
Figure 1: System Reliability and Fleet Size
MTBF
A
2,000 1000
Semiconductor Industry
MTBF
MTBF  MDT 1,600 800
Cumulative Fleet Size
MTBF(hours)
1,200 600
System
Population
800 400
400 200
• MTBF=100 hours, MDT=5 hours
0 0
105
120
135
15
30
45
60
75
90
1
100
A  0.95 Weeks
100  5 160,000
Cumulative Installed WT (1998 to 2030) 150,000
140,000 1.5 MW (2010-2014 )
Wind Power Industry
2.0 MW (2015-2020)
120,000
Installed WT Population
2.5 MW (2020-2025)
100,000 3.0 MW (2025-2030)
• MTBF=200 hours, MDT=10 hours 80,000
60,000
200 40,000
22,500
A  0.95 20,000
200  10 0
98-99
02-03
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030

20. Operational Availability under CM Policy
1
Ao ( , s,  , n, t r ) 
 s ( n t ) x e  n t r 
1  t s  t r 1  
 x 0
r


 x! 
 =system or subsystem inherent failure rate
s =base stock level for spares
β =usage rate, and 0β1
n =system fleet size
tr =defective part repair turn-around time
ts =time for conducting repair-by-replacement
Ref: Jin, Wang, “Planning performance based contracts considering reliability and uncertaint system usage,” Journal of the
18
Operational Research Society , 2012

21. 19
Operational Availability under PM Policy

A0 ( s, , t p , t r ) 
 0
R(t )dt
R(t )dt  t s  t p R( )  t r F ( ) Pr{O  s}

0
R(t) =reliability function
O =spares demand, a random variable
s =base stock level
tp =parts reconditioning turn-around time
tr =defective repair turn-around time
ts =hands-on repair-by-replacement time
Ref: Jin, Tian, Xie, “A Multi-Criteria Approach to Performance-Based Service with Variable System Usage,” Texas State University Working paper (2012).

22. Topic Three
Multi-Criteria Approach to
PBC Planning
20

23. 21
Reliability Optimization and Spare Parts Logistics
Reliability Optimization Spare Parts Logistics
r5 (t) s32 Fleet 1
r1(t) s21
r6(t) r8(t)
s s32 Fleet 2
r2(t) r4(t) s22
r7(t) s3,n-1 Fleet n-1
s3,n
max E[ Rsys (r(t ), n)] Fleet n
min var Rsys (r(t ), n)  max Ap (s, x)
min Cost r(t ), n  min Cost (s, x), EBO (s, x)
• Tillman et al. (1977) • Scherbrooke (1968, 1992)
• Kuo et al. (1987) • Muckstadt (1973)
• Chen (1992) • Graves (1985)
• Jin & Coit (2001) • Lee (1987)
• Levitin & Lisnianski (2001) • Cohen et al. (1990)
• Coit et al. (2004) • Diaz & Fu (1996)
• Ramirez-Marquez et al. (2004) • Alfredsson (1997)
• Marseguerra et al. (2005) • Zamperini & Freimer (2005)
• Jin & Ozalp (2009) • Lau & Song (2008)
• Ramirez-Marquez & Rocco (2010) • Kutanoglu et al. (2009)
• More ..... • More .....

24. 22
Performance Based Logistics/Contract
New Features:
• Integrating reliability management with spares provisioning
• Focus on system performance outcome
• Lifecycle cost analysis
• Variable installed base and uncertain usage
• Profit-centric decision making
• Kim, Cohen, Netessine (2007) Operation Availability
• Nowicki et al. (2008)
• Jin and Liao (2009) MTBF
Ao 
• Jeet, Kutanoglu, Partani (2009) MTBF  MTTR  MLDT
• Kang & McDonald (2010)
• Oner et al. (2010)
MTBF=mean time between failures
• Jalil et al. (2011) MTTR=mean time to repair
• Mirzahosseinian & Piplani (2011) MLDT=mean logistics delay time
• Jin & Wang (2012)
• Jin & Tian (2012)
Ref: Jin & Wang (2011)

25. Generic Decision Model for PBC
Objective Functions:
Max: Service profit (OEM and 3PL)
Min: Cost per unit usage (customer)
Subject to:
System Operational Available>A(min)
System Reliability>MTBF(min)
Logistics Response Time<Time(min)
Logistics Footprint<Cost(min)
23

26. Total Lifecycle Cost Management
Fleet Costs: C ( , s )  D( )  nc ( )  I ( , s )
 max   
D( )  B1 exp 
   

Design Costs:  min 
 1 1 
Mfg. Costs: c( )  B2  B3     
 max 
 
Spares/Repair (1   )  1
I ( , s)  sc ( )  c r n
Logistics Costs:  (1   )
1. K.B. Öner, G.P. Kiesmüller, G.J. van Houtum (2010) in European Journal of Operational Research, vol. 205, no. 3, 2010, pp. 615-624.
2. H.Z. Huang, Z.J. Liu, D.N.P. Murthy (2007) in IIE Transactions, vol. 39, no. 8, 2007, pp. 819-827.
3. T. Jin, P. Wang (2012) in Journal of the Operational Research Society, 2012, doi:10.1057/jors.2011.144, (forthcoming). 24

27. Design and Manufacturing Cost
Design Cost   max   
D( )  B1 exp k
    
 min 
Unit Production Cost c( )  B3 ( A)  B2 (  v   max )
v
Design Cost vs. Reliability Manufacturing Cost vs. Reliability
2.0 600,000
B1=$1106 B2=$1105
Cost ($) (106)
1.8 500,000
=0.05 B3=$2,000 =0.6
400,000
1.6
Cost($)
300,000
1.4 =0.5
200,000
1.2
=0.02 100,000 =0.4
1.0 0
20000 24000 28000 32000 36000 40000 20,000 25,000 30,000 35,000 40,000
1/ 1/
References: 1) A. Mettas (2000), RAMS
2) H.-Z. Huang, H.J. Liu, D.N.P. Murthy (2007), IIE Transactions
3) A.G. Loerch (1999), Naval Research Logistics 25

28. 26
Spares Inventory and Repair Cost
m
I ( , s)  smc( )  mc1  mc2   c3 si  R( , s)
i 1
Repair cost
Capital Cost
Cost for Holding cost
parameter
updating Order cost
Ref: Jin et al. 2011, ICRMS 2011

29. Linear and Exponential Reward Model
• Cost Plus Fixed Fee (CPFF)
• Cost Plus Award Fee (CPAF)
• Cost Plus Incentive Fee (CPIF)
* linear incentive
a  b( A  Amin ) A  Amin
R( A)  
a A  Amin
* exponential incentive
exp   ( A  Amin )  A  Amin
R( A)  
a A  Amin
27
Ref: Nowicki et al. (2008)

30. 28
Profit-Centric Servitization
Maximize:
K K K
E [ P(λ, s;  )]  R( As (λ, s;  ))   Di (i )  B1,i   n mi ci (i )  B2,i    I i (i , si ;  )
i 1 i 1 i 1
Subject to:
min,iimax,i for i=1, 2, …., K
mi
 
K
As (λ, s;  )   Ai (i , si ;   Amin
i 1
1 1 1 2 K

31. Numerical Example-Wind Turbine
Index i=1 i=2 i=3
subsystem Blade Mainshaft/Bearing Gearbox
mi 3 1 1
max(faults/year) 0.2898 0.0312 0.1306
min(faults/year) 0.1560 0.0168 0.0703
B1($) 3,330,000 675,000 1,936,500
B2($) 333,000 67,500 193,650
B3($) 20,000 7,000 12,000
 0.02 0.02 0.02
 0.6 0.6 0.6
cr ($/defective part) 40,000 50,000 60,000
tr (days) 45 90 120
ts (days) 3 4 6
29

32. 30
Results Comparison
=5 years, Amin=0.97, =1, and n=50 systems
Option Linear Exponential
i 1 2 3 1 2 3
mi 3 1 1 3 1 1
Name Blade MS/B GX Blade MS/B GX
 0.180 0.031 0.120 0.179 0.031 0.119
5-Year
s 7 0 5 7 0 5 contract
Asub 0.9981 0.9972 0.9974 0.9981 0.9972 0.9975
Acluster 0.9942 0.9972 0.9974 0.9943 0.9972 0.9975
Asys 0.9889 0.9890
Profit $25.06M $24.78M
=10 years, Amin=0.97, =1, and n=50 systems
Optio
Linear Exponential
n
i 1 2 3 1 2 3
mi 3 1 1 3 1 1
Name Blade MS/B GX Blade MS/B GX
10-Year  0.172 0.025 0.103 0.172 0.024 0.0990
s 8 0 5 8 0 5
Contract Asub 0.9985 0.9981 0.9980 0.9985 0.9982 0.9982
Acluster 0.9954 0.9981 0.9980 0.9954 0.9982 0.9982
Asys 0.9916 0.9918
profit $57.59M $57.11M

33. Topic Four
Potential Research Thrusts
under PBC Theme
31

34. Reliability Growth and Increased Install Base
MTBF Run Chart and Cumulative Field Systems
Reliability and Field Shipment for a Type ATE
Equipment
1,600 800
Installed Systems
1,200 MTBF 600
Cum Systems
MTBF(hours)
800 400
400 200
0 0
15
30
60
90
45
75
105
135
120
1
Weeks
32

35. (Q, r) Spare Parts Inventory Control
Inventory
Basic model
q
Q
qQ
r
l
Time t
Multi-resolution (s, s-1) model
L for i=1 L for i=2 i=3
q2
q1
r2
r1
0 t11 t12 t13 t21 t22 t23 t24 t31 time 33
Ref: Jin and Liao 2009 , Computer and Industrial Engineering

36. A Lifecycle Approach to Spares Provisioning
Ramp-up Mature Phase out
Installed
base Spare parts
Quantity
demand
New
Shipment
Time
34
Ref. Inderfurth and Mukherjee (2008)

37. 35
Lifecycle for Major USA Aircraft Systems
Planned Phase Out
Development Start (Last Model)
1946 B-52 94
1954 KC-135 86
1953 AIM-9 72
1970 SSN-688 56
1969 F-15 51
1955 UH-1 49
1969 F-14 41
0 10 20 30 40 50 60 70 80 90 100
Years
Reference : Timothy Smith, “Reliability Growth Planning Under Performance Based
Logistics” Master Thesis, Texas Tech University, 2003.

38. Conclusion
1. PBM represents a new paradigm in designing,
marketing, and operating capital-intensive equipment.
2. PBM merges two distinct bodies of literature:
reliability optimization and spares supply
management.
3. PBM is a lifecycle approach to system reliability
commitment involving the users and the suppliers.
4. A long-term PBM contract drives the reliability
growth.
36

39. Key Terminologies
1. Original equipment manufacturer (OEM)
2. 3rd party logistics (3PL) supplier
3. Maintenance, repair and overhaul (MRO)
4. Material-based contract (MBC)
5. Performance based logistics/contracting/maintenance
6. Power-by-hour (PBH)
7. Servitization
8. Lifecycle cost analysis
9. Operational availability
10. Mean-time-between-replacements (MTBR)
11. Mean downtime (MDT)
12. Mean-time-to-repair (MTTR)
13. Mean logistics delay time (MLDT)
14. Mean-time-between-failures (MTBF)
15. Performance measures
16. Spare/service parts logistics (SPL)
17. Multi-criteria optimization
18. Multi-echelon, multi-item repairable inventory
19. Reliability allocation/optimization
37

40. Selected References
Reliability Modeling
1. D.W. Coit, “System reliability confidence intervals for complex systems with estimated component
reliability,” IEEE Transactions on Reliability, vol. 46, no. 4, 1997, pp. 487-493.
2. J.E. Ramirez-Marquez, and W. Jiang, “An improved confidence bounds for system reliability,” IEEE
Transactions on Reliability, vol. 55, no. 1, 2006, pp. 26-36.
3. E. Borgonov, “A new uncertainty measure”, Reliability Engineering and System Safety, vo;. 92, pp. 771-
784, 2007.
4. T. Jin, D. Coit, "Unbiased variance estimates for system reliability estimate using block
decompositions," IEEE Transactions on Reliability , vol. 57, 2008, pp.458-464.
5. H. Guo, T. Jin, A. Mettas, “Designing reliability demonstration test for one-shot systems under zero
component failures," IEEE Transactions on Reliability , vol. 60, no. 1, 2011, pp. 286-294
Reliability, Maintenance, and Spares Logistics Management
1. H.-Z. Huang, H.J. Liu, D.N.P. Murthy. 2007. Optimal reliability, warranty and price for new products.
IIE Transactions, vol. 39, no. 8, pp. 819-827.
2. K. Kang, M. McDonald. 2010. Impact of logistics on readiness and life cycle cost: a design of
experiments approach, Proceedings of Winter Simulation Conference. pp. 1336-1346.
3. S.H. Kim, M.A. Cohen, S. Netessine. 2007. Performance contracting in after-sales service supply chains.
Management Science, vol. 53, pp. 1843-1858.
4. D. Nowicki, U.D. Kumar, H.J. Steudel, D. Verma. 2008. Spares provisioning under performance-based
logistics contract: profit-centric approach. Journal of the Operational Research Society. vol. 59, no. 3,
2008, pp. 342-352.
5. K.B. Öner, G.P. Kiesmüller, G.J. van Houtum. 2010. Optimization of component reliability in the design
phase of capital goods. European Journal of Operational Research, vol. 205, no. 3, pp. 615-624.
6. T. Jin, P. Wang, “Planning performance based contracts considering reliability and uncertain system
usage,” Journal of the Operational Research Society , 2012 (forthcoming)
7. T. Jin, Y. Tian, “Optimizing reliability and service parts logistics for a time-varying installed base,”
European Journal of Operational Research, vol. 218, no. 1, 2012, pp. 152-162 38

41. Thanks
&
Questions
39