Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems
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Transcript of Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems
Contracting for Infrequent Restoration and Recovery of Mission-Critical
Systems
Serguei Netessine
The Wharton SchoolUniversity of Pennsylvania
(visiting INSEAD)
(Joint work with Sang-Hyun Kim, Yale,Morris Cohen and Senthil Veeraraghavan, Wharton)
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 2
Joint Strike Fighter (F-35 Lightning II)
“Two-thirds of the cost ofowning an aircraft comes after it is delivered” - Senior VP, Lockheed Martin
Facts:
• Projected quantity:• Unit cost: $48M - $63M
2,443
$347B
$40B$257B
• Development cost:• Production cost:• Support cost:
(Source: GAO report, 2006)
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 3
After-sales service marketIt is estimated that service support…• represents 8% of US GDP, and• $1 trillion annual spend (to support previously purchased assets)
(Source: “Winning in the Aftermarket”, HBR, May 2006)
Profit contribution of after-sales services
0
20
40
60
80
100
120
76%
24%
80%
20%45%
55%Products
(initial sales)
Services(aftermarket)
(Source: AMR Research, Aberdeen Group, 2002)Revenue IT Spend Profit
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 4
Supply chains compared
Manufacturing supply chain
After-sales service supply chain
Origin of demand Consumer demands Product failures
Nature of demand Frequent, large quantity Intermittent, sporadic
Shortage cost Moderate Very high
Required response Can be scheduled ASAP (same or next day)
Resource positioning A few selected locations Close to customer sites
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 5
Aftermarket in US defense industry• Very expensive products with long lifecycles• DoD annual budget of $70B (‘06) for product support
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 6
Performance-Based Logistics (PBL)• DoD’s new contracting policy for service acquisition
• Mandated since 2003
• Buy service outcome, not service products– “Instead of buying set levels of spares, repairs, tools, and
data, the new focus is on buying a predetermined level of availability to meet the customer’s objectives.”
• Example– “Contractor is penalized by x dollars per 1% of fleet
availability below 95% target.”
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 7
Evidence of PBL success
F-14 LANTIRN
Navy Program Pre-PBL
H-60 Avionics
F/A-18 Stores Mgmt System (SMS)
Tires
APU
56.9 Days 5 Days
22.8 Days 5 Days
52.7 Days 8 Days
35 Days 6.5 Days
28.9 Days 2 Days CONUS4 Days OCONUS
Aircraft and Equipment Logistics Response Times
decreased average of 70%- 80%
Post-PBL
42.6 Days 2 Days CONUS*7 Days OCONUS**
ARC-210
*CONUS = Continental US**OCONUS = Outside Continental US
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 8
PBL as an incentive mechanism
Buyer
Materialproducts
Supplier
Traditional relationshipConflicting incentives
Buyer
Value of servicesthrough products
ServiceProvider
PBL relationshipAligned incentives
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 9
Wharton group PBL research
• Uncertainty in cost• Ownership structure• Product reliability
• Cost sharing• Performance incentives
• Cost reduction effort• Stocking levels• Reliability improvement• Service capacity
• Cost reduction• Availability• Service time
Performanceoutcomes
Managerial decisions
Exogenous factors
ContractsCost sharing and PBL
Kim, Cohen, Netessine (2007a)Mgmt Science 53(12), 1843-58
Reliability or Inventory?Kim, Cohen, Netessine (2007b)
Under review
Infrequent product failuresToday’s talk
Under review
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 10
Infrequent equipment failuresEngine services due to malfunction (March 2006 – March 2007)
Regional airline company with installed base of 60 engines
March 2006 September 2006 March 2007
Compressor degradation
Linerdamage
Vibration
Vane burn through
Fan case corrosion
Oil system debris
Oil leakOil leak
Vane burn through
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 11
Dealing with infrequent failures• Equipment failures are infrequent but detrimental
– Samsung: power outage for < 24 hours → $40M loss– Intel: 15-min response requirement for equipment failures
• Restoration activities (“service”)Service Time = Equipment Downtime
Time
Machine Down Awaiting Part (MDAP)
On-site repair
Repair jobcompleted,machine is up
Parts arriveCSE orders
additional parts if necessary
Customercalls CSE arrives with
some or all of the required parts
On-site diagnosis
RemoteDiagnosis
Machinefails
• Parts Availability• Logistics• Transportation
CSE Response
Time
Repair Time
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 12
Incentivizing readiness• Low-frequency challenge
– Fast problem resolution is essential to minimize downtime → high service capacity should be maintained
– However, equipment failures occur only once in a while! → service capacity will be idle for most of the time
• How to ensure high service capacity level in a decentralized supply chain?– Capacity investment is difficult to monitor– Low incentive to invest in capacity, which will be
underutilized– Contracts
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 13
Contracting for restoration services• Limitation of traditional warranties
– Based on service promise, not outcome– Difficult to guarantee consistent service delivery
• Performance-based contracts– Financial bonus/penalty based on equipment downtime– Commercial: SLA (Telecom), Power by the Hour (Airline)– Government: Performance-Based Service Acquisition, PBL (DoD),
EPA.
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 14
Research agenda• How well do performance-based contracts work?
• Potentially great risks in low-frequency environment– Example 1: Equipment failed once. Supplier completed the
service very late. Does this mean that the supplier did not reserve much service capacity? (limited information)
– Example 2: Equipment never failed (no information)
• Does choice of performance measure matter?– Multiple ways to construct a performance measure– Potential impact on contracting efficiency
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 15
Related literature
Queuing systems•Effect of congestion (e.g. call center)•Gilbert & Weng (’98), Plambeck & Zenios (’03), Ren & Zhou (’07)
Risk management and insurance•Risk mitigation and insurance•Kleindorfer & Saad (’06), Tomlin (’06)
Service parts inventory management•Forecasting and inventory planning•Sherbrooke (’68), Muckstadt (’05), Cohen et al. (’90)
Economics•Abreu, Milgrom, Pearce (’91): repeated partnership game with imperfect signals
No contracting and no incentive issues
Opposite end of spectrum (heavy traffic)
Focus on prevention, not restoration
AMP: No performance-based contracting or service outsourcing
Economic model of contracting forlow-frequency, high-impact services
•Principal-agent model•Twist: performance realization depends on exogenous events (random failures)
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 16
Principal-agent model: quick review
Principal
Agent(risk-averse)
Offers a contract that dependson performance outcome X(a)
Exerts effort a*, which is unobservable to Principal and hence cannot be contracted on
Observes realized outcome X(a*)and pay according to contract terms
Efficiency loss comes from Principal’s inability to give high incentive,since doing so increases income risk of Agent, who demands
risk premium as a condition for participating in the trade
Receives stochastic income
Decides to participate inthe trade
a*
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 17
Model: sequence of eventsObserves realized downtimes and pay according to contract terms
Receives stochastic income
Risk-averse Supplier decides to participate inthe trade
Chooses service capacity *≥privately
Risk-neutralCustomeroffers a contract Tthatpenalizes downtimes
S1 S2 S3
Poisson failure process with rate ~ O(1)
i.i.d. downtimes {Si} are realized* = 1/E[Si] > >>
Supplier’s service performance(downtime) is realized only when equipment failure occurs
Contractinglength = 1
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Slide 18
Assumptions
• By increasing service capacity (= service rate),1) Expected service time goes down, and2) Service time variability does not go up
• Linear penalty contract:– Performance measure X is positively correlated with
downtime
• Mean-variance utility for Supplier:
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 19
Assumption on Customer’s objectiveMinimize downtime cost
+ contracting cost without downtime constraint
Minimize contractingcost subject to total
downtime constraint
Minimize contractingcost subject to per-incident
downtime constraint
• Works if downtime cost is well-known• Many commercial settings• Example: Samsung
• Downtime cost is difficult to assess• Government and commercial• Example: Navy
• Downtime cost is difficult to assess• Government and commercial• Example: Air Force
Potential problem: Customer discounts rare failures→ When a failure occurs, Customer may experiencea long downtime with serious consequences
Customer valuesfast service
delivery after eachfailure incident
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Slide 20
Customer’s contract design problem
subject to (Service constraint)
(IR)
(IC)
subject to
(IC)
(Service constraint)
= Risk premium
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 21
Which performance measure?
1. Penalize cumulative downtimes
S1 S2 S3
2. Penalize average downtime
Sample mean estimator
Both incentivizethe Supplier toinvest in capacity
Compound Poisson variable
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Slide 22
Supplier’s response to contract termsAverage-performance contract
1
No-failure effect:Little benefit of sampling
Cumulative-performance contract
1
Exp. total penalty =
Income risk = Income risk =
Exp. total penalty =
Sample-mean variance reduction→ more willing to take a chance
Capacity as a means to hedge against risk
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Slide 23
Optimal penalty ratesCumulative-performance contract
pCUM
1
Average-performance contract
pAVE
1
Take advantage of Supplier’s voluntarycapacity increase → to induce m, only
small contractual incentive pCUM needed
Non-monotonicity of * results innon-monotonicity of pAVE
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Slide 24
Efficiency loss in supply chain
= Risk premium = efficiency loss
Average-performancecontract
Cumulative-performancecontract
Cumulative-performancecontract
Average-performancecontract
Efficiency loss is greatest when equipment is most reliable!
Risk pooling occurs as more performance realizations are collected, revealingmore information about Supplier’s capacity decision larger , better efficiency
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 25
Which contract is better?
Average-performance contract more efficient
Cumulative-performance contract more efficient
1.4Average-performance contract
better if v = CV(Si) < 1.4
Average-performance contract removes uncertainty in N more effectively throughnormalization, but it also adds noise through division by a random variable N
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 26
Extensions: Alternative customer objectives• Total downtime constraint/profit maximization• Potential problem
– For low , Customer discounts rare failure events → Customer is content with low capacity → but when a failure occurs, potentially long downtime can be encountered
• Main difference– “High reliability → large inefficiency” no longer holds in general
r/c = 5 x 103
r/c = 104
r/c = 5 x 103
r/c = 104
= 0.01 = 0.001CUM
AVE
CUM
AVE
CUM
AVE
CUM
AVE
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 27
Some more extensions• Endogenous reliability decisions by the
supplier– Cumulative-performance contract provides better
incentives to improve reliability.• More complex contracts
– Key insights are preserved• Multiple customers served by the same
supplier– Capacity pooling mitigates effects of low-
frequency failures
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 28
Summary of results• First study on service contracting in a low-frequency
environment• High reliability may lead to a contracting challenge
– If per-incident downtime standard is established, agency cost is greatest when equipment is most reliable
• Choice of performance metric (average or total performance) makes a difference– Although designed to achieve the same goal, two
contracts may result in very different supplier responses– Contract based on average performance brings the benefit
of variance reduction through sampling
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 29
Managerial implications• Use performance-based contracts with discretion
– Environmental characteristics (e.g. reliability) may limit the effectiveness of performance-based contracting
– In-sourcing or auditing, however expensive, may be better alternatives in some cases
– Warning against blanket PBL mandate
• Reliability improvement vs. prompt restorations– Preventing equipment from failing may interfere with
restoring it quickly– The right contract depends on whether the supplier can
affect reliability
Infrequent restoration servicesSerguei Netessine, The Wharton School
Slide 30
Applications and extensions• Outsourcing emergency services
– Emergency services in government sector– Disaster recovery in IT (IBM, HP, Sungard, etc.) and
hazardous waste (government of Canada).• Extensions
– Theoretical framework: contracting when events occur intermittently
– Multi-item product: contract on end-product downtime or component downtimes?
– Empirical investigation