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)


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


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


Slide 5

Aftermarket in US defense industry• Very expensive products with long lifecycles• DoD annual budget of $70B (‘06) for product support


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.”


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

http://www.clipart.com/en/close-up?o=903057&memlevel=C&a=c&q=tires&s=1&e=30&show=all&c=&cid=&findincat=&g=&cc=&page=


Slide 8

PBL as an incentive mechanism

Buyer

Materialproducts

Supplier

Traditional relationshipConflicting incentives

Buyer

Value of servicesthrough products

ServiceProvider

PBL relationshipAligned incentives


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


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


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


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


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.


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


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)


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*


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


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:


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


Slide 20

Customer’s contract design problem

subject to (Service constraint)

(IR)

(IC)

subject to

(IC)

(Service constraint)

= Risk premium


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


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


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


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


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


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


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


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


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


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

contracting for infrequent restoration and recovery of mission-critical systems

Documents

service acquisitionmandated

service productsinstead

service promise

high service capacity

downtime high service

buy service outcome

equipment downtimecommercial

capacity investment