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Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems. Serguei Netessine The Wharton School University of Pennsylvania (visiting INSEAD) (Joint work with Sang-Hyun Kim, Yale, Morris Cohen and Senthil Veeraraghavan, Wharton). Facts:. Projected quantity:. 2,443.

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Contracting for infrequent restoration and recovery of mission critical systems

Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems

Serguei Netessine

The Wharton School

University of Pennsylvania

(visiting INSEAD)

(Joint work with Sang-Hyun Kim, Yale,

Morris Cohen and Senthil Veeraraghavan, Wharton)


Joint strike fighter f 35 lightning ii

Facts: Mission-Critical Systems

  • Projected quantity:

2,443

  • Unit cost:

$48M - $63M

  • Development cost:

$40B

  • Production cost:

$257B

  • Support cost:

(Source: GAO report, 2006)

Joint Strike Fighter (F-35 Lightning II)

$347B

“Two-thirds of the cost of

owning an aircraft comes

after it is delivered”

- Senior VP, Lockheed Martin

Infrequent restoration services

Serguei Netessine, The Wharton School


After sales service market

Profit contribution of after-sales services Mission-Critical Systems

Products

(initial sales)

55%

76%

80%

Services

(aftermarket)

45%

24%

20%

Revenue

IT Spend

Profit

(Source: AMR Research, Aberdeen Group, 2002)

  • It 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)

After-sales service market

Infrequent restoration services

Serguei Netessine, The Wharton School


Supply chains compared
Supply chains compared Mission-Critical Systems

Infrequent restoration services

Serguei Netessine, The Wharton School


Aftermarket in us defense industry
Aftermarket in US defense industry Mission-Critical Systems

  • Very expensive products with long lifecycles

  • DoD annual budget of $70B (‘06) for product support

Infrequent restoration services

Serguei Netessine, The Wharton School


Performance based logistics pbl
Performance-Based Logistics (PBL) Mission-Critical Systems

  • 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 services

Serguei Netessine, The Wharton School


Evidence of pbl success
Evidence of PBL success Mission-Critical Systems

NavyProgram

Pre-PBL

Post-PBL

5 Days

56.9 Days

F-14 LANTIRN

Aircraft and Equipment Logistics Response Times decreased average of 70%- 80%

22.8 Days

5 Days

ARC-210

H-60 Avionics

8 Days

52.7 Days

42.6 Days

2 Days CONUS*7 Days OCONUS**

F/A-18 Stores Mgmt System (SMS)

2 Days CONUS4 Days OCONUS

28.9 Days

Tires

APU

*CONUS = Continental US

**OCONUS = Outside Continental US

6.5 Days

35 Days

Infrequent restoration services

Serguei Netessine, The Wharton School


Pbl as an incentive mechanism

Traditional Mission-Critical Systems relationship

Conflicting incentives

PBLrelationship

Aligned incentives

Service

Provider

Supplier

Buyer

Buyer

Material

products

Value of services

through products

PBL as an incentive mechanism

Infrequent restoration services

Serguei Netessine, The Wharton School


Wharton group pbl research
Wharton group PBL research Mission-Critical Systems

  • Cost sharing

  • Performance incentives

Contracts

Cost sharing and PBL

Kim, Cohen, Netessine (2007a)

Mgmt Science 53(12), 1843-58

  • Cost reduction

  • Availability

  • Service time

Performance

outcomes

  • Cost reduction effort

  • Stocking levels

  • Reliability improvement

  • Service capacity

Managerial decisions

Reliability or Inventory?

Kim, Cohen, Netessine (2007b)

Under review

  • Uncertainty in cost

  • Ownership structure

  • Product reliability

Exogenous factors

Infrequent product failures

Today’s talk

Under review

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Serguei Netessine, The Wharton School


Infrequent equipment failures

March 2006 Mission-Critical Systems

September 2006

March 2007

Vibration

Oil system debris

Liner

damage

Fan case corrosion

Oil leak

Vane burn through

Oil leak

Compressor degradation

Vane burn through

Infrequent equipment failures

Engine services due to malfunction (March 2006 – March 2007)

Regional airline company with installed base of 60 engines

Infrequent restoration services

Serguei Netessine, The Wharton School


Dealing with infrequent failures

Service Mission-Critical Systems Time = Equipment Downtime

Repair Time

CSE

Response

Time

Machine Down Awaiting Part (MDAP)

Time

  • Parts Availability

  • Logistics

  • Transportation

Remote

Diagnosis

On-site

diagnosis

On-site

repair

Customer

calls

Parts arrive

CSE orders

additional parts if necessary

Repair job

completed,

machine is up

CSE arrives with some or all of the required parts

Machine

fails

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”)

Infrequent restoration services

Serguei Netessine, The Wharton School


Incentivizing readiness
Incentivizing readiness Mission-Critical Systems

  • 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 services

Serguei Netessine, The Wharton School


Contracting for restoration services

Limitation of traditional warranties Mission-Critical Systems

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.

Contracting for restoration services

Infrequent restoration services

Serguei Netessine, The Wharton School


Research agenda
Research agenda Mission-Critical Systems

  • 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 services

Serguei Netessine, The Wharton School


Related literature
Related literature Mission-Critical Systems

  • Queuing systems

  • Effect of congestion (e.g. call center)

  • Gilbert & Weng (’98), Plambeck & Zenios (’03), Ren & Zhou (’07)

  • Service parts inventory management

  • Forecasting and inventory planning

  • Sherbrooke (’68), Muckstadt (’05), Cohen et al. (’90)

  • Economic model of contracting for

  • low-frequency, high-impact services

  • Principal-agent model

  • Twist: performance realization depends on

  • exogenous events (random failures)

Opposite end of spectrum (heavy traffic)

No contracting and no incentive issues

Focus on prevention, not restoration

AMP: No performance-based contracting or service outsourcing

  • Risk management and insurance

  • Risk mitigation and insurance

  • Kleindorfer & Saad (’06), Tomlin (’06)

  • Economics

  • Abreu, Milgrom, Pearce (’91):

  • repeated partnership game with

  • imperfect signals

Infrequent restoration services

Serguei Netessine, The Wharton School


Principal agent model quick review

a Mission-Critical Systems *

Principal

Agent

(risk-averse)

Principal-agent model: quick review

Offers a contract that depends

on performance outcome X(a)

Observes realized

outcome X(a*)

and pay according to contract terms

Decides to participate in

the trade

Exerts effort a*, which is unobservable to Principal and hence cannot be contracted on

Receives stochastic income

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

Infrequent restoration services

Serguei Netessine, The Wharton School


Model sequence of events

Risk-neutral Mission-Critical Systems

Customer

offers a

contract T

that

penalizes

downtimes

Observes realized

downtimes and pay according to contract terms

S1

S2

S3

Risk-averse Supplier decides to participate in

the trade

Chooses service

capacity m*≥m

privately

Receives stochastic income

Model: sequence of events

Poisson failure process with rate l~ O(1)

Contracting

length = 1

i.i.d. downtimes {Si} are realized

m* = 1/E[Si] > m >> l

Supplier’s service performance

(downtime) is realized only

when equipment failure occurs

Infrequent restoration services

Serguei Netessine, The Wharton School


Assumptions
Assumptions Mission-Critical Systems

  • By increasing service capacity m (= service rate),

    • Expected service time goes down, and

    • 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 services

Serguei Netessine, The Wharton School


Assumption on customer s objective

Potential problem: Customer discounts rare failures Mission-Critical Systems

→ When a failure occurs, Customer may experience

a long downtime with serious consequences

Customer values

fast service

delivery after each

failure incident

Assumption on Customer’s objective

Minimize downtime cost

+ contracting cost without

downtime constraint

Minimize contracting

cost subject to total downtime constraint

Minimize contracting

cost 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

Infrequent restoration services

Serguei Netessine, The Wharton School


Customer s contract design problem

subject to Mission-Critical Systems

(Service constraint)

(IR)

(IC)

p = Risk premium

subject to

(Service constraint)

(IC)

Customer’s contract design problem

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Serguei Netessine, The Wharton School


Which performance measure

Compound Poisson variable Mission-Critical Systems

Both incentivize

the Supplier to

invest in capacity

S1

S2

S3

Sample mean estimator

Which performance measure?

1. Penalize cumulative downtimes

2. Penalize average downtime

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Serguei Netessine, The Wharton School


Supplier s response to contract terms

Cumulative-performance contract Mission-Critical Systems

m*

m*

1

l

1

l

No-failure effect:

Little benefit of sampling

Exp. total penalty =

Exp. total penalty =

Income risk =

Income risk =

Supplier’s response to contract terms

Average-performance contract

Capacity as a means to hedge against risk

Sample-mean variance reduction

→ more willing to take a chance

Infrequent restoration services

Serguei Netessine, The Wharton School


Optimal penalty rates
Optimal penalty rates Mission-Critical Systems

Cumulative-performance contract

Average-performance contract

pCUM

pAVE

1

l

1

l

Take advantage of Supplier’s voluntary

capacity increase → to induce mm, only

small contractual incentive pCUM needed

Non-monotonicity of m* results in

non-monotonicity of pAVE

Infrequent restoration services

Serguei Netessine, The Wharton School


Efficiency loss in supply chain

Average Mission-Critical Systems

-performance

contract

p = Risk premium

= efficiency loss

Cumulative

-performance

contract

Cumulative

-performance

contract

Average

-performance

contract

l

Efficiency loss in supply chain

Risk pooling occurs as more performance realizations are collected, revealing

more information about Supplier’s capacity decision  larger l, better efficiency

Efficiency loss is greatest when equipment is most reliable!

Infrequent restoration services

Serguei Netessine, The Wharton School


Which contract is better

Cumulative-performance contract more efficient Mission-Critical Systems

1.4

Average-performance contract more efficient

Which contract is better?

Average-performance contract

better if v = CV(Si) < 1.4

Average-performance contract removes uncertainty in N more effectively through

normalization, but it also adds noise through division by a random variable N

Infrequent restoration services

Serguei Netessine, The Wharton School


Extensions alternative customer objectives

p Mission-Critical Systems CUM

h = 0.01

h = 0.001

pCUM

r/c = 104

r/c = 104

pAVE

pAVE

pCUM

pCUM

pAVE

r/c= 5 x 103

r/c = 5 x 103

pAVE

Extensions: Alternative customer objectives

  • Total downtime constraint/profit maximization

  • Potential problem

    • For low l, 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

Infrequent restoration services

Serguei Netessine, The Wharton School


Some more extensions
Some more extensions Mission-Critical Systems

  • 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 services

Serguei Netessine, The Wharton School


Summary of results
Summary of results Mission-Critical Systems

  • 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 services

Serguei Netessine, The Wharton School


Managerial implications
Managerial implications Mission-Critical Systems

  • 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 services

Serguei Netessine, The Wharton School


Applications and extensions
Applications and extensions Mission-Critical Systems

  • 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

Infrequent restoration services

Serguei Netessine, The Wharton School


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