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Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems Serguei Netessine The Wharton School University of Pennsylvania (visiting.

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Presentation on theme: "Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems Serguei Netessine The Wharton School University of Pennsylvania (visiting."— Presentation transcript:

1 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)

2 Infrequent restoration services Serguei Netessine, The Wharton School Slide 1 Joint Strike Fighter (F-35 Lightning II) Two-thirds of the cost of owning 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)

3 Infrequent restoration services Serguei Netessine, The Wharton School Slide 2 After-sales service market 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) Profit contribution of after-sales services 76% 24% 80% 20% 45% 55% Products (initial sales) Services (aftermarket) (Source: AMR Research, Aberdeen Group, 2002) RevenueIT SpendProfit

4 Infrequent restoration services Serguei Netessine, The Wharton School Slide 3 Supply chains compared Manufacturing supply chain After-sales service supply chain Origin of demandConsumer demandsProduct failures Nature of demandFrequent, large quantityIntermittent, sporadic Shortage costModerateVery high Required responseCan be scheduledASAP (same or next day) Resource positioningA few selected locationsClose to customer sites

5 Infrequent restoration services Serguei Netessine, The Wharton School Slide 4 Aftermarket in US defense industry Very expensive products with long lifecycles DoD annual budget of $70B (06) for product support

6 Infrequent restoration services Serguei Netessine, The Wharton School Slide 5 Performance-Based Logistics (PBL) DoDs 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.

7 Infrequent restoration services Serguei Netessine, The Wharton School Slide 6 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 Days5 Days 52.7 Days 8 Days 35 Days 6.5 Days 28.9 Days 2 Days CONUS 4 Days OCONUS Aircraft and Equipment Logistics Response Times decreased average of 70%- 80% Post-PBL 42.6 Days2 Days CONUS* 7 Days OCONUS** ARC-210 *CONUS = Continental US **OCONUS = Outside Continental US

8 Infrequent restoration services Serguei Netessine, The Wharton School Slide 7 PBL as an incentive mechanism Buyer Material products Supplier Traditional relationship Conflicting incentives Buyer Value of services through products Service Provider PBL relationship Aligned incentives

9 Infrequent restoration services Serguei Netessine, The Wharton School Slide 8 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 Performance outcomes Managerial decisions Exogenous factors Contracts Cost sharing and PBL Kim, Cohen, Netessine (2007a) Mgmt Science 53(12), 1843-58 Reliability or Inventory? Kim, Cohen, Netessine (2007b) Under review Infrequent product failures Todays talk Under review

10 Infrequent restoration services Serguei Netessine, The Wharton School Slide 9 Infrequent equipment failures Engine services due to malfunction (March 2006 – March 2007) Regional airline company with installed base of 60 engines March 2006September 2006March 2007 Compressor degradation Liner damage Vibration Vane burn through Fan case corrosion Oil system debris Oil leak Vane burn through

11 Infrequent restoration services Serguei Netessine, The Wharton School Slide 10 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 job completed, machine is up Parts arrive CSE orders additional parts if necessary Customer calls CSE arrives with some or all of the required parts On-site diagnosis Remote Diagnosis Machine fails Parts Availability Logistics Transportation CSE Response Time Repair Time

12 Infrequent restoration services Serguei Netessine, The Wharton School Slide 11 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

13 Infrequent restoration services Serguei Netessine, The Wharton School Slide 12 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.

14 Infrequent restoration services Serguei Netessine, The Wharton School Slide 13 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

15 Infrequent restoration services Serguei Netessine, The Wharton School Slide 14 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 for low-frequency, high-impact services Principal-agent model Twist: performance realization depends on exogenous events (random failures)

16 Infrequent restoration services Serguei Netessine, The Wharton School Slide 15 Principal-agent model: quick review Principal Agent (risk-averse) Offers a contract that depends on 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 Principals 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 in the trade a*a*

17 Infrequent restoration services Serguei Netessine, The Wharton School Slide 16 Model: sequence of events Observes realized downtimes and pay according to contract terms Receives stochastic income Risk-averse Supplier decides to participate in the trade Chooses service capacity * privately Risk-neutral Customer offers a contract T that penalizes downtimes S1S1 S2S2 S3S3 Poisson failure process with rate ~ O(1) i.i.d. downtimes { S i } are realized * = 1/E [ S i ] > >> Suppliers service performance (downtime) is realized only when equipment failure occurs Contracting length = 1

18 Infrequent restoration services Serguei Netessine, The Wharton School Slide 17 Assumptions By increasing service capacity (= service rate), 1)Expected service time goes down, and 2)Service time variability does not go up Linear penalty contract: –Performance measure X is positively correlated with downtime Mean-variance utility for Supplier:

19 Infrequent restoration services Serguei Netessine, The Wharton School Slide 18 Assumption on Customers 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 Potential problem: Customer discounts rare failures When a failure occurs, Customer may experience a long downtime with serious consequences Customer values fast service delivery after each failure incident

20 Infrequent restoration services Serguei Netessine, The Wharton School Slide 19 Customers contract design problem subject to (Service constraint) (IR) (IC) subject to (IC) (Service constraint) = Risk premium

21 Infrequent restoration services Serguei Netessine, The Wharton School Slide 20 Which performance measure? 1. Penalize cumulative downtimes S1S1 S2S2 S3S3 2. Penalize average downtime Sample mean estimator Both incentivize the Supplier to invest in capacity Compound Poisson variable

22 Infrequent restoration services Serguei Netessine, The Wharton School Slide 21 Suppliers response to contract terms Average-performance contract 1 No-failure effect: Little benefit of sampling Cumulative-performance contract 1 Exp. total penalty = Income risk = Exp. total penalty = Sample-mean variance reduction more willing to take a chance Capacity as a means to hedge against risk

23 Infrequent restoration services Serguei Netessine, The Wharton School Slide 22 Optimal penalty rates Cumulative-performance contract p CUM 1 Average-performance contract p AVE 1 Take advantage of Suppliers voluntary capacity increase to induce m, only small contractual incentive p CUM needed Non-monotonicity of * results in non-monotonicity of p AVE

24 Infrequent restoration services Serguei Netessine, The Wharton School Slide 23 Efficiency loss in supply chain = Risk premium = efficiency loss Average -performance contract Cumulative -performance contract Cumulative -performance contract Average -performance contract Efficiency loss is greatest when equipment is most reliable! Risk pooling occurs as more performance realizations are collected, revealing more information about Suppliers capacity decision larger, better efficiency

25 Infrequent restoration services Serguei Netessine, The Wharton School Slide 24 Which contract is better? Average-performance contract more efficient Cumulative- performance contract more efficient 1.4 Average-performance contract better if v = CV ( S i ) < 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

26 Infrequent restoration services Serguei Netessine, The Wharton School Slide 25 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 10 3 r/c = 10 4 r/c = 5 x 10 3 r/c = 10 4 = 0.01 = 0.001 CUM AVE CUM AVE CUM AVE CUM AVE

27 Infrequent restoration services Serguei Netessine, The Wharton School Slide 26 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

28 Infrequent restoration services Serguei Netessine, The Wharton School Slide 27 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

29 Infrequent restoration services Serguei Netessine, The Wharton School Slide 28 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

30 Infrequent restoration services Serguei Netessine, The Wharton School Slide 29 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


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