Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems

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. Facts: • 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

3. Profit contribution of after-sales services 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

4. Supply chains compared Infrequent restoration services Serguei Netessine, The Wharton School

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

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 services Serguei Netessine, The Wharton School

7. Evidence of PBL success 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

8. Traditional 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

9. Wharton group PBL research • 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 Infrequent restoration services Serguei Netessine, The Wharton School

10. March 2006 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

11. Service 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

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 services Serguei Netessine, The Wharton School

13. 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. Contracting for restoration services Infrequent restoration services Serguei Netessine, The Wharton School

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 services Serguei Netessine, The Wharton School

15. Related literature • 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

16. a* 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

17. Risk-neutral 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

18. Assumptions • 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

19. 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 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

20. subject to (Service constraint) (IR) (IC) p = Risk premium subject to (Service constraint) (IC) Customer’s contract design problem Infrequent restoration services Serguei Netessine, The Wharton School

21. Compound Poisson variable 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 Infrequent restoration services Serguei Netessine, The Wharton School

22. Cumulative-performance contract 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

23. Optimal penalty rates 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

24. Average -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

25. Cumulative-performance contract more efficient 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

26. pCUM 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

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 services Serguei Netessine, The Wharton School

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 services Serguei Netessine, The Wharton School

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 services Serguei Netessine, The Wharton School

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 Infrequent restoration services Serguei Netessine, The Wharton School