Skip to main content
SpringerLink
Log in
Book cover

Advances in Through-life Engineering Services pp 309–329Cite as

Product-Service Systems Under Availability-Based Contracts: Maintenance Optimization and Concurrent System and Contract Design

  • Chapter
  • First Online:

Part of the book series: Decision Engineering ((DECENGIN))

Abstract

Product-service systems (PSSs) are the result of a shifting business focus from designing and selling physical products, to selling a system consisting of products and services in an ongoing relationship with the customer that fulfills customer satisfaction. A PSS contract can take several forms (e.g., fixed price, capability-contract, and availability-based). The focus of this chapter is on PSSs that use availability-based contracts. In these cases the customer does not purchase the product, instead they purchase the utility of the product and the availability of service in order to obtain a lower cost while still meeting their needs. This chapter addresses the optimization of system maintenance activities, and the concurrent design of the PSS and the contract.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    We assume that ideally the design of the PSS means designing the hardware, software, service, and logistics associated with the system concurrently. Section 19.4 of this chapter includes contract design in this process as well.

  2. 2.

    Classically “inventory” refers to an inventory of items (e.g., spare parts), however, more generally, it could mean a maintenance “opportunity inventory”, which is a combination of all the resources necessary to support the system, i.e., workforce, facilities, favorable weather, and spare parts. This broader interpretation of inventory (previously eluded to by [10]) is a departure from mainstream operations research that only thinks of inventory as parts.

  3. 3.

    This is not the difference between the predictive and corrective maintenance actions, but rather the cost of just a corrective maintenance event. The predictive maintenance event cost is subtracted later when the real option value is determined, i.e., in Eq. (19.2).

  4. 4.

    The value construction in this section assumes that the system is revenue earning, e.g., a wind turbine or an airplane used by an airline.

  5. 5.

    For example, if the system is a wind turbine, path uncertainties could be due to variations in the wind over time.

  6. 6.

    This could be due to the limited availability of maintenance resources or the limited availability of the system being maintained.

  7. 7.

    The decision-maker may also have the flexibility not to implement the predictive maintenance on a particular date but to wait until the next possible date to decide, which makes the problem an American option style as has been demonstrated and solved by [12]. The Haddad et al. solution in [12] is correct for the assumption that an optimal decision will be made on or before some maximum waiting duration and the solution delivered is the maximum “wait to date”. Unfortunately, in reality maintenance decision-makers for critical systems face a somewhat different problem: given that the maintenance opportunity calendar is known when the RUL indication is obtained, on what date should the predictive maintenance be done to get the maximum option value. This makes the problem a European option style.

  8. 8.

    In [18] the inventory lead time (ILT) was considered to be a logistics parameter determined from an availability requirement. It is also possible that ILT is a contract parameter that is flowed down to subcontractors.

  9. 9.

    Although we include warranty design in the list of possible contract design activities that could be driven by the product parameters, for most products that have warranties, the type of warranty and its length are determined by marketing, and are not based on the product’s predicted reliability. More commonly, the warranty type and length (which are a contract) are passed to the engineering design to determine the appropriate warranty reserve fund, which would be an example of the first category.

  10. 10.

    Mechanism design theory is an economic theory that seeks to determine when a particular strategy or contract mechanism will work efficiently.

  11. 11.

    This problem is also evidenced by the choice of a single value for the cost of money, i.e., the cost of money is not constant over time (nor the same for all projects within an organization).

  12. 12.

    While there are some major manufacturers who appear to (or claim to) use an integrated approach in concurrently designing contract and product parameters, they are unpublished and no details are available.

References

  1. Gansler JS, Lucyshyn W, Vorhis C (2011) Performance-based services acquisition. UMD-CM-11-005, University of Maryland School of Public Policy, February, p 83

    Google Scholar 

  2. Levitt T (1972) Production-line approach to service. Harv Bus Rev 50(5):41–52

    Google Scholar 

  3. Meier H, Roy R, Seliger G (2010) Industrial Product-Service Systems—IPS2. CIRP Ann Manuf Technol 59(2):607–627

    Article  Google Scholar 

  4. Sundin E (2009) Life-cycle perspectives of product/service-systems: in design theory. In: Sakao T, Lidnahl M (eds) Introduction to product/service-system design. pp 31–49

    Google Scholar 

  5. Datta PP, Roy R (2010) Cost modelling techniques for availability type service support contracts: a literature review and empirical study. In: Proceedings of 1st CIRP Industrial Product-Service Systems (IPS2) conference. Cranfield University, p 216

    Google Scholar 

  6. Arora V, Chan FTS, Tiwari MK (2010) An integrated approach for logistic and vendor managed inventory in supply chain. Expert Syst Appl 37(1):39–44

    Article  Google Scholar 

  7. Ferguson J, Sodhi M (2011) Using simulation for setting terms of performance based contracts. In: Hu B, Morasch K, Pickl S, Siegle M (eds) Operations research proceedings 2010. Springer, Berlin, p 465

    Google Scholar 

  8. Bowman RA, Schmee J (2001) Pricing and managing a maintenance contract for a fleet of aircraft engines. Simulation 76(2):69–77

    Article  Google Scholar 

  9. Kim S, Cohen MA, Netessine S (2007) Performance contracting in after-sales service supply chains. Manag Sci 53(12):1843–1858

    Article  MATH  Google Scholar 

  10. Gabriel SA, Sahakij P, Balakrishnan S (2004) Optimal retailer load estimates using stochastic dynamic programming. J Energ Eng 130(1):1–17

    Google Scholar 

  11. Murthy DNP, Asgharizadeh E (1999) Optimal decision making in a maintenance service operation. Eur J Oper Res 116(2):259–273

    Article  MATH  Google Scholar 

  12. Haddad G, Sandborn PA, Pecht MG (2014) Using maintenance options to maximize the benefits of prognostics for wind farms. Wind Energy 17(5):775–791

    Article  Google Scholar 

  13. Lei X, Sandborn PA (2016) PHM-based wind turbine maintenance optimization using real options. Int J Progn Health Manag 7(1)

    Google Scholar 

  14. Vestas (2013) 3 MW platform. http://pdf.directindustry.com/pdf/vestas/3-mwplatform/20680-398713.html. Accessed 6 June 2016

  15. Bruck M, Goudarzi N, Sandborn P (2016) A levelized cost of energy (LCOE) model for wind farms that includes power purchase agreement (PPA) energy delivery limits. In: Proceedings of the ASME power conference, Charlotte, NC, USA

    Google Scholar 

  16. Lei X, Sandborn P, Bakhshi R, Kashani-Pour A, Goudarzi N (2015) PHM based predictive maintenance optimization for offshore wind farms. In: Proceedings IEEE conference on prognostics and health management (PHM), Austin, TX, June 2015

    Google Scholar 

  17. Nowicki D, Kumar UD, Steudel HJ, Verma D (2006) Spares provisioning under performance-based logistics contract: profit-centric approach. J Oper Res Soc 59(3):342–352

    Article  MATH  Google Scholar 

  18. Jazouli T, Sandborn P, Kashani-Pour A (2014) A direct method for determining design and support parameters to meet an availability requirement. Int J Perform Eng 10(2):211–225

    Google Scholar 

  19. Jazouli T, Sandborn P, Kashani-Pour A (2014) A direct method for determining design and support parameters to meet an availability requirement—parameters affecting both downtime and uptime. Int J Perform Eng 10(6):649–652

    Google Scholar 

  20. Jin T, Wang P (2011) Planning performance based logistics considering reliability and usage uncertainty. Working Paper for Ingram School of Engineering, Texas State University, San Marcos, TX

    Google Scholar 

  21. Sharma DK, Cui Q, Chen L, Lindly JK (2010) Balancing private and public interests in public-private partnership contracts through optimization of equity capital structure. Transp Res Rec: J Transp Res Board 2151(1):60–66

    Article  Google Scholar 

  22. Hamidi M, Liao H, Szidarovszky F (2014) A game-theoretic model for outsourcing maintenance services. Proceedings—annual reliability and maintainability symposium. doi:10.1109/RAMS.2014.6798514

    Google Scholar 

  23. Deng Q, Zhang L, Cui Q, Jiang X (2014) A simulation-based decision model for designing contract period in building energy performance contracting. Build Environ 71:71–80

    Article  Google Scholar 

  24. Hong S, Wernz C, Stillinger JD (2015) Optimizing maintenance service contracts through mechanism design theory. Appl Math Model. doi:10.1016/j.apm.2015.07.009

    Google Scholar 

  25. Zhu Q, Fung RYK (2012) Design and analysis of optimal incentive contracts between fourth-party and third-party logistics providers. In: Proceedings of the automation and logistics (ICAL). IEEE, pp 84–89. doi:10.1109/ICAL.2012.6308175

  26. Arora V, Chan FTS, Tiwari MK (2010) An integrated approach for logistic and vendor managed inventory in supply chain. Expert Syst Appl 37(1):39–44

    Article  Google Scholar 

  27. Wang W (2010) A model for maintenance service contract design, negotiation and optimization. Eur J Oper Res 201(1):239–246

    Article  MathSciNet  MATH  Google Scholar 

  28. Zhao H, Yin Y (2011) A dynamic mechanism of risk allocation of construction project from perspective of incomplete contract theory: a theoretical model. Ind Eng Eng Manag 1816–1820

    Google Scholar 

  29. Rodrigues D, Erkoyuncu J, Starr A, Wilding S, Dibble A, Laity M (2015) Review of the modelling approaches for availability contracts in the military context. Procedia CIRP 30:451–456

    Article  Google Scholar 

  30. Hockley CJ, Smith JC, Lacey LJ (2011) Contracting for availability and capability in the defence environment. In: Ng I, Parry G, Wild P, McFarlane D, Tasker P (eds) Complex engineering service systems: concepts and research. Springer, New York, pp 237–256

    Chapter  Google Scholar 

  31. Kashani Pour AR, Sandborn P, Cui Q (2016) Review of quantitative methods for designing availability-based contracts. J Cost Anal Parametr 9:69–91

    Article  Google Scholar 

  32. Sols A, Nowicki D, Verma D (2008) n-dimensional effectiveness metric-compensating reward scheme in performance-based logistics contracts. Syst Eng 14(3):305–326

    Google Scholar 

  33. Erkoyuncu JA, Roy R, Shehab E, Wardle P (2009) Uncertainty challenges in service cost estimation for product-service systems in the aerospace and defence industries. In: Proceedings of the 1st CIRP Industrial Product-Service Systems (IPS2) conference, April 2009

    Google Scholar 

  34. Knight JT, Singer DJ (2014) Applying real options analysis to naval ship design. In: Proceedings of ASNE Day

    Google Scholar 

  35. Skaf J, Boyd SP (2010) Design of affine controllers via convex optimization. IEEE Trans Autom Control 55(11):2476–2487

    Article  MathSciNet  Google Scholar 

  36. U.S. Government Accountability Office (2008) Improved analysis and cost data needed to evaluate the cost-effectiveness of performance-based logistics

    Google Scholar 

  37. Shen ZJ (2007) Integrated stochastic supply-chain design models. Comput Sci Eng 9(2):50–59

    Article  Google Scholar 

Download references

Acknowledgements

Funding for the wind turbine real options work in this chapter was provided by Exelon for the advancement of Maryland’s offshore wind energy and jointly administered by MEA and MHEC, as part of “Maryland Offshore Wind Farm Integrated Research (MOWFIR): Wind Resources, Turbine Aeromechanics, Prognostics and Sustainability”. Additional funding for the study of contract engineering was provided by the Naval Postgraduate School (Grant Number N00244-16-1-0003) and the National Science Foundation Division of Civil, Mechanical and Manufacturing Innovation (Grant No. CMMI1538674).

Author information

Authors and Affiliations

  1. Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD, 20742, USA

    Amir Kashani Pour, Xin Lei & Peter Sandborn

  2. Mechanical Engineering Department, University of North Carolina Charlotte, Charlotte, NC, 28223, USA

    Navid Goudarzi

Authors
  1. Amir Kashani Pour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Navid Goudarzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter Sandborn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Sandborn .

Editor information

Editors and Affiliations

  1. Cranfield University, Cranfield, Bedfordshire, United Kingdom

    Louis Redding

  2. Department of Manufacturing & MSAS, Cranfield University, Cranfield, Bedfordshire, United Kingdom

    Rajkumar Roy

  3. Cranfield University, Cranfield, Bedfordshire, United Kingdom

    Andy Shaw

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Kashani Pour, A., Goudarzi, N., Lei, X., Sandborn, P. (2017). Product-Service Systems Under Availability-Based Contracts: Maintenance Optimization and Concurrent System and Contract Design. In: Redding, L., Roy, R., Shaw, A. (eds) Advances in Through-life Engineering Services. Decision Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-49938-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49938-3_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49937-6

  • Online ISBN: 978-3-319-49938-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation