Advertisement

Product Architecture Decision Under Lifecycle Uncertainty Consideration: A Case Study in Providing Real-time Support to Automotive Battery System Architecture Design

  • Qi D. Van Eikema HommesEmail author
  • Matthew J. Renzi
Chapter
Part of the Springer Series in Advanced Manufacturing book series (SSAM)

Abstract

Flexibility is valuable when the future market and customer needs are uncertain, especially if the product development process is long. This chapter focuses on what the firm can do to increase their flexibility before a product is produced and sold. The flexibility is built into the product architecture, which then enables the firm to take a staged decision process. Flexibility-in-the-Project approach was developed by de Neufville and Sholtes (2011), and has been successfully applied to large infrastructure projects. Real options analysis has only been utilized in high-level product planning decisions. The case study described in this chapter is the first successful application of the Flexibility-in-the-Project framework, providing real-time engineering design decision support to Ford Motor Company engineering efforts in future vehicle electrification. In hybrid and electric vehicle applications, the high voltage battery pack hardware and control system architecture will experience multiple engineering development cycles in the next 20 years. Flexibility in design could mitigate risk due to uncertainty in both engineering and consumer preferences. Core engineering team decisions on battery pack voltage monitoring, thermal control, and support software systems will iterate as technology evolves. The research team valued key items within the technology subsystems and developed flexible strategies to allow Ford to capture upside potential while protecting against downside risk, with little-to-no extra cost at this early stage of development. The methodology used to evaluate the uncertainty, identify flexibility, and provide the real options value of flexibility is presented.

Keywords

Cash Flow Multidisciplinary Design Optimization Product Development Process Battery Pack Gasoline Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Aaker DA, Kumar V, Day GS, Leone RP (2010) Marketing research. 10th edn. Wiley, New YorkGoogle Scholar
  2. Abernathy W (1978) Productivity dilemma: roadblock to innovation in the automobile industry. The Johns Hopkins University Press, BaltimoreGoogle Scholar
  3. Allada V, Jiang L (2001) Design for robustness of modular product families for current and future markets. In: Proceedings of the DETC01, ASME design engineering and technical conference and computers and information in engineering conference, Pittsburgh, PA, 9–12 Sept 2001Google Scholar
  4. Allada V, Jiang L (2002) New modules launch planning for evolving modular product families. DETC2002/DFM-34190. In: Proceedings of ASME 2002 design engineering technical conferences and computer and information in engineering conference montreal, Canada, Sept 29–Oct 2, 2002Google Scholar
  5. Andrea D (2010) Battery management systems. Artech House, NorwoodGoogle Scholar
  6. Balakrishnan A, Geunes J (2003) Production planning with flexible product specifications: an application to specialty steel manufacturing. Oper Res 51(1):94–112CrossRefzbMATHGoogle Scholar
  7. Baldwin CY, Clark KB (2000) Design Rules, vol 1. MIT Press, CambridgeGoogle Scholar
  8. Black F, Scholes M (1973) The pricing of options and corporate liabilities. J Polit Econ 81(3):637–654Google Scholar
  9. Brace I (2004) Questionnaire design : how to plan, structure, and write survey material for effective market research. Kogan Page, LondonGoogle Scholar
  10. Chen W, Yuan C (1999) A probabilistic design model for achieving flexibility in design. ASME J Mech Des 121(1):77–83CrossRefGoogle Scholar
  11. Clarkson PJ, Simons CS, Eckert CM (2004) Predicting change propagation in complex design. ASME J Mech Des 126(5):788–797CrossRefGoogle Scholar
  12. Copland T, Antikarov V (2003) Real options: a practitioner’s guide. Texere EIA (US Energy Information Administration) (2012). http://www.eia.gov/energyexplained/index.cfm?page=gasoline_factors_affecting_prices. Accessed May 2012
  13. de Weck O, de Neufville R, Chaize M (2004) Staged deployment of communications satellite constellations in low Earth orbit. J Aerosp Comput Inf Commun 1:119–136CrossRefGoogle Scholar
  14. de Neufville R, Scholtes S, Wang T (2006) Real options by spreadsheet: parking garage case example. J Infrastruct Syst 12(3):107–111CrossRefGoogle Scholar
  15. de Neufville R, Scholtes S (2011) Flexibility in engineering design. MIT Press, CambridgeGoogle Scholar
  16. Donndelinger J, Ferguson S, Lewis K (2003) Exploring mass trade-offs in preliminary vehicle design using pareto sets. AIAA conference 2003Google Scholar
  17. Eckert CP, Clarkson J, Zanker W (2004) Change and customisation in complex engineering domains. Res Eng Des 15:1–21CrossRefGoogle Scholar
  18. Engel A, Browning TR (2008) Designing systems for adaptability by means of architecture options. Syst Eng 11(2):125–146Google Scholar
  19. Ferguson S, Lewis K (2004) Effective development of flexible systems in multidisciplinary optimization, AIAA 2004Google Scholar
  20. Ferguson S, Siddiqi A (2007) Flexible and reconfigurable systems: nomenclature and review. DETC 2007-35745. In: Proceedings of the ASME 2007 international design engineering technical conferences & computers and information in engineering conference, Las Vegas, NV, 4–7 September 2007Google Scholar
  21. Ferguson S, Kasprzak E, Lewis K (2007) Designing a family of reconfigurable vehicles using multilevel mutidiscilinary design optimization. AIAA-2007-1880, the 48th AIAA/ASMR/ASCE/AHS/ASC structures, structural dynamics, and materials conference, 2007Google Scholar
  22. Ford DN, Durward KS (2005) Adapting real options to new product development by modeling the second Toyota Paradox. IEEE Trans Eng Manage 52(2)Google Scholar
  23. Ford (2011) New Ford focus electric keeps its cool when the temperature heats up. http://media.ford.com/article_display.cfm?article_id=34774. Accessed 28 May 2012
  24. Ford Battery Research Team (2011) Personal conversationsGoogle Scholar
  25. Fricke E, Schulz AP (1999) Incorporating flexibility, agility, robustness, and adaptability within the design of integrated systems—Key to success. In: Proceedings of 18th digital avionics systems conference, 1999. IEEE. St. Louis, MO, vol. 1, pp 1.A.2-1–1.A.2-8Google Scholar
  26. Fricke E, Schulz AP (2005) Design for changeability (DfC): principles to enable changes in systems throughout their entire lifecycle. Syst Eng 8(4):342–358Google Scholar
  27. General Motors (2011) Cooling fins help keep chevrolet volt battery at ideal temperature. http://media.gm.com/product/public/us/en/volt/home.detail.html/content/Pages/news/us/en/2011/Feb/0214_battery.html
  28. Gerwin D (1982) Do’s and Don’ts of computerized manufacturing. Harv Bus Rev 60:107–116Google Scholar
  29. Gladwell M (2005) Blink, the power of thinking without thinking. Little Brown and Company, New YorkGoogle Scholar
  30. Gustavsson SO (1984) Flexibility and productivity in complex production processes. Int J Prod Res 22(5):801–808CrossRefGoogle Scholar
  31. Harkness JA, Van de Vijver FJR, Mohler P (2003) Cross-cultural survey methods. Wiley, New YorkGoogle Scholar
  32. Haubelt C, Telch J, Richter K (2002) System design for flexibility. Design, automation and test in europe conference and exhibition, Paris, FranceGoogle Scholar
  33. Henderson RM, Clark KB (1990) Architectural innovation. Adm Sci Q 35(1990):9–30CrossRefGoogle Scholar
  34. Hybrid Market Forecasts (2006) Hybridcars.com: http://www.hybridcars.com/hybrid-drivers/hybrid-market-forecasts.html. Accessed 20 Mar 2012
  35. Hybridcars.com (2012) Dashboard: Sales Still Climbing. http://www.hybridcars.com/news/December-2011-dashboard-sales-still-climbing-35093.html. Accessed 20 Mar 2012, from Dec 2011
  36. Kahneman D, Tversky A (1979) Prospect theory, an analysis of decision under risk. Econometrica 47:263–291CrossRefzbMATHGoogle Scholar
  37. Kahneman D, Knetsch JL, Thaler RH (1990) Experimental tests of the endowment effect and the coase theorem. J Polit Econ 98:1325–1347CrossRefGoogle Scholar
  38. Kazmer DO, Roser C (1999) Evaluation of product and process design robustness. Res Eng Des 11(1):21–30Google Scholar
  39. Kapoor D, Kazmer D (1997) The definition and use of the process flexibility index. 1997 ASME Design engineering technical conference, DFM-97-110 Sacramento, CaliforniaGoogle Scholar
  40. Keese DA, Takawale NP, Seepersad C, Wood K (2006) An enhanced change modes and effects analysis (CMEA) tool for measuring product flexibility with applications to consumer products. ASME international design engineering technical conferences and computers and information in engineering conferences, DETC2006-99478, Philadelphia, PAGoogle Scholar
  41. Keese DA, Tilstra C, Seepersad C, Wood KL (2007) Empirically-derived principles for designing products with flexibility for future evolution, 2007 ASME International Design Engineering Technical Conference, Las Vegas, NV, Sept 2007. DETC2007-35695Google Scholar
  42. Lieke A, Chmarra MK, Tomiyama T (2008) Modularization method for adaptable products. In: Proceedings of the 2008 design engineering technical conferences, New York, NY, 3–6 Aug 2008Google Scholar
  43. Maier MW, Rechtin E (2002) The Art of Systems Architecting. CRC Press, Boca RentonGoogle Scholar
  44. Martin MV, Ishii K (2002) Design for variety: developing standardized and modularized product platform architectures. Res Eng Des 13:213–235Google Scholar
  45. Mathews SH, Datar VT, Johnson B (2007) A practical method for valuing real options. J Appl Corp Finan 9(2):95–104CrossRefGoogle Scholar
  46. Meyer MH, Lehnerd AP (1997) The power of product platforms. Free Press, New York 1997Google Scholar
  47. Nissan (2012) Why did Nissan develop and EV battery? Nissan technology magazine. http://www.nissan-global.com/EN/TECHNOLOGY/MAGAZINE/ev_battery.html. Accessed May 2012
  48. Olewnik A, Brauen T, Ferguson S, Lewis K (2004) A framework for flexible systems and its implementation in multiattribute decision making. ASME J Mech Des 126(3):412–419CrossRefGoogle Scholar
  49. Olewinik A, Lewis K (2006) A decision support framework for flexible system design. J Eng Des 17(1):75–97Google Scholar
  50. Omotoso M (2008) Alternative powertrain sales forecast. University of Michigan: http://umtri.umich.edu/content/Mike.Omotoso.Forecast.pdf. Accessed 12 Mar 2012
  51. Pandey V, Thurston D (2008) Copulas for demand estimation for portfolio reuse design decisions. In: Proceedings of the 2008 design engineering technical conferences, DETC2008-49406, New York, NY, Aug 3–6 2008Google Scholar
  52. Papalambros P, Wilde D (2000) Principles of optimal design. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  53. Parkinson AR, Chase KW (2000) An introduction to adaptive robust design for mechanical assemblies. ASME design engineering technical conferences DETC2000/DAC-14241, Baltimore, MarylandGoogle Scholar
  54. Phadke MS (1989) Quality engineering using robust design. Prentice-Hall, Englewood CliffsGoogle Scholar
  55. Qureshi A, Murphy JT, Kuchinsky B, Seepersad C, Wood K, Jensen D (2006) Principles of product flexibility. ASME international design engineering technical conferences and computers and information in engineering conference, DETC2006-99583, Philadelphia, PAGoogle Scholar
  56. Rechtin E (1991) Systems architecting, creating and building complex systems. Prentice Hall, Englewood CliffsGoogle Scholar
  57. Renzi M (2012) System architecture decisions under uncertainty: a case study on automotive battery system design, MS Thesis. MIT CambridgeGoogle Scholar
  58. Ross AM, Donna RH, Hastings DE (2008) Design for changeability (DfC): principles to enable changes in systems throughout their entire lifecycle. Syst Eng 11(4)Google Scholar
  59. Sage AP, Rouse WB (1999) Handbook of systems engineering and management. Wiley, New YorkGoogle Scholar
  60. Saleh JH, Hastings DE, Newman DJ (2003) Flexibility in system design and implications for aerospace systems. Acta Astronaut 53(12):927–944CrossRefGoogle Scholar
  61. Savage S (2009) The flaw of averages. Wiley, New YorkGoogle Scholar
  62. Sethi AK and Sethi SP (1990) Flexibility in manufacturing: a survey. Int J Flex Manuf Syst. 2:289–328 (Kluwer Academic Publishers, Boston. Manufactured in The Netherlands)Google Scholar
  63. Shah NBJ, Viscito WL, Ross AM, Hastings DE (2008) Quantifying flexibility for architecting changeable systems. 6th Conference on engineering research, Long Beach, CA, April 2008Google Scholar
  64. Simpson TW, Siddique Z, Jiao J (2006) Product platform and product family design. Springer, HeidelbergGoogle Scholar
  65. Siri D (2010) In race to market, Nissan’s electric car takes shortcuts. Wired http://www.wired.com/autopia/2010/01/nissan-leaf-2/. Accessed 21 Feb 2012
  66. Skiles SM, Singh V, Krager J, Wood K, Jensen D, Szmerekovsky A (2006) Adapted concept generation and computation techniques for the application of a transformer design theory. ASME International design engineering technical conferences and computers and information in engineering conference, DETC2006-99584, Philadelphia, PAGoogle Scholar
  67. Stock JH, Watson MW (2007) Introduction to econometrics. 2nd edn. Pearson, BostonGoogle Scholar
  68. Suh E, de Weck O, Zhang D (2007) Determining product platform extent. Res Eng Des 18(2):67–89CrossRefGoogle Scholar
  69. Tilstra AH, Backlund PB, Seepersad CC, Wood KL (2008) Industrial case studies in product flexibility for furture evoluation: an application and evaluation of design guidelines. In: Proceedings of the ASME 2008 international design engineering technical conferences & computers and information in engineering conference, IDETC/CIE 2008, Brooklyn: ASME, pp 1–14Google Scholar
  70. Tran L (2012) NHTSA statement on conclusion of chevy volt investigation. NHTSA: http://www.nhtsa.gov/About+NHTSA/Press+Releases/2012/NHTSA+Statement+on+Conclusion+of+Chevy+Volt+Investigation. Accessed 15 Feb 2012
  71. Train K (2003) Discrete choice methods with simulation. Cambridge University Press, New YorkGoogle Scholar
  72. Trigeorgis L (1996) Real options: managerial flexibility and strategy in resource allocation. The MIT Press, CambridgeGoogle Scholar
  73. Tversky A, Kahneman D (1981) The framing of decisions and psychology of choice. Science 211(1981):453–458CrossRefzbMATHMathSciNetGoogle Scholar
  74. Ulrich KT, Eppinger SD (2008) Product design and development. McGraw Hill, New YorkGoogle Scholar
  75. Utterback JM (1996) Mastering the dynamics of innovation. Harvard Business Press, BostonGoogle Scholar
  76. Wang CY (2011) Vehicle Battery Engineering, Summer 2011 Short Course. Dearborn, MI, USAGoogle Scholar
  77. Wang T, de Neufville R (2005) Real options “in” projects. 9th Real options annual international conference, ParisGoogle Scholar
  78. Westbrook M (2001) The electric and hybrid electric car. Society of Automotive Engineers, WarrendaleGoogle Scholar
  79. Whitney DE (2004) Mechanical assemblies: their design, manufacture, and role in product development. Oxford University Press, New YorkGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Engineering Systems DivisionMassachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations