Practical Modeling in Automotive Production

  • Jonathan H. Owen
  • Robert R. Inman
  • Dennis E. Blumenfeld
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 152)

Abstract

We all want to make a difference. We all want our work to enrich the world. As production planners, we have a great opportunity. Since the industrial revolution, production planning has enabled industry to extract the most from the era’s manufacturing technology. Henry Ford’s assembly line, with its associated production planning, dramatically improved production efficiency. While production planning continues to advance productivity thereby enhancing society’s prosperity and quality of life, the benefits from production systems modeling are often not realized in practice. As noted by the editors, there is a widening gap between research and the needs of industry. The cause is not that the models are not sophisticated enough to capture the complexities of the real world. Neither is it that there is a lack of technology transfer. From our experience in industry, the gap arises from underdeveloped modeling. Underdeveloped modeling is diverting us from making a bigger difference. To impact production, we need models that can be put into practice. Not necessarily simple, but actionable. If a firm cannot act on a model, the model (and its associated solution methodologies) will not enhance the firm’s performance. The authors admit to straying from this advice themselves. But we have learned that models implemented gratify the most. Therefore, we propose that the most fruitful future research direction is practical modeling.

References

  1. Ackoff RL (1979) “The future of operational research is past.” J Oper Res Soc 30(2):93–104Google Scholar
  2. Alden JM, Burns LD, Costy T, Hutton RD, Jackson CA, Kim DS, Kohls KA, Owen JH, Turnquist MA and Vander Veen DJ (2006) “General Motors increases its production throughput.” Interfaces 36(1):6–25CrossRefGoogle Scholar
  3. Blumenfeld DE, Burns LD, Diltz JD, Daganzo CF (1985) “Analyzing trade-offs between transportation, inventory and production costs on freight networks.” Transport Res 19B(6): 361–380CrossRefGoogle Scholar
  4. Blumenfeld DE, Burns LD, Daganzo CF, Frick MC, Hall RW (1987) “Reducing logistics costs at General Motors.” Interfaces 17(1):26–47CrossRefGoogle Scholar
  5. Blumenfeld DE, Li J (2005) “An analytical formula for throughput of a production line with identical stations and random failures.” Math ProblEng 2005(3):293–308CrossRefGoogle Scholar
  6. Burns LD, Daganzo CF (1987) “Assembly line job sequencing principles.” Int J Prod Res 25(1):71–99CrossRefGoogle Scholar
  7. Burns LD, Hall RW, Blumenfeld DE, Daganzo CF (1985) “Distribution strategies that minimize transportation and inventory costs.” Oper Res 33(3):469–490CrossRefGoogle Scholar
  8. Buzacott JA (1968) “Prediction of the efficiency of production systems without in-ternal storage.” Int J Prod Syst 6(3):173–188CrossRefGoogle Scholar
  9. Buzacott JA (1990) “Abandoning the moving assembly line: models of human op-erators and job sequencing.” Int J Prod Syst 28(5):821–839CrossRefGoogle Scholar
  10. Conway R, Maxwell W, McClain JO, Thomas LJ (1988) “The role of work-in-process inventory in serial production lines.” Oper Res 36(2):229–241CrossRefGoogle Scholar
  11. Cross M, Moscardini AO (1985) Learning the art of mathematical modelling. Ellis Horwood Ltd., Chichester and Wiley, New YorkGoogle Scholar
  12. Daskin MS (2006) “Models vs. Problems.” OR/MS Today 33(4):6Google Scholar
  13. De Kok AG (1990) “Computationally efficient approximations for balanced flow-lines with finite intermediate buffers.” Int J Prod Syst 28(2):401–419CrossRefGoogle Scholar
  14. Dilworth JB (1989) Production and operations management. Random House, New York pp. 77Google Scholar
  15. Fourer R, Gay DM, Kernighan B (2003) AMPL: A modeling language for mathematical programming. Brooks/Cole-Thomson Learning, Pacific Grove, CAGoogle Scholar
  16. Geoffrion AM (1976) “The purpose of mathematical programming is insight, not numbers.” Interfaces 7(1):81–92CrossRefGoogle Scholar
  17. Hall RW (1985) “What’s so scientific about MS/OR?” Interfaces 15(2):40–45CrossRefGoogle Scholar
  18. Hamming RW (1962) Numerical methods for scientist and engineers. McGraw-Hill, New YorkGoogle Scholar
  19. Hopp WJ, Spearman ML (1996) Factory physics: foundations of manufacturing management. Irwin/McGraw-Hill, New YorkGoogle Scholar
  20. Hunt GC (1956) “Sequential arrays of waiting lines.” Oper Res 4:674–683CrossRefGoogle Scholar
  21. Inman RR (1998) “In-plant material buffer sizing for just-in-time systems in the automotive industry.” Research Publication R&D-8860, General Motors R&D Center, Warren, MichiganGoogle Scholar
  22. Inman RR, Bhaskaran S, Blumenfeld DE (1997) “In-plant material buffer sizes for pull system and level-material-shipping environments in the automotive industry.” Int J Prod Res 35(5):1213–1228CrossRefGoogle Scholar
  23. Jordan WC, Graves SC (1995) “Principles on the benefits of manufacturing process flexibility.” Manag Sci 41(4):577–594CrossRefGoogle Scholar
  24. Kingman JFC (1961) “The single server queue in heavy traffic.” Proc Camb Phil Soc 57(1): 902–904CrossRefGoogle Scholar
  25. Kletter DB (1994) Determining production lot sizes and safety stocks for an auto-mobile stamping plant. Master’s thesis, Massachusetts Institute of TechnologyGoogle Scholar
  26. Michalewicz Z, Fogel DB (2004) How to solve it: Modern heuristics. Springer, BerlinGoogle Scholar
  27. Miser HJ (1993) “The easy chair: avoiding the corrupting lie of a poorly stated problem.” Interfaces 23(6):114–119CrossRefGoogle Scholar
  28. Pidd M (1999) “Just modeling through: a rough guide to modeling.” Interfaces 29(2):118–132CrossRefGoogle Scholar
  29. Pollock SM (1976) “Mathematical modeling: applying the principles of the art studio.” Eng Educ 66:167–171Google Scholar
  30. Powell SG (1995) “The teachers’ forum: teaching the art of modeling to MBA stu-dents.” Interfaces 25(3):88–94CrossRefGoogle Scholar
  31. Sakasegawa H (1977) “An approximation formula \({L}_{q}\cong\alpha\cdot{\rho }^{\beta }\left /\right. \left (1 - \rho \right )\).” Ann Inst Stat Math 29A:67–75CrossRefGoogle Scholar
  32. Shugan SM (2002) “Marketing science, models, monopoly models, and why we need them.” Market Sci 21(3):223–228CrossRefGoogle Scholar
  33. Tersine RJ (1985) Production/operations management: concepts, structure, and analysis. North-Holland, New YorkGoogle Scholar
  34. Volkema RJ (1995) “Creativity in MS/OR: Managing the process of formulating the problem.” Interfaces 25(3):81–87CrossRefGoogle Scholar
  35. Winston WL (1987) Operations research: applications and algorithms. Duxbury, BostonGoogle Scholar

Copyright information

© Springer New York 2011

Authors and Affiliations

  • Jonathan H. Owen
    • 1
  • Robert R. Inman
  • Dennis E. Blumenfeld
  1. 1.General Motors CompanyWarrenUSA

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