The Ongoing Challenge for a Responsive Demand Supply Network: The Final Frontier—Controlling the Factory

  • Kenneth Fordyce
  • R. John Milne


Over the past 20 years organizations have put significant energy into making smarter decisions in their enterprise wide central planning and “available to promise” processes to improve responsiveness (more effective use of assets and more intelligent responses to customer needs and emerging opportunities). However, firms have put only limited energies into factory floor decisions and capacity planning and almost none into generating a tighter coupling between the factory and central planning. The bulk of the work to make “smarter factory decisions” has focused on two simple metrics: increasing output and reducing cycle time—often without accommodating the need to run lots at different velocities and without recognizing how the operating curve (trade-off between lead time and tool utilization—Appendix 3) links them. In fact, many of the recent Lean initiatives have focused on eliminating variability to induce simplicity to achieve improved output or cycle time without concern for the impact on responsiveness or capacity. The purpose of this paper is to (a) make clear the critical, and often overlooked, role of factory responsiveness with respect to central planning; (b) explain how traditional factory planning and the current application of Lean can severely impact the firm’s responsiveness; (c) elaborate on touch points between central and factory planning demonstrating simple tactical methods that can improve responsiveness and protect the factory from churn; (d) explain why smarter dispatch scheduling is critical to successful responsiveness; and (e) outline the basics of smarter dispatch scheduling. Although the focus of this paper is the factory, many of the core concepts apply to a wide range of industries from restaurants to health care delivery.


Cycle Time Wait Time Operating Curve Central Planning Average Wait Time 
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  1. 1.
    Bermon S, Hood S (1999) Capacity optimization planning system (CAPS). Interfaces 29(5): 31–50CrossRefGoogle Scholar
  2. 2.
    Bitran G, Tirupati D (1989) Tradeoff curves, targeting and balancing in manufacturing networks. Oper Res 37(4):547–555CrossRefGoogle Scholar
  3. 3.
    Bixby R, Burda R, Miller D (2006) Short-interval detailed production scheduling in 300 mm semiconductor manufacturing using mixed integer and constraint programming. Semiconductorfabtech, 32nd edn, http://, pp 34–40
  4. 4.
    Buchholz J (2005) Interview with Nick Donofrio. IBM on the spot series, posted on Accessed 9 Aug 2005
  5. 5.
    Chen H, Harrison M, Mandelbaum A, Ackere A, Wein L (1988) Empirical evaluation of a queuing network model for semiconductor wafer fabrication. Oper Res 36(2):202–215CrossRefGoogle Scholar
  6. 6.
    Dennis P (2007) Lean production simplified. Productivity Press, New YorkGoogle Scholar
  7. 7.
    Denton B, Forrest J, Milne RJ (2006) Methods for solving a mixed integer program for semiconductor supply chain optimization at IBM. Interfaces 36(5):386–399CrossRefGoogle Scholar
  8. 8.
    Fordyce K, Bixby R, Burda R (2008) Technology that upsets the social order—a paradigm shift in assigning lots to tools in a wafer fabricator—the transition from rules to optimization. In: Proceedings of the 2008 winter simulation conferenceGoogle Scholar
  9. 9.
    Fordyce K, Wang C-T, Chang C, Degbotse A, Denton B, Lyon P, Milne RJ, Orzell R, Rice R, Waite J (2011a) In: Kempf, Keskinocak, Uzsoy (ed). The ongoing challenge—creating an enterprise-wide detailed supply chain plan for semiconductor and package operations. Planning production and inventories in the extended enterprise: a state of the art handbook, Vol 2 (Chapter 14)Google Scholar
  10. 10.
    Fordyce K, Fournier J, Milne RJ (2011b) Basics of the operating curve—classical planning meets its uncertainty principle. Working paper,, jmilne@clarkson.eduGoogle Scholar
  11. 11.
    Fox B, Kempf K (1985) Complexity uncertainty, and opportunistic scheduling. In: Proceedings of the IEEE second conference on artificial intelligence applications: the engineering of knowledge based systems, Miami, FL, pp 487–492Google Scholar
  12. 12.
    Gross D, Harris C (1998) Fundamentals of queueing theory, 3rd edn. Wiley, New YorkGoogle Scholar
  13. 13.
    Hackman ST, Leachman RC (1989) A general framework for modeling production. Manag Sci 35(4):478–495CrossRefGoogle Scholar
  14. 14.
    Hopp W, Spearman M (2008) Factory physics, 3rd edn. McGraw-Hill Irwin, New YorkGoogle Scholar
  15. 15.
    Horn G, Podgorski W (1998) A focus on cycle time-vs.-tool utilization “paradox” with material. In: Advanced semiconductor manufacturing conference and workshop proceedings, pp 405–412Google Scholar
  16. 16.
    Kempf K, Pape D, Smith S, Fox B (1991) Issues in the design of AI based schedulers. AI Mag 11(5):37–45Google Scholar
  17. 17.
    Kempf K (1989) Manufacturing scheduling: intelligently combining existing methods. In: Working notes of AAAI AI in manufacturing symposium. Fox M (ed.), AAAI, Burgess Drive Menlo ParkGoogle Scholar
  18. 18.
    Kempf K (1994) Intelligent scheduling semiconductor wafer fabrication. In: Mark Fox, Monte Zweben (eds) Intelligent scheduling. Morgan Kaufman Publishers, pp 473–516 (Chapter 18)Google Scholar
  19. 19.
    Kempf K (2004) Control-oriented approaches to supply chain management in semiconductor manufacturing. In: Proceedings of the 2004 American control conference, Boston, MA, pp 4563–4576Google Scholar
  20. 20.
    Leachman R, Benson R, Liu C, Raar D (1996) IMPReSS: an automated production planning and delivery-quotation system at Harris corporation–semiconductor sector. Interfaces 26(1):6–37CrossRefGoogle Scholar
  21. 21.
    Little J (1992) Tautologies, models and theories: can we find laws of manufacturing? IEEE Trans 24(3):7–13CrossRefGoogle Scholar
  22. 22.
    Liu J, Yang F, Wan H, Fowler J (2010) Capacity planning through queuing analysis and simulation-based statistical methods: a case study for semiconductor wafer FABS. Scholar
  23. 23.
    Morrison J, Dews E, LaFreniere J (2006) Fluctuation smoothing production control at IBM’s 200mm wafer fabricator: extensions, application and the multi-flow production index (MFPx). In: Proceedings of the 2006 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, Boston, MAGoogle Scholar
  24. 24.
    Morrison J, Martin D (2006) Cycle time approximations for the G/G/m queue subject to server failures and cycle time offsets with applications. In: ASMC 2006 Proceedings, p 322Google Scholar
  25. 25.
    Orlicky J (1975) Material requirements planning: the new way of life in production and inventory management. McGraw-Hill, New YorkGoogle Scholar
  26. 26.
  27. 27.
    Shirodkar S, Kempf K (2006) Supply chain collaboration through shared capacity models. Interfaces 36(5):420–432CrossRefGoogle Scholar
  28. 28.
    Shobrys D (2003) History of APS. Supply chain consultants (, Wilmington, DE 19808, USAGoogle Scholar
  29. 29.
    Simon HA (1957) Administrative behavior, 2nd edn. The Free Press, New YorkGoogle Scholar
  30. 30.
    Singh H (2009) Supply chain planning in the process industry. Supply Chain Consultants, WilmingtonGoogle Scholar
  31. 31.
    Singh H (2009) Practical guide for improving sales and operations planning. Supply Chain Consultants, WilmingtonGoogle Scholar
  32. 32.
    Sullivan G (1990) IBM Burlington’s logistics management system (LMS). Interfaces 20(1): 43–61CrossRefGoogle Scholar
  33. 33.
    Sullivan G (1994) Logistics management system (LMS): integrating decision technologies for dispatch scheduling in semiconductor manufacturing. Intelligent scheduling. Morgan Kaufman Publishers, San Francisco pp 473–516Google Scholar
  34. 34.
    Sullivan G (1995) A dynamically generated rapid response fast capacity planning model for semiconductor fabrication facilities. the impact of emerging technologies on computer science and operations research, Kluwer Academic Publishers, Boston (presented at Winter 1994 computers and operations research conference)Google Scholar
  35. 35.
    Uzsoy R, Lee C, Martin-Vega LA (1992) A review of production planning and scheduling modules in the semiconductor industry, Part 1: system characteristics, performance evaluation, and production planning. IIE Trans Sched Logist 24(4):47–60CrossRefGoogle Scholar
  36. 36.
    Uzsoy R, Lee C, Martin-Vega LA (1994) A review of production planning and scheduling modules in the semiconductor industry, Part 2: shop floor control. IIE Trans Sched Logist 26(5):44–55CrossRefGoogle Scholar
  37. 37.
    Zisgen H, Ments I, Wheeler B, Hanschle T (2008) A queuing network based system to model capacity and cycle time for semiconductor fabrication. In: Proceedings of the 2008 winter simulation conferenceGoogle Scholar
  38. 38.
    Zisgen H, Brown S, Hanschke T, Meents I, Wheeler B (2010) Queuing model improves IBM’s semiconductor capacity and lead-time management. Interfaces 40(5):397–407CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.IBM Systems and Technology GroupPoughkeepsieUSA

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