The Ongoing Challenge: Creating an Enterprise-Wide Detailed Supply Chain Plan for Semiconductor and Package Operations

  • Kenneth Fordyce
  • Chi-Tai Wang
  • Chih-Hui Chang
  • Alfred Degbotse
  • Brian Denton
  • Peter Lyon
  • R. John Milne
  • Robert Orzell
  • Robert Rice
  • Jim Waite
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 152)


In the mid-1980s, Karl Kempf of Intel and Gary Sullivan of IBM independently proposed that planning, scheduling, and dispatch decisions across an enterprise’s demand-supply network were best viewed as a series of information flows and decision points organized in a hierarchy or set of decision tiers (Sullivan 1990). This remains the most powerful method to view supply chains in enterprises with complex activities. Recently, Kempf (2004) eloquently rephrased this approach in today’s supply chain terminology, and Sullivan (2005) added a second dimension based on supply chain activities to create a grid (Fig. 14.1) to classify decision support in demand-supply networks. The row dimension is decision tier and the column dimension is responsible unit. The area called global or enterprise-wide central planning falls within this grid.


Supply Chain Planning Horizon Supply Chain Management Efficiency Frontier Supply Chain Plan 
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.


  1. .
    Arntzen B, Brown G, Harrison T, Trafton L (1995) Global supply chain management at digital equipment corporation. Interfaces 25(1):69–93CrossRefGoogle Scholar
  2. .
    Bermon S, Hood S (1999) Capacity optimization planning system (CAPS). Interfaces 29(5):31–50CrossRefGoogle Scholar
  3. .
    Burda R, Degbotse A, Dews B, Milne RJ, Sullivan G (2007) Who would have thought – optimization in fabricator dispatch and artificial intelligence in enterprise wide planning. In: (Working paper) IBM Strategic Systems, 1000 River Road, Essex Junction, VT 05452, USAGoogle Scholar
  4. .
    Dangat GS, Gokhale AR, Li S, Milne RJ, Orzell RA, Reid RL, Tang X, Yen C (1999) Best can do matching of assets with demand in microelectronics manufacturing. U.S. Patent 5,971,585, 26 Oct 1999Google Scholar
  5. .
    Davenport T (2006) Competing on analytics. Harvard Business Review, January 2006, pp 1–9Google Scholar
  6. .
    Denton B, Milne RJ (2006) Method for optimizing material substitutions within a supply chain. U.S. Patent 6,983,190, 3 Jan 2006Google Scholar
  7. .
    Denton B, Hedge S, Orzell RA (2004) Method of calculating low level codes for considering capacities. U.S. Patent 6,584,370, 18 May 2004Google Scholar
  8. .
    Denton B, Forrest J, Milne RJ (2005) A method for considering hierarchical preemptive demand priorities in a supply chain optimization model. U.S. Patent Application: 2005–0171828, also, IBM docket: BUR9–2003–0198US1Google Scholar
  9. .
    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
  10. .
    Duchessi P (1987) The conceptual design for a knowledge based system as applied to the production planning process. In: Silverman B (ed) Expert systems for business, pp 163–194Google Scholar
  11. .
    Fogarty D, Hoffman T (1983) Production and inventory management. South-West Publishing, Cincinnati, OHGoogle Scholar
  12. .
    Fordyce K (1998) Matching assets with demand engines for PROFIT and supply chain management. MicroNews (a publication of the IBM Microelectronics Division, 3rd Quarter, 1998) 4(3).
  13. .
    Fordyce K (2001) New supply chain management applications provide better customer service: serious gets exciting. MicroNews (a publication of the IBM Microelectronics Division, 2nd Quarter, 2001) 6(3).
  14. .
    Forrester J (1961) Industrial dynamics. M.I.T. Press, Cambridge, MAGoogle Scholar
  15. .
    Galbraith J (1973) Designing complex organizations. Addison-Wesley, Reading, MAGoogle Scholar
  16. .
    Glover F, Jones G, Karney D, Klingman D, Mote J (1979) An integrated production, distribution, and inventory planning system. Interfaces 9(5):21–35CrossRefGoogle Scholar
  17. .
    Goldman S (2004) Science in the twentieth century. Great Courses on CD by the Teaching Company, Chantilly, VAGoogle Scholar
  18. .
    Graves RJ, Konopka JM, Milne RJ (1995) Literature review of material flow control mechanisms. Prod Plan Contr 6(5):395–403CrossRefGoogle Scholar
  19. .
    Hackman ST, Leachman RC (1989) A general framework for modeling production. Manag Sci 35(4):478–495CrossRefGoogle Scholar
  20. .
    Hegde SR, Milne RJ, Orzell RA, Pati MC, Patil SP (2004) Decomposition system and method for solving a large-scale semiconductor production planning problem. United States Patent No. 6,701,201 B2Google Scholar
  21. .
    IBM white paper G510–6402–00 (2005) DIOS – dynamic inventory optimization, IBM Corporation, 1133 Westchester Avenue, White Plains, NY 10604, USAGoogle Scholar
  22. .
    IBM white paper G299–0906–00 (2006) Collaboration with IBM E&TS Helps ADI Stay ahead of Customer Demand, IBM Corporation, 1133 Westchester Avenue, White Plains, NY 10604, USAGoogle Scholar
  23. .
    Kempf K (1994) Intelligently scheduling wafer fabrication. In: Intelligent scheduling. Morgan Kaufmann, San Francisco, CA, pp 517–544 (Chapter 18)Google Scholar
  24. .
    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
  25. .
    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
  26. .
    Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43(4) (special issue on frontier research in manufacturing and logistics):546–558Google Scholar
  27. .
    Lin G, Ettl M, Buckley S, Yao D, Naccarato B, Allan R, Kim K, Koenig L (2000) Extended enterprise supply chain management at IBM personal systems group and other divisions. Interfaces 30(1):7–25CrossRefGoogle Scholar
  28. .
    Little J (1992) Tautologies, models and theories: can we find “laws” of manufacturing? IIE Trans 24(3):7–13CrossRefGoogle Scholar
  29. .
    Lyon P, Milne RJ, Orzell R, Rice R (2001) Matching assets with demand in supply-chain management at IBM microelectronics. Interfaces 31(1):108–124CrossRefGoogle Scholar
  30. .
    Milne RJ, Orzell RA, Yen C (1999) Advanced material requirements planning in microelectronics manufacturing. U.S. Patent 5,943,484, 28 Aug 1999Google Scholar
  31. .
    Norden P (1993) Quantitative techniques in strategic alignment. IBM Syst J 32(1):180–197CrossRefGoogle Scholar
  32. .
    Orlicky J (1975) Material requirements planning: the new way of life in production and inventory management. McGraw-Hill, New YorkGoogle Scholar
  33. .
    Orzell R, Patil S, Wang C (2004) Method for identifying product assets in a supply chain used to satisfy multiple customer demands. U.S. Patent 20050177465A1, 17 Oct 2004Google Scholar
  34. .
    Promoting O.R.: The Science of Better (2005) Matching assets to supply chain demand at IBM microelectronics.
  35. .
    Simon HA (1957) Administrative behavior, 2nd edn. The Free Press, New YorkGoogle Scholar
  36. .
    Shobrys D (2003) “History of APS,” Supply Chain Consultants (, 460 Fairmont Drive, Wilmington, DE 19808, USA
  37. .
    Shobrys D, Fraser J (2003) Planning for the next generation (supply chain planning). Manuf Eng 82(6):10–13CrossRefGoogle Scholar
  38. .
    Singh H (2007) Personal communication with Ken Fordyce supply chain consultants (, 460 Fairmont Drive, Wilmington, DE 19808, USA
  39. .
    Sullivan G (1990) IBM Burlington’s logistics management system (LMS). Interfaces 20(1):43–61CrossRefGoogle Scholar
  40. .
    Sullivan G (2005) PROFIT: decision technology for supply chain management at IBM microelectronics division. In: Applications of supply chain management and E-commerce research. Springer, New York, pp 411–452Google Scholar
  41. .
    Sullivan G (2007) Evaluating planning engines in 1994, working paper, chapter in memoirsGoogle Scholar
  42. .
    Sullivan G, Jantzen J, Morreale M (1991) Using Boolean matrices or integer vectors to analyze networks. In: APL91 Proceedings editor Jan Engel, APL Quote Quad, vol 21(4), pp 174–185Google Scholar
  43. .
    Swaminathan J, Smith S (1998) Modeling supply chain dynamics: a multi agent approach. Decis Sci 29(3):607–632CrossRefGoogle Scholar
  44. .
    Tayur S, Ganeshan R, Magazine M (1998) Quantitative models for supply chain management. Kluwer Academic, Boston, MAGoogle Scholar
  45. .
    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, Scheduling Logistics 24(4):47–60Google Scholar
  46. .
    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, Scheduling Logistics 26(5):44–55Google Scholar
  47. .
    Wolfson R (2000) Einstein’s relativity and the quantum revolution. Great Courses on CD by the Teaching Company, Chantilly, VAGoogle Scholar
  48. .
    Woolsey G (1979) Ten ways to go down with your MRP. Interfaces 9(5):77–80CrossRefGoogle Scholar
  49. .
    Zisgen H (2005) EPOS – stochastic capacity planning for wafer fabrication with continuous fluid models. IBM Global Engineering Services, Decision Technology Group Mainz, GermanyGoogle Scholar

Copyright information

© Springer New York 2011

Authors and Affiliations

  • Kenneth Fordyce
    • 1
  • Chi-Tai Wang
  • Chih-Hui Chang
  • Alfred Degbotse
  • Brian Denton
  • Peter Lyon
  • R. John Milne
  • Robert Orzell
  • Robert Rice
  • Jim Waite
  1. 1.Strategic Systems DepartmentIBM CorporationHurleyUSA

Personalised recommendations