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Model Predictive Control in Semiconductor Supply Chain Operations

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Book cover Planning Production and Inventories in the Extended Enterprise

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 152))

Abstract

Maintaining agility in a multi-echelon multi-product multi-geography supply chain with long and variable manufacturing lead times, stochastic product yields, and uncertain demand is a difficult goal to achieve. The approach advocated here is based on a practical application of control theory that includes a model of the system being controlled, feedback from previous results, feed-forward based on demand forecasts, and optimization of both the financial results and the control actions applied to achieve them. This Model Predictive Control (MPC) approach has been employed in the continuous-flow process industry for many years, and has been independently suggested for supply chains by a number of academic research teams. This chapter describes a large-scale application of the approach in the semiconductor industry.

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References

  • Axsater S (1985) “Control theory concepts in production and inventory control.” Int J Syst Sci 162: 161–169

    Article  Google Scholar 

  • Bessler SA, Zehna PW (1968) “An application of servomechanisms to inventory.” Nav Res Logist Q 15:157–168

    Google Scholar 

  • Bose S, Pekny JF (2000) “A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain”. Comput Chem Eng 24:329–335

    Article  Google Scholar 

  • Braun MW, Rivera DE, Flores ME, Carlyle WM, Kempf KG (2003a) “A model predictive control framework for robust management of multi-product multi-echelon demand networks.” Ann Rev Contr 272: 229–245

    Article  Google Scholar 

  • Braun MW, Rivera DE, Carlyle WM, Kempf KG (2003b) “Application of model predictive control to robust management of multi-echelon demand networks in semiconductor manufacturing.” Simulation Trans Soc Model Simulat Int 79(3):139–156

    Article  Google Scholar 

  • Camacho EF, Bordons C (1995) Model predictive control in the process industries. Springer, Berlin

    Google Scholar 

  • Disney S, Towill D (2002) “A discrete transfer function model to determine the dynamic stability of a vendor managed inventory supply chain.” Inter J Prod Res 40(1):179–204

    Article  Google Scholar 

  • Evans GN, Naim MM, Towill DR (1998) “Application of a simulation methodology to the redesign of a logistical control system.” Int J Prod Econ 56–57:157–168

    Article  Google Scholar 

  • Flores ME, Rivera DE, Smith-Daniels V (2000) “Managing supply chains using model predictive control.” Proc. AIChE 2000 Annual Meeting (Los Angeles, CA), paper 262f

    Google Scholar 

  • Garcia CE, Prett DM, Morari M (1989) “Model predictive control: theory and practice – a survey”. Automatica 25(3):335–348

    Article  Google Scholar 

  • Godding G, Sarjoughian H, Kempf K (2007) “Application of combined discrete-event simulation and optimization models in semiconductor enterprise manufacturing systems.” Proc. IEEE Winter Simulation Conf. (Washington DC), p. 1729–1736

    Google Scholar 

  • Holt CC, Modigliani F, Muth JF, Simon HA (1960) Planning production, inventories and work force. Prentice-Hall, Englewood Cliffs.

    Google Scholar 

  • Huang D, Sarjoughian HS, Godding GW, Rivera DE, Kempf KG (2006) “Flexible experimentation and analysis for hybrid DEVS and MPC models.” Proc. IEEE Winter Simulation Conf. (Monterey, CA), p. 1863–1870

    Google Scholar 

  • Huang D, Sarjoughian H, Wang W, Godding G, Rivera D, Kempf K, Mittelmann H (2009) “Simulation of semiconductor manufacturing supply-chain systems with DEVS, MPC, and KIB”. IEEE Trans Semicond Manuf 22(1):164–174

    Article  Google Scholar 

  • Hwang CL, Fan LT, Erickson LE (1967) “Optimum production planning by the maximum principle.” Manag Sci 18(9):751–755

    Article  Google Scholar 

  • Kempf K (2004) “Control-oriented approaches to supply chain management in semiconductor manufacturing.” Proc. American Control Conference, (Boston), p. 4563–4576

    Google Scholar 

  • Kleindorfer P, Kriebel C, Thompson G, Kleindorfer G (1975) “Discrete optimal control of production plans. Manag Sci 22(2):261–273

    Article  Google Scholar 

  • Kothare SL, Morari M (2000) “Contractive model predictive control for constrained nonlinear systems”. IEEE Trans Automatic Contr 45(6):1053–1071

    Article  Google Scholar 

  • Lee JH, Morari M, Garcia CE (1994) “State-space interpretations of Model Predictive Control. Automatica 30(4):707–717

    Article  Google Scholar 

  • Maciejowski JM (2002) Predictive control with constraints.. Pearson Education Limited, Essex, England

    Google Scholar 

  • Moore GE (1965) “Cramming more components onto integrated circuits.” Electronics April 19, p 114–117

    Google Scholar 

  • Moore GE (1998) “Cramming more components onto integrated circuits.” Proc IEEE 86(1):82–85

    Article  Google Scholar 

  • Ogunnaike BA, Ray WH (1994) Process dynamics, modeling, and control. Oxford University Press, New York

    Google Scholar 

  • Ortega M, Lin L (2004) “Control theory applications to the production-inventory problem: a review”. Int J Prod Res 42(11):2303–2322

    Article  Google Scholar 

  • Perea E, Grossman I, Ydstie E, Tahmassebi T (2000a) “Applications of model predictive control for supply chain management.” Proc. AIChE 2000 Annual Meeting (Los Angeles, CA), paper 262e

    Google Scholar 

  • Perea E, Grossman I, Ydstie E, Tahmassebi T (2000b) “Dynamic modeling and classical control theory for supply chain management.” Comput Chem Eng 24:1143–1149

    Article  Google Scholar 

  • Perea-Lopez E, Grossman I, Ydstie E (2001) “Dynamic modeling and decentralized control of supply chains.” Ind Eng Chem Res 40(15):3369–3383

    Article  Google Scholar 

  • Perea-Lopez E, Ydstie BE, Grossman IE (2003) “A model predictive control strategy for supply chain optimization.” Comput Chem Eng 27:1201–1218

    Article  Google Scholar 

  • Qin SJ, Badgwell TA (2003) “A survey of industrial model predictive control technology”. Contr Eng Practice 11:733–764

    Article  Google Scholar 

  • Sarjoughian H, Huang D, Wang W, Rivera D, Kempf K, Godding G (2005) “Hybrid discrete event simulation with model predictive control for semiconductor supply chain management.” Proc. IEEE Winter Simulation Conf. (Orlando), p. 255–266

    Google Scholar 

  • Schwartz JD, Rivera DE, Kempf KG (2005) “Towards control-relevant forecasting in supply chain management”. Proc. American Control Conference (Portland, OR), p. 202–207

    Google Scholar 

  • Schwartz JD, Wang W, Rivera DE (2006) “Optimal tuning of process control-based decision policies for inventory management in supply chains.” Automatica 42:1311–1320

    Article  Google Scholar 

  • Schwartz JD, Arahal MR, Rivera DE, Smith KD (2009a) “Control-relevant demand forecasting for tactical decision-making in semiconductor supply chain management.” IEEE Trans Semiconductor Mfg 22(1):154–163

    Article  Google Scholar 

  • Schwartz JD, Rivera DE (2009b) “Control-relevant estimation of demand models for closed-loop control of a production-inventory system.” 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (Shanghai, China) 416–421

    Google Scholar 

  • Schwartz JD, Rivera DE (2009c) “A system identification approach to PDE modeling of a semiconductor manufacturing process.” Proc. 15th Annual IFAC Symposium on System Identification, (St. Malo, France). 15:1, doi: 10.3182/20090706–3-FR-2004.00160

    Google Scholar 

  • Schwartz JD, Rivera DE (2010) “A process control approach to tactical inventory management in production-inventory systems”. Int J Prod Econ 125(1):111–124

    Article  Google Scholar 

  • Seferlis P, Giannelos NF (2004) “A two-layered optimization-based control strategy for multi-echelon supply chain networks”. Comput Chem Eng 28:799–809

    Article  Google Scholar 

  • Simon HA (1952) “On the application of servomechanism theory in the study of production control.” Econometrica 20(2):247–268

    Article  Google Scholar 

  • Sprzeuzkouski AY (1967) “A problem of optimal stock management.” J Optim Theor Appl 1(3):232–241

    Article  Google Scholar 

  • Thompson G, Sethi S (1980) “Turnpike horizons for production planning.” Manag Sci 26(3):229–241

    Article  Google Scholar 

  • Towill D (1982) “Dynamic analysis of an inventory and order based production control system.” Inter J Prod Res 20(6):671–687

    Article  Google Scholar 

  • Towill D (1992) “The practical use of control theory in manufacturing systems design and manufacturing management.” J Syst Eng 2:2–14

    Google Scholar 

  • Towill D, Del Vecchio A (1994) “The application of filter theory to the study of supply chain dynamics.” Prod Plann Contr 5(1):82–96

    Article  Google Scholar 

  • Tzafestas S, Kapsiotis G, Kyriannakis E (1997) “Model-based predictive control for generalized production planning problems.” Comput Ind 34(2):201–210

    Article  Google Scholar 

  • Vargas-Villamil FD, Rivera DE, Kempf KG (2003) “A hierarchical approach to production control of reentrant semiconductor manufacturing lines.” IEEE Trans Contr Syst Technol 11(4):578–87

    Article  Google Scholar 

  • Vassian HJ (1955) “Application of discrete variable servo theory to inventory control.” Oper Res 3(3):272–282

    Article  Google Scholar 

  • Wang W, Rivera DE, Kempf KG (2003) “Centralized model predictive control strategies for inventory management in semiconductor manufacturing supply chains.” Proc. American Control Conference (Denver), 585–590

    Google Scholar 

  • Wang W, Rivera DE, Kempf KG, Smith KD (2004) “A model predictive control strategy for supply chain management in semiconductor manufacturing under uncertainty.” Proc. American Control Conference (Boston), 4577–4582

    Google Scholar 

  • Wang W, Rivera DE, Kempf KG (2005) “An improved model predictive control algorithm for supply chain management”. Proc. American Control Conference (Portland, OR), 208–213

    Google Scholar 

  • Wang W, Rivera DE, Kempf KG (2007) “Model predictive control strategies for supply chain management in semiconductor manufacturing”. Int J Prod Econ 107:56–77

    Article  Google Scholar 

  • Wang W, Rivera DE (2008) “A model predictive control algorithm for tactical decision-making in semiconductor manufacturing supply chain management”. IEEE Trans Contr Syst Technol 16(5):841–855

    Article  Google Scholar 

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Acknowledgements

Three groups need to be acknowledged for collaboration in realizing the work described here. First is Dr. Daniel E. Rivera, Professor of Chemical Engineering and Program Director of the Control Systems Engineering Laboratory at Arizona State University. Professor Rivera was the first to suggest applying Model Predictive Control to supply chain problems at Intel. With the aid of three of his PhD students – Martin Braun, Wenlin Wang, and Jay Schwartz – he constructed the theoretical foundation for our work. Second are Dr. Joseph Lu, Chief Scientist and Senior Fellow at Honeywell International, Honeywell Process Solutions in Phoenix, Arizona and Dr. Duane Morningred, Engineering Fellow at Honeywell International, Honeywell Process Solutions in Moorpark, California. Together they facilitated the implementation of the initial Model Predictive Controller in Intel’s supply chain management system in collaboration with Intel’s Kirk Smith (formerly with Honeywell International). Third are Intel’s Customer Planning and Logistics Group (CPLG) Vice Presidents Keith Reese and Frank Jones. The former supported the early research and the latter the implementation of Model Predictive Control systems in Intel’s supply chain. Their strategic vision was vital to the success of the work described here. The critical contributions of tens of additional members of CPLG are gratefully acknowledged.

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Authors and Affiliations

  1. Decision Technologies Group, Intel Corporation, 5000 W. Chandler Blvd., Chandler, Arizona, USA, 85226

    Karl Kempf

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  1. Karl Kempf
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  2. Kirk Smith
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  3. Jay Schwartz
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  4. Martin Braun
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Corresponding author

Correspondence to Karl Kempf .

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Editors and Affiliations

  1. , Decision Technologies C3-111, Intel Corporation, W. Chandler Blvd. 5000, Chandler, 85226, USA

    Karl G Kempf

  2. School of Industrial &, Systems Engineering, Georgia Institute of Technology, Ferst Drive NW., 765, Atlanta, 30332-0205, Georgia, USA

    Pınar Keskinocak

  3. , Edward P. Fitts Department of Industrial, North Carolina State University, Daniels Hall, Campus Box 7906 300, Raleigh, 27695, USA

    Reha Uzsoy

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© 2011 Springer New York

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Kempf, K., Smith, K., Schwartz, J., Braun, M. (2011). Model Predictive Control in Semiconductor Supply Chain Operations. In: Kempf, K., Keskinocak, P., Uzsoy, R. (eds) Planning Production and Inventories in the Extended Enterprise. International Series in Operations Research & Management Science, vol 152. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8191-2_16

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