The Effects of Production Planning on the Dynamic Behavior of a Simple Supply Chain: An Experimental Study

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

Abstract

Sophisticated supply chain planning systems, also known as Advanced Planning and Scheduling (APS) systems, have become commonplace in industry, and constitute a multibillion dollar software industry (Musselman and Uzsoy 2001; de Kok and Fransoo 2003; Stadtler and Kilger 2004). Many of these models rely to some degree on mathematical programming formulations of multistage productioninventory systems, which have been discussed extensively by (Saad 1982; Voss and Woodruff 2003; Johnson andMontgomery 1974; Hax and Candea 1984) and in this volume by Missbauer and Uzsoy. However, there has been little study in the literature of the effects of these production planning models on the dynamic behavior of supply chains. The dynamic behavior of supply chains over time has been studied in the system dynamics literature for several decades (Sterman 2000; Forrester 1962), leading to a growing understanding of the effects of information and material delays on the behavior of these systems, such as the bullwhip effect (Chen et al. 1998; Chen et al. 2000; Dejonckheere et al. 2003; Dejonckheere et al. 2004). However, the production planning procedures used in these models are generally feedback control procedures, with little ability to predict future states of the system and behave in a reactive manner. It is also quite difficult to interface optimization-based production planning models to standard system dynamics software. Hence, there is very little work of which we are aware that examines the effect of optimization-based planning procedures on the dynamic behavior of the supply chain in a systematic manner.

Keywords

Petroleum Shipping 

Notes

Acknowledgements

We thank Dr. Karl Kempf of Intel Corporation for providing the data for the testbed, and for his thoughtful suggestions throughout this work. The development of the SCOPE environment has been supported by The Laboratory for Extended Enterprises at Purdue (LEEAP), the UPS Foundation, and NSF Grants DMI-0075606 and DMI-0122207.

References

  1. Akif JC (1991) Consistency analysis of PMS based on GRAI modeling. In: Doumeinigts G, Browne J, Tomljanovich M (eds.) Computer applications in production and engineering: integration aspects. Elsevier Science Publishers, Amsterdam, pp. 269–277Google Scholar
  2. Angerhofer BJ, Angelides MC (2000) System dynamics modelling in supply chain management: a research review. Winter Simulation ConferenceGoogle Scholar
  3. Arntzen BC, Brown GG, et al. (1995) “Global supply chain management at digital equipment corporation.” Interfaces 25:69–93CrossRefGoogle Scholar
  4. Asmundsson JM, Rardin RL, et al. (2009) “Production planning models with resources subject to congestion.” Nav Res Logist 56:142–157CrossRefGoogle Scholar
  5. Asmundsson JM, Rardin RL, et al. (2006) “Tractable nonlinear production planning models for semiconductor wafer fabrication facilities.” IEEE Trans Semicond Manuf 19:95–111CrossRefGoogle Scholar
  6. Aviv Y (2001) “The effect of collaborative forecasting on supply chain performance.” Manag Sci 47(10):1326–1343CrossRefGoogle Scholar
  7. Biswas S, Narahari Y (2004) “Object oriented modeling and decision support for supply chains.” Eur J Oper Res 153:704–726CrossRefGoogle Scholar
  8. Buckley SJ, An C (2005) Supply chain simulation. In: Fromm H, An C (eds.) Supply chain management on demand: strategies and technologies. SpringerGoogle Scholar
  9. Burns JF, Sivazlian BD (1979) “Dynamic analysis of multi-echelon supply systems.” Comput Ind Eng 7:181–193Google Scholar
  10. Chen F, Drezner Z, et al. (1998). The bullwhip effect: managerial insights on the impact of forecasting and information on variability. In: Tayur S, Magazine M, Ganeshan R (eds.) Quantitative models for supply chain management. KluwerGoogle Scholar
  11. Chen F, Drezner Z, et al. (2000) “Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, leadtimes and information.” Manag Sci 46:269–286Google Scholar
  12. Chen HB, Bibner O, et al. (1999) Esca: a thin-client/server/web-enabled system for distributed supply chain simulation, 1999. Winter Simulation ConferenceGoogle Scholar
  13. Choi TY, Dooley KJ, et al. (2001) “Supply networks and complex adaptive sytems: control vs. emergence.” J Oper Manag 19:351–366CrossRefGoogle Scholar
  14. Coyle RG (1977) Management system dynamics. Wiley, New YorkGoogle Scholar
  15. Davenport TH (2000) Mission critical: realizing the promise of enteprise systems. Harvard Business School Press, Cambridge, MAGoogle Scholar
  16. de Kok AG, Fransoo JC (2003) Planning supply chain operations: definition and comparison of planning concepts. In: de Kok AG, Graves SC (eds.) OR handbook on supply chain management. Amsterdam, Elsevier, pp. 597–675Google Scholar
  17. Dejonckheere J, Disney SM, et al. (2002) “Transfer function analysis of forecasting induced bullwhip in supply chains.” Int J Prod Econ 78:133–144CrossRefGoogle Scholar
  18. Dejonckheere J, Disney SM, et al. (2003) “Measuring and avoiding the bullwhip effect: a control theoretic approach.” Eur J Oper Res 147:567–590CrossRefGoogle Scholar
  19. Dejonckheere J, Disney SM, et al. (2004) “The impact of information enrichment on the bullwhip effect in supply chains: a control engineering perspective.” Eur J Oper Res 153:727–750CrossRefGoogle Scholar
  20. Duarte B, Fowler J, et al. (2007) “A compact abstraction of manufacturing nodes in a supply network.” Int J Simulat Process Model 3(3):115–126CrossRefGoogle Scholar
  21. Ettl M, Feigin G, et al. (2000) “A supply chain network model with base-stock control and service requirements.” Oper Res 48:216–232CrossRefGoogle Scholar
  22. Fordyce K, Degbotse A, et al. (2011) The ongoing challenge: creating an enterprise-wide detailed supply chain plan for semiconductor and package operations. In: Kempf KG, Keskinocak P, Uzsoy R (eds.) Planning in the extended enterprise: a state of the art handbook. Kluwer, New YorkGoogle Scholar
  23. Forrester JW (1962) Industrial dynamics. MIT, Cambridge, MAGoogle Scholar
  24. Fox M, Barbuceanu M, et al. (2000) “Agent-oriented supply chain management.” Int J Flex Manuf Syst 12:165–188CrossRefGoogle Scholar
  25. Goetschalckx M, Vidal CJ, et al. (2002) “Modeling and design of global logistics systems: a review of integrated strategic and tactical models and design algorithms.” Eur J Oper Res 143:1–18CrossRefGoogle Scholar
  26. Hackman ST, Leachman RC (1989) “A general framework for modeling production.” Manag Sci 35:478–495CrossRefGoogle Scholar
  27. Hax AC, Candea D (1984) Production and inventory management. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  28. Hopp WJ, Spearman ML (2001) Factory physics: foundations of manufacturing management. Irwin/McGraw-Hill, BostonGoogle Scholar
  29. Huang GQ, Lau JSK, et al. (2003) “The impacts of sharing production information on supply chain dynamics: a review of the literature.” Int J Prod Res 41(7):1483–1517CrossRefGoogle Scholar
  30. Huang P, Lee YM, et al. (2004) Utilizing simulation to evaluate business decisions in a sense-and respond environment, 2004 Winter Simulation ConferenceGoogle Scholar
  31. Johnson LA, Montgomery DC (1974) Operations research in production planning, scheduling and inventory control. Wiley, New YorkGoogle Scholar
  32. Karmarkar US (1989) “Capacity loading and release planning with work-in-progress (WIP) and lead-times.” J Manuf Oper Manag 2(105–123)Google Scholar
  33. Kempf KG (2004) “Control-oriented approaches to supply chain management in semiconductor manufacturing.” IEEE Trans Automat ContrGoogle Scholar
  34. Kleijnen JPC (2004) “Supply chain simulation tools and techniques: a survey.” Int J Simulat Proc Model 1(1/2):82–89Google Scholar
  35. Kleindorfer PR, Kriebel CH, et al. (1975) “Discrete optimal control of production plans.” Manag Sci 22(3):261–273CrossRefGoogle Scholar
  36. Law AM, Kelton WD (1991) Simulation modeling and analysis. McGraw-Hill, New YorkGoogle Scholar
  37. Leachman RC, Benson RF, et al. (1996) “Impress: an automated production planning and delivery quotation system at harris corporation - semiconductor sector.” Interfaces 26:6–37CrossRefGoogle Scholar
  38. Leachman RC, Kang J, et al. (2002) “SLIM: short cycle time and low inventory in manufacturing at Samsung electronics.” Interfaces 32(1):61–77CrossRefGoogle Scholar
  39. Lee HL, Padmanabhan P, et al. (1997) “Information distortion in a supply chain: the bullwhip effect.” Manag Sci 43:546–558CrossRefGoogle Scholar
  40. Lee YH, Cho MK, et al. (2002) “Supply chain simulation with discrete-continuous combined modeling.” Comput Ind Eng 43:375–392CrossRefGoogle Scholar
  41. Lendermann P, Gan BP, et al. (2001) Distributed simulation with incorporated aps procedures for high-fidelity supply chain optimization, 2001. Winter Simulation ConferenceGoogle Scholar
  42. Macal CM, North MJ (2005) Tutorial on agent-based modeling and simulation, 2005. Winter Simulation ConferenceGoogle Scholar
  43. Maloubier H, Breuil D, et al. (1984) Use of GRAI method to analyse and design production management system. In: Doumeinigts G, Carter WA (eds.) Advances in production management systems. Elsevier Science publishers, pp. 127–142Google Scholar
  44. Min H, Zhou G (2002) “Supply chain modeling: past, present and future.” Comput Ind Eng 43:231–249CrossRefGoogle Scholar
  45. Missbauer H, Uzsoy R (2011). Optimization models of production planning problems. In: Kempf KG, Keskinocak P, Uzsoy R (eds.) Planning production and inventories in the extended enterprise: a state of the art handbook, vol. 1. Springer, Norwell, MA, pp. 437–508CrossRefGoogle Scholar
  46. Musselman K, Uzsoy R (2001) Advanced planning and scheduling for manufacturing. In: Salvendy G (ed.) Handbook of industrial engineering. Wiley, New YorkGoogle Scholar
  47. Orcun S, Asmundsson JM, et al. (2007) “Supply chain optimization and protocol environment (SCOPE) for rapid prototyping of supply chains.” Prod Plann Contr 18:388–406CrossRefGoogle Scholar
  48. Ovacik IM (2011) “Advanced planning and scheduling systems: the quest to leverage ERP for better planning.” International series in operations research & management science 1:33–43CrossRefGoogle Scholar
  49. Persson F, Olhager J (2002) “Performance simulation of supply chains.” Int J Prod Econ 77:231–245CrossRefGoogle Scholar
  50. Petrovic D (2001) “Simulation of supply chain behavior and performance in an uncertain environment.” Int J Prod Econ 71:429–438CrossRefGoogle Scholar
  51. Ptak CA, Schragenheim E (1999) ERP: tools, techniques and applications for integrating the supply chain. St. Lucie Press, Boca Raton, FLGoogle Scholar
  52. Saad GH (1982) “An overview of production planning models: structural classification and empirical assessment.” Int J Prod Res 20(1):105–114CrossRefGoogle Scholar
  53. Sadeh NM, Hildum DW, et al. (2001) “MASCOT: an agent-based architecture for dynamic supply chain creation and coordination in the internet company.” Prod Plann Contr 13:212–223CrossRefGoogle Scholar
  54. Schieritz N, Grossler A (2002) Emergent structures in supply chains: a study integrating agent-based and system dynamics modeling. 36th Hawaii International Conference on System Sciences, IEEE Computer SocietyGoogle Scholar
  55. Shapiro JF (1993). Mathematical programming models and methods for production planning and scheduling. In: Graves SC, Rinnooy Kan AHG, Zipkin P (eds.) Handbooks in operations research and management science, vol. 4, logistics of production and inventory. Elsevier Science Publishers B.V.Google Scholar
  56. Simchi-Levi D, Wu SD, et al. (eds.) (2004) Handbook of quantitative supply chain analysis: modeling in the e-business era. International series in operations research and management science. Kluwer, New YorkGoogle Scholar
  57. Stadtler H, Kilger C (eds.) (2004) Supply chain management and advanced planning. SpringerGoogle Scholar
  58. Sterman JD (2000) Business dynamics: systems thinking and modeling for a complex world. McGraw-Hill, New YorkGoogle Scholar
  59. Swaminathan JM, Smith SF, et al. (1998) “Modeling supply chain dynamics: a multiagent approach.” Decis Sci 29(3):607–632CrossRefGoogle Scholar
  60. Tayur S, Magazine M, et al. (1998) Quantitative models for supply chain management. Kluwer, AmsterdamGoogle Scholar
  61. Terzi S, Cavalieri S (2004) “Simulation in the supply chain context: a survey.” Comput Ind 53:3–16CrossRefGoogle Scholar
  62. Thomas DJ, Griffin PM (1996) “Coordinated supply chain management.” Eur J Oper Res 94:1–15CrossRefGoogle Scholar
  63. Towill DR (1982) “Dynamic analysis of an inventory and order based production control system.” Int J Prod Res 20(6):671–687CrossRefGoogle Scholar
  64. Towill DR (1996) “Industrial dynamics modeling of supply chains.” Logist Inform Manag 9(4):43CrossRefGoogle Scholar
  65. Towill DR, Del Vecchio A (1994) “The application of filter theory to the study of supply chain dynamics.” Prod Plann Contr 5:82–96CrossRefGoogle Scholar
  66. Uzsoy R, Lee CY, et al. (1992) “A review of production planning and scheduling models in the semiconductor industry part i: system characteristics, performance evaluation and production planning.” IIE Trans Schedul Logist 24(47–61)Google Scholar
  67. Van Dyke Parunak H (2001). Industial and practical applications of DAI. In: Weiss G (ed.) Multiagent systems: a modern approach to distributed artificial intelligence. MIT, Cambridge, MAGoogle Scholar
  68. Van Dyke Parunak H, Savit R, et al. (1999) Dasch: dynamic analysis of supply chains. Center for Electronic Commerce, ERIM Inc. Ann Arbor, MIGoogle Scholar
  69. Vidal CJ, Goetschalckx M (2001) “A global supply chain model with transfer pricing and transportation cost allocation.” Eur J Oper Res 129:134–158CrossRefGoogle Scholar
  70. Voss S, Woodruff DL (2003) Introduction to computational optimization models for production planning in a supply chain. Springer, Berlin; New YorkGoogle Scholar
  71. Weiss G (ed.) (2001) Multiagent systems: a modern approach to distributed artificial intelligence. MIT, Cambridge, MAGoogle Scholar
  72. Wooldridge M (2001). Intelligent agents. In: Weiss G (ed.) Multiagent systems: a modern approach to distributed artificial intelligence. MIT, Cambridge, MA, pp. 27–77Google Scholar
  73. Wooldridge M (2001) Multiagent systems. Wiley, Chichester, U.K.Google Scholar
  74. Zäpfel G, Missbauer H (1993a) “Production Planning and Control (PPC) systems including load-oriented order release - Problems and research perspectives.” Int J Prod Econ 30:107–122CrossRefGoogle Scholar

Copyright information

© Springer New York 2011

Authors and Affiliations

  1. 1.Laboratory for Extended Enterprises at Purdue, e-Enterprise Center at Discovery ParkPurdue UniversityWest LafayetteUSA

Personalised recommendations