The "physics" of capacity and backlog management in service and custom manufacturing supply chains

System Dynamics Review

Volumn 21, Issue 3, 2005, Pages 217-247

  Anderson Jr , Edward G ^a   Morrice, Douglas J ^a   Lundeen, Gary ^b  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b PiaDux Consulting   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INDUSTRIAL PRODUCTION; MANAGEMENT; MANUFACTURING;

EID: 28444492339     PISSN: 08837066     EISSN: None     Source Type: Journal    
DOI: 10.1002/sdr.319     Document Type: Article

References (44)

1. Akkermans HA, Romme AGL. 2004. Opportunism and partnership in a high-tech supply network: an agent-based-model. Working paper, Eindhoven University of Technology.
2. Akkermans HA, Vos B. 2003. Amplification in service supply chains: an exploratory case study from the telecom industry. Production and Operations Management 12(2): 204-223.
3. Akkermans HA, Bogerd P, van Doremalen J. 2004. Travail, transparency, and trust: a case study of computer-supported collaborative supply chain planning in high-tech electronics. European Journal of Operational Research 153: 445-456.
4. Anderson EG. 1997. The effects of business cycles upon capital supplier productivity and technological capability. Doctoral dissertation, Massachusetts Institute of Technology.
5. Anderson EG. 2001a. The non-stationary staff planning problem with business cycle and learning effects. Management Science 47(6): 817-832.
6. Anderson EG. 2001b. Managing the impact of high market growth and learning on knowledge worker productivity and service quality. European Journal of Operational Research 134(3): 508-524.
7. Anderson EG. 2004. Approximating Gaussian white noise in continuous time simulations without a pink noise filter. Working paper, University of Texas McCombs School of Business, Austin, TX.
8. Anderson EG, Morrice DJ. 1999. A simulation model to study the dynamics in a service-oriented supply chain. In Proceedings of the 1999 Winter Simulation Conference Proceedings.
9. Anderson EG, 2000. A simulation game for service-oriented supply chain management. Journal of Production and Operations Management 9(l): 40-55.
10. Chen FY, Ryan JK, Simchi-Levi D. 1997a. The impact of exponential smoothing forecasts on the bullwhip effect. Working paper, Northwestern University.
11. Chen FY. 1997b. The bullwhip effect: managerial insights on the impact of forecasting and information on variability in the supply chain. Working paper, Northwestern University.
12. Compustat Industrial Annual Data. 2004. Wharton Research Data Services, http://wrds.wharton.upenn.edu/, The Wharton School, University of Pennsylvania.
13. Dell M, Magretta J. 1998. The power of virtual integration. Harvard Business Review March-April: 72-84.
14. Drezner Z, Ryan JK, Simchi-Levi D. 2000. Quantifying the bullwhip effect: the impact of forecasting, leadtime, and information. Management Science 46(3): 436-443.
15. Fitzsimmons JA, Fitzsimmons MJ. 2001. Service Management: Operations, Strategy, and Information Technology. 3rd Edition. Irwin/McGraw-Hill: Boston, MA.
16. Forrester JW. 1958. Industrial dynamics: a major breakthrough for decision makers. Harvard Business Review 36(4): 37-66.
17. Forrester JW. 1961. Industrial Dynamics. MIT Press, Cambridge, MA. (Now available from Pegasus Communications, Waltham, MA).
18. Gavirneni S, Kapuscinski R, Tayur S. 1999. Value of information in capacitated supply chains. Management Science 45(l): 16-24.
19. Goldratt EM, Cox J. 1986. The Goal. North River Press: Croton-on-Hudson, NY.
20. George ML. 2003. Lean Six-Sigma for Services: How to Use Lean Speed and Six Sigma Quality to Improve Services and Transactions. McGraw-Hill: New York.
21. Gonçalves P. 2003. Demand bubbles and phantom orders in supply chains. PhD dissertation, Massachusetts Institute of Technology, Cambridge, MA.
22. Gonçalves P, Hines J, Sterman J. 2005. The impact of endogenous demand on push-pull production systems. System Dynamics Review 21(3): 187-216.
23. Graves SC. 1986. A tactical planning model for a job shop. Operations Research 34(4): 522-533.
24. Holt CC, Modigliani F, Muth JF, Simon HA. 1960. Planning Production, Inventories, and Work Force. Prentice-Hall: Englewood Cliffs, NJ.
25. Homer JB. 1999. Macro- and micro-modeling of field service dynamics. System Dynamics Review 15(2): 139-162.
26. Johnson ME, Pyke DF. 2000. A framework for teaching supply chain management. Journal of Production and Operations Management 9(1): 2-18.
27. Law AM, Kelton WD. 2000. Simulation Modeling and Analysis (3rd edn). McGraw-Hill: New York.
28. Lee HL, Padmanabhan P, Whang S. 1997a. Information distortion in a supply chain: the bullwhip effect. Management Science 43(4): 516-558.
29. Lee HL. 1997b. The bullwhip effect in supply chains. Sloan Management Review 38(3): 93-102.
30. Lee HL, So KC, Tang CS. 2000. The value of information sharing in a two-level supply chain. Management Science 46(5): 626-643.
31. Li L, Zhang H. 2000. The multistage service facility start-up and capacity model. Operations Research 48(3): 490-497.
32. Maister DH. 1984. The psychology of waiting lines. In The Service Encounter, Czepiel J (ed.), Lexington Books: Lexington, MA; 113-123.
33. Mills PK, Margulies N. 1980. Toward a core typology of service organizations. Academy of Management Review 2: 118-131.
34. Morecroft JDW. 1980. A systems perspective on materials requirement planning. Decision Sciences 14: 1-18.
35. Morrice DJ, Anderson EG, Bharadwaj S. 2004. A simulation study to assess the efficacy of linear control theory models for the coordination of a two-stage customized service supply chain. In Proceedings of the 2004 Winter Simulation Conference, RG Ingalls, MD Rossetti, JS Smith, BA Peters, eds. 1128-1135. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
36. Oliva R. 1996. A dynamic theory of service delivery: implications for managing service quality. Doctoral dissertation, Massachusetts Institute of Technology.
37. Repenning N. 1996. The improvement paradox: three essays on process improvement initiatives. Massachusetts Insititute of Technology, PhD Dissertation. Cambridge, MA.
38. Sethi SP, Thompson GL. 2000. Optimal Control Theory: Applications to Management Science and Economics. Kluwer: Boston, MA.
39. Shanmugan KS, Breipohl AM. 1988. Random Signals: Detection, Estimation, and Data Analysis. Wiley: New York.
40. Stengel RF. 1994. Optimal Control and Estimation. Dover: New York.
41. Sterman JD. 1988. Deterministic chaos in models of human behavior: methodological issues and experimental results. System Dynamics Review 4: 148-178.
42. Sterman JD. 1989a. Misperceptions of feedback in dynamic decision making. Organizational Behavior and Human Decision Processes 43(3): 301-335.
43. Sterman JD. 1989b. Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment. Management Science 35(3): 321-339.
44. Sterman JD. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin/McGraw-Hill: Boston, MA.