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Dynamic simulation of the supply chain for a short life cycle product – lessons from the Tamagotchi case. (English) Zbl 1046.90500
Summary: Supply chain phenomena such as the bullwhip effect and boom and bust have been widely studied. However, their interaction with other factors has not been elaborated. We use scenario-based dynamic simulations to study the short product life cycle case, exemplified by Tamagotchi\(^{\text{TM}}\), which was the first of the virtual pet toys. Our model has three components, market, retail and factory. To simulate the supply chain dynamics, all parts consist of scenarios based on the Tamagotchi\(^{\text{TM}}\) case and are integrated into a dynamic model. Our model should be helpful to decision makers and planners faced with similar short life cycle product introductions.

90B05 Inventory, storage, reservoirs
Full Text: DOI
[1] STELLA and STELLA Research software Copyright 1985, 1987, 1988, 1990-1197, 2000, 2001 High Performance Systems, Inc. All rights reserved.
[2] Bradley, P.J.; Thomas, T.G.; Cooke, J., Future competitionsupply chain vs. supply chain, Logistics management and distribution report, 39, 3, 20-21, (1999)
[3] Tompkins JA. No boundaries. North Carolina: Tompkins Press, 2000. p. 50.
[4] Bowersox JD, Closs DJ. Logistical management: the integrated supply chain process. New York: McGraw-Hill, 1996. p. 37.
[5] Gunasekaran, A.D.; Macbeth, K.; Lamming, R., Modeling and analysis of supply chain management systemsan editorial overview, Journal of the operational research society, 51, 1112-1115, (2000)
[6] Ballou RH. Business logistics management. New Jersey: Prentice-Hall, 1992. p. 443.
[7] Sunil C, Meindl P. Supply chain management. New Jersey: Prentice-Hall, 2001. p. 4.
[8] Gopal C, Cahill G. Logistics in manufacturing. Illinois: Richard D. Irwin, Inc., 1992. p. 113 and 208.
[9] Magee JF, Copacino WC, Rosenfield DB. Modern logistics management: integrating marketing, manufacturing, and physical distribution. New York: Wiley, 1985. pp. 42.
[10] Bowersox JD, Closs DJ, Helferich OK. Logistical management: a systems integration of physical distribution, manufacturing support, and materials procurement. 3rd ed. New York: Macmillan Publishing Company, 1986. pp. 412-421.
[11] Nersesian, R.L.; Boyd, S.G., Computer simulation in logistics, (1996), Quorum Books London
[12] Vendemia, W.G.; Patuwo, B.E.; Ming, S.H., Evaluation of lead time in production/inventory systems with non-stationary stochastic demand, Journal of the operational research society, 46, 221-233, (1995) · Zbl 0827.90066
[13] Takeda, K.; Kuroda, M., Optimal inventory configuration of finished products in multi-stage production/inventory system with an acceptable response time, Computers & industrial engineering, 37, 251-255, (1999)
[14] Schwarz, L.B.; Weng, K.Z., The design of JIT supply chainsthe effect of leadtime uncertainty on safety stock, Journal of business logistics, 20, 1, 141-163, (1999)
[15] Gavirneni, S.; Kapuscinski, R.; Tayur, S., Value of information in capacitated supply chains, Management science, 45, 1, 16-24, (1999) · Zbl 1231.90088
[16] Chen, F., Decentralized supply chains subject to information delay, Management science, 45, 8, 1076-1090, (1999) · Zbl 1231.90017
[17] Lee HL, Padmanabhan V, Whang S. Information distortion in a supply chain: the bullwhip effect. Management Science 1997a;43(4):546-58. · Zbl 0888.90047
[18] Lee HL, Padmanabhan V, Whang S. The bullwhip effect in supply chains. Sloan Management Review 1997b;38(3):93-102.
[19] Nehmias S. Production and operations analysis. 3rd ed. New York: McGraw-Hill, 1997. pp. 791-4.
[20] Winker, J.; Towill, D.R.; Naim, M., Smoothing supply chain dynamics, International journal of production economics, 22, 231-248, (1991)
[21] Pidd M. Computer simulation in management science. 2nd ed. Chichester: Wiley, 1984. pp. 219-26 and 250-61.
[22] Forrester, J.W., Industrial dynamics, (1961), MIT Press Massachusetts
[23] Senge, P.M.; Sterman, J.D., System thinking and organizational learningacting locally and thinking globally in the organization of the future, European journal of operational research, 59, 137-150, (1992)
[24] Paich, M.; Sterman, J.D., Boom, bust and failures to learn in experimental markets, Management science, 39, 12, 1439-1458, (1993)
[25] Vennix, J.M., Group model building, (1996), Wiley Chichester
[26] Cheng, H., Enterprise integration and modeling: the meta base approach, (1996), Kluwer Academic Publishers Massachusetts
[27] Shapiro JF. Modeling the supply chain. California: Duxbury, 2001. p. 468.
[28] Bowerman LB, O’Connell RT. Forecasting and time series. 3rd ed. California: Duxbury Press, 1993. p. 8.
[29] Barker KR, Kropp DH, Management science. New York: Wiley, 1985. p. 388.
[30] Kotler P. Marketing management, the Millennium ed. New Jersey: Prentice-Hall, 1999. p. 342.
[31] Lowson B, King R, Hunter A. Quick response. New York: Wiley, 1999. pp. 99-102.
[32] Corbett, C.J.; Blackburn, J.D.; Van Wassenhove, L.N., Partnerships to improve supply chains, Sloan management review, 40, 4, 71-82, (1999)
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