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Information transformation in a supply chain: a simulation study. (English) Zbl 1077.90502
Summary: We study the information transformation by simulating a multi-stage supply chain when the end customer’s demand is a general autoregressive integrated moving average (ARIMA) process, and the information, represented in the form of orders, is propagated from downstream to upstream in the supply chain. Our simulation results indicate several important and novel phenomena that need further theoretical analysis: (1) the anti-bullwhip effect and the transition from the regular bullwhip effect; (2) the trend of information transformation at higher stages of a supply chain; (3) the impact of lead-time on information transformation and the so-called lead-time paradox. In this paper, we will demonstrate these aspects via extensive computational experiments.

90B05 Inventory, storage, reservoirs
68U20 Simulation (MSC2010)
Full Text: DOI
[1] Lee, H.L.; Padmanabhan, V.; Whang, S., Information transformation in a supply chainthe bullwhip effect, Management science, 43, 4, 546-558, (1997) · Zbl 0888.90047
[2] Forrester, J.W., Industrial dynamics, (1961), MIT Press Cambridge, MA
[3] Sterman, J.D., Modeling managerial behaviormisperceptions of feedback in a dynamic decision making experiment, Management science, 35, 3, 321-339, (1989)
[4] Sterman, J.D., Teaching takes off, fight simulators for management education, OR/MS today, 19, 5, 40-44, (1992)
[5] Sterman, J.D., The beer distribution game, (), 101-112
[6] Kaminsky P, Simchi-Levi D. A new computerized beer game: a tool for teaching the value of integrated supply chain management, POMS series in technology and operation management, vol. 1. Global supply chain and technology management, USA, 1998.
[7] Blinder, A.S., Can the production smoothing model of inventory behavior be saved?, Quarterly journal of economics, 101, 3, 431-454, (1986)
[8] Blanchard, O.J., The production and inventory behavior of the American automobile industry, Journal of political economy, 91, 365-400, (1983)
[9] West, K.D., A variance bounds test of the linear quadratic inventory model, Journal of political economy, 94, 4, 374-401, (1986)
[10] Kahn, J., Inventories and the volatility of production, The American economic review, 77, 667-679, (1987)
[11] Cachon, G.P.; Lariviere, M.A., Capacity choice and allocationstrategic behavior and supply chain performance, Management science, 45, 8, 1091-1108, (1999) · Zbl 1231.90012
[12] Kelle, P.; Milne, A., The effect of (s,S) ordering policy on the supply chain, International journal of production economics, 59, 113-122, (1999)
[13] Cachon, G.P., Managing supply chain demand variability with scheduled ordering policies, Management science, 45, 6, 843-856, (1999) · Zbl 1231.90011
[14] Drezner Z, Ryan J, Simchi-Levi D. Quantifying the bullwhip effect: the impact of forecasting, leadtime and information. Working Paper, Northwestern University, Evanston, IL, 1996.
[15] Graves, S.C., A single-item inventory model for a nonstationary demand process, Manufacturing & service operations management, 1, 50-61, (1999)
[16] Chen, F.; Drezner, Z.; Ryan, J.; Simchi-Levi, D., Quantifying the bullwhip effect in a simple supply chainthe impact of forecasting, lead-times, and information, Management science, 46, 3, 436-443, (2000) · Zbl 1231.90019
[17] Aviv, Y., The effect of collaborative forecasting on supply chain performance, Management science, 47, 10, 1326-1343, (2001) · Zbl 1232.90009
[18] Aviv, Y., A time-series framework for supply chain inventory management, Operations research, 51, 2, 210-227, (2003) · Zbl 1163.90534
[19] Aviv, Y., Gaining benefits from joint forecasting and replenishment processes, Manufacturing and service operations management, 4, 1, 55-74, (2002)
[20] Dejonckheere, J.; Disney, S.M.; Lambrecht, M.R.; Towill, D.R., Measuring and avoiding the bullwhip effect: a control theoretic approach, European journal of operational research, 147, 3, 567-590, (2003) · Zbl 1026.90030
[21] Fair, R.C., The production-smoothing model is alive and well, Journal of monetary economics, 24, 3, 353-370, (1989)
[22] Krane, S.D.; Braun, S.N., Production smoothing evidence from physical product data, Journal of political economy, 99, 3, 558-581, (1991)
[23] Allen DS. Seasonal production smoothing. Federal Reserve Bank of ST. Louis. Economic Review 1999;(9/10):21-40.
[24] Michael, F.G.; Brannon, J.I., Seasonality and the production-smoothing model, International journal of production economics, 65, 173-178, (2000)
[25] Fitzgerald TJ. Inventories and the business cycle: an overview. Federal Reserve Bank of Cleveland. Economic Review 1997;(3):11-22.
[26] Box, G.E.P.; Jenkins, G.M.; Reinsel, G.C., Time series analysis forecasting and control, (1994), Holden-Day San Francisco, CA
[27] Heyman D, Sobel M. Stochastic models in operations research, vol. II. New York: McGraw-Hill; 1984. · Zbl 0531.90062
[28] Johnsom, G.D.; Thompson, H.E., Optimality of myopic inventory policies for certain dependent demand processes, Management science, 21, 11, 1303-1307, (1975) · Zbl 0307.90019
[29] Lee, H.L.; So, K.; Tang, C., The value of information sharing in a two-level supply chain, Management science, 46, 626-643, (2000) · Zbl 1231.90044
[30] Raghunathan, S., Information sharing in a supply chaina note on its value when demand is nonstationary, Management science, 47, 4, 605-610, (2001) · Zbl 1232.90084
[31] Gilbert K. An autoregressive integrated moving average supply chain model. Working Paper, University of Tennessee, Knoxville, TN, 2002.
[32] Li G, Wang SY, Yu G, Yan H. Order process in supply chain when facing ARIMA(p,d,q) demand process. Working Paper, Key Laboratory of Management, Decision and Information Systems, Chinese Academy of Sciences, Beijing, China, 2002.
[33] Montgomery, D.C.; Johnson, L.A., Forecasting and time series analysis, (1976), McGraw-Hill New York · Zbl 0411.62067
[34] Li G, Wang SY, Yu G, Yan H. The bullwhip effect and the anti-bullwhip effect. Working Paper, Key Laboratory of Management, Decision and Information Systems, Chinese Academy of Sciences, Beijing, China, 2002.
[35] Baganha, M.; Cohen, M., The stabilizing effect of inventory in supply chains, Operations research, 46, 572-583, (1998)
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