Design and simulation of demand information sharing in a supply chain

Abstract On the premise of discrete simulation technology, the study developed a simulation approach to quantify firms’ business operations and performances in a multi-tier supply chain. By careful simulation scenario design and statistical validation, the simulation model was applied to understand one practical business problem, i.e., how to evaluate the business model and its trade-off of implementing demand information sharing strategy. The results showed that with high demand variance, low demand correlation, and/or high demand covariance, the supply chain without the intermediate tier performed better than that with the intermediary. However, bypassing the intermediate tier in the chain might cause companies less responsive to demand variability. The simulation and analytical approaches presented in the paper can help firms make better decision on business model design and inter-organizational collaboration in supply chains.

[1]  Frank Y. Chen,et al.  Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information.: The Impact of Forecasting, Lead Times, and Information. , 2000 .

[2]  Vineet Padmanabhan,et al.  Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect" , 1997, Manag. Sci..

[3]  Edward A. Silver,et al.  Changing the givens in modelling inventory problems: the example of just-in-time systems , 1992 .

[4]  W. R. Buckland,et al.  Contributions to Probability and Statistics , 1960 .

[5]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[6]  M. Jaksic,et al.  Bullwhip Effect in a Supply Chain , 2007 .

[7]  David R. Brillinger,et al.  The collected works of John W. Tukey , 1984 .

[8]  Linus Schrage,et al.  “Centralized Ordering Policies in a Multi-Warehouse System with Lead Times and Random Demand” , 2004 .

[9]  Alexander Shapiro,et al.  Finding Optimal Material Release Times Using Simulation-Based Optimization , 1999 .

[10]  George Tagaras,et al.  A Periodic Review Inventory System with Emergency Replenishments , 2001, Manag. Sci..

[11]  Michael J. Shaw,et al.  Evaluating Information Sharing Strategies in Supply Chains , 2000, ECIS.

[12]  Daniel Thiel,et al.  System dynamics modeling and simulation of a particular food supply chain , 2000, Simul. Pract. Theory.

[13]  Terry P. Harrison,et al.  The Bullwhip Effect—Impact of Stochastic Lead Time, Information Quality, and Information Sharing: A Simulation Study , 2004 .

[14]  A. I. Sivakumar,et al.  Multiobjective dynamic scheduling using discrete event simulation , 2001, Int. J. Comput. Integr. Manuf..

[15]  Lee J. Krajewski,et al.  A model for comparing supply chain schedule integration approaches , 2000 .

[16]  Philip M. Kaminsky,et al.  Designing and managing the supply chain : concepts, strategies, and case studies , 2007 .

[17]  Gek Woo Tan,et al.  Sharing shipment quantity information in the supply chain , 2006 .

[18]  Stephen M. Disney,et al.  The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective , 2004, Eur. J. Oper. Res..

[19]  Christopher S. Tang,et al.  The Value of Information Sharing in a Two-Level Supply Chain , 2000 .

[20]  Jayashankar M. Swaminathan,et al.  Modeling Supply Chain Dynamics: A Multiagent Approach , 1998 .

[21]  Hau L. Lee,et al.  Information distortion in a supply chain: the bullwhip effect , 1997 .

[22]  M. Naim,et al.  Industrial Dynamics Simulation Models in the Design of Supply Chains , 1992 .

[23]  John W. Tukey,et al.  Multiple comparisons: 1948-1983 , 1994 .

[24]  Fu-Ren Lin,et al.  Using multi-agent simulation and learning to design new business processes , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[25]  Richard B. Chase,et al.  Production and operations management : manufacturing and services , 1995 .

[26]  Richard L. Nolan,et al.  Sense and Respond: Capturing Value in the Network Era , 1998 .

[27]  Steven Orla Kimbrough,et al.  Computers play the beer game: can artificial agents manage supply chains? , 2002, Decis. Support Syst..

[28]  Rajesh Piplani,et al.  Supply-side collaboration and its value in supply chains , 2004, Eur. J. Oper. Res..

[29]  Christopher C. Yang,et al.  A new approach to solve supply chain management problem by integrating multi-agent technology and constraint network , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[30]  Af Farhoomand,et al.  Dell: Selling Directly, Globally , 2000 .

[31]  J. F. Howell,et al.  Pairwise Multiple Comparison Procedures with Unequal N’s and/or Variances: A Monte Carlo Study , 1976 .