The governing dynamics of supply chains: The impact of altruistic behaviour

This paper analyses an infinite horizon two-echelon supply chain inventory problem and shows that a sequence of the optimum ordering policies does not yield globally optimal solutions for the overall supply chain. First-order autoregressive demand pattern is assumed and each participant adopts the order-up-to (OUT) policy with a minimum mean square error forecasting scheme to generate replenishment orders. To control the dynamics of the supply chain, a proportional controller is incorporated into the OUT policy, which we call a generalised OUT policy. A two-echelon supply chain with this generalised OUT policy achieves over 10% inventory related cost reduction. To enjoy this cost saving, the attitude of first echelon player to cost increases is an essential factor. This attitude also reduces the bullwhip effect. An important insight revealed herein is that a significant amount of benefit comes from the player doing what is the best for the overall supply chain, rather than what is the best for local cost minimisation.

[1]  James A. Kahn.  Inventories and the volatility of production , 1986 .

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[3]  R. Kapuściński,et al.  Value of Information in Capacitated Supply Chains , 1999 .

[4]  Marc Lambrecht,et al.  Creating win-win scenarios from the bullwhip problem - By design not accident , 2003 .

[5]  S. Disney,et al.  Vendor-managed inventory and bullwhip reduction in a two-level supply chain , 2003 .

[6]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

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

[8]  S. Disney,et al.  On variance amplification in a three-echelon supply chain with minimum mean square error forecasting , 2022 .

[9]  Stephen C. Graves,et al.  A Single-Item Inventory Model for a Nonstationary Demand Process , 1999, Manuf. Serv. Oper. Manag..

[10]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

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

[12]  Stephen M. Disney,et al.  Measuring and avoiding the bullwhip effect: A control theoretic approach , 2003, Eur. J. Oper. Res..

[13]  K. Arrow,et al.  Optimal Inventory Policy. , 1951 .

[14]  Mohamed Mohamed Naim,et al.  Utilising a production system archetype to attenuate bullwhip in a grocery supply chain , 2003 .

[15]  J. Ryan,et al.  The cost impact of using simple forecasting techniques in a supply chain , 2003 .

[16]  B. F. Caviness,et al.  Quantifier Elimination and Cylindrical Algebraic Decomposition , 2004, Texts and Monographs in Symbolic Computation.

[17]  Herbert J. Vassian.  Application of Discrete Variable Servo Theory to Inventory Control , 1955, Oper. Res..

[18]  W. Van de Velde,et al.  Variance amplification and the golden ratio in production and inventory control , 2004 .

[19]  Srinivasan Raghunathan,et al.  Information Sharing in a Supply Chain: A Note on its Value when Demand Is Nonstationary , 2001, Manag. Sci..

[20]  HosodaTakamichi,et al.  The governing dynamics of supply chains , 2006 .

[21]  Yossi Aviv,et al.  The Effect of Collaborative Forecasting on Supply Chain Performance , 2001, Manag. Sci..

[22]  Xiaolong Zhang.  The impact of forecasting methods on the bullwhip effect , 2004 .

[23]  Marshall L. Fisher,et al.  Supply Chain Inventory Management and the Value of Shared Information , 2000 .

[24]  D. Simchi-Levi,et al.  The value of information sharing in a two-stage supply chain with production capacity constraints , 2003 .

[25]  S. Karlin.  Dynamic Inventory Policy with Varying Stochastic Demands , 1960 .

[26]  Paul Zipkin.  Performance Analysis of a Multi-Item Production-Inventory System Under Alternative Policies , 1995 .

[27]  Layth C. Alwan,et al.  Stochastic characterization of upstream demand processes in a supply chain , 2003 .

[28]  Howard E. Thompson,et al.  Optimality of Myopic Inventory Policies for Certain Dependent Demand Processes , 1975 .

[29]  Stephen Michael Disney,et al.  Eliminating drift in inventory and order based production control systems , 2005 .

[30]  David F. Pyke,et al.  Exploiting timely demand information to reduce inventories , 1996 .

[31]  K. N. Singh,et al.  Value of Information in a Capacitated Supply Chain , 2008, INFOR Inf. Syst. Oper. Res..