Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study

2011 ◽  
Vol 49 (3) ◽  
pp. 765-803 ◽  
Author(s):  
Partha Priya Datta ◽  
Martin G. Christopher
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Simulation Study ◽  
Coordination Mechanisms

Information sharing in decentralised supply chains with partial collaboration

2021 ◽  
Author(s):  
Roberto Dominguez ◽  
Salvatore Cannella ◽  
Borja Ponte ◽  
Jose M. Framinan
Keyword(s):  
Supply Chains ◽  
Information Sharing

scholarly journals Revisiting the value of information sharing in two-stage supply chains

2018 ◽  
Vol 270 (3) ◽  
pp. 1044-1052 ◽  
Author(s):  
Ruud H. Teunter ◽  
M. Zied Babai ◽  
Jos A.C. Bokhorst ◽  
Aris A. Syntetos
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Value Of Information ◽  
Two Stage

IT-Based Classification for Supply Chain Coordination Mechanisms

2013 ◽  
pp. 148-162 ◽  
Author(s):  
Fereshteh Ghahremani ◽  
Mohammad Jafar Tarokh
Keyword(s):  
Information Technology ◽  
Supply Chain ◽  
Supply Chains ◽  
Supply Chain Coordination ◽  
Coordination Mechanisms ◽  
Effective Solution ◽  
New Classification ◽  
Role Of It

Managing dependencies via coordination is an effective solution for the problems that arise from these interdependencies in supply chains. This can be practical via a set of methods called coordination mechanisms. Numerous coordination mechanisms have been discussed before in literature. This paper develops a new classification of these mechanisms on the basis of information technology (IT) impact on them. This classification proves the important role of IT in better coordinating supply chains and help managers distinguish between coordination mechanisms that are created and improved by information technology and thus lead them to have the best choice based on their infrastructures and organization type.

Anticipation of Demand in Supply Chains

2019 ◽  
pp. 1-45
Author(s):  
Youssef Tliche ◽  
Atour Taghipour ◽  
Béatrice Canel-Depitre
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Moving Average ◽  
Demand Management ◽  
Inventory Level ◽  
Squared Error ◽  
Interfirm Collaboration ◽  
Overall Performance

The main objective of studying decentralized supply chains is to demonstrate that a better interfirm collaboration can lead to a better overall performance of the system. Many researchers studied a phenomenon called downstream demand inference (DDI), which presents an effective demand management strategy to deal with forecast problems. DDI allows the upstream actor to infer the demand received by the downstream one without information sharing. Recent study showed that DDI is possible with simple moving average (SMA) forecast method and was verified especially for an autoregressive AR(1) demand process. This chapter extends the strategy's results by developing mean squared error and average inventory level expressions for causal invertible ARMA(p,q) demand under DDI strategy, no information sharing (NIS), and forecast information sharing (FIS) strategies. The authors analyze the sensibility of the performance metrics in respect with lead-time, SMA, and ARMA(p,q) parameters, and compare DDI results with the NIS and FIS strategies' results.

Sign in / Sign up

Export Citation Format

Share Document