Dynamic supply network

It follows that the two factors - supplier s capability and the interface between the supplier and the buyer -determine the supply chain effectiveness. The interface governs the relationships between the supplier and the buyer, and helps operationalize the business models of collaboration. The supplier and buyer must be able to formalize such collaboration by leveraging the individual supplier capabilities. Clearly, the buyer would be effective if s/he can build a dynamic supply network that is responsive to the needs of consumers, including rapid fulfillment of orders. The buyer may also need expertise to manage relationships with offshore suppliers. 

Jammernegg, W. and Kischka, P. 2005. Dynamic, customer-oriented improvement of supply networks. European Journal of Operational Research, 167, 413-426. 

We are not aware of any existing models for addressing the issue of dynamic configuration of a supply network. 

To illustrate the actual importance of dynamic properties for the functioning of metabolic networks, we briefly describe and summarize a recent computational study on a model of human erythrocytes [296]. Erythrocytes play a fundamental role in the oxygen supply of cells and have been subject to extensive experimental and theoretical research for decades. In particular, a variety of explicit mathematical models have been developed since the late 1970s [108, 111, 114, 123, 338 341], allowing us to test the reliability of the results in a straightforward way. 

Very often a mixture of these two approaches is used to determine the values of the parameters. Good examples are Dano et al. [78] and Chassagnole et al. [79]. In these studies many parameters were taken from the literature and, in a parameter estimation approach, were allowed to vary within experimental error to fit the unknown parameters. When considering dynamics, the boundary conditions of the network have to be supplied as explicit functions of time, and therefore they have to be measured in order to give good values for parameter with parameter estimation [74, 79]. Many detailed and core models can be interrogated online at JWS online (www.jjj.bio.vu.nl) [80]. 

In a network of autocatalytic feedback loops (of which we here do consider only those processes related to Mg) control functions which give rise to either autocatalyst supply (sequestration from the enviromnent) or loss (abstraction or precipitation by parasitic byproducts) several different chemical species may interact or cooperate to achieve the above kinds of control (Eiswirth et al. 1991b), two such side-loops cause somewhat increased topological network complexity rather than fundamentally changing network dynamics. 

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