Creating the Value Chain Model

Virtual value chain The virtual, information-based equivalent of the value chain model where value is created by gathering, selecting, synthesizing, and distributing information. (From Harvard Business Review, Exploiting the Virtual Value Chain, J.F. Rayport and J.J. Sviokla, November-December, 1995)... 

The virtual, information-based equivalent of the value chain model where value is created by gathering, selecting, synthesizing, and distributing information. 

To compete with the traditional fossil-based refineries, biorefineries have to exploit optimally raw materials firom plants and create multiple value chains. Therefore, the concept of a whole-plant biorefinery appears as a more convenient model. Despite the diversity of oil crops, such as soy, rapeseed, sunflower, and palm, the whole-plant biorefinery concept can be applied similarly to all of them. Differences can emerge due to the nature of the plant and the way to recover its seeds containing the vegetable oil. Palm trees, for instance, remain in the soil, and their fruits are harvested, where plants such as rapeseed or sunflower are cut every year. In both cases, the first step of the biorefinery process is to separate the oil-rich seeds firom the lignocellulosic fraction of the plant. 

The value chain is a systematic approach to examining the development of competitive advantage. The chain consists of a series of activities that create and build value. They culminate in the total value delivered by an organization. To analyse the specific activities through which a firm can create a competitive advantage, it is useful to model the firm as a chain of value-creating activities. A value network is a business analysis perspective that describes social and technical resources within and between businesses (Porter 1985). 

Polymer models consisting of one single chain of 300 monomers were created using a Monte Carlo-based method similar to the short-chain models described above. This was done to study the effect on diffusion and solubility of the presence in these short-chain models of too many chain ends (as compared to the real material) and, therefore, of added free volume. It has been suggested that this is one of the main factors for the discrepancy between simulated and experimental values. The models were validated using the similar procedure described in Section 9.2.I.I. 

A series of articles were published by Ennari et al. on MD simulation of transport processes in Poly(Ethylene Oxide) and sulfonic acid-based polymer electrolyte.136,137 The work was started by the determination of the parameters for the ions missing from the PCFF forcefield made by MSI (Molecular Simulations Inc.), to create a new forcefield, NJPCFF. In the models, the proton is represented as a hard ball with a positive charge. Zhou et al. used the similar approach to model Nation.138 The repeating unit of Nafion (Fig. 17) was optimized using ab initio VAMP scheme. The protons were modeled with hydronium ions. Three unit cell or molecular models were used for the MD simulation. The unit cell contains 5000 atoms 20 pendent side chains, and branched Nafion backbone created with the repeating unit. Their water uptakes or water contents were 3, 13, or 22 IEO/SO3, which correspond to the room temperature water uptakes at 50% relative humidity (RH), at 100% RH, and in liquid water respectively.18 The temperature was initially set at a value between 298.15 and 423.15 K under NVE ensemble with constant particle number, constant volume (1 bar), and constant energy. 

There are several ways to include detectability of disruption risks in supply chain risk models. One way is to directly use the values in the MFPT matrix, the ntij values, and create an objective function to minimize the number of transitions between suppliers and the buyer. Otherwise, the MFPT values may not be suitable to use directly in risk quantification since values in the MFPT matrix are in transitions and need to be transformed to actual time units (e.g., hours, days, or weeks) for proper use in disruption quantification. This transformation to time units is supply chain specific, since the speed with which the information spreads through the nodes depends on the information technology systems implemented at each node and the availability and strength of connection among the nodes. For instance, if a buyer has implemented an ERP system that allows communication with all tiers of his supply chain, he would have much better connectivity to any supplier and the transition times would be much shorter than buyers that do not have a similar visibility. We call the time it takes any disruption news to reach from node i to j as the disruption delay between nodes i and j and denote it as Aij. 

In accordance with the second approach the polymeric solutions viscosity anomaly is explained by the effect of the hydrodynamic interaction between the links of the polymeric chain, such links represent by themselves the beads into the necklace model. Accordingly to this effect the hydrodynamic flow aroimd the presented bead essentially depends on the position of the other beads into the polymeric ball. An anomaly of the viscosity was conditioned by the anisotropy of the hydrodynamic interaction which creates the orientational effect (prior work by Peterlin and Copic [7]). High values of the viscosity for the concentrated solutions and its strong gradient dependence cannot be explained only by the effect of the hydrodynamic interactioa... 

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