Networks, knowledge research

The types of supply chain activities that benefit from Web 2.0 include marketing and advertising collaborating and strengthening relationships with customers and suppliers information and knowledge transfer and networking for research. These activities may be classified as either internal such as communications within and across departments, or external such as communicating with customers, suppliers, and partners. 

In Chapter 43 the incorporation of expertise and experience in data analysis by means of expert systems is described. The knowledge acquisition bottleneck and the brittleness of domain expertise are, however, the major drawbacks in the development of expert systems. This has stimulated research on alternative techniques. Artificial neural networks (ANN) were first developed as a model of the human brain structure. The computerized version turned out to be suitable for performing tasks that are considered to be difficult to solve by classical techniques. 

V.M. Ashley P. Linke, 2004, A novel approach for reactor network network synthesis using knowledge discovery and optimization techniques, Chemical Engineering Research Design, 82 (A8) 952-960... 

McGovern et al.26 analyzed the expression of heterologous proteins in E. coli via pyrolysis mass spectrometry and FT-IR. The application was to a2-interferon production. To analyze the data, artificial neural networks (ANN) and PLS were utilized. Because cell pastes contain more mass than the supernatant, these were used for quantitative analyses. Both the MS and IR data were difficult to interpret, but the chemometrics used allowed researchers to gain some knowledge of the process. The authors show graphics indicating the ability to follow production via either technique. 

Knowledge based approaches such as fuzzy logic, neural networks or multiagents model currently constitute an important axis of research and application in bioprocesses. They have shown their usefulness particularly when one does not have an analytical model but that a certain expertise is available. Harmand and Steyer [37] have addressed that when this expertise comprises a sufficiently important know-how, approaches such as fuzzy logic will be preferred. If, on the other hand, one has only a limited experience but lays out of a rather important data base, the statistical approaches such as neural networks can be used. 

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