Planning model evaluation

Developed frameworks are applied to the specific industry problem to monthly plan a global chemical commodity value chain by volumes and values. Sub-objectives are to elaborate characteristics and planning requirements for a global commodity value chain in the chemical industry and to develop, implement and evaluate the respective model. Research question 2 is directed to a real industry case study demonstrating the real existence of formulated requirements, showing the applicability of the developed model in reality and evaluating the model using industry data. 

These demand forecasts developed through the application of network models are only a first step in establishing an overall planning model such as a venture simulator. There have been numerous presentations (I, 2) of these more complex simulation systems in the past, and for a new product in a new market, a venture simulation as outlined in Figure 2 provides a useful means of evaluating the outlook for such a project. This approach involves the development of a series of simulation models which when complete contain at least four sectors ... 

It is a common experience in synthetic chemistry that a truly optimal ordering of a synthetic route may not be possible in the planning stage, but may have to determined experimentally. The precise information necessary for the complete and unambiguous evaluation of each step in a possible synthesis is hardly ever available. Nonetheless it is clearly wise to try to optimize a synthetic plan on the basis of available information before the experimental approach begins. Such an effort may suggest certain preliminary or "model" experiments that can be helpful in the choice or refinement of a synthetic plan. It is also obviously desirable to devise and consider alternate or bypass paths for each problematic step of a synthetic sequence. 

The small NBS program in chemical engineering should receive substantially greater funding to fulfill critical needs for evaluated data and predictive models. The committee supports NBS plans to focus on data needs in emerging technology areas such as biotechnology and advanced materials. 

Statistical and algebraic methods, too, can be classed as either rugged or not they are rugged when algorithms are chosen that on repetition of the experiment do not get derailed by the random analytical error inherent in every measurement,i° 433 is, when similar coefficients are found for the mathematical model, and equivalent conclusions are drawn. Obviously, the choice of the fitted model plays a pivotal role. If a model is to be fitted by means of an iterative algorithm, the initial guess for the coefficients should not be too critical. In a simple calculation a combination of numbers and truncation errors might lead to a division by zero and crash the computer. If the data evaluation scheme is such that errors of this type could occur, the validation plan must make provisions to test this aspect. 

Numerous workers tested the HELP model, and it is in general use throughout the United States by regulators, design engineers, and others for planning and evaluating barrier-type landfill covers. 

One can identify two major categories of uncertainty in EIA data (scientific) uncertainty inherited in input data (e.g., incomplete or irrelevant baseline information, project characteristics, the misidentification of sources of impacts, as well as secondary, and cumulative impacts) and in impact prediction based on these data (lack of scientific evidence on the nature of affected objects and impacts, the misidentification of source-pathway-receptor relationships, model errors, misuse of proxy data from the analogous contexts) and decision (societal) uncertainty resulting from, e.g., inadequate scoping of impacts, imperfection of impact evaluation (e.g., insufficient provisions for public participation), human factor in formal decision-making (e.g., subjectivity, bias, any kind of pressure on a decision-maker), lack of strategic plans and policies and possible implications of nearby developments (Demidova, 2002). 

This phase is intended as a final check of the model as a whole. Testing of individual model elements should be conducted during earlier phases. Evaluation of the model is carried out according to the evaluation criteria and test plan established in the problem definition phase. Next, carry out sensitivity testing of the model inputs... 

Finally, the model needs to prove its applicability in reality and its performance in an industry case study. The model is evaluated with comprehensive industry case data and the relevance of the end-to-end value chain planning approach is evaluated. Opportunities for model extensions are outlined at the end. 

State of the art literature is analyzed to evaluate the coverage of value chain planning requirements in related models. The review helps to iden-... 

Model implementation and case study evaluation need to prove that the model supports value chain planning decisions towards global optima that the model is applicable in practice based in industry case data and that solution times are acceptable for application in the global monthly planning process. 

The model is implemented and evaluated with an industry case. The technical implementation is described first. Then, the industry case is introduced and model-relevant case data are presented. Model reaction tests are conducted for various industry case data sets to analyze model applicability, sensitivity and model planning results. Model performance tests are conducted to analyze technical parameters such as solution time or approximation methods quality. The case evaluation inspired several model extension possibilities presented at the end of the chapter. 

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