Value Scenario Evaluation

Sales scenario evaluation considers the impact of sales prices, sales flexibility and elasticity on volumes and values. Initially, sales prices are varied starting from a basis plan and consider the impact on volumes and values shown in fig. 86. 

Production scenario evaluation investigates the influences of production and campaign control parameters on volumes and values. All experiments share the hypothesis that restrictive production control leads to lower profits due to lower optimization flexibility. 

Selection of Parameter Values - For the scenarios evaluated, were standard factors employed Were these average values or reasonable worst-case values If alternative values were used, what was the justification Was the reasoning correct ... 

Application of the age of air concept can be justified by the fact that the content of contaminants found in the exhaust air normally rises from the value found in supply air entering the room. On its voyage through the room, the air is likely to pick up more contaminants the longer it stays in the room. This is a very simple assumption. It can be argued, however, that using the age of air concept is the best way to evaluate ventilation design for scenarios where little or no information is available on use of the room and locations and emission rates for heat and contaminant sources. 

Interroute extrapolation. The values for pharmacokinetic variables in the O Flaherty Model are independent of the route of exposure. However, the model does incorporate media-specific estimates of absorption from the gastrointestinal tract. Different exposure scenarios have been evaluated with the O Flaherty Model for children and adults (O Flaherty 1993, 1995a). 

The current version of CalTOX (CalTOX4) is an eight-compartment regional and dynamic multimedia fugacity model. CalTOX comprises a multimedia transport and transformation model, multi-pathway exposure scenario models, and add-ins to quantify and evaluate variability and uncertainty. To conduct the sensitivity and uncertainty analyses, all input parameter values are given as distributions, described in terms of mean values and a coefficient of variation, instead of point estimates or plausible upper values. 

Suppose you are asked to evaluate the purchase of the multicone cyclone referred to in Example 3.4. The capital investment is 35,000 (see Example 3.4), and the equipment has a class life of 5 years, after which it will be sold for the salvage value of 4000. The income stream generated by the machine is on line A in Tables EB.5A and EB.5B. As the equipment ages, its operating and maintenance costs increase, and line B lists the expense profile. Assume a tax rate of 35 percent with no investment tax credit. Evaluate two possible scenarios (a) 100 percent use of equity and (b) 100 percent debt financing. Use straight-line depreciation for debt financing, for simplicity assume equal annual payments (principal plus interest) to the lender for the 5 years at a rate of 10.5%. 

The model is evaluated by means of provided case test data. Industry case data are modified by values and selected volume parameters for confidentiality reasons, Therefore, optimization results will not match directly with the actual business. However, the provided data are realistic in order to test sensitivity of the model and to compare model reactions applying different scenarios. Two test types are conducted ... 

The model has been evaluated by means of a global commodity industry case. The evaluation proved the importance of value chain planning to integrate volume and value decisions from sales to procurement exchange rate, sales and raw material price and elasticity scenarios have key influences on total profit and volume planning decisions within the global value chain network. 

The PBPK model development for a chemical is preceded by the definition of the problem, which in toxicology may often be related to the apparent complex nature of toxicity. Examples of such apparent complex toxic responses include nonlinearity in dose-response, sex and species differences in tissue response, differential response of tissues to chemical exposure, qualitatively and/or quantitatively difference responses for the same cumulative dose administered by different routes and scenarios, and so on. In these instances, PBPK modeling studies can be utilized to evaluate the pharmacokinetic basis of the apparent complex nature of toxicity induced by the chemical. One of the values of PBPK modeling, in fact, is that accurate description of target tissue dose often resolves behavior that appears complex at the administered dose level. 

For some chemicals a large database was available that allows statistical evaluation. In such cases, mean values were considered as typical PECs and the 90th percentile as a worst-case scenario. 

Marchini et al. examined the consequences of population structure on association studies and evaluated the effectiveness of GC in correcting for population structure (17). In simulated scenarios that involved two or three populations, GC did not remove the effects of population structure in association studies when a small number of loci were used. When a large number of loci were used, the correction by GC became conservative. This problem of GC became severe with small p values and large sample sizes. 

In this study, operational risk was accounted for in terms of variance in both projected benefits, represented by first stage variables, and forecasted demand, represented by the recourse variables. The variability in the projected benefit represents the solution robustness where the model solution will remain close to optimal for all scenarios. On the other hand, variability of the recourse term represents the model robustness where the model solution will almost be feasible for all scenarios. This approach gives rise to a multiobjective analysis in which scaling factors are used to evaluate the sensitivity due to variations in each term. The projected benefits variation was scaled by 0i, and deviation from forecasted demand was scaled by 02, where different values of 0i and 02 were used in order to observe the sensitivity of each term on the final petrochemical complex. The objective function with risk consideration can be written as follows ... 

Evaluation of a number of irradiation scenarios and approaches to combining the Hp(lO) values from the two personal monitors indicates that an algorithm developed by the NCRP using the /fp(lO) values for the front and back personal monitors of the form ... 

The same example problem as used in Chapter 7 and A5.9.2 above will be used. A reactor of volume 3.5 m3 has a design pressure of 14 barg (maximum accumulated pressure 16.41 bara). A worst case relief scenario has been identified in which a gassy decomposition reaction occurs. The mass of reactants in the reactor would be 2500 kg. An open cell test has been performed in a DIERS bench-scale apparatus, in which the volume of the gas space in the apparatus was 3800 ml, and the mass of the sample was 44.8 g. The peak rate of pressure rise was 2263 N/m2s at a temperature of 246°C, and the corresponding rate of temperature rise was 144°C/minute. These have been corrected for thermal inertia. The pressure in the containment vessel corresponding to the peak rate was 20.2 bara. The liquid density at 246°C is estimated as 820 kg/m3. The gas generated by the runaway has a Cp/Cv value of 1.3. The problem is to evaluate the relief size required. 

An important element of risk analysis is assessing the importance of the uncertainties in the data now being used to evaluate various scenarios. By varying the value of the parameters that are used in the analysis, one can understand the importance of present uncertainties in significant variables by their impact on the calculated risk from the repository. These results will be used to direct the research program toward those elements which are most significant. 

As each alternative network configuration already possesses an optimum objective function value based on the scenarios/restrictions underlying the calculation, evaluation of the alternatives has to focus on aspects other than financial optimization. 

As with the shifting probability p in the probabilistic shifting scheme, the threshold T is also an application-specific parameter, whose value affects the tradeoff between the shifting cost (unicast) and the rekey cost (multicast). Analyzing the tradeoff is difficult and may vary for different application scenarios. Thus, we evaluate the choice of different thresholds through simulation, as discussed in Section 4.2. 

For most health economic evaluations, which include cost-benefit, cost-effectiveness, and cost-utility analyses, one needs to choose a base-case scenario and the perspective of evaluation. Often, the societal perspective is adopted, but it can be from an individual s or a payer s perspective. The costs or benefits are valued in two approaches ... 

Realistic predichons of study results based on simulations can be made only with realistic simulation models. Three types of models are necessary to mimic real study observations system (drug-disease) models, covariate distribution models, and study execution models. Often, these models can be developed from previous data sets or obtained from literature on compounds with similar indications or mechanisms of action. To closely mimic the case of intended studies for which simulations are performed, the values of the model parameters (both structural and statistical elements) and the design used in the simulation of a proposed trial may be different from those that were originally derived from an analysis of previous data or other literature. Therefore, before using models, their appropriateness as simulation tools must be evaluated to ensure that they capture observed data reasonably well [19-21]. However, in some circumstances, it is not feasible to develop simulation models from prior data or by extrapolation from similar dmgs. In these circumstances, what-if scenarios or sensitivity analyses can be performed to evaluate the impact of the model uncertainty and the study design on the trial outcome [22, 23]. 

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