Scenario optimistic/pessimistic

Fig. 4. Projected trend of the Earth surface temperature increase. Upper curve, pessimistic scenario with no emission curtailment lower curve, optimistic scenario with significant emission curtailment middle curve, in-between scenario. (Adapted from IPCC 2001.)...

Figure 6.14. Forecasting the carbon dioxide content in the atmosphere obtained under different anthropogenic scenarios 1 Keeling and Bacastow pessimistic scenario 2 Bjorkstrom optimistic scenario 3 IPCC scenario 4 Kondratyev realistic scenario. The ordinate A = Ca(t)/Ca(1900).

The ten-year projection should be provided for three scenarios base, optimistic, and pessimistic. These cases are not meant to be simple percentage changes of the sales projechons. Instead, the team should try to identify the drivers of the project s success and construct alternahves for the future that lead to different results for the project The base case should be the most likely case. The optimistic scenario should be based on the positive development of some (not all) key success factors. The pessimistic scenario is usually the minimum feasible case, meaning a situation where the organization would still pursue the project, but some factors do not develop in a positive way. 

Table 3. Probability of loss (%) forthe proposed Jenkins scenarios using the optimistic (min.) and pessimistic (max.) Kaplan-Meier statistics. 

Each scenario comprises a mixture of missions in different environments. The extended Kaplan-Meier approach was used to estimate the probability of survival for each mission. The prohahihty of surviving a scenario was calculated hy simply using the joint probability expression. Results for the probability of loss for each scenario, using the optimistic and the pessimistic risk models, are summarized in Table 3. 

The general methodology is based on the scenario method three scenarios are developed for each country (central case, pessimistic, and optimistic). 

In line with the previous approach is the scenario technique. It describes the possible benefit of a measure regarding accidents of relevance [13]. An exact effectiveness is not determined, but the true effect is approximated using two scenarios as upper and respectively lower boundary. The scenarios are defined using an optimistic and pessimistic approach with respect to the benefit [13, 14]. The analysis is commonly conducted using accident data and assumptions on the effect of a measure. 

Detectability is defined as the time it takes for the buyer to realize that a disruption to supply network has occurred. Modeling the propagation of disruption information in a supply chain depends on the way nodes communicate with one another. Under the best-case scenario, each supplier, independent of its tier, communicates directly with the buyer. This is a very optimistic scenario since it requires every supplier to know the end destination of its product and to establish a direct connection with the buyer. The next best scenario assumes that every disrupted supplier reports to its immediate buyer. This situation is more realistic however, it does not ensure that the information will reach the primary buyer promptly since the immediate buyer of the disrupted supplier may not share the information with his customers or with the main buyer. Another scenario would be to assume that the information flows randomly in the supply chain network. This scenario may be perceived as pessimistic at first, but the method can be customized to better model the reality. 

Mortality trends from mesothelioma for French men aged 50 to 79 years continue to increase, reaching a peak averaging between 1140 (optimistic scenario) and 1300 deaths (pessimistic scenario) annually around the years 2030 and 2040, respectively (Banaei et al. 2000). In Japan, from 1958 to 1996, a total of 1,846 (0.17%) malignant mesothelioma cases (1,287 male, 558 female, 1 unknown) were registered among 1,056,259 autopsy cases (Murai 2001). The frequency of mesothelioma (number of cases/total autopsy cases) was 0.10% (461/440,334) for the term 1958-1979, 0.18% (716/390,124) for 1980-1989, and 0.30% (669/225,801) for... 

The results in Table 10.4 show that, in the worst-case scenario, the NPV is very negative and the project will lose money. In the best-case scenario, the NPV is increased over the base case by approximately 45 million. From this result, the decision on whether to go ahead and build the plant is not obvious. On one hand, the process could be highly profitable, but on the other hand, it could lose nearly 73 million over the course of the ten-year plant life. By taking a very conservative philosophy, the results of the worst-case scenario suggest a decision of do not invest. However, is the worst-case scenario realistic Most likely, the worst-case (best-case) scenario is unduly pessimistic (optimistic). Consider each of the three parameters in Table 10.3. It will be assumed that the value of the parameter has an equal chance of being at the high, base-case, or low value. Therefore, in terms of probabilities, the chance of the parameter taking each of these values is 1/3, or 33.3%. Because there are three parameters (R, FCfy, and COM ),... 

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