Spiderplots versus Tornado Diagrams for Sensitivity Analysis

Eschenbach, Ted

doi:10.1287/inte.22.6.40

Cited by 134 publications

(71 citation statements)

References 4 publications

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“…The Tornado Chart (Eschenbach 1992) shown in Figure 5 indicates the range of profitability with possible range in the horizontal axis and the cost elements in the vertical axis. A wider range, meaning longer bar in the chart, means a larger fluctuation in the potential profitability or the risk at the end.…”

Section: Evaluation Results and Discussionmentioning

confidence: 99%

Proposal on Hybrid Risk Evaluation Method (HREM) for bidding decision in international infrastructure project

Isaka

Yoneda

Koga

2017

JAMDSM

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This paper describes a proposal on a new method that enables the appropriate and practical decision making in bidding before submitting a proposal to participate in the bidding for a large-scale international infrastructure project. Such a method has been demanded by potential bidders in order to overcome the recent trend that Japanese companies have given up in participating in such projects in spite of opportunities to extend their business in highly potential overseas market. For this, we have established a comprehensive evaluation method for risk assessment with reference to actual experiences of one of the leading Japanese consultants in establishing the basis of the proposed method, and conducted a post-evaluation to verify and validate the proposed method based on assumptions for the Suvarnabhumi Airport Rail Link (ARL) construction project in Thailand. Based on the assessment, we have obtained a positive result to ensure the effectiveness and the validity of the theory assumed as well as the proposed method, with suggestion for further improvement. Keywords IntroductionThe Japanese Government has been keen on exporting the Japanese Technology in infrastructure as a package, which has been the core of the strategy for sustainable development of Japanese industries. One of the main components of the said package is the railway systems that comprise rolling stock, train control system, power supply system, automatic fare collection system, etc. for urban mass transits and high speed railways, which Japanese railway industries have spent years to accomplish safe, international competitive and highly reliable system. The Japanese high speed railway technology, well known as Shinkansen, has been successfully introduced in Taiwan and the mass transit systems by Japanese technologies have been widely adopted in various countries, which were financed by the Japanese ODA and with the management by Japanese consulting firms, e.g. Delhi Metro in India, Purple Line in Thailand, etc. These projects were bid through the International Competitive Bidding (ICB) and the contractors wishing to win the project have to participate in the bidding process. Although the projects were partly or entirely financed by the Japanese Government, there were some projects in which no Japanese firm has participated in the bidding due to high risks residing in the bidding process and in the contract conditions, e.g. non-familiarity with modern practice of project implementation (Niraula et al., 2008), unfair modification of FIDIC based conditions (Matsuba et al., 2011). Disputes at the court in case the arbitration on the claims by the contractor is not successful could bring about serious financial impacts on the business model of the contractor due to the increase of the project cost by the result of disputes (Onishi, et al., 2002). Upon these cases, the Overseas Construction Association of Japan (OCAJI) has submitted a request to Japan International Cooperation Agency (JICA) to improve the bidding conditions in order to avoid h...

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Section: Evaluation Results and Discussionmentioning

confidence: 99%

Proposal on Hybrid Risk Evaluation Method (HREM) for bidding decision in international infrastructure project

Isaka

Yoneda

Koga

2017

JAMDSM

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“…A tornado diagram (Eschenback 1992) would show the lower and upper values of P obtained from variation of each parameter, with the parameter with the widest limits displayed on top, and the parameter having smallest limits on the bottom. Tornado diagrams (Fig.…”

Section: Sensitivity Coefficientsmentioning

confidence: 99%

“…Another visual presentation is a spider plot showing the impact of uncertainty in each parameter on the variable in question, all on the same graph (Eschenback 1992;DeGarmo 1993, p. 401). A spider plot, Fig.…”

Section: Sensitivity Coefficientsmentioning

confidence: 99%

System Sensitivity and Uncertainty Analysis

Loucks

Beek

2017

Water Resource Systems Planning and Management

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The usefulness of any model is in part dependent on the accuracy and reliability of its output data. Yet, because all models are abstractions of reality, and because precise input data are rarely if ever available, all output values are subject to imprecision. The input data and modeling uncertainties are not independent of each other. They can interact in various ways. The end result is imprecision and uncertainty associated with model output. This chapter focuses on ways of identifying, quantifying, and communicating the uncertainties in model outputs. IntroductionModels are the primary way we have to estimate the multiple impacts of alternative water resource system design and operating policies. Models are used to estimate the values of various system performance indicators resulting from specific design and/or operating policy decisions. Model outputs are based on model structure, hydrologic and other time series inputs and a host of parameters whose values characterize the system being simulated. Even if these assumptions and input data reflect, or are at least representative of, conditions believed to be true, we know the model outputs or results will be wrong. Our models are always simplifications of the real systems we are analyzing. Furthermore, we simply cannot forecast the future with precision. So we know the model outputs defining future conditions are uncertain estimates, at best. Some input data uncertainties can be reduced by additional research and further data collection and analysis. Before spending money and time to gather and analyze additional data, it is reasonable to ask what improvement in estimates of system performance or what reduction in the uncertainty associated with those estimates would result if all data and model uncertainties could be reduced if not eliminated. Such information helps determine how much one would be willing to "pay" to reduce model output uncertainty. If the uncertainty on average is costing a lot, it may pay to invest in additional data collection, in more studies, or in developing better models, all aimed at reducing that uncertainty. If that uncertainty only a very modest, impact on the likely decision that is to be made, one should find other issues to worry about.If it appears that reducing uncertainty is worthwhile, then the question is how best to do it. If doing this involves obtaining additional information, then it is clear that the value of this additional information, however measured, should exceed the cost of obtaining it. The value of such information will be the benefits of more precise estimates of system performance, or the reduction of the uncertainty, that one can expect from obtaining such information. If additional information is to be obtained, it should be focused on that which reduces the uncertainties considered important, not the unimportant ones.This chapter reviews some methods for identifying and communicating model output uncertainty. The discussion begins with a review of the

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“…Since the P index function is linear with respect to all input variables, the Sensitivity Coefficient for each input is the slope of a plot of P index score versus the percent change in that variable Comparison of factor Sensitivity Coefficients are best visualized in a so-called "spider" plot (Eschenbach, 1992), shown in Figure 2b. This figure shows the biosolids-P source factors (rate, application method, and PSC) superimposed as a single steeply sloping line, indicating that these factors are High-Impact variables.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The difference between the low and high P index scores is the Swing associated with each input factor. The impact of many independent input variables can be conveniently summarized in a "tornado" diagram (Eschenbach, 1992). In this diagram (Figure 2a), input variables are arranged in decreasing order, based on the magnitude of their total possible impact on the P index score.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Sustaining Biosolids Recycling Under Phosphorus-Based Nutrient Management

Brandt¹,

Elliott²

2008

proc water environ fed

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Forty-nine states have developed site assessment tools (Phosphorus (P) indices) to rank agricultural fields based on vulnerability to P loss. Because long-term biosolids application causes accumulation of soil P above concentrations needed for optimum crop yields, land-based recycling programs may be adversely affected by deployment of P management policies. Ninety-five active biosolids recycling fields were evaluated using the Pennsylvania P index. Without accounting for biosolids P solubility, only 22 of 95 fields qualify for standard nitrogen (N)-based biosolids application rates. Using biosolids-specific P source coefficients, based on laboratory testing, 76 fields could be managed by applying biosolids to satisfy crop N needs. Changes in crop management practices, biosolids application methods, and variables affecting crop / conservation practices were also evaluated as options for decreasing the overall P index ratings for the fields. A combination of measures could be used to qualify nearly all fields for Nbased biosolids application. Adapting this approach to other state P site assessment tools is essential for sustaining land application as a biosolids recycling option.

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Spiderplots versus Tornado Diagrams for Sensitivity Analysis

Cited by 134 publications

References 4 publications

Proposal on Hybrid Risk Evaluation Method (HREM) for bidding decision in international infrastructure project

Proposal on Hybrid Risk Evaluation Method (HREM) for bidding decision in international infrastructure project

System Sensitivity and Uncertainty Analysis

Sustaining Biosolids Recycling Under Phosphorus-Based Nutrient Management

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