Will Aviation Biofuels Fly?

Will Aviation Biofuels Fly?
@RISK Helps Assess Two Different Government
Policies in Purdue University Study

Developing biofuels for aircraft is a risky endeavor, as transforming plant material into liquid fuel is still very expensive compared to the price of fossil fuels. Dr. Wallace Tyner and his colleagues at Purdue University in Indiana used @RISK to conduct a cost-benefit analysis of building an aviation biofuel plant, and to determine the potential impacts of two different government policies to jump-start this technology: reverse auction and capital subsidy. They found that both policies reduced risk in investment of aviation biofuels, however a reverse-auction policy reduced the risk of this investment more.

Researchers Amanda Bittner, Dr. Wallace Tyner, and Xin Zhao at the Agricultural Economics Department at Purdue University used a discounted cash flow (DCF) model to find the net present value (NPV) of a theoretical aviation biofuel plant. They incorporated four variables that have a large impact on the non-risk adjusted breakeven fuel price: capital cost, feedstock cost, final fuel yield, and hydrogen cost (the price of hydrogen input used in producing biofuels). They created empirical distributions for each variable from literature and/or experts, and then used @RISK to incorporate uncertainty into the variables using a PERT distribution. The researchers created projections to forecast what the fuel prices would be in the future, and what the breakeven biofuel price would have to be in two different situations: one in which the fuel price has no overall trend, and one case in which the fuel price has an increasing trend.

The researchers next wanted to investigate what kind of government policy would be most effective in reducing the risks around investing in aviation biofuels, and at what point in the probability distribution would investment firms be willing to place a bid.

Then, they examined which lowers risk more: reverse auction or capital subsidy. They looked at each under three different contract lengths: 5-year, 10-year, and 15-year, to see how contracts of increasing duration would impact the probability of financial loss for investors. “At those different contract lengths, the numbers are pretty startling,” says Dr. Tyner.

Dr. Tyner uses @RISK for research, as well as for teaching his course in benefit cost analysis.

 » Read the full case study  

@RISKI use [@RISK] to teach all my students
     to introduce uncertainty into project
     evaluation. It’s been a tool in my
     portfolio for a long, long time. You can
     do something with much less expense
     and much easier with @RISK today
     compared to what I painstakingly did
     years ago.
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                                  Dr. Wallace Tayner
                                  Professor of Agricultural Economics
                                  Purdue University

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Teaching Tips & Examples

The DecisionTools Suite is a great way to present quantitative techniques in a straightforward, easy-to-understand way for students.

Optimizing Channel Conflicts
Excerpted from Chapter 61 Financial Models Using Simulation and Optimization, by Wayne Winston, published by Palisade

An important problem in marketing is how to set prices in different channels in order to maximize a company's profitability. Selling in different channels opens up a vast number of potential sales as it exposes the product to different consumers, however, a company must realize that charging a low price in one channel can and will cannibalize sales in another channel. Download the example model and step-by-step instructions to see how Solver and Evolver are used to determine the demand curves of each channel, and the optimal price for each channel so that profitability is maximized despite the conflict between channels.

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Tech Tip of the Month

Number of Bins in a Histogram

How does @RISK choose the number of bars when making a histogram? In other words, how many bins or intervals does @RISK divide the data range into? Can I change this?

By default, @RISK determines the number of bins from the number of iterations or data points n, as follows:

n  Bins
Less than 25  5
25 to 100  Nearest integer to n/5
More than 100  Largest integer below 10 x log(n)

If you want to change this for a particular histogram, right-click the graph and select Graph Options. On the Distribution tab, the lower section lets you specify a number of bins (bars), as well as a minimum (left edge of the first bar). "Automatic" uses the calculation shown above, but you can specify a number from 2 to 200.

» View other Techniques and Tips here


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Palisade is the world's leading provider of risk and decision analysis software and solutions. Founded in 1984, its products @RISK and the DecisionTools Suite are used by over 95% of the Fortune 100, in nearly every industry around the world. Palisade is headquartered in New York State, and has offices in London, Sydney, Rio de Janeiro, and Tokyo.

What is The DecisionTools Suite?
The DecisionTools Suite is the world’s only integrated set of risk and decision analysis programs. The Suite includes @RISK for Monte Carlo simulation, BigPicture for mind mapping and data exploration, PrecisionTree for decision trees, and TopRank for “what if” sensitivity analysis. In addition, DecisionTools Suite comes with StatTools for statistical analysis and forecasting, NeuralTools for predictive neural networks, and Evolver and RISKOptimizer for optimization. All programs work together seamlessly, and all integrate completely with Microsoft Excel for ease of use and maximum flexibility.

The Suite is offered in three affordable and flexible licensing options for those in academia: Student Versions, Course Licenses and Full Academic Versions.

» Schools using Palisade software
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In This Issue

» Will Aviation Biofuels Fly?
   @RISK Helps Assess Two
   Different Government
   Policies in Purdue U Study

» Licensing Options

» Teaching Tips &

   Optimizing Channel Conflicts

» Tech Tip
   Number of Bins in a

» About Palisade and the
   DecisionTools Suite


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