FutureMetrics Uses @RISK to Hedge Wood Prices in Production of Burning Wood Pellets
Wood pellets are commonly used in Europe to fuel hundreds of thousands of home heating systems. They are also in high demand in Europe in power plants as a carbon neutral substitution for coal. They are comprised of highly densified wood in terms of both volume and energy content in a uniform shape. One of the key input drivers of ROI for a biomass energy project or pellet project is the price of wood. FutureMetrics, a consultancy for project development and policy research in the biomass thermal energy sector, uses Palisade’s @RISK to accurately estimate wood costs. The consultancy uses @RISK to analyze the uncertainty surrounding wood prices and thereby measures the potential excess or shortfall of cash flows in present value terms for upcoming projects.
Wood Pellet Popularity and Potential ROI
“I have been using @RISK since the early 1990’s, so I have a perspective on the commitment to the users that the Palisade team has,” said Dr. William Strauss, president and founder of FutureMetrics. “Every new version has new and very useful features that often are based on feedback from users. Palisade’s @RISK software has the richest feature set and has the most relevant tools for analyzing models that contain uncertainty.”
@RISK Offers Insight to Appropriate Price-Hedging
The project’s success was measured by net present value and return on investment. @RISK enabled the team to see the range of possible values for these metrics in the form of probability distributions, so the actual likelihood of a project making money or losing money could be easily quantified. The @RISK tornado charts assisted FutureMetrics and INRS to quantify the sensitivity of the project to changes in input costs.
Findings Using @RISK
The research showed that there was a nine percent chance that the plant would have a negative NPV over a the life of the project period.
Figure 1: Without hedging, there was a nine percent probability of loss over a three-year period.
Looking at the sensitivity of the model to wood prices in @RISK, it became apparent that the price of the wood going into the pellet mill was critical. @RISK’s tornado chart on the model showed that for each $8.80 (which equals one standard deviation in the price of the wood) increase in wood prices, the NPV was lowered by $22.96 million. Since the expected NPV was only $35.7 million to begin with, the project was exposed to significant price risk.
Figure 2: @RISK’s tornado graph shows that the price of wood going into the pellet mill had by far the greatest impact on NPV.
This risk can be mitigated by hedging their input wood prices. FutureMetrics and INRS then considered the case of the wood aggregator who buys wood from various sources and resells it to pellet manufacturers at a fixed price. In order for the aggregator to reduce its risks if it is going to offer a stable price to the pellet manufacturers, it too must find a way to hedge against market price fluctuations.
Using an econometric model, a simulated set of wood prices, and @RISK, FutureMetrics and INRS determined that the aggregator had a huge 89.5% change of losing money without a hedge, with a mean expected loss of $143.9 million. With a price hedging strategy in place, the mean loss was drastically reduced to $18,000.
Figure 3: The probability of the aggregator losing money without a hedging strategy to insulate against market price fluctuations is 89%, with a mean loss of $143.9 million.
Figure 4: The wood aggregator’s potential loss with a hedging strategy are greatly reduced.
The ease by which complex Excel models can be altered to accept uncertain inputs, and the ease with which the simulations can be used to determine the outputs’ sensitivity to input variation, make @RISK a valuable tool for pricing and investment strategy.
“@RISK is powerful, yet easy to learn, and has the ability to show complex solutions with intuitive visual outputs,” said Dr. Strauss. “Just like Excel, which is easy to learn but has a never-ending depth of features, @RISK allows new users to build models and run simulations very quickly from the Excel framework. But as the user learns to use @RISK, the software has the maturity, breadth and depth to support increasingly sophisticated models and techniques. However, no matter the complexity of the model, the product can show solutions in ways that communicate valuable information to decision makers who may or may not share the technical skills of the model designer.”