Ecostrat releases white paper on biomass supply chain analytics
Ecostrat Inc. recently released another white paper in its series on de-risking feedstock supply. The white paper series provides evidence to industry developers and lenders on why they should stay up to speed with the latest developments in biomass supply chain research and analytics, as the economic viability of bioenergy projects depends on de-risking feedstock supply.
The white paper, “Why Should We Care About Supply Chain Analytics?” follows another published in August, which provides an overview of five wood procurement mistakes and solutions.
This most recent white paper noted the complexity of biomass supply chains, no matter the feedstock. “Whether it’s getting wood from the forest to the biopower plant, corn stover to the biofuel plant, or organics to the anaerobic digester, the fact of the matter is that biomass supply chains contain great numbers of dependent variables that interact to determine the final cost of feedstock,” Jordan Solomon and Marcin Lewandowski of Ecostrat detailed in the white paper.
Feedstock cost depends on a number of interacting variables, which many are characterized by uncertainty, like diesel cost, stumpage and weather. According to Ecostrat, when modeling potential outcomes based on a large set of dependent variables, two model approaches can be followed. The first is to develop a deterministic model, based on a potentially large set of fixed value assumptions, the validity of which determine the effectiveness of the model, or second, develop a probabilistic, or stochastic, model where each variable is given a range based on the probability of occurrence.
The white paper likened the way deterministic models make determinations of risk and prediction cost to a compass bearing—a single bearing will produce a fix on something along one path, which is only as good as variable affecting the ship’s direction. This is why a bearing has to be constantly updated, adjusting for earlier errors or assumptions—its path, or projection, is never correct and always has to be adjusted.
Stochastic modeling using computer simulation allows for the uncertainty of occurrence and impact of the events (like diesel cost, drought, weather, quality issues, insolvency of suppliers) over extended periods of time. “Stochastic models have the computational power to take into account the hundreds of thousands of possible outcomes that happen when we forecast uncertainty and variation over multiple variables,” Solomon and Lewandowski explained in the paper.
The white paper mentioned that one of the most popular types of stochastic modeling is Monte Carlo simulation, or probability simulation, a technique used to understand the impact of risk and uncertainty on forecasting models.
The white paper emphasized that one of the reasons that bioenergy, biogas, biochemical and biofuel projects tend to accrue high debt cost is due to the perceived risk debt providers add on top of the actual risk. One of the main obstacles to lowering debt cost is investors’ understanding of feedstock prices and future trends, which is inherently complex due to the number of dependent interacting variables that are hard to predict.
According to Ecostrat, multi-variable problems like this have been solved with Monte Carlo methods, which involve analysis of historical data and probabilities. Solomon and Lewandowski referred to dealing with probabilities—such as the probability of a competitor increasing production levels—as a stochastic problem. The more analyses for each problem that are run, the more results obtained, and the more confidence in a certain value being most likely to occur.
The white paper indicated that, with such an approach, questions about the probability of feedstock cost being a certain amount, or what the range of future feedstock cost is within a certain percentage of risk tolerance, can be answered. The paper concluded that this is information investors need, and although deterministic may appear simpler, most deterministic forecasts are wrong.
“Having just one line indicating future values has limited applications, as that line, just like the initial compass bearing, will turn out to be incorrect,” Solomon and Lewandowski provided in the paper. “In contracts, being able to claim with statistical certainty that, for instance, there is 90 percent chance that the cost of feedstock will not exceed $X is much more meaningful. Investors and insurers operate on such probabilities, and the bioenergy industry needs to do the same if we are to lower debt costs.”
The full white paper can be accessed here.