First of Kind, Financing Advanced Bioenergy Pt 3: Dealing with Deal Breakers

February 27, 2011 |

Part 3-Dealing with Deal Breakers. How financial modeling can be used in quantifying risk and identifying “Deal Breakers”

By Biofuels Digest special correspondent Tim Sklar

In Part I of this three part series titled “On Identifying Risks”, discussions were included that focused upon the high-risk nature of advanced bio-refinery projects, why financing is hard to obtain what can be done to improve the odds.

In Part 2 titled “On Quantifying Risks” a summary of due diligence inquiries that project developers can expect to be subjected to is presented along with a list of typical concerns potential financial backers have with respect to perceived risks. In addition, a set of bio-refinery specific “what if” questions are included, because many of them will be asked.

It was hoped that the material included in Part 2 will ultimately be used as a useful guide in assembling information and in conducting analyses that will be needed when preparing loan requests, applications for loan guarantees, investment memoranda and prospectuses.

In Part 3 titled “Quantifying Risk Using Robust Financial Models”, insights are presented as to the how financial modeling can be used in quantifying risk and identifying “Deal Breakers”. In the sub-section titled “The Anatomy of a Model”, a detailed description is given of the computational framework used in the financial model. Another sub-section describes how to use this model.

Also presented are mathematical formula used in the model for calculating measures of financial viability. Finally, a case study is presented for typical but hypothetical bio-refinery project. The data used is in tabular form, showing input assumptions used for optimistic, most likely and pessimistic scenarios. Also presented in tabular form is a set of key measures of financial viability that were actually calculated by the model for each of the three scenarios.

Major findings are then presented and the “known-unknowns” that could become “Deal Breakers” are identified.  The Deal Breakers that are highlighted are those that will require specific risk mitigation efforts. Part 3 does not include a sub-section on how this can be done, as it is varies from project to project and is beyond the scope of this article.

Quantifying Risks Using Robust Financial Models

There are many ways in which risks identified in response to “What-if” questions can be quantified. This author prefers using robust financial models, because risks are expressed as changes in financial outcomes that all parties to the project understand. Typically, all of these answers have financial implications that can be computed by rerunning a financial model.

In quantifying risks, model input is modified to reflect possible most likely values associated with each known-unknown are input and financial viability measurements are calculated. This exercise is repeated using pessimistic values for the same set of known-unknowns, and the resulting measurements of financial viability are compared against the results obtained for the most likely values. Those known-unknowns that have most adverse impact are then deemed to be “Deal Breakers”.

The easiest risk assessment to make is one that reflects an input assumption, such as an expected selling price decreases or cost increased. More difficult risk assessments may involve shifting series of data, such as costs of construction that could be delayed, or delayed ramp-ups in production. Still others would require changes in the calculations themselves, such as not being able to attain expected quality or efficiencies that had been expected from a bio-refinery component.

In the following section titled “Anatomy of a Model” data was obtained for a hypothetical bio-refinery project and sensitivity tests were run for three sets of assumptions. And a set financial viability assessments were computed for each scenario, and risk assessments were included based on results obtained.

The Anatomy of a Model

For Evaluating The Financial Viability of Advanced Bio-refinery Projects

Included below is a skeletal description of a computational framework  (or “Model”) for recalculating financial projections of the project over the required planning horizon, using sets most likely, optimistic and pessimist assumptions? This capability is needed to facilitate the quantification of risks that are inherent in bio-refinery projects. The “Model” described is easily modified to reflect a wide variety of bio-refinery projects including those that incorporate more than one feedstock, more than one process and more than one product. The structure of the model allows for addition of supporting calculations and for reflecting partially integrated operations.

The Anatomical Description

Using The Model

Creating Input-Basic Input Variables Used

  • For each Asset Class:
    • Capital Costs and Expenses
    • Construction, Start-up and Ramp up Schedule by month
    • Amortizable Cost; Useful Life; Depreciation Method; Residual Value
    • Ramp-up Schedule, Normal and Maximum Process Capacity
    • Normal Input Volumes By Type (Daily/Ann’l)
    • Normal Output by Product @ Normal Capacity (Daily/Ann’l)
    • Feedstock consumed per gallon produced in normal operations
    • Direct Operating costs expected per gallon (such as labor, chemicals, catalysts, fuels, utilities, maintenance, lab costs)
  • Base Period:
    • Product Prices Expected (Low/Avg/High)
    • Feedstock Costs per Unit Low/Avg/High)
    • Fixed Operating Costs per Year (Marketing, Maintenance, G & A)
    • Ann’l Expected Rate of Inflation for Operating Costs

Validation of Information & Assumption

  • Perform correlation analyses between Conventional Fuels and Biofuels pricing.
  • Project Prices for:
    • Biofuels and Conventional Fuels
    • Crude Oil
    • Biorefinery  Process Feedstocks
    • Biofuels Process Bi-Products
    • Retail, Rack and Refinery Gate prices
  • Reflect impact of Government Price Support Programs and Incentives on Biofuels sales revenue.
  • Obtain supply and demand outlook and develop possible scenarios for adjusting future prices.
  • Validate feedstock to biofuels dt/gal conversion rates using published metrics from alternative processes.

Developing Optimistic, Most Likely and Pessimistic Scenarios

  • Develop “Optimistic”,” Most Likely” and “Pessimistic Values” for Prices, Production and Sales Levels, and Operating Costs.
  • Develop Alternative Start-up and Ramp-up Scenarios
  • Identify Possible Potential Delays
    • In process validation,
    • In construction,
    • In ramp-up, and
    • In financing.
  • Compile Projections reflecting alternative time-tables:
  • For perfecting and testing technology being used;
  • For making capital outlay for technology development, demo-unit & pilot plant development, and construction of a commercial scale plant;
  • For receiving tranches of financing that will be required at each stage;
  • For commissioning a  commercial scale Biorefinery;  and,
  • For sustaining normal operations over the required test period.

Running the Model Cockpit

  • Select Base Year Avg Selling Prices for Each Biofuel.
  • Select Base Year Direct Costs for Each Biofuel Process.
  • Prepare a schedule of technology development costs and capital outlays through start-up.
  • Prepare a biofuels production schedule thru ramp-up.
  • Develop “base case” projections  reflecting most likely assumptions for prices, production, direct operating costs and capital outlays, as well as most likely modest start-up delays.
  • Develop a pessimistic case that reflects lower biofuels  prices, higher costs and delays in start-up and ramp-up;
  • Develop a optimistic case that reflects higher biofuels prices, lower capex and opex costs and no snafus in start-up and ramp-up schedules;
  • Enter a “Case Selection Code into the Model Cockpit, and rerun the model to test alternative sets of assumptions.

Basic Calculations Performed

Revenue, Direct Cost & Gross Margin Projections

1) For each Case selected and each Biofuel to be produced and sold, calculate annual projections of revenue, direct cost and gross margins generated over a 10 year planning horizon using projections of production and sales volumes and inflation adjusted base prices and unit costs.

Revenue tin= Selling Price tin x Gallons Sold ti n x Inflation%t

Direct Cost tin = Cost/Gal tin x Gals Produced ti n x Infl. %t

Gross Margin tin = Revenue tin – Direct Cost tin

Where: t = year, i = case selected, and n = Type of biofuel

2) Aggregate for all products and incorporate into the “Projections of Earnings and Cash Flow” on Exhibit 4

Sources and Uses of Funds and Loan Amortization Schedules

5)            For each Case selected and each Biofuel process to be constructed, calculate annual projections of capital outlays that will be required.

6)            Aggregate Financing requirements, then compute  construction loans interest that is expected to be paid and the amount of debt and equity funding that will be needed.

7)            Identify various mixes of financing to be used by source.

8)                 -For debt obligations, obtain details on repayment periods, interest rates,  guarantees and other collateral to be used.

9)                 – For equity to be offered, identify class of stock and number of shares to be issued, as well as  and preferences and conversion features to be used.

10)         4)  Using terms for each funding source, develop amortization schedules that reflect repayments by year and portions that are to be treated as interest, principal Also, compute dividend distributions to be made following dividend policies for various classes of stock..

11)         Aggregate and incorporate annual  amounts into the “Depreciation, Amortization and Debt Service Schedules ” on Exhibit 5.

Fixed Operating Cost, D&A and Loan Amortization Schedules

For each Case selected and each Biofuel process to be constructed:

  • Calculate annual amounts of S, G&A, Lease and Rental payments, Insurance premiums and non-income related Taxes expected and apply inflation factors where appropriate.
  • Calculate annual amounts of Depreciation and Amortization for various classes of fixed assets having the same guideline lives and depreciation rates.

Using data obtained from Exhibit 6, compute  debt service repayment schedules for each type of loan, showing interest and principal by year and resulting outstanding loan balances.

Aggregate these amounts into the annual projections for incorporation into the “Projections of Earnings and Cash Flow” on Exhibit 4.

Projections of Earnings and Cash Flows

For each Case selected and each Biofuels process to be operated, and using annual projections previously calculated, compute the following measures of earnings and cash flows:

Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA tin ) = Gross Margin tin – Fixed Operating  Cost tin

Earnings Before Interest & Taxes, (EBIT tin )

= EBITDA tin – Depreciation and Amortization (D&A tin )

Pre Tax Income (PTI tin ) = EBIT tin – Interest tin

Net After Tax Income (NATI tin ) = PTI tin – Taxes tin

Net Operating Cash Flows (NCF tin )

= NATI tin + D& A tin – Principal Payments on Debt (P’Pmts tin)

Where: t = year, i = case selected, and n = biofuels process selected

For each Case selected and each Biofuels process to be operated, and using annual projections previously calculated, compute the following measures of financial viability:

–       Gross Margin as a % Total Revenues & as $/gallon sold

–       EBITDA, EBIT PTI, NATI as a % Total Revenues

–       Reversion Value as a multiple of EBITDA in Year 10, at (the end of the project life

Using total amounts invested and total equity invested, net cash flows projected  as well as estimated reversion value, compute Internal Rates of Return (IRR).

Consolidate projections of earnings and cash flows  for all biofuels processes being evaluated and recompute measures of financial viability on a consolidated basis.

Compare results for each of the three cases and assess risks based on differences obtained.

Projected Balance Sheets

Using the consolidated projections of earnings and cash flows over the project life and supporting  details contained in Exhibits 5 and 6:

  • Project capitalized costs net of depreciation.
  • Project outstanding loan balances after debt servicing.
  • Project changes in retained earnings after reflecting capital contributions, projections of net after tax income and scheduled dividend distributions.
  • Then derive estimates of undistributed cash generated from operations over the project life.

Compute Balance Sheet Strength measures such as the Current Ratio; the Debt: Equity Ratio ,the  Debt Coverage Ratio and Asset Turnover.

Evaluating Impact on Project Financial Viability

Model Cockpit Input Assumptions for Scenarios Tested

Base Case Case 2 Case 3
Most Likely Pessimistic Optimistic
Capex/gal 0.9363 1.0300 0.8427
Opex/gal 0.4172 0.4588 0.3754
Biomass Cost/g 0.3618 0.3980 0.3256
Yield Gal/ton 90 81 99
Avg. SP/gal 4.00 4.14 3.73
Normal Ann’l O/P 26,700,000 26,700,000 26,700,000
1st Yr O/P 23,350,000 20,000,000 26,700,000
Start-up 6 mo. Delay 12 mo. Delay On Time

Financial Viability Results Obtained for Scenarios Tested

Major Findings:

  • Variations in Prices and Costs of +- 10% matter.
  • Modest Delays in Ramp-up and in Modest Increases in Capital Outlays over 10 years don’t.
  • 80% Debt Improves ROR Significantly, Reversion Values don’t
Base Case Case 2 Case 3
($ Amts in mils) Most Likely Pessimistic Optimistic
EBITDA 36 26 42
Reversion Value 236 196 251
NATI as % Rev. 18.4% 13.6% 20.8%
Net Cash Flow 22 16 26
ROR on Investment 36.2% 26.5% 42.0%
ROR on Equity 71.6% 51.8% 85.1%

Key Factors Requiring Further Analysis of Risk Mitigation Alternatives

  • Capex
  • Opex
  • Biomass Cost/ton
  • Yield T/gal SP/Gal)
  • Debt: Equity Ratio

More about Sklar & Associates, Biofuels Project Developers, here.

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