Project feasibility analysis

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• Read Chapter 13

• Read three articles on web site

– J Agribusiness Including Risk in Economic

Feasibility Analysis: The Case of Ethanol

Production in Texas

– IAMA Journal Article on economic feasibility of Bio-ethanol Production for Wheat in South Africa

– SJAE Use of Probabilistic Cash Flows

• Lecture 20 Ethanol Feasibility.xls

• Lecture 20 Project Feasibility.xls

• Lecture 20 Changing Risk Over Tme.xls

• Lecture 20 Growth Functions.xls

Materials for Lecture

• Ice Plant article by Richardson and Mapp,

Southern Journal of Agricultural Economics (SJAE), 1976 – use risk for feasibility analysis

– Demonstrated methodology for risk based

feasibility studies

– Probability of Economic Success

• Probabilistic Cash Flows to meet cash needs– Recent Feasibility Studies on Class Website

• Including Risk in Economic Feasibility Analysis

• Bio-ethanol Production for Wheat in South Africa

Project Feasibility Analysis

Project Feasibility Analysis

• Most feasibility studies are done using Excel spreadsheets

– This trend by business started in mid-90s

• Feasibility studies often ignore risk – many do

a “What if …” study for the Best Case and

Worst Case scenarios

• Some analysts think they considered risk by including a 10 year “average” price

• Excel feasibility models are easily converted

to be stochastic simulation models

– Just make the forecasted variables stochastic

using the residuals from the forecast models

Project Feasibility Analysis

• Risks to consider for a feasibility analysis are:

– Price of raw inputs, as fuel and labor

– Price of the product or output

– Production risk

– Black Swans

– Competition and market share over the life of

investment

– Cost of the plant and product development

– Cost of production for the finished product

• Project feasibility is where we put it all

together in an analysis of Time, Money, and Economic Viability

Project Feasibility Analysis

• Project Feasibility: consider the Time, Money (Cost), and Economic Viability of the finished business

• Simulate the Time to complete the plant

• Simulate the Cost of developing the plant

incorporating risk into the plant’s development costs

• Simulate the Economic Viability of the completed

plant (business)

Project Management

Bid Analysis

Project Feasibility

Rate of Return Cost (money)

Time P(T)

P(C)

P( )

Project Management

Analysis

Project Feasibility

Rate of Return Cost (money)

Time P(T)

P(C)

P( )

Project Feasibility Analysis

• Proposed business with a new product

• Tasks and duration/costs

Tasks Description Time (mo.) Costs ($1,000)

1 Plant Modification 3-5 300-325

2 Product Development 1-3 200-300

3 Distribution System 2-3 50-100

4 Marketing Program 3-4 100-150

• Finance 100% of project costs @ 9%

• Cost of production/unit Uniform(10,15), can be a scenario variable

Project Feasibility Analysis

• Setting the proposed project up in a Project Management setting yields the following cost and time to complete the project

• If these answers are acceptable to

management the next questions is –

– Will the business be economically viable?

Project Feasibility Analysis

• The stochastic final cost of building the plant becomes input into the project analysis phase of study

Project Feasibility Analysis

• 10 Year analysis gives way to lots of reports

– Annual rate of return to assets

– Things look bad after 6th year

Project Feasibility Analysis

• 10 Year analysis gives way to lots of reports

– Annual net cash income

– Things look bad after 6th year

Project Feasibility Analysis

• Scenario analysis of management control variables to see if the plant could be more profitable.

Project Feasibility – Ethanol Plants in Texas

• Ethanol production is dependent on

– Inputs could be: corn, sorghum, wheat, potatoes, etc – Fuel requirements are: natural gas and electricity

– Sale of co-product of DDGS – Sale of ethanol

• Local communities want a plant because it hires 35

to 50 people year around, farmers have dependable market for grain, and given a $2.25/gallon

construction cost it generate jobs (at least for 6-9 months)

• Develop a Feasibility Model for an

Ethanol plant in Texas

• Location: High Plains due to feedlots

and local corn/sorghum supplies

• Rail transportation facilities available to

import corn and ship ethanol

• KOVs

– Net Present Value– Annual cash flows– Probability of cash flow deficits– Probability investors get their money back or the P(Increase Real Net Worth)

Project Feasibility – Ethanol Plants in Texas

• Stochastic variables

– Corn and sorghum prices – DDGS price

– Ethanol price – Electricity and natural gas prices

• Develop MVE distribution for these prices based on

prices for the past 10 years

• Problem with stochastic prices

– Must use Texas prices and we have forecasts for National prices

Texas Price = a + b National Price + e Simulate a stochastic national price and use in Texas price

Project Feasibility – Ethanol Plants in Texas

• Develop a Financial Simulation Model :

Income Statement, Cash Flow, and Balance Sheet

• Simulate 10 years using corn as the feed

stock

• Assume a learning curve for management to

bring the plant up to its full capacity

• Validation exercises

– 4 Ps – Touring test with other economists– Presented results to local investors – Presented results to politicians

Project Feasibility – Ethanol Plants in Texas

• A new business may need a few

months or years to grow sales to their potential

• It may take months or years to learn

how to reach potential of prod function

• In either case, assume a stochastic

growth function and simulate it, if

nothing else is available, use a Uniform distribution

• Example of a growth function for 8

years

Learning Curve or Demand Cycle

• A new concept in project feasibility

analysis

• Explicitly consider externalities

– Such as cleanup costs at end of business

• Buried gas tanks

• Disposal of dirt with oil and gasoline spills

• Disposal of old parts and tires/batteries

• Renovation of land after a strip mine

• Cleanup after oil well is drilled

• Oil spills (ship wreck, train wreck w/ haz chem)

Life Cycle Costing

• We may want to change the uncertainty for a random variable as time

progresses

• This is done by adding an Expansion

factor (Et)to the stochastic variable

• For an Empirical Distribution

Ỹ= Ŷ *(1+EMP(.) * Et)

where Et is a fraction from 0 to infinity

Et = 1 causes risk to equal historical risk

Et = 2 doubles historical risk

Changing Uncertainty Over Time

• For a Normal Distribution the process is slightly more complicated

Ỹt = NORM(Ŷt , σ * Jt * Et)

where Jt normalizes the standard

deviation for changes in the mean

– Jt = (Ŷt / Historical Ŷ)

– Et is a fraction from 0 to infinity

Et = 1 causes risk to equal historical risk

Et = 2 doubles historical risk

Changing Uncertainty Over Time

• Steps to Life Cycle Costing Analysis

– Identify the potential externalities

– Determine costs of these externalities – Assign probabilities to the chance of

experiencing each potential cost

• Assume distributions with GRKS or Bernoulli

– Simulate costs given the probabilities

– Incorporate costs of cleanup and

prevention in the project

Life Cycle Costing

• Bottom line is that LCC will increase the costs

of a project and reduce its feasibility

• Affects the downside risk

• Does nothing to increase the positive returns

• Need to consider the FULL costs of a

proposed project to make the correct decision

• J Emblemsvag – Life Cycle-Costing: Using Activity-Based Costing and Monte Carlo Simulation to manage Future Costs

Life Cycle Costing