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I. Criteria for Project Selection Models• A. Realism

– 1. The model should reflect the reality of the manager's decision situation, including the multiple objectives of both the firm and its managers.

– 2. Need a common measurement system to evaluate projects.

– 3. Must take into account the firms limitations on facilities, capital, personnel, etc.

– 4. Should include risk factor.

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• B. Capability– 1. Must deal with multiple time frames– 2. Must simulate various situations.

• a) Internal situations (e.g., strikes)

• b) External situations (e.g., interest rate changes)

– 3. Must optimize the decision.

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• C. Flexibility– 1. Must provide valid results within range of

conditions the firm might experience.– 2. Should be easily modified or self-adjusting

to changes in the firm's environment.

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• D. Ease of Use– 1. Reasonably convenient, low execution time,

easy to use and understand– 2. Should need no special interpretation, no

hard-to-acquire data, no excessive personnel or unavailable equipment.

– 3. Model's variables should rate one to one with real world variables that the manager finds significant to the project.

– 4. Expected outcomes should be easily simulated

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• E. Cost – 1. Data gathering and modeling costs should be

low relative to the project cost.– 2. All costs should be considered, and their

total should definitely not be greater than the potential benefits of the project.

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• F. Easy Computerization– 1. Convenient to gather and store information

in a computerized data base.– 2. Easy to manipulate the data in the model

such as through a spreadsheet.

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II. The Nature of the Project Selection Models

• A. Two basic model types– 1. Numeric - use numbers as inputs.– 2. Nonnumeric - don't use numbers as inputs.

• B. Important model facts to remember– 1. Models don't make decisions; people do.– 2. Models only partially reflect reality. True

model optimality only exists within the limited framework of the model itself

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• C. Necessary for evaluation of a model to develop a fist of the firm's objectives.– 1. List generated by top management.– 2. Items on the list should be weighted to represent the

degree of contribution the item has on the set of goals.– 3. Projects are accepted or rejected based on how much

their predicted outcomes contribute to goal achievement.– 4.Some suggested list categories.

• a) Production, marketing finance, personnel, administrative

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– 5.Some list factors are recurring, others have one-time impact.

– 6. Ranges of uncertainty are helpful for hard-to-estimate factors.

– 7.Thresholds or critical values of acceptance or rejection can be assigned.

– 8. Items will contain differing levels of specificity.

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III. Type of Project Selection Models

• A. Nonnumeric Models– 1.The Sacred Cow

– a) Suggested by a senior and powerful official

– b) Stopped at successful conclusion or when suggesting official realizes it was a mistake

– 2.The Operating Necessity– a) Project is required to keep the system going

– b) Ask if system is still worth operating..

– 3.The Competitive Necessity– a) Needed to compete.

– b) Operating necessity projects take precedence over competitive necessity projects

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– 4. The Product Extension Line• a) Judged on degree to which it fits the firm's existing

product line, fills a gap, strengthens a weak link or extends a line into a new desirable direction

– 5.Comparative Benefit Model• a) Q Sort

– 1) Divide a list of projects into groups of good - fair - poor.

– 2) Rank projects within groups.

– 3) One person or a selecting committee does it.

• b) Models widely used.

• c) Peer review - outside referee picks projects.

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• B. Numeric Models - Profit - Profitability– 1.Payback Period

• a) Initial fixed investment in the project divided by the estimated annual cash inflows from the project.

– 2. Average Rate of Return (ARR)• a.) Ratio of average annual profit to the initial or

average investment in the project. • b.) Both payback and ARR methods ignore the time

value of money– 3. Discounted Cash Flow - Present Value Method

• a) Determine net present value (NPV) of all cash flows by discounting them by the required rate of return (hurdle rate).

• b) Project is acceptable if sum of the NPV is positive.

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– 5. Profitability Index (benefit-cost ratio)• a) NPV of all future expected cash flows divided by the

initial cash investment.

• b) Acceptable if ratio is greater than 1.0.

– 6. Other Profitability Models• a) Subdivide net cash flows into the elements that

comprise the net flow.

• b) Introduce terms to consider risk.

• c) Consider effects on other projects in the organization.

• d) Pacifico's Method

• e) Dean's Profitability Method

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– 7. Advantages of profit-profitability numeric models

– a) Undiscounted models simple to use and easy to understand.

– b) Cash flows come from readily available accounting information.

– c) Output in familiar terms for decision makers.

– d) Models usually provide "absolute" go/no‑go decisions.

– e) Some models account for risk.

– f) Dean's model incorporates impact of project on the rest of the organization

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– 8.Disadvantages of profit-profitability numeric models

• a) Ignore all nonmonetary factors except risk.

• b) Undiscounted models ignore time value of money and timing of cash flows.

• c) Present value models are short‑run biased.

• d) Payback model ignores cash flows beyond the payback period.

• e) Sensitive to data errors from early years of project.

• f) All discounting nonlinear, so effects of parameter changes are not obvious to most managers.

• g) Models with research risk can mislead the decision maker.

• h) Some only apply to projects resulting in new products.

• i) Not clear how cash flows are properly defined for project evaluation purposes.

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• C. Numeric Models - Scoring– 1. Unweighted 0-1 Factor Model

• a) List relevant factors on a preprinted form and have top management evaluators determine whether or not the project qualifies for each of the factors. Sum the responses and see if the project qualifies for enough factors to accept.

• b) Weighs all factors as equally important.

• c) No gradation of degree which factor is met.

– 2. Unweighted Factor Scoring Model• a) Grade level of how well a factor is met by a project on a

graded scale, usually 5 pt. (e.g.,5-very good 1 -very bad).

• b) All factors still weighted equally

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– 3. Weighted Factor Scoring Model• a) Add weights reflecting the relative importance of the

factors and multiply the weight of each factor by its score. Sum these values and compare to a threshold value.

• b) This method can provide a sensitivity analysis to point out area for project improvement.

• c) Don't include marginally relevant factors

– 4. Constrained Weighted Factor Model• a) Similar to above except additional criteria enter the

model as constraints (things that must be present or absent in order for the project to be acceptable) rather than as Weighted factors

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– 5. Dean and Nishry's Model• a) Integer programming model that selects the highest

scoring project from the scoring model, and then selects them one after another until the resources and depleted

– 6. Goal Programming with Multiple Objectives• a) Variation of the general linear programming method

which optimizes an objective function with multiple objectives.

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– 7. Advantages of Numeric Scoring Models• a) Allow multiple criteria to be used for evaluation and

decision.

• b) Structurally simple and easy to use.

• c) A direct reflection of management policy.

• d) Easily altered to meet changes in the environment and in management policy.

• e) Weighted scoring models allow for the fact that some factorsare more important than others.

• f) Easy sensitivity analysis to see trade-offs between several criteria.

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– 8. Disadvantages of Numeric Scoring Models• a) Output is a relative measure, no utility is reflected,

thus no direct indication of project support.

• b) Generally linear, elements are assumed to be 'independent.

• c) Tendency to include too many criteria.

• d) Unweighted models assume equal importance of all criteria.

• e) To the extent to which profit-profitability is included in the scoring model, its advantages and disadvantages appear.

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• D. Suggestions on Model Selection– 1. Scoring models are suggested

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IV. Analysis Under High Uncertainty

• A. Uncertainty in Organizations– 1. Most uncertainty is about the timing and the costs.

– 2. Three areas of uncertainty

• a) Timing of project and cash flows.

• b) What will the project accomplish.

• c) Side effects or unforeseen consequences

– 3. Pro-forma documents help reduce uncertainty.• a) Monte Carlo Simulation - exposes the many

possible consequences of a project.– 1) Risk Analysis

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• B. Risk Analysis– 1. Focuses decision maker's attention on the

nature and the extent of the uncertainty of some variables in the decision-making process.

– 2. Uses probability distributions for each of the uncertain variables, instead of the point estimates that financial analysis utilizes.

– 3. Decision Analysis

• C. General Simulation Analysis– 1. Avoid the full-cost approach. Only count costs

and times that are really results of the new proposed project.

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V. Comments on the Information Base for Evaluation-Selection

• A. Comments on Accounting Data– 1. Costs and revenue are assumed to vary linearly

with associated changes in inputs and outputs.– 2. The standards used to provide cost-revenue

information may or may not be accurate representations of the physical systems they are supposed to represent.

– 3.Incremental analysis should be used to guard against the full-cost approach.

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• B. Comment on Measurements– 1.Subjective vs. Objective

• a. Objective - Measurement taken by reference to an external system.

• b. Subjective - Reference to a standard that is internal to the system.

– 2.Quantitative vs. Qualitative• a. Difference is that one may apply the law of addition

to quantitative data and not to qualitative

– 3.Reliable vs. Unreliable• a. Data source is reliable if repetitions of a

measurement vary by less than a prespecified amount

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– 4. Valid vs. Invalid• a. Validity - extent to which a piece of information

means what we think it means.

• C. Comment on Technological Shock– 1. Dealing in projects unfamiliar to the

organization will usually lead to underestimating costs and time. This is mostly due to the system reacting in ways that we did not predict.

– 2. Whenever possible include build past knowledge of system actions and reactions into estimates of future project performance.

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VI. Project Proposals • A. Proposal Overview

– 1.Project Proposal - set of documents submitted for evaluation.

– 2.Can be extensive, usually to prospective outside clients, or brief, usually internal projects.

• B. The Technical Approach– 1. General Description of problem to be attacked.

– 2.Method for resolving critical problems presented.

– 3. Tests and inspection procedures noted.

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• C. The Implementation Plan

– 1.Contains estimates of the time required, the cost, and the materials used.

– 2. PERT-CPM, time charts, etc. here, also milestones for the project and period by period resource usage.

• D. The Plan for Logistic Support and Administration

– 1. Description of the ability of the proposer to supply the routine facilities equipment and skills needed during the project.

– 2.Section describing how the project will be administered.• E. Past Experience

– 1.Describes past experience of the proposing group.

– 2. List of key personnel with their titles and qualifications.

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VII. The Past and Future of Project Evaluation/Selection Models

• A. Post WW II - mostly payback period was used.• B. 50's and 60's - profit-profitability was majority.

– 1.Led to short time-horizon projects

• C. 70’s - High interest rates raised cut-off rate. Projects with long term payoff horizons were cut back.

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• D. Late seventies early eighties - Mostly profit, but a growth in multiple criterion models.

• E. Recently - Computers are making sensitivity analysis easy, but math programming models are still not being used. Scoring models are now being used.

• F. Future - More usage of the math programming and scoring models.

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