The Nature Of Project Selection Models

There are two basic types of project selection models, numeric and nonnumeric. Both are widely used. Many organizations use both at the same time, or they use models that are combinations of the two. Nonnumeric models, as the name implies, do not use numbers as inputs. Numeric models do, but the criteria being measured may be either objective or subjective. It is important to remember that the qualities of a project may be represented by numbers, and that subjective measures are not necessarily less useful or reliable than so-called objective measures. (We will discuss these matters in more detail in Section 2.5.)

Before examining specific kinds of models within the two basic types, let us consider just what we wish the model to do for us, never forgetting two critically important, but often overlooked, facts.

• Models do not make decisions-, people do. The manager, not the model, bears responsibility for the decision. The manager may "delegate" the task of making the decision to a model, but the responsibility cannot be abdicated.

• All models, however sophisticated, are only partial representations of the reality they are meant to reflect. Reality is far too complex for us to capture ,more than a small fraction of it in any model. Therefore, no model can yield an optimal decision except within its own, possibly inadequate, framework.

We seek a model to assist us in making project selection decisions. This model should possess the characteristics discussed previously: ease of use, flexibility, low cost, and so on. Above all, it must evaluate potential projects by the degree to which they will meet the firm's objectives. (In general, we will not differentiate between such terms as goals, objectives, aims, etc.) To construct a selection/evaluation model, therefore, it is necessary to develop a list of the firm's objectives.

Such a list should be generated by the organization's top management, it is a direct expression of organizational philosophy and policy. The list should go beyond the typical clichés about "survival" and "maximizing profits," which are certainly real goals but are just as certainly not the only goals of the firm. Others might include maintenance of share of specific markets, development of an improved image with specific clients or competitors, expansion into a new line of business, decrease in sensitivity to business cycles, maintenance of employment for specific cat egories of workers, and maintenance of system loading at or above some percent of capacity, just to mention a few.

A model of some sort is implied by any conscious decision. The choice between two or more alternative courses of action requires reference to some objective(s), and the choice is thus made in accord with some, possibly subjective, "model."

In the past two or three decades, largely since the development of computers and the establishment of operations research as an academic subject area, the use of formal, numeric models to assist in decision making has expanded. A large majority of such models use financial measures of the "goodness" of a decision. Project selection decisions are no exception, being based primarily on the degree to which the financial goals of the organization are met |35|. As we will see later, this stress on financial goals, largely to the exclusion of other criteria, raises some serious problems for the firm, irrespective of whether the firm is for-profit or not-for-profit.

When the list of objectives has been developed, an additional refinement is recommended. The elements in the list should be weighted. Each item is added to the list because it represents a contribution to the success of the organization, but each item does not make an equal contribution. The weights reflect the different degree of contribution of each element in the set of goals.

Once the list of goals has been developed, one more task remains. A project is selected or rejected because it is predicted to have certain outcomes if implemented. These outcomes are expected to contribute to goal achievement. If the estimated level of goal achievement is sufficiently large, the project is selected. If not. it is rejected. The relationship between the project's expected results and the organization's goals must be understood. In general, the kinds of information required to evaluate a project can be listed under production, marketing, financial, personnel, administrative, and other such categories.

The following is a list of factor« that contribute, positively or negatively, to these categories. In order to give focus to this list, we assume that the projects in question involve the possible substitution of a new production process for an existing one. The list is meant to be illustrative. It certainly is not exhaustive.

Production Factors

1. Time until ready to install

2. Length of disruption during installation

3. Degree of disruption during installation

4. Learning curve—time until operating as desired

5. Effects on waste and rejects

6. Energy requirements

7. Facility and other equipment requirements

8. Safety of process

9. Other applications of technology

10. Consistency with current technological know-how

11. Change in cost to produce a unit output

12. Change in time to produce a unit output

13. Change in raw material usage

14. Availability of raw materials

15. Required development time and cost

16. Impact on current suppliers

17. Change in quality of output

18. Change in quality control procedures

Marketing Factors

1. Size of potential market for output

2. Probable market share of output

3. Time until market share is acquired

4. Impact on current product line

5. Ability to control quality

6. Consumer acceptance

7. Impact on consumer safety

8. Estimated life of output

9. Shape of output life cycle curve 10. Spin-off project possibilities

Financial Factors

1. Profitability, net present value of the investment

2. Impact on cash flows

3. Payout period

4. Cash requirements

5. Time until break-even

6. Size of investment required

7. Impact on seasonal and cyclical fluctuations

8. Cost of getting system up to speed

9. Level of financial risk

Personnel Factors

1. Training requirements

2. Labor skill requirements

3. Availability of required labor skills

4. Level of resistance from current work force

5. Other worker reactions

6. Change in size of labor force

7. Change in sex, age, or racial distribution of labor force

8. Inter- and intra-group communication requirements

9. Support labor requirements 10. Impact on working conditions Administrative and Miscellaneous Factors

1. Meet government safety standards

2. Meet government environmental standards

3. Impact on information system

4. Impact on computer usage

5. Need for consulting help, inside and outside

6. Reaction of stockholders and securities markets «

7. Patent and trade secret protection

8. Impact on image with customers, suppliers, and competitors

9. Cost of maintaining skill in new technology

10. Vulnerability to single supplier

11. Degree to which we understand new technology

12. Elegance of new process

13. Degree to which new process differs from current process

14. Managerial capacity to direct and control new process

Some factors in this list have a one-time impact and some recur. Some are difficult to estimate and may be subject to considerable error. For these, it is helpful to identify a range of uncertainty. In addition, the factors may occur at different times. And some factors may have thresholds, critical values above or below which we might wish to reject the project.

Clearly, no single project decision need include all these factors. Moreover, not only is the list incomplete, but it contains redundant items. Perhaps more important, the factors are not at the same level of generality: profitability and impact on organizational image both affect the overall organization, but impact on working conditions is more oriented to the production system. Nor are all elements of equal importance. Change in production cost is usually considered more important than impact on computer usage. Later in this chapter we will deal with the problem of generating an acceptable list of factors and measuring their relative importance. At that time we will discuss the creation of a DSS (Decision Support System) for project evaluation and selection. The same subject will arise once more in Chapters 12 and 13 when we consider project auditing and termination.

Although the process of evaluating a potential project is time-consuming and difficult, its importance cannot be overstated. A major consulting firm has argued |37| that the primary cause for the failure of R & D projects is insufficient care in evaluating the proposal before the expenditure of funds. What is true of R & D projects also appears to be true for other kinds of projects. Careful analysis of a potential project is a sine qua non for profitability in the construction business. There are many horror stories |43| about firms that undertook projects for the installation of a computer information system without sufficient analysis of the time, cost, and disruption involved.

Later in this chapter we will consider the problem of conducting an evaluation under conditions of uncertainty about the outcomes associated with a project. Before dealing with this problem, however, it helps to examine several different evaluation/selection models and consider their strengths and weaknesses. Recall that the problem of choosing the project selection model itself will be discussed later in this chapter.

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Project Management Made Easy

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