Transnational Research Associates

Engineering Economics

Investment Proposals and Decision-Making

Art Madsen, M.Ed.

Much like the formal science of pure economics, the field of engineering also incorporates interdisciplinary sub-specialties which make it an appropriate and effective tool for both designing major structural projects and for working out, using quantitative methods, the financial package to do so. It is as crucial to able to create a safe and solid facility as it is to make sure that the financing is in place to complete it. Many third world nations have towering monoliths on their horizons that were never completed due to poor financial planning and an inadequate mastery of engineering economics. Industrial and project managers who have a professional grasp of engineering economics and high finance can avoid such third-world half-finished eyesores and can do so well within originally forecast budgets. To make sure that adequate funding remains available for project completion, astute assessment of investment proposals is an important part of generating company income.

Within the complex realm of engineering economics there is a vast array of topics, but none more important, in my estimation, than investment proposal assessment and decision-making. Tens of millions of dollars can be lost through ill-conceived decisions based on partial or inaccurate data. In this paper, following introductory observations, the most managerially advanced methods of making wise investment decisions will be discussed. Reference will be made to known models for (1) choosing the proper investment proposal, for (2) recovering capital and for (3) streamlining methodologies currently employed to profitably complete an engineering project on schedule, and to make sure the capital is there to do so.

Corporate managers, many of whom are degreed engineers, are well aware of the Scientific Method that essentially involves a combination of economic assessment skills and hard analysis. Within this context, experienced industrial managers are well aware of the predictive power of figures, data and input. From such sources of information decisions emerge, decisions which impact the image and reputation of an entire firm. These factors are instrumental in the winning of new design and construction contracts. If contracts are completed on schedule, and within the price structure forecast, the company moves ahead and expands. In the internal management process during the design and construction phases, decisions -- sometimes hourly -- are the critical factors in meeting project objectives.

The decisions under analysis in this paper will, of course, deal directly with cost issues and project financing generated, in part, through investment portfolios. This is the case since we are focusing on engineering economics, as a sub-specialty within the greater field of project engineering and management.

Many projects must be delayed because of funding delays caused by financial institutions' reluctance to provide cash-flow. They may have a variety of valid reasons for curtailing funds. An industrial manager has huge responsibilities, often affecting the employment of hundreds of workers; if cash becomes unavailable for continuation of normally scheduled project work, then everything necessarily grinds to a halt. Financial decisions have to be made well in advance to eliminate this occurrence. Intelligent investment decisions, while funding is readily available, and cost-control measures at all stages of project work are the determining factors in ensuring smooth cash-flow for continuance of all operations. In Thuesen's Engineering Economy, Fourth Edition (1971), and later in his Eighth Edition (1993), strategies are discussed at length for making, what the author calls in his original Chapter 7, "decisions among alternatives". Investment proposals must be generated and often there are many of them, some are dependent proposals and others are independent. These two categories are determined by the degree to which their elements are interdependent. In other words, if element "A" can only be achieved by implementing element "B" then this proposal is categorized as dependent; the converse is true, of course, for independent proposals. Key decisions, affecting cash availability in lean times, are direct outgrowths of analysis of both categories of investment proposals. Based on the types of financing required for on-going project needs, investment proposals can be quite simple, involving rotating lines of credit for example, or quite complex involving perhaps a web of accounts, investment vehicles or stock-issuance, debentures, derivatives or other sophisticated strategies. The objective, whether proposals are internally dependent or independent, is to provide a bridge of financing which can effectively support on-going project or corporate activities when cash unexpectedly or temporarily stops flowing from banks, clients or lenders.

There are a number of risk factors associated with any construction project, however well designed from an engineering viewpoint it may have been. Industrial managers, when implementing many of the fundamental concepts of economics as vital decisions are being made, keep profit and cost-effectiveness firmly in mind. If the cost of capital is in the neighborhood of 10% per annum, then they will seek investment instruments that, with minimal risk, will yield at least 15% annually. Normally, an engineering firm's clients will ensure adequate capital flow, but there are contingency situations that develop during construction necessitating low-cost capital instantly, from other sources. By maximizing income on side-lined capital assets, the firm can tap into these proceeds at will, without destabilizing the underlying financial integrity of the company. It can do so for fairly lengthy periods of time, in accordance with a formula for determining when, precisely, the risks of continuing to provide such bridging support outweigh the probable benefits of keeping the non-paying client's project on schedule. In real-world situations, decisions of this type have to be made, balancing all the critical factors in the firm's investment portfolio, in relation to the client's financial stability and integrity.

Government clients, for example, are considered notoriously irregular when it comes to providing smooth-flowing capital for project needs, particularly on large projects. Engineering firms working with government agencies, whether foreign or domestic, need to plan carefully for delays and anticipated cash-flow blockages. By having a healthy investment portfolio, originally derived from one of several investment proposals, the firm can responsibly weather a shortage of operating funds on one or more of its on-going projects. Decisions made early in the game determine how effectively a firm can deal with these interim crises.

Quantitative methods, such as the Present-Worth Model, plug several variables into a graphed equation and a decision can be made on the basis of incremental investment parameters displayed (Thuesen, 1993, 170-172). The present worth concept is very useful since it provides a means of comparison of investment possibilities, and weighs the company's disbursements in relation to receipts at a fixed point in time, taking into account the prevailing interest rate and other factors.

In addition to using funds invested and profits reaped to provide bridging finance for major projects not principally financed by the firm, but rather by an external client firm, capital wisely generated through astute investment can also be used by the engineering firm to float its own projects. This was the case of Bechtel Corporation, International Engineering Company and Fluor Utah in the 1970s and 1980s. Their massive capital backing, derived from a significant number of banks, holding companies, subsidiaries and private investment portfolios, enabled them to embark on self-financed projects, in addition to their world-wide project commitments financed by government and private clients. Decisions made in the executive offices of these firms, often employing the relatively simple quantitative methods referred to, resulted in phenomenal success on a number of projects, ranging from the Yangtse River to the Sahara, where bridging financing was either required or where total in-firm funding was provided.

Within a given project smaller decisions often are made which can measurably enhance profitability. Toward project completion, the choice between salvaging used equipment and materials or disposing of these items at a substantial loss must be made. This is an especially important decision on overseas work-sites from which heavy equipment cannot be cost-effectively re-shipped back to the United States. Once again, quantitative methodologies come into play and are used by managers whose highest priority is maximizing revenue for the firm, while providing the best possible quality product for the client. Depreciation must be carefully figured and cost-recovery strategies implemented (Pansini, 1995). These concepts do not apply only to heavy equipment used during the construction phase, of course, but also to depreciation of the entire physical plant designed and constructed to endure for a finite period of time. Depending on the terms of the engineering or construction firm's contractual arrangement and the specific nature of project, decisions to (1) salvage, (2) discard, (3) sell, or (4) re-use equipment or plant facilities must be made. By examining in some detail the original cost of the equipment, the net salvage value and the average service life, an annual rate of depreciation can be calculated. As is the case with decisions of greater magnitude, these 'less significant' matters can still have great impact on a given project's bottom-line, as any accountant can attest.

These are just some of the considerations which must be entered into the proverbial decision equation to achieve cost recovery, high profitability and improved overall corporate financial performance. Capital saved or created can, of course, be re-injected into investment portfolios.

From a theoretical perspective, engineering economics operates on the assumption that a dollar invested in the present is worth far more than one available for investment in the years ahead. Not only is the fluctuating inflation rate an operative factor in this regard, but the actual placing of funds in present-day financial instruments and vehicles results in return on investment (ROI) which is further compounded through reinvestment strategies. Since the funds available in most industrial portfolios are considerable, even reinvestment of interest or profit can prove to be lucrative and advantageous.

This brings us full-circle back to the role of the investment proposal in financial decision-making. Given the climate of uncertainty prevailing in many of the world's key economies, there needs to be at least one methodic approach, or perhaps several valid approaches, for making investment proposal decisions. It turns out that there are at least five recognized strategies incorporating fairly complex elements, used generally when actual 'probabilities' have not been made available to management. Figure I, below, displays the names of these well-known models, and indicates the principal economic assumptions governing each.

These models offer means and methods of selecting from among investment alternatives. Each has its own advantages and disadvantages and must be adapted for use in specific investment climates and situations. The remainder of this paper will focus on some of the rules and priorities assigned by these recognized theories to the wide range of investment dilemmas faced frequently by industrial engineers.

1. Laplace: No economic probability is more likely to occur than any other.

2. Maximin: An entirely pessimistic model that selects the best of the worst alternatives.

3. Maximax: The most optimistic model assuming that the ideal outcome will occur.

4. Hurwicz: A flexible model incorporating a fluctuating pessimism/optimism coefficient.

5. Minimax Regret: A model with a matrix to allow for what could have been achieved.

Source: Adapted from Thuesen (1993, 569)


Because uncertainty is inherent to the investment world, risk is a major factor in placing funds in any financial institution, holding company, or investment vehicle. When probabilities are assigned to each investment proposal decision, risk is sometimes minimized and important criteria are honored. However, when specialists are unwilling to commit themselves to calculating probabilities, then another set of rules comes into play. Incidentally, the entire field of probability is a separate subject requiring elucidation, but perhaps not in the present paper. Needless to say, investment proposal decisions are usually based on elaborate results of probability calculations in keeping with standard procedures in the field of investment strategy. When probability figures are not available, then one or more of the five models shown on Figure I are frequently used.

Certain assumptions as to the investment climate are made in all of the five models depicted above, and are then converted to mathematical or arithmetic form. The most favorable alternative is then selected from among four or five scenarios that result from 'working through' a theoretical equation provided by engineering economists.

The first accepted model revolves around the premise known as the "principle of insufficient reason" which is recognized in economic circles as being predicated on the idea that any investment atmosphere is as likely to occur, naturally, as any other atmosphere. This notion is also called Laplace's Rule. Mathematically, this concept is recorded as "1/n", that is one over n, in which "n" represents the number of possible investment climates or possibilities. Calculations invariably result in Alternative 4, of 5, being selected under this system of evaluation. (Thuesen, 1993)

Two similar models for selecting investment proposals and options with varying degrees of optimism and pessimism are the MAXIMIN and the MAXIMAX models. Both of these reflect widely divergent assumptions about either the general economic mood or the actual investment being considered. In most cases, for our purposes, it is the latter, i.e. the actual investment being assessed. The first model, Maximin, is quite pessimistic in its approach and, worked into the formula for generating alternative outcomes, is this overlay of bleakness. Maximin thinking, in fact, results in an investment alternative being selected which is the best of the worst possible outcomes, according to Thuesen. Alternatives A2 and A5 are equally poor, but marginally profitable in a conservative sense.

Under Maximax thinking, the rosiest of pictures is painted and the formula reflects an optimistic outlook, generating a result that may be somewhat on the 'high side.' The results of the three foregoing models are expressed as Alternatives (A) in the form of dollar payoffs (P). In these formulas, exponents and coefficients reflect the degree of economic certainty, variability, or what economists call "states of nature."

The final two models used to determine which of pre-existing investment alternatives is best (in the absence of reliable probability data) differ somewhat in their complexity. The Hurwicz model includes a variable coefficient for optimism/pessimism in order to accommodate those engineering economists who may find weakness in the Maximin and Maximax extremes. In order to properly use this Hurwicz Rule, several plots need to be generated on a graph so as to project investment income under a variety of 'states of nature', i.e. economic outlooks either in the macro-sphere or within the specific investment proposal being evaluated. Several potential payoffs can therefore be projected with a degree of accuracy as fine-tuned as the data plugged into the Hurwicz coefficient.

Lastly, the Minimax Regret paradigm should be mentioned. When investment and portfolio managers, be they engineers, financiers or economists, calculate what they could have earned by having predicted in advance all market conditions, but were unable, of course, to do so, they experience a "regret factor". This rule or model helps them overcome this factor by inserting a "regret matrix" into their calculations. The idea is to minimize the regret factor to the extent possible, thus (somehow) favorably impacting decisions in the future. The underpinning logic of this model's formula is designed to position the decision-maker in the safest possible position before selecting a proposal or option.

Each of these five conceptual rules is useful when resolving a specific investment dilemma or when analyzing certain types of proposals and options. Some tend to play up the likelihood of success, while others down play the magnitude of payoffs. The final two systems seem to provide a more complex mechanism for calculating and evaluating inherent risks and uncertainties.

This paper, although fairly concise, has reviewed the importance of corporate investment decision-making with regard to:


If any one observation could be reiterated, it would probably be that for whatever strategic or financial planning purposes, proper evaluation of investment proposals, and the decision-making that accompanies this assessment, are indispensable for all engineering and industrial firms that wish to remain competitive and viable in contemporary markets.




Pansini, A. Engineering Economic Analysis Guidebook, The Fairmont Press, Lilburn, Georgia, 1995.

Park, W. Cost Engineering Analysis: A Guide to Economic Evaluation of Engineering Projects, John Wiley & Sons, New York, N.Y., 1973.

Riggs, J., Bedworth, D., and Randhawa, S. Engineering Economics, Fourth Edition, McGraw-Hill, New York, N.Y., 1996.

Thuesen, G. and Fabrycky, W. Engineering Economy, Eighth Edition, Prentice Hall, Englewood Cliffs, N.J., 1993.

Thuesen, H., Fabrycky, W. and Thuesen, G. Engineering Economy, Fourth Edition, Prentice Hall, Englewood Cliffs, N.J., 1971.