Companies invest hundreds of billions of dollars every year in fixed assets. By their nature, these investment decisions have the potential to affect a firm’s fortunes over several years. A good decision can boost earnings sharply and dramatically increase the value of the firm. A bad decision can lead to bankruptcy[1]. The reason is that most of these decisions involve committing a big sum of money and the results heavily depend on forecasting and creating the future in a competitive and ever-changing business environment. Thus the risk and uncertainty is inherent in these investments. Fortunately decision science has developed for many years and methodologies have been devised to help the decision-maker evaluate capital investment projects and minimize or avoid the risk and uncertainty inherent within these decisions. However, there is no such a thing as one best way to solve all these decision problems. An engineering manager must know how to select the evaluation methods and combine them with the management expertise to make the best decision for the company.

This paper will give a general review of current methodologies on capital investment appraisal and followed by some discussions about the weaknesses and virtues of different methodologies. Suggestions and conclusion are made based on the point that a sound decision can only be reached by combining the scientific methodology with decision makers’ expertise.

1. Traditional Approach—Financial and Economic Methodologies

It is very natural to evaluate investment on the financial and economic basis. After all, the fundamental purpose of any investment is to augment wealth which is measured by profits. According to Proctor and Canada [22], capital investment evaluation in the civilian sector has traditionally been performed through the capital budgeting process. The definition of capital budgeting is articulated by Canada and White[4] as "the series of decisions by individual economic units as to how much and where resources will be obtained and expended for future use, particularly in the production of future goods and services." Hirschey and Pappas [1] give its definition as "Capital budgeting is the process of planning expenditures that generate cash flows expected to extend beyond one year."

Concerned primarily with measurement of capital productivity, Dean[13] proposes an umbrella capital expenditure management program with ten elements tied together in a step by step process. The process provides "top management…an objective means of measuring the economic worth of individual investment proposals in order to have a realistic basis for choosing among them and selecting those which will mean the most to the company’s long-run prosperity." Emphasis is on "efficiency" and "productivity of capital which…means its power to produce profits."

The growth of capital budgeting practices and in particular discounted cash flow (DCF) models (such as Net Present Value (NPV) and Internal Rate of Return (IRR)) is well documented over the past decades. Overseen often by controllers and financial department, capital budgeting practices of large corporations widely incorporate DCF models in the financial analysis of capital investment proposals [22]. While Non-DCF methods such as payback period and accounting rate of return is often used by small business firms [6,28,29,30].

1—1. DCF methodologies

The basic principle underlying the discounted cash flow methodologies is the time value of money, which is generally based on the cost of capital [1,3,4]. By analyzing the historical financial data of the company and investigating market, the marketing, engineering, and financial departments make estimates of cash flows of an investment proposal. When all the cash flows are changed into the present value at a certain discount rate, the present value should be greater than or at least equal to zero if the proposal is to be accepted.

In practice, mainly two methods are used to justify the investment [5,6,22]: NPV (by ranking proposal in order of descending positive NPV) and IRR (by ranking proposal in order of descending IRR when IRR is greater than minimum attractive rate of return or discount rate). Although both methods are theoretically correct, but they don’t always give the same ranking.

Regarding the choice of the two methods, Evans and Forbes [5] argue that capital budgeting practices reveal that IRR is much preferred over the NPV as an investment decision tool even though business scholars prescribe the NPV as theoretically optimal. IRR is treated as display method. As such it is more compatible with decision-makers’ expectations and therefore, is more cognitively efficient. Because the IRR is expressed as an interest rate, it more closely resembles an analogy display, in which the IRR is simply compared to the required return. In contrast, the NPV is stated in dollars, resembling more a very precise digital display. Evans and Forbes finally suggest that academicians should reorient their efforts from promoting the NPV teaching methods to ameliorate the pitfalls of the IRR.

To suit the needs of business decision-makers and avoid the problem of different ranking of IRR from NPV, other scholars devised different rate-of-return methods which are supposed to be ranking-compatible with NPV. Shull [23] introduced a yield-based capital budgeting methodology, and stated that, by integrating this technique, each adjusted Overall Rate of Return method can provide NPV-consistent evaluations for an entire set of projects being evaluated over a common time horizon. He further cited an Adjusted MIRR method derived from Beaves [25] to verify the technique. Later he introduced a Scale-Adjusted Rate of Return Method [9] to enable the NPV-compatibility of the Overall Rate of Return (ORR) and Perpetuity Rate of Return (PRR) methods. Asquith and Bethel [8] also introduced a viewpoint to prove that ranking project by IRR can be superior to a NPV method. However, these methods of ranking projects by rate of return prove not to be completely compatible with NPV. Hajdasinsky [11] pointed out that Shull’s project ranking procedures are conceptually improper. Current definitions of ORRs are not fully NPV-compatible. In his another paper [10], he also pointed out the fallacy of Asquith and Bethel and stated that the incremental project ranking approach is the proper approach regardless of which NPV-compatible profitability measure is used. This viewpoint is also supported by Thuesen [3], who stated that ranking on rate of return will guarantee to select the set of proposals that maximize the total present-worth amount only if all the proposals are independent and there is no limitation on the money available for investments.

As stated, DCF methodologies heavily depend on the estimating and forecasting of future financial data, the inherent risk and uncertainty becomes a major concern of decision makers. The current approach toward risk and uncertainty can be found in works of Rose [2], Thuesen [3] and Canada [4]. Probabilistic methods are generally employed to deal with the analysis of risk, but their use is largely limited due to the lack of historical data and information. As pointed by Canada, these probabilities are not generally objectively verifiable, and hence are generally subjective probabilities. The evidence supporting any given probability in any analysis may differ markedly in both quality and quantity from that for any other probability. As long as uncertainty is concerned, the present methodologies have very limited help. Recently, a new approach by utilizing fuzzy set theory is explored by some scholars [14,15] to deal quantitatively with imprecision or uncertainty. Due to the development of computers and easy-to-use software packages, there is a strong increase in the use of both sensitivity analysis and adjustment of hurdle rates for risk in DCF models [26].

1—2. Other Accounting Methodologies

Among other financial analysis tools, the most used methods include Payback Period (PBB) and Accounting Rate of Return (ARR) [6,7,12]. These methods are considered theoretically incorrect and are dissuaded by scholars [4,6,29]. The shortcomings of Payback Period method lies in its ignoring the time value of money as well as the cash flows after the payback period. Some scholars suggest adding a discount rate in the calculation of payback period [4,29], but it still falls short of the economic value standard. Due to its easy use, the Payback Period method is mostly used in small business firms [6] and used by large corporations as a supplementary tool for risk and liquidity analysis [22]. In a survey conducted by Stanley Block [6], payback period is still the preferred approach by 42.7% of firms. While DCF methods are used by 27.6% small business firms, which is a higher rate of utilization than that indicated in other surveys of small business over the last few decades. Block pointed that in recent times, small business firms created 80% of new jobs in the U.S. [27]. Thus, their methodology for capital investment decisions is very important, though it is somewhat different from that used by large business firms. One of the reasons presented by Block is that financial institutions can indirectly exert strong pressures on the analytical methods used by small business firms in making investment decisions [28]. Bankers are primarily interested in the firm’s ability to meet short-term obligations associated with a loan, rather than maximizing the wealth of the owners of the firm. When the small business owner approaches his/her banker for a loan to finance a capital investment, he/she better be prepared to demonstrate his/her capacity to repay the loan within a set period of time rather than to specify the project has a positive net present value or that the IRR exceeds the weighted average cost of capital. While there are approaches to integrate more theoretically correct methods into the Payback Period calculation [29,30], these are often beyond the scope of the small business owner and may require more sophistication than any of the four primary methods (PBP, ARR, NPV, IRR) standing alone.

Average Accounting Rate of Return (ARR) also overlooks the time value of money. It is used by small business firms because it is easy to understand and manipulate [4,6,12,28].

2. Nontraditional Approach—Multi-attribute and Strategic Methodologies

A number of criticisms of the capital budgeting process surfaced since the 1980s [18,21,26,31,32,33,34] when U.S. manufacturing companies were confronted by the strong competition from Japanese products. Scholars began questioning the traditional approach adopted by U.S. manufacturers in evaluating advanced manufacturing technology. Proctor and Canada [22] point that one particular criticism of the traditional approach involves the fundamental problem of estimating cash flows. Project selection is primarily based on financial considerations and hence, project definition is often formulated by practitioners based on the easily-conceptualized and immediately identifiable cost savings.

Kaplan [31] identifies many inadequacies in traditional accounting methods which, although not accepted by everyone, have caused some reconsideration of accounting practices. In his paper, Kaplan highlights quality, inventory, productivity, innovation and work force capabilities as critical non-financial aspects of manufacturing performance. In a later paper focusing on computer integrated manufacturing justification, Kaplan [32] challenges the evaluation of new investment against a status quo alternative of making no investment at all. He contends that such an alternative assumes that current market share, selling price, and costs will continue as usual. Instead, Kaplan asserts that the status quo should reflect probable declining cash flow, market share, and profit margin. Finally, he points out additional "intangible benefits" of new investments in manufacturing technology including: greater flexibility, shorter throughput and lead times and increased learning.

Kaplan’s viewpoint were positively responded by many scholars who later provide more and detailed supporting evidences through case studies. Meredith and Suresh [33] state that it is now well recognized that the major roadblocks to automation our factories are not engineering shortcomings in the equipment or manufacturing processes but rather managerial attitudes and policies. Foremost among such roadblocks is the justification problem. They argue that conventional financial justification models, in many cases, are inappropriate on their own to justify advanced technology projects. They can not cope with the nature of the benefits offered, such as flexibility and synergy, and the risks inherent in today’s advanced manufacturing technologies.

Considerable evidence is available to support the claim that the financial appraisal methods used by industry to evaluate capital investments may be inappropriate for today’s high technology business environment [16,18,21,26,34]. A manufacturing strategy survey of senior managers of large manufacturers in Europe, North America and Japan conducted by Demeyer et al, reveals that the competitive priorities of those executives are dimensions of cost (productivity), quality, flexibility, and dependability/delivery [34]. Other surveys [22] also indicate product quality as a top priority in America. Other less tangible benefits most widely cited are: (1) improved competitive position, (2) increased manufacturing flexibility, (3) reduced delivery time, and (4) reduced product development time.

Strategic evaluation of capital investment which used to be performed by top management beyond the range of financial appraisal are now brought in line with justification of investment in advanced manufacturing system and other high technology, long-range capitalization projects.

Analytic Hierarchy Process (AHP) [4] was developed and documented primarily by Thomas Saaty. It is presently most widely used analytic tool for multi-attribute decision making [4,19,24]. The strength of the AHP method lies in its ability to structure a complex, multi-person, multi-attribute, and multi-period problem hierarchically. The analytic process includes identifying strategic objectives and their supporting sub or sub-sub objectives of each objective, assigning different weights to each objective (attribute), comparing alternatives against the attributes from the lowest level and computing the overall scores. This process allow decision maker to assign different weights to each attributes (generally strategic goals) and comparing alternatives based on the decision maker’s overall strategic policy. However, AHP method is not flawless when applied to capital investment decision problems. One of the major criticisms of AHP is the manner in which the attribute weights are elicited and assessed [4]. Another well documented problem is the ranking reversal. Ranking reversal is encountered when the introduction of a new alternative reverses the rankings of previously evaluated alternatives. Boucher [19] argues that if the AHP is used for economic decision making in which criteria quantified in economic terms and difficult-to-quantify criteria are combined in arriving at the overall value of an alternative, one must assume that the Decision Maker is implicitly weighting the criteria based on the average value (importance) of the alternatives on that criteria. Otherwise, the final vector weights do not make economic sense.

The success of AHP has prompted researchers to develop new multi-criteria decision making methodologies that incorporate some of the features of the AHP. The use of a hierarchy to structure the decision problem and a pairwise comparison process to weight decision elements has appeared in newer methodologies. Kleindorfer [24] introduced a model based on Ackoff’s interactive planning process to establish a strategic performance hierarchy for a given line of business. It is this performance hierarchy which, together with an evaluation methodology based on Saaty’s AHP, leads to the proposed procedure for prioritizing alternative technologies. Boucher [19] developed a multi-criteria capital investment justification technique that incorporates the finer features of the AHP but overcomes many of its perceived deficiencies. He call this model Non-Traditional Capital Investment Criteria (NCIC), and illustrated that the outcome of this model can be mapped to the outcome of AHP and vice versa. However, it overcomes the deficiency of ranking reversal problem in AHP.

The Kaplan model is in fact a two level model. Kaplan [32] argues that conventional discounted cash flow method should be used based on the financial data available and based on this information, the project shows a positive net present value, then it should be accepted. If the NPV is negative, then it becomes necessary to estimate how much the annual cash flows must increase before the investment does give a positive NPV. Then the management must decide if the value of the intangible benefits will be greater than this figure. If the answer is yes, the project is still acceptable.

Meredith and Suresh identify three main groups of AMT projects: 1. Stand alone (such as robots, numerical control machines, CAD), 2. Linked (such as flexible manufacturing systems, CAD plus computer aided process planning etc), and 3. Integrated (fully integrated FMS, CIM). For the stand alone projects, they suggest that the standard economic evaluation models (payback, ROI, IRR, NPV, etc) be used with an allowance being given form any additional economic benefits or costs which are peculiar to this type of project. With the second group, a more analytical approach is required. This will involve such techniques as Value analysis, Portfolio analysis, and Risk analysis. The third group will require a more strategic justification approach and involve such factors as technical importance, business objectives, competitive advantage, research and development. This will usually involve the setting up of a pilot project before the full project is accepted.

It is argued that the strategic approach to the justification problem should not be used in isolation, but that both the economic and analytical approaches should be used to supplement the evaluation process for the more sophisticated level of AMT to reveal the full implication of the decision.

There are many ways to structure a multi-attribute model. They include [22]: single stage scoring, two stage scoring, AHP, and goal programming. These models are generally designed according to the management’s preference and their business strategy, which may include financial and non-financial factors. Canada [4] introduced some general analytic tools in establishing a multi attribute model. The process include identifying criteria, which are generally lists of possible attributes or strategic objectives, selecting measurement scales, selecting analysis techniques, making evaluation against the criteria.

Walls [20] introduces a good example of goal programming. In the paper, he presented a case of choosing and allocating limited investment funds among array of projects according to the company’s strategic objectives, which are considered vital to long term business viability by the senior management. Multi-attribute utility theory is used to establish a systematic and comprehensive strategic choice model that integrates strategic objectives. With the help of computer, different portfolios are produced and prescriptive result aims at the portfolio with the highest utility, which will strike a balance among different strategies, such as lowest risk, minimum cost, maximum NPV, maximum resource reserve, etc. Sensitivity analysis is easy to be conducted to provide extra information on the promising portfolios.

The principle advantages [22] of these approaches include comprehensive structuring of the problem, little or no reliance on a massive accounting and measurement system, and finally, ease of use. These non-traditional models permit flexibility in selection of criteria, are inexpensive and do not require detailed economic analysis in order to implement. On the other hand, they can incorporate DCF information if desired. Weights can be used to ensure criteria reflect the business strategy. Finally, the wealth of knowledge possessed by experienced personnel can be channeled in manners which enhance its impact.

Disadvantages include the potential for faulty structuring of criteria, skewing of the evaluation due to the dominance of particular players or other misapplications due to the lack of knowledge or insight on the part of the personnel involved in the process. Finally non-traditional techniques do involve some difficulties in communication with decision makers by creating evaluation scores or other results which are not as easily conceptualized as are monetary rates of return.

No contention can be made against the intention of using capital budgeting methods in evaluation of investment proposals. As stated, the ultimate purpose of any capital investment is to augment wealth which is measured by economic value. Thus, any economy-driven (cost-saving, short-term opportunity, and expansion) investment proposal should be evaluated using capital budgeting methods.

Selecting capital budgeting methods requires understanding business environment. Since capital budgeting methods are only analysis tools assisting management in making investment decisions, which tool to employ depends on the efficiency of the tool in different situations. The theoretically correct methods such as NPV and IRR are favored in most situations of capital budgeting analysis, but they are not the sole measure and norm of accepting investment proposals.

Most business owners favor a rate of return measure in helping them evaluate proposals because it is both easy to understand and comparable to interest rate on capital markets. That is why IRR is a better-favored DCF model among practitioners [5]. However, IRR requires much more calculation than NPV, and doesn’t rank proposals according to its total economic value (vis present worth or net present value). In order to be compatible with NPV, an incremental approach has to be adopted when making comparison among different alternatives. There are also situations of multiple solutions when cash flow changes its direction more than once. Such calculation malaise of IRR needs to be taken care of when adopting this method. Another criticism of IRR is somewhat more fundamental that IRR as an interest rate is more or less misleading because it assumes the money recovered during the middle of the period would be generating the same profit as the original investment, which is often not the case.

NPV is now gradually gaining popularity because of the advocacy of academicians. It avoids the calculation malaise of IRR, thus is less misleading. However, as pointed out, NPV doesn’t resemble an analogy display, which is more efficient in evaluation process. The idea could be easily demonstrated. During evaluation process, the hurdle rate (or discount rate) may frequently change in mind because management may have different considerations regarding risk and uncertainty. The NPV can not adjust itself to these changes of considerations in mind and have to be calculated each time when different consideration happens. While IRR can easily tell if the proposal still falls within the range of different consideration.

ARR could be said to be a simplified substitute of IRR in that it is not only a particular case of IRR when the discount rate of cash flow is zero, but it also takes on the same display method. Perhaps that is why ARR is still popular among some practitioners. As pointed by some scholars, many small business firms resort to such method simply because it is difficult for them to determine their cost of capital, which is recommended to be used as the discount rate if DCF models are to be used. Many business owners and middle or lower level decision-makers are familiar with such simple ideas and terms. They are used to and still can make relatively sound decision based on such terms, especially when they need fast numbers to make quick decisions. This is what scholars refer to as back-of-envelope decisions among lower-level managers and small business owners.

Payback Period method is the most disputable yet most popular techniques used by practitioners. It is strongly criticized among academicians [4,35] because it not only overlooks the time value of money, but also the cash flow after the payback period, thus loses the whole picture of the proposal. Despite these criticisms, it turns out to be the most favored budgeting tool because of its simplicity, emphasis on liquidity, and response to external financing pressures[6,36]. This conflict between academicians and practitioners exists as long as the practical business environment differs from what scholars presume in their evaluation model. DCF models take an internal-oriented position of maximizing the economic value of the investment, while PBP, on the other hand, focuses and responses to the dictation of external environment. That is why large corporations elect to evaluate the proposals by DCF methods while still employing PBP as supplementary tool for risk and liquidity analysis [22]. Another reason of favoring PBP is suggested by Longmore [36] as short-termism of managers over corporation owners. One of the main benefits of PBP not emphasized by other scholars is that it naturally takes into account the volatility of forecasted cash flow of later years. Such consideration is important in many industries, especially in consumer goods industry, which is highly populated by small business firms.

In light of the above discussion, we can reach the following conclusion regarding capital budgeting methods:

1. A combined approach of DCF method and PBP is optimal for any situation. PBP could be used as an effective screening tool in situations where there are considerations of external constraints. In such a case, PBP is emphasized while NPV or IRR could be used as supplementary tool in situations of stringent external financial pressures. Where external financial pressure is not a big concern, NPV or IRR is emphasized as a major measure of economic effectiveness and PBP as supplementary analysis tool for liquidity and risk evaluation.
2. Regarding the choice of the NPV or IRR, it depends on the acquaintance of the management with each tool. When the management are inclined to increase the required return as means of risk consideration, it is more effective to use IRR in the budgeting process. However, this tool requires more sophistication of practitioners and management, and might be misleading to someone not having full knowledge of it. It is advisable that practitioners should try not to use both of them because it might cause confusion to the management and other interested parties. It would be a good practice to stick to one of them in a corporation rather than using diversified methods.
3. ARR should be avoided whenever the management and other decision-makers are ready to accept DCF methods. For less sophisticated corporations, NPV should be readily available because it is easy to calculate and fallacy-free.
4. Payback Period method is a very effective evaluation tool and should not be overlooked. It is strongly recommended in industries featured by the volatility of product market and to corporations experiencing financial pressure. Whether or not using discounted cash flow in calculating payback period is not a great significance but should be made clear when coming up with the number.

Criticisms of capital budgeting practices mainly focus on its ignorance of intangible benefits and resulting in under-investment in organizational capabilities[31,37,38,39]. As pointed out by Harvard Business School professor Baldwin [37] in his 1994 study, "Even the most skilled practitioners of discounted-cash-flow methodology have difficulty fitting crucial strategic investments into clear-cut revenue and cost projections. As a result, under-investment in organizational capabilities is a pervasive problem in many U.S. companies." And he further argues that the present capital budgeting procedures may well be a culprit in the decline of U.S. global competitiveness. It is clear, therefore, that strategic methods should be employed in a strategy-driven (technological innovations and revolutions, long-term investment in volatile markets, R & D projects) investment analysis.

As a matter of fact, a survey by Cook and Rizzuto [40] showed that most U.S. companies do not use any formal economic analysis to evaluate their basic research projects which are clearly defined as being "strategic" investments, and seldom use it to evaluate applied research projects. However, in the evaluation of development projects DCF models are widely utilized. Thus most of the questions arise in the appraisal of such projects that have characteristics of both strategy-driven and economy-driven investments (applied research and development projects).

Since no criticism has been directed to the overestimation of strategy-driven projects by traditional approach, Kaplan’s two level model [32] could be qualified in such analysis. The main impediment of using Kaplan’s model involves converting intangibles into measurable economic worth, which is highly unlikely and unreliable. Therefore, there is not much meaning in trying to convert intangibles into financial terms. A modification can be made to the model. If the economic evaluation fails, a strategic evaluation is employed. Utility measure could be used as an efficient tool in integrating strategies into the evaluation process with any multi-attribute methodology. If the estimated economic value of investment is believed to be underestimating and unreliable, the data obtained will not serve much purpose in the strategic analysis of independent project. However, it can be combined into the multi-attribute methods in comparison of alternative proposals where a choice must be made.

The current multi-attribute methodologies are generally based on the comparison of alternative investments. Analytical Hierarchy Process could be a sophisticated representative of the multi-attribute methods. The merit of AHP is that it can be easily fitted into any strategic analysis process and give a sequential and hierarchical subdivision, thus breaks down the problem and makes the decision-making more easily and objectively. The logic is easy to follow and weights assigned to each attributes is easy to determine. However, as pointed out, the ranking reversal problem may surrender the technique to null. This is mainly due to its manner of eliciting and assessing the weight for different alternatives. During the analytical process, the decision-maker is constantly making a balance and differentiation of relative weights of different alternatives. A way of improving the technique could be comparing only two alternatives every time and eliminating the inferior before conducting next round of comparing. Another way is using somewhat absolute measure, say, the utility measure, not the relative measure. But the process will be more complicated.

In the case of independent investment proposals, there hasn’t been any clear cut or dominating strategic methodology. With a bunch of proposals, a portfolio approach can be employed to strike a balance in allocating limited funds to satisfy the company’s strategic goals, as demonstrated by Walls [20] and Stevens [41]. However, portfolio management bases its assumption on the plentitude of investment opportunities which are generally pre-justified by other evaluation process, say, DCF methods. Such methodology can well fit into large companies’ planning process. Small companies are seldom confronted with such chances. When it boils down to the evaluation of each single proposal, the problem evolves into the analysis of financial risk of investing and competitive risk of not investing.

In his paper " The Risk of Not Investing in a Recession", Ghemawat [39] stresses that companies should think long-term and focus on competitive position. He cites the semi-conductor industry to emphasize the importance of investing in order to sustain a competitive position. In 1970s, U.S. companies’ share in semi-conductor industry is around 70% while Japanese companies’ share is around 30%. In the down turn of 1975-76, major U.S. companies hesitated in investing (in capacity to produce 16K DRAMs) while Japanese companies didn’t. When the upturn came, IBM and other U.S. customers began to turn to Japanese suppliers for newer products for the first time. This shifted the balance of trade in semiconductors to favor the Japanese and the situation has never turned back. Till late 1980s, Japanese companies captured nearly 60% market while U.S. companies hold a share of slightly over 40%. In conclusion, Ghemawat suggests that investing to create and sustain a competitive advantage is still the best recipe for dealing with downturns and other challenges if an advantage can be achieved cost effectively.

How to sustain a competitive position and achieve an advantage cost effectively while avoiding a serious risk of financial bankruptcy by accepting too many investment opportunities? Ghemawat gives the following guideline as the frame work to help managers make a hard decision between investing and not investing: 1. Think long-term; 2. Focus on competitive position; 3. Recognize the moving baseline. The analysis of long-term competitive position provides a useful benchmark for deciding whether to invest. The importance of investment’s effect on sustainability is, if anything, even greater when a company faces capable competitors than it doesn’t. To assume, as seems common, that the alternative to investment is perpetuation of the competitive status quo is to fail to grasp this point.

With regard to the decision of technological investment, the timing and significance of technological innovations or revolutions in the technological environments can be important. Grenadier and Weiss [42] provides a useful tool of option pricing approach that can be helpful for making a cost effective choice. For example, when markets are prone to rapid innovation, companies may consider postpone their innovation investments until future innovation arrives. They can adopt leapfrog (skipping an early innovation but adopting the next generation of innovation) or laggard (waiting until a new generation of innovation arrives before purchasing the previous innovation) strategy. When markets forecast greater expected benefits to future innovations, the compulsive (purchasing every innovation—thus learning by doing) and leapfrog strategy may be adopted. For markets with rapid and significant progress, the leapfrog strategy should dominate; for markets with slow but significant progress, a compulsive strategy should dominate.

Courtney et al [43] also provides a set of generic strategies for dealing with uncertainty in business environments. They first divide the environment into four clearly cut uncertainty levels: 1. A clear-enough future, 2. Alternate futures, 3. A range of futures, and 4. True ambiguity. The strategies that can be used in the four levels of uncertainty include shaping the future, adapting to it, or reserving the right to play at a later time. These strategies can be implemented through a combination of three basic types of actions: big bets, options, and no-regret moves. Big bets are generally adopted by shapers in the levels two and three, like setting standards and creating demand. For level one, most companies are adapters. For level two, most companies may choose options approach, which are generally designed to secure the big payoffs of the best case scenarios while minimizing losses in the worst scenarios. No-regret moves are an essential element of any strategy that will pay off no matter what happens, like initiatives aimed at reducing cost, gathering competitive intelligence, or building skills.

Whatever strategic methods are used in the capital investment evaluation process, one of the most important factor that should not be neglected is the expertise of management and capability of the organization that will be committed to the investment. It is not saying too much to say the success of an investment largely depends on the management commitment and the organizational capability. The same project may prove to be a success in one company but a fail in another. It is especially true in the technological investment where such an investment may dictates business reengineering, new management style and organizational culture. As pointed by Hayes and Jaikumar [44], that though U.S. companies are supplied with the advanced manufacturing technologies, such as CAD, CAM, CAE (computer aided engineering), robotics, FMS, and CIM, and these advances promise to improve everything (a recent study of FMS in 20 U.S. companies showed that they have reduced by more than 50% the amount of labor, total product cost by as much as 75%, achieved significant reduction in indirect worker and staff, in reject rates, in time required to introduce products), most U.S. managers are having difficulty reaping these advantages. Just like replacing horses with truck, the whole process and the way of doing business must change. Companies must understand and prepare for the revolutionary capabilities of these systems. Otherwise, they will become as much an inconvenience as a benefit—and a lot more expensive.

According to Ghemawat [39], the mean lag in returns for R&D expenditures tends to be four to six years. Major changes in human resource practices (as opposed to policies) may, it has been suggested, require as many as seven years. And the restructuring of a corporate portfolio may take a decade or longer to implement. However the typical business cycle of the U.S. average since 1920 has been about 4 years. Thus, investments in organization capabilities should never be hampered by the traditional capital budgeting procedures. Effective funding of capabilities will require U.S. companies to coordinate accounting, technology, strategy, and finance in ways they haven’t before [37].

This paper reviewed the current practices of capital investment decision-making methodologies. It is shown that Net Present Value method, Internal Rate of Return method, Payback Period method, and Accounting Rate of Return method are the most popular capital budgeting methods used by practitioners. It is also shown that even though the Discounted Cash Flow methods are theoretically correct, other non-DCF methods persist in the practice because business environment dictates. The best practice is combining DCF method with Payback Period method in evaluating investment proposals. Payback Period method could be a very effective tool in helping management making investment decisions, particularly under volatile markets and stringent external financial pressures. However, capital budgeting techniques are very limited with regard to investment evaluation in today’s competitive environment where technological and strategic issues play important role. Analytical Hierarchy Process is an effective multi-attribute methods but it can only be used to make a choice between pre-justified alternatives. Defects of AHP are identified and ways of avoiding them are suggested. For those strategy-driven independent investment proposals, evaluation process is still an intuitive analytical process. Some guidelines are offered. Practitioners should be aware of the merits and shortcomings of different methods and combine them with the management expertise to assist management in making best investment decisions for the corporate.

[25] Beaves, Robert G. "Net Present Value and Rate of Return: Implicit and Explicit Reinvestment Assumptions," The Engineering Economist, Summer 1988,Vol. 33