Term
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Explanation
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real options analysis
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A method for valuing projects and assets based on concepts originally developed to value financial options. Real options analysis is most useful for large capital budget decisions
in situations involving significant uncertainties (especially market uncertainties) and where management has flexibility to adapt decisions to unexpected developments. For example,
real options analysis is often used for mergers and acquisitions, facility expansion decisions, oil exploration, contract valuation, and prioritizing R&D projects.
Real options analysis is based on the recognition that there is an important similarity between financial investments and business projects. In finance, a stock option
(specifically, a "call-option contract") allows (but does not force) the owner to buy a fixed number of shares of the stock at a specified date for a specified price. The owner will
want to exercise the option and buy the stock if its price goes up, but not if the stock price goes down. Similarly, projects involve options. For example, a project to construct a
new factory provides options to postpone construction if the economy slows. Building a new factory also includes options to temporarily shut down or abandon the plant, as well as to
expand its size to meet an unexpected demand for more production. The options inherent in physical assets are termed "real" to distinguish them from classic financial options.
In the 1997, Robert Merton, Myron Scholes, and Fischer Black won a Nobel price for deriving a model for computing the price of call- and other types of options. This work provided
the foundation for developing an analogous method for valuing the options (flexibility) inherent in projects. As with financial options, the value of the options implicit in a
project increases the value of that project. For example, a factory with an option is more valuable than an identical factory that does not include options, and the extra value is
the value of the option.
Traditional financial valuation methods, including net present value (NPV), typically under-value projects because they fail to adequately account
for the value of management flexibility to exercise the project's inherent options. Expected net present value (ENPV) can include the value of
flexibility (if "downstream decisions" are represented in the decision tree), and most authors include ENPV as subset of the methods available for real options analysis. Similarly,
multi-attribute utility analysis (MUA) can account for the value of management flexibility by including project evaluation criteria related to the
option value of the project. However, real options analysis also includes solution methods that derive option values from the market prices of underlying assets. Thus, for example,
a real options analysis of an oil drilling project could derive the project's value in part based on market prices for barrels of oil, similar to the way the value of a call option
on a stock can be derived from the behavior of the market price of that stock.
A major benefit of real options analysis is the insights that it provides for managing projects so as to leverage uncertainty and limit downside risks. Although in many cases it may
not be practical or even possible to apply the most sophisticated solution techniques, real options theory has shown how simpler methods, including ENPV and MUA can be used to more
accurately account for option value by recognizing multiple decision pathways and better accounting for the cost of risk.
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return on investment (ROI)
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The ratio of project income to project cost. Typically, project income is specified as the average annual net income from the project, and project cost is the total invested capital:
For example, a project that costs $100,000 and is expected to return $20,000 annually would have an ROI of 20%.
As indicated, ROI is a measure of the financial benefits obtained from a project over a specified time period in return for the required
investment, with the result expressed as a percentage. ROI is widely used (especially in the private sector) both to justify a planned project and to evaluate the extent to which the desired return was
achieved.
Like IRR, ROI is most often used as a go/no-go screen for selecting projects. A minimum required ROI is specified, typically referred to as the "hurdle rate." The hurdle rate is often based on the
company's weighted average cost of capital (WACC), the average return on a portfolio of all the firm's securities (equity and debt). Alternatively, the hurdle rate may be set higher
or lower depending on the company's appetite for risk and shareholders' expectations for company performance. Projects with ROI's less than the hurdle rate are
rejected.
ROI is similar to internal rate of return (IRR) in that it provides a measure of "bang-for-the-buck." However, it is even simpler in that in that it does not distinguish,
nor is it sensitive to, when project cash flows occur. ROI involves significant biases and is therefore not very useful for aiding project-selection decisions.
When used to rank projects, ROI has significant limitations. If project income varies from year to year, ROI will depend significantly on the period chosen for computing average income. ROI also has most of the limitations of IRR. It ignores non-financial project benefits, can't properly account for project risks and
interdependencies, and does not provide a basis for quantifying the value of alternative project portfolios. Since future cash flows are not discounted, ROI ignores
the preference that should be given to projects whose cash inflows occur sooner in time. Thus, ROI is particularly unsuitable for ranking projects that produce incremental
revenues or cost savings that persist over multiple years.
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sensitivity analysis
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A method for determining how the variation in the outputs of a model depend on variations in the model's various inputs and other assumptions. In the simplest form of sensitivity analysis, each input variable is varied over a range representing its uncertainty, and the
impact on model outputs is observed. Those variables that produce the biggest changes to model outputs are identified as the variables whose uncertainties are most critical to model predictions.
Other forms of sensitivity analysis involve varying the structure of the model, or its underlying assumptions, and observing the affect on outputs. Simulation is a form of sensitivity analysis which can be used to explore how simultaneous variations in the values of input
variables affect model outputs. Other forms of sensitivity analysis show how variations in the outputs of a model can be apportioned to different sources of variation in inputs.
Sensitivity analysis is useful for many purposes. For example, it can indicate where additional effort might be most useful for improving confidence in model predictions. Suppose a sensitivity analysis showed that a small change in the assumed growth rate for the
market served by a new product results in a very large change in the computed value to be derived from that product. The result would suggest that it might be useful to use a probability distribution to describe uncertainty in market growth rate and to use a probabilistic analysis
to characterize the resulting uncertainty in the value of the new product. Additionally, the result would suggest that it may be worthwhile to devote additional effort to estimating market growth rate before committing to produce the new product. Furthermore, it would suggest that,
after introducing the new product, the growth in market size should be measured and tracked closely to support future decisions regarding the product.
Sensitivity analysis can be used to test a model and explore how closely it corresponds to the real world processes that it is meant to represent. In this way, sensitivity analysis can be used to increase the confidence in a model and its predictions by providing an understanding of how the
model responds to changes in its inputs, data used to calibrate it, the structure of the model, and other factors.
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simulation
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A technique for predicting or analyzing the outcomes of a real world situation using an analytic model represented within a computer program. In the context of project evaluation, simulation
involves predicting the impacts that a project will produce that contribute to or detract from the fundamental objectives of the
organization. Monte Carlo simulation is a form of simulation that involves a built-in random process whereby different possibilities are generated and used to estimate the probabilities
of possible outcomes.
A dynamic simulation is one that represents the time sequence by which the various relevant changes and impacts occur. For example, a model for simulating a new product development project might first represent the
attributes of the product likely to result from the project, then represent the sales likely to occur based on those product attributes, and, finally, translate those sales into
a corresponding revenue stream for the organization.
In theory, any project outcomes that can be anticipated and represented as mathematical cause-effect or influencing relationships can be simulated. In practice, however, simulation is often
difficult because there are so many factors that influence outcomes and those influences are complex and only partially understood. A good simulation captures only those factors and influences that are most important.
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strategic alignment
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The extent to which an organization's operational decisions are consistent with and implement its strategy. The concept is that organizations define strategies for achieving their
fundamental goals. Success then requires two things. First, the strategy must provide the organization with the right plan, one that makes sense given the organization's resources,
strengths, and the business environment. Second, the day-to-day decisions that are made within the organization must successfully implement the strategy. Strategic alignment deals
with the second issue. If the organization is not choosing projects that are consistent with its strategy, the likelihood that its strategy will allow it to achieve its goals is reduced considerably.
Strategic alignment seems intuitively to be a good thing, and this, no doubt, is one of the reasons that it is so often talked about in the context of project portfolio management. However, as often happens with
fuzzy concepts, the discussion tends to break down when we try to apply it in the real world. How do we measure how well projects align with strategy? Even more importantly, why would having a portfolio
of projects aligned with strategy necessarily create the greatest value for the organization? Projects should contribute to the implementation of effective strategy. However, just because a set
of projects is consistent with strategy does not mean that those specific projects will create the greatest value for the organization.
The approach most often used to quantify strategic alignment involves creating a balanced scorecard composed of measures linked to elements of the organization's
stated strategy. For example, if policy makers have declared that one component of the strategy is to "become the low-cost provider," projects would be scored on, among other things, the
degree to which they would permit price reductions. If another element of the firm's strategy is to "be acquired by a larger firm," projects would be scored based on whether they would make the firm
appear more or less attractive to other companies. (Click here for a typical example of strategic alignment scoring.) The scores indicate the scorers sense of the degree to
which proposed projects are consistent with the various elements of strategy.
By itself, the balanced scorecard doesn't provide an overall measure of strategic alignment, only numbers that reflect judged alignment with different elements of strategy. Therefore, weights are assigned to the
strategy elements to allow the scores to be aggregated. The weights typically reflect some judged assessment of the relative importance of the various strategy elements, for example,
how critical each element is to achieving the organization's stated vision.
Although the above application of the balanced scorecard approach might provide useful insights on projects, it would be of no use for project prioritization. The goal of project prioritization is to
identify projects that create maximum value. Just because a project achieves a high aggregate score because it reflects numerous elements of the corporate strategy does not mean it will create more value. Strategic
alignment is not a surrogate for value, so prioritizing projects based on strategic alignment does not make sense.
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total cost of ownership (TCO)
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All of the costs associated with a project, including those associated with deploying, managing, and ultimately disposing of any assets produced by that project. For example, the
total cost of ownership of a car is not just the purchase price, but also expenses incurred through its lifetime of use, such as repairs, insurance, and fuel. Sometimes, TCO is
expressed as an average annual cost figure.
TCO is a useful management tool for uncovering what might otherwise be overlooked costs. It's major disadvantage from the perspective of project prioritization is that it fails to
address project benefits.
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theory of constraints (TOC)
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A popular management approach originally developed in the 1980's in a series of books and articles by Eliyahu M. Goldratt . TOC promotes a "thinking process" and various "solutions" based on the
idea of identifying and relaxing the constraints that limit an organization's ability to achieve its goals. Recent papers by TOC advocates have argued that the approach is applicable to project portfolio management. Although TOC does not
dictate a specific model or logic for selecting projects, its perspective and techniques are most certainly useful for project portfolio management in some situations.
According TOC, every organization has at least one factor that inhibits its ability to meet its objectives. The constraining factors can be broadly classified as internal resource
constraints, market constraints, and policy constraints. To better achieve its goals (e.g., profit maximization), the organization must increase throughput at the bottleneck process.
According to Goldratt and others, the steps are: (1) identify the active constraint, (2) decide how to exploit the active constraint (how to increase its throughput utilization), (3)
subordinate all other processes (manage all other processes to exploit the active constraint), (4) elevate the system's constraint (increase capacity, find alternatives to the constraint,
etc.) and (5) repeat and continue for the next constraint that becomes active.
TOC's various "problem-solving tools" are potentially useful in the context of project portfolio management. In particular, the TOC thinking process is aimed at answering three questions
central to designing and choosing projects: What to change?, What to change to?, and How to cause the change? Project communication and management tools are provided to promote agreement.
The loosely affiliated consulting organizations dedicated to applying TOC market numerous application-specific "solutions" for areas such as production, supply chain management, technology development,
and sales and marketing.
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value at risk (VaR)
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A risk metric that describes the potential for loss for a portfolio of investments. Typically, VaR is defined as the amount or percentage of available portfolio value such that there is a 95% (or 99%) probability
of the portfolio losing less than that amount over a certain time horizon. VaR is often used as a measure of financial risk, but it applies to non-financial risks as well.
VaR is popular because it
addresses the concept of "maximum potential loss." A major weakness is that it is not additive; that is, the VaR for a set of portfolios is not the sum of the VaR's of the individual portfolios.
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