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Term
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Explanation
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risk tolerance
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A measure of an individual's or organization's willingness to accept risk when making choices. Risk tolerance may be assessed and
quantified as a parameter in a utility function. See the section of the paper on risk for how this may
be accomplished.
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S
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SaaS
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Stands for Software as a Service (typically pronounced "sass"). A means for making software applications available to customers, typically over the internet. The
software is not sold for local installation but is made available as a service on a subscription basis. Many project portfolio management (PPM) tools,
especially those with less sophisticated analytics, are provided as SaaS. More information on SaaS is provided in the paper chapters on PPM tool differences
and on PPM tool costs and risks.
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sandbox
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A term used to describe a safe testing environment with controlled or limited access within which a user or application program can "play" without risking damage to
the a larger system. Project portfolio management (PPM) tools are often advertised as providing a "sandbox" for users to enter and analyze project data
without committing that data to the project database available to other users.
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scaling function
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Also called a value curve or value scaling function, a scaling function is a functional relationship used in a decision model that translates a level of performance, as expressed by a performance measure, into a
number that indicates the value or desirability of that level of performance. In the example below, which might apply to an electric utility concerned with quickly restoring service to
customers without power, the x axis denotes the amount of time the customer is without power. The y-axis is a relative measure of value, defined such that 100 indicates maximum value
and 0 represents the value associated with the worst level of performance that the utility expects could occur (in this case, an outage lasting 24 hours). In the example, the scaling
function is non-linear to reflect that fact that residential customers will often suffer greater losses when the duration of an electric outage approaches 4 to 8 hours, because, for
example, an outage of such duration may cause refrigerated food to spoil.
A sample scaling function
A decision model may require a scaling function for each of its performance measures. However, if the incremental value of
obtaining a unit of improvement as expressed by the performance measure does not depend on the current level of performance, the scaling function will be linear (a straight line).
Performance measures are often defined in such a way that this assumption can be made.
Mathematically, a scaling function has the form V = S(p), where "p" is the performance measure, "S" is the scaling function, and "V" is the measure of value. The
differences in the values of V produced under various levels of performance p indicate by how much the higher levels of performance are preferred. As in the above example, by
convention, a scaling function often expresses value on a zero-to-100 scale. In technical terms, a scaling function is a single-attribute utility
function—a utility function with only a single independent variable for measuring performance.
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scoring
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An assessment of performance that involves assigning a score based on some predefined scale.
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scenario
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An internally consistent sequence of events producing some outcome and based on assumptions chosen by the scenario creator. Scenarios have been likened to "mental
movies," and the term is the same as that used in the film and television industry to describe the script that ties a story's events together. Creating scenarios is a common technique
for forecasting the possible consequences of situations or actions, especially in support of long-range planning.
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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. Depending on the
results of such tests, sensitivity analysis will identify errors that need to be corrected or build confidence in the model and its predictions. In this way, sensitivity analysis
promotes model improvement via application of the Scientific Method.
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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 portfolio management, simulation typically involves predicting the consequences of individual projects or
portfolios of projects. The simulation model takes as input assumptions regarding the project and produces as output project consequences relevant to the achievement of the
organization's objectives (these outcomes are project or portfolio performance measures). The
simulation process involves generating scenarios consisting of assumptions for the project or project portfolio and using the model to determine
(simulate) what the corresponding business consequences might be. Monte Carlo simulation is a popular form of simulation that involves using a
built-in random process to select assumptions for the scenarios. The distribution of model outputs is then used to assign probability
distributions representing uncertainty over project or portfolio consequences.
A dynamic simulation is one wherein the model 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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Six Sigma
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A popular business methodology, developed originally by Motorola in the 1970s, for improving the quality of business process outputs. Some project portfolio management tools incorporate templates and aids to support Six Sigma as applied to projects.
The Six Sigma methodology aims to identify and remove the causes of defects (errors or variations in process outputs) that lead to customer dissatisfaction. There are
five steps in the methodology (abbreviated DMAIC): (1) define the customer and business goals for the process, (2) measure defects in the performance of the current process, (3) analyze
the data to identify root causes of defects, (4) improve the process to reduce defects, and (5) control the variables that cause defects. Six Sigma defines metrics for measuring process
quality, employs statistical analysis, and establishes an infrastructure of people within the organization to advance the methodology ("Green Belts," "Black Belts," etc.). The term "six
sigma" refers to a concept in statistics for measuring how far a given process deviates from perfection, and suggests that errors be reduced to at most a few per million.
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SMART
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A multi-criteria analysis method originally developed in the 1970s by behavioral psychologist and decision analyst Ward Edwards.
SMART is an acronym describing the desired characteristics when specifying decision objectives—the objectives should be Specific,
Measurable, Achievable, Realistic, and Time-based. SMART has been incorporated into many decision aiding tools and several project portfolio management
tools rank projects using the technique. Compared to multi-attribute utility analysis, SMART makes simplifying
assumptions for the purpose of enabling quick assessment techniques.
SMART recommends a multi-step ranking method that begins with identifying the criteria, or value dimensions to be used for evaluating alternatives. The value
dimensions are then ranked based on judged importance, and the least important dimension is assigned a value weight of 10. The next-to-least-important dimension is assigned an
importance weight representing the ratio of its relative importance to that of the least-important dimension. For example, if this dimension was viewed as twice as important as the
least important dimension, it would be assigned a weight of 20. Weights are assigned to the other dimensions in the same way, preserving importance ratios. The weights are then
normalized to sum to one by dividing each weight by the sum of all of the weights. Each alternative is then rated on each dimension using a zero-to-100 scale. The ratings are weighted
and summed, and the results used to rank the alternatives.
The simplifications inherent in SMART can lead to errors. In particular, if the value dimensions are not preferentially independent (e.g., if the importance of a dimension depends on the performance of the alternatives with respect to some
other dimension) or if value is not directly proportional to rating (e.g., if a rating of 50 is not half as valuable as a rating of 100, in which case a scaling function is needed), then there may be significant errors in the rankings produced by SMART. Edwards and colleagues also developed
"improved" versions of SMART, called SMARTS and SMARTER. SMARTS is simply SMART using the more defensible swing weight method for
eliciting weights.
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source code
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The lines of code and algorithms as originally written by a computer programmer that determine how the software works. Source
code is typically written in human-readable form. In the case of most tools for project portfolio management that incorporate decision models, access to the source code is required to modify in any significant way the model or logic by which projects are evaluated and prioritized.
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SQL
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Stands for standard query language, a standardized computer language used to create, modify, retrieve and manipulate data from a relational database. SQL uses regular English words for many of its commands, which makes it easy to learn and understand. The original version, called
SEQUEL (structured English query language) was designed by an IBM research center in 1974 for use on mainframe computes. SQL was first introduced as a commercial database system
in 1979 by Oracle Corporation.
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standard deviation
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A measure of the spread or variability within a set of data. The standard deviation is usually calculated as the square root of the sum of the squares of the distance
of each data point from the mean divided by the number of data points minus 1:
Where the xi are the various data values, n is the number of values, and xAvg is the average value of the
xi. The standard deviation is the square root of the variance, another measure of data variability. However, the standard deviation is often preferred because
it has the same units as the quantity being measured.
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stochastic programming
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A general category of mathematical solution techniques for problems involving uncertainty. In stochastic programming, probability distributions are typically used to quantify uncertainties. Stochastic programming is employed in some project portfolio management tools where the goal is to identify project decisions that maximize either the expected value or
certain equivalent of the project portfolio subject to budget constraints.
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