
Term

Explanation

S




SaaS

Stands for Software as a Service (typically
pronounced "sass"). Also called ondemand software, SaaS is means
for making software applications available to customers, wherein the
application is hosted by a vendor or service provider and made available to
customers over a network, typically the internet. Instead of installing the
software application on each enduser's computer or device, SaaS software
uses native web technology to deliver HTML and objects directly to the
user's browser. 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.


sandbox

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.


simple additive weighting

See weighted sum model (WSM).


scale

A graduated range of numbers used as a means for
measuring something. In the context of project prioritization, scales are used as a
basis for assigning numbers to projects or to specified attributes of the projects. The numbers are
combined using an aggregation
equation, and the results of the computations used to select or
prioritize the projects.
Because the goal for project selection is to choose
projects that collectively create the most value, the scales and associated aggregation
equation should produce estimates of project and portfolio value, although
that is not always the case. For example, in the most common scoring model used by many project portfolio management (PPM) tools, the
assigned numbers (scores) are simply weighted and added, without
regard to whether the specified scales allow such operations (or whether
the results have anything to do with project value). One requirement for
producing estimates of project value is the use of the proper kinds of
scales.
The most familiar type of scale for measuring project
attributes is a natural scale. A natural scale is a commonly used
scale for the attribute, expressed in original or natural units. For
example, project cost is commonly measured in dollars (or other currency),
and time to complete may be measured in months.
Although it might seem that it would always be best to
use natural scales for quantifying project attributes, that is not the
case. A natural scale for the attribute in question may not exist, or there
may be so many considerations relevant to prioritization that defining a
natural scale for each would make project assessment too complex. When
natural scales do not exist or are inconvenient to use, contextspecific
constructed scales may be defined. For example, a fourlevel
constructed scale for "impact on community jobs" might be defined
as:
Instructions: Select a score, that on balance, best
reflects your judgment of the impact on jobs.
Note: You may choose a score between the integer
values (e.g., 2.5)
A sample constructed scale.
In this example, the scores on the scale for impact on
jobs are defined in terms of two attributes, the number of jobs created and
salary, both expressed in natural units.
A critical consideration for defining scales is whether
those scales will allow the computations needed to compute project
priorities. In this regard, there are three major categories of scales:
ordinal scales, interval scales, ratio scales and cardinal
scales.
An ordinal scale is one that merely indicates how
something is ranked. For example, 10 projects could be ranked in terms of
preference. The project ranked 2 is preferred over the project ranked 3,
but the scale doesn't indicate by how much. Ordinal scales tend to be easy
for people to apply, but, because the differences between scale numbers are
not specified, no mathematical operations can be meaningfully applied to
the scores from such scales. Thus, for example, if a scoring model uses an
ordinal scale to rank projects based on two criteria, say "impact on
corporate image" and "financial attractiveness," adding or averaging those
rankings would lead to meaningless results.
A scale is interval scaled if the numbers assigned
are in units of equal magnitude. The Fahrenheit and Celsius scales for
temperature are examples of interval scales—in each case the scale
units (degrees) don't change at different locations of the scales. The
difference between 100 degrees and 90 degrees is the same difference as
between 90 degrees and 80 degrees. Because distances between numbers in an
interval scale have meaning, the numbers assigned with such scales can be
added or subtracted. Thus, for example, if you're using interval scales you
can use aggregation equations that weight, add, or subtract the assigned
numbers. Weighting and adding scores from scales that are not interval scaled produces meaningless results.
Although you can meaningfully weight and add interval
scaled numbers, you cannot generally multiply them. This is because
interval scales may have arbitrary zero points. The Celsius temperature
scale arbitrarily defines zero as the temperature at which water freezes.
This makes ratios expressed using scale numbers meaningless—a
temperature of 50 degrees is not twice as hot as a temperature of 25
degrees, as demonstrated by the fact that the corresponding temperatures on
the Celsius scale, 10 and 3.9 degrees are not in the ratio 2 to
1.
A scale is ratio scaled if, in addition to being
interval scaled, it has a zero level that corresponds to "none of the
attribute" (also referred to as an absolute scale). With ratio scales,
attributes are assigned numbers such that (1) the differences between the
numbers reflect differences in the amount of the attribute and (2) ratios
between the numbers reflect ratios of the attribute. This ensures that the
numbers assigned to various degrees or amounts of the attribute bear a
direct relationship to the absolute amount of the attribute. With such a
scale, we can say not only that one project has so many units more of a
attribute than a second project, but also that the first project has so
many times "as much" of the attribute than the second project. Time
measured in months, for example, is a ratio scale. A project that requires
4 months takes twice as long as a project that requires 2 months. Costs and
probabilities are defined on ratio scales. Also, the weights defined in the
additive value function are defined along a ratio scale. All arithmetic
operations are permitted on numbers that fall along a ratio scales. Also,
you can multiply ratio scale values by interval scaled values.
A cardinal scale is an umbrella term for interval
and ratio scales. A ratio scale is a cardinal scale with a zero level.
The figure below summarizes differences in the
information communicated depending on the type of scale used for a ranking.
If an ordinal scale is used to communicate the results of a horse race, the
only thing you'd know is the order in which the horses finished. If an
interval scale is used, you'd know the order in which the horses finished
and the differences in the times run by each horse. If a ratio scale is
used, you'd know everything: the order in which the horses finished and
their times. For example, you might be able to determine that the first
place horse finished 3rd all time for races at that track.
Different scales provide different levels of
information.
To help avoid errors in project selection decision
models, you should strive to define scales that are ratio scaled, or be
careful not to apply computations that aren't permitted for the types of
scales you've defined. The example scale for job creation shown above is
neither interval scaled nor ratio scaled. However, it is anchored in
natural units that give meaning to differences in scale values and has an
absolute zero. In such cases it is often possible to use a scaling function to translate the such
scales to alternative units such that the result is ratio scaled. For
example, if total income from new jobs is viewed as a reasonable measure
for the impact on jobs, a scaling function for making the measure ratio
scaled would be the product of the number of jobs created and the average
salary for those jobs.


scaling function

A functional relationship for translating scales. For example, if the units on a scale
aren't of equal magnitude (e.g., as in a logarithmic scale), a scaling
function might be applied to convert the scale into one with equalsized
units. This approach is often used to obtain interval scales that allow
differences in scale values to reliably indicate differences in the amount
of the measure.
A common use of scaling functions for project
prioritization is to convert a measure of project performance relative to
some objective into a measure indicating the value of that level of
performance. In this instance, the scaling function may be referred to as 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 yaxis 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
nonlinear 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 the linear assumption holds.
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 zeroto100 scale. In technical
terms, a scaling function is a singleattribute utility function—a utility function with
only a single independent variable for measuring performance.


scenario

An internally consistent description of a possible
sequence of events, or situation, based on assumptions and factors chosen
by the scenario creator. Scenarios are commonly used as a basis for
estimating the implications of taking some action. For example, a project might be evaluated assuming one or
more scenarios, or visions of what the future might bring.
Scenario building refers to the process of
generating scenarios. Scenario analysis involves using multiple
scenarios and considering the implications of those possible futures.
Scenario analysis is a form of risk analysis in that it helps to create
understanding of the implications of uncertainty.
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 longrange planning.


scenario analysis

Similar to sensitivity analysis except that cominations of variables are simultaneously varied to
mimic the combined impact of changes to related assumptions.


score

When used as a noun, a number assigned to a project to represent some characteristic of the
project relevant to assessing its merit. Project scores are inputs to a model for the comparative evaluation of
projects. Oftentimes, score refers to a number selected from a constructed
scale. The type of model that relies on scores for input may be a scoring model, though the term may also be used to describe
subjective estimates generated for input to other types of models as well. When used as a verb, the term refers to the task
of generating a judgmental input for a model.


scorecard

A table, usually displayed on a single page or screen, that
summarizes the results of generating inputs for a scoring model. The purpose is to quickly
convey information relevant to specifying judged performance (e.g., project performance)
relative to various dimensions or objectives of interest.


scorer

An individual who provides estimates used by a project prioritization or project selection model to assess
project performance. The individual may be labeled an "expert" to indicate that person was selected for his or her
knowledge or expertise needed for providing the desired estimate. For example, if the scores
are inputs needed for a project prioritization model, the scorer would normally be someone particularly knowledgeable about the projects
being prioritized and the consequences that would likely result from conducting those projects. The process of generating
scores is termed scoring because providing estimates of
performance may involve selecting inputs to the model from one or
more predefined scales.


scoring

An assessment of performance that involves assigning a
score, usually a number based on one or more predefined scales.


scoring model

A type of decision
model often used for project
selection that involves scoring
projects against multiple criteria.
Various criteria (considerations) for choosing projects are identified.
These typically include financial criteria (e.g., net present value), plus criteria related to
customer service, safety, contribution to strategy, risk, etc. Each project
is evaluated (scored) against each criterion, and the scores are combined
in some way to obtain an overall measure intended to represent the
attractiveness or to provide a figure of merit for each project.
Scoring models differ mainly in how the criteria are defined and measured,
and how the individual assessments are aggregated to obtain an overall
project figure of merit. Such differences significantly affect the
complexity, information requirements, reliability, and defensibility of the
model.
Although there are a number of sophisticated,
multicriteria decision models that involve scoring, including AHP, ELECTRE,
goal programming, and PROMETHEE, the term scoring model typically
refers to the least complicated type of multicriteria model wherein the
project figure of merit is obtained by simply adding, or, more commonly,
weighting and adding, the scores assigned to the individual criteria. This
results in a method of evaluation that is very simple to implement and
understand, but one that, typically, is not very reliable or defensible.
Many, if not most, project portfolio
management tools are limited to using this type of simple scoring model
for evaluating or ranking projects.
There are three types of scoring models: checklist
models, unweighted scoring models, and weighted scoring models:
 With a checklist model, the criteria are expressed as yes/no
statements (e.g., "Payback
period less than 5 years", "Project involves no safety risk") listed
in a table. Individuals then evaluate each project by indicating
(checking) those criteria from the list that the project satisfies. The
checks are counted for each project, and the totals are used as the
measure for ranking the projects. Since a check counts as a score of "1"
when totaling scores, the checklist model is sometimes referred to an
unweighted, 0/1 factor model.
 An unweighted scoring model is similar to a checklist model,
but allows for gradations in project scores. Instead of expressing the
criteria as yes/no statements, a scale is used. Often, a 5point scale is
selected, where 5 means the project is very good with respect to the
criterion, 4 means good, 3 means fair, 2 means poor, and 1 means very
poor. The scores are summed, and the totals are used as the measure of
project attractiveness.
 A scoring model is a weighted scoring model if it allows
weights to be assigned to the criteria. A weighted scoring model has the
mathematical form:

where S_{j} is the total
score for the jth project, N is the number of
criteria, w_{i} is the weight assigned to the
ith criterion, and s_{ij} is the score of
the jth project on the ith criterion. The weights are
typically assumed to represent some concept of the relative importance
of each criterion. Methods used for assigning weights include paired comparison, AHP and the
swing weight method. Although
it is not necessary and has no effect on relative rankings, weights are
often scaled to sum to one, expressed mathematically as:
This allows the weight on each criterion to be
interpreted as the percent of the total weight assigned to that
particular criterion.
The main advantage of scoring models is that they provide
a way to capture the multiple considerations that are relevant when
deciding whether or not to conduct a project. Scoring models are very easy
to create and simple to understand. A scoring model can easily be
implemented in Excel or one of the other standard computer spreadsheet
tools. The model is flexible and can be easily altered or changed to
accommodate changes in organizational preferences or managerial policy.
Another advantage of a scoring model is that although the model is
developed to support project selection, that same model can be used as a
guide for project improvement. A project's scores on each criterion can be
compared with the best possible score. The differences, when multiplied by
the weights, indicate the types of improvements that would most improve the
project's attractiveness as measured by the scoring model.
The main disadvantage of scoring models is that the model
output is typically not a reasonable measure of the value of doing the project. Without a sound
measure of project value, it is impossible to know whether the project is
worth its costs or to identify the portfolio of projects that produces the
most value given the resources available. Under the standard scoring model,
the mathematical equation for computing total scores is linear, implicitly
assuming that a unit improvement on any criterion always contributes the
same amount to project attractiveness regardless of how well the project
performs against that criterion or on any other criterion. Many relevant
criteria, such as risk, can't be
reasonably captured using linear equations.
Also, because it is so easy to define criteria, it is
common for scoring models to contain many criteria. The criteria often
overlap or represent similar or related objectives, and this overlap can
produce significant biases. Such errors can be reduced by placing
restrictions on how criteria are defined and measured, but this
complication effectively means applying a different approach (see multiattribute utility analysis). Such
complications, though needed for accuracy, eliminate the simplicity of
design that is the main attraction of scoring models.


sensitivity analysis

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.


server

A software program, or the computer on which it runs,
that shares data and software resources in response to requests made by to
other programs or computers referred to as clients. This architecture,
known as the clientserver model, may be established within a single
computer, a local area network (LAN), or a wide area network (WAN) over the
Internet. The client provides the user interface, for example, a GUI (graphical user interface) and performs some
or all of the processing related to the requests it makes from the server.
The server maintains the data and processes the requests.
Clientserver architecture
Servers are named after the primary functions they
perform: for example, file servers receive, store, and send files, web servers store webpages, mail servers
receive, store, and forward emails, and application servers install,
operate and host applications. Many project portfolio management
applications are constructed using clientserver architecture.


shadow price

Term used in optimization to indicate the amount by which the
objective function under the
optimal solution would increase if a constraint were relaxed by one unit.
Thus, in a business application, a shadow price could indicate the maximum
price that management should be willing to pay for an extra unit of a given
limited resource. Suppose, for example, that a production line is set to
operate for a normal, 40hour week. The shadow price for operating hours
would be the maximum price the manager should be willing to pay for
operating the line an additional hour, based on the benefits to be obtained
from the additional hour of operation. Computing shadow prices can help
organizations identify constraints that ought to be relaxed.
Some project portfolio
management tools allow computation of shadow prices for portfolio
resource constraints. Suppose the tool uses an optimization engine to
identify the project portfolio that produces the greatest portfolio
value subject to meeting some
constraint, such as a maximum allowable budget year cost. A natural
question would be, "By how much would the value of the optimal portfolio
increase if the constraint were relaxed?" The shadow price for the budget
constraint is the amount by which portfolio value could be increased if the
constraint were relaxed by one dollar.


simulation

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 form of
simulation that involves using a builtin 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 causeeffect 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. An efficient
simulation captures only those factors and influences that are most
important.


single attribute utility function

Analogous to a single attribute value
function, but describes a singlevariable utility
function rather than to a value
function. Unlike a value function, which applies in cases where there is no uncertainty over the
performance outcomes for alternatives, a utility function applies to conditions of uncertainty and
must capture decision maker attitudes to taking risks.
Singleattribute utility functions are typically encountered in two circumstances. First, if
objectives and performance
measures have been defined such that
additive independence is satisfied, then the multiattribute utility
function can be expressed as a sum of singleattribute
functions. Singleattribute utility functions are much easier to assess. The second
circumstance where singleattribute utility functions are encountered is where a value function has been defined to collapse
the multiple performance measures relevant to the decision problem down to an equivalent monetary value. In this case, the purpose of the
singleattribute function is to account for the decision maker's attitude toward risk.
The minimum number of points that need to be established in order to approximate a singleattribute utility function
is probably five. If the function is normalized by assigning a utility of zero
to the worse level of performance and a
utility of one to the best level of performance, then the certain equivalents method can then be used to obtain the performance
levels associated with three additional utilities equal to 0.25, 0.5, and 0.75.
Alternatively, a singleattribute utility function can be obtained by selecting a mathematical form for the function and
and then fitting that function to a few assessed points. Commonly used mathematical forms include:
 Exponential. The exponential function is convenient for representing the risk attitude of the decision maker. The
constant a is the risk tolerance, which for a = 0 indicate
risk neutrality, a < 0 indicates risk preferring, and a > 0 indicates risk aversion. The exponential
function is said to have constant absolute risk aversion, since a is a constant. The exponential utility function
is often simply written as U(x) = e^{ax}, since the constant 1 has no effect on the performance levels
xthat maximize the function.
 Logarithmic The natural logarithm exhibits declining marginal utility with the level of x, in
fact, marginal risk tolerance is 1/x. Note that the function is defined only for x > 0.
It attributes great undesirability for
performance levels that approach zero. In fact, if x is uncertain and there is any probability of x = 0
the expected utility with be ∞. If an individual has a utility function with this shape, then it can be shown
that he/she will be willing to pay, in some circumstances, for insurance.
 Power Some
empirical studies suggest that individuals exhibit constant relative risk aversion but diminishing absolute risk aversion, meaning
that they are willing to take on more risk as they gain wealth. The power utility function has this characteristic.
It is also known as the isoelastic function for utility. The value η is a measure of risk aversion,
with η = 0. This is the only utility function with constant relative risk aversion. Power utility is appealing
because it implies stationary risk premiums and interest rates even in the presence of longrun economic growth. Also,
it is tractable in the presence of multiplicative lognormally distributed risks. A negative η represents a
subsistence level.
 Quadratic. The quadratic utility function is increasing only for x < 1. This function has
increasing absolute risk aversion and a "bliss point" where the parabola achieves its maximum. Thus when working with a quadratic
utility function we want to have the bliss point at a level of consumption far above anything that the consumer might actually
realize. Increasing absolute risk aversion and the existence of a bliss point are important disadvantages, although quadratic
utility is tractable in models with additive risk. In financial economics, the utility function most frequently used to describe
investor behaviour is the quadratic utility function. Its popularity stems from the fact that, under the assumption of quadratic utility,
meanvariance analysis is optimal.
A risk attitude that is sometimes appropriate (especially for personal investment decisions) is one
where the decision maker's willingness to accept risks increases as total wealth increases. A mathematical form
with this characteristic is U(x) = h + k (e^{ax}  be^{cx}), where a, b, c & k ≥ 0


single attribute value function

Also called, preference function, marginal value function, partial value function,
and a scaling function,
a value
function that contains only one attribute, expressed, for example, as
V(x). The function defines a relationship between value, V, and the level specified for the
attribute, x. A single attribute value function is also referred to
as a scaling function, is a particular application of a scaling function because it
transforms and scales (if needed) the measure used to express performance (the attribute) into a
a number indicated the decision maker's preference for that preference.
In the context of valuing projects, for example, a relevant attribute
might be the amount of learning provided by the project. A single attribute
value function would indicate the assumption that the value of the learning
provided by the project is a function only of the attribute indicating the
amount of learning provided. If the value function for learning is single
attribute, then no other attributes defined for characterizing projects,
for example the type of project, matter when estimating the value of the
learning.


Six Sigma

A popular business and project management 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.


skewness

In probability theory and statistics, skewness is a
measure of the asymmetry of the probability distribution of a random
variable about its mean. The skewness can be positive or negative depending
on which direction the "tail" of the distribution extends.
Probability distributions with positive and negative
skew


slope

The steepness of a curve at some designated point. The
slope of a curve or line indicates how much change in the dependent
yvariable occurs when the independent xvariable changes one
unit. A horizontal line has a slope of zero. A line that makes a 45 degree
angle with the xaxis has a slope of one.


SMART

A multicriteria
analysis method originally developed in the 1970s by behavioral
psychologist and decision analyst Ward Edwards. SMART is an acronym
describing desired characteristics when specifying decision objectives—the objectives should be
Specific, Measurable, Achievable, Realistic, and Timebased. SMART has been
incorporated into many decision aiding tools and several project portfolio management tools rank projects using the technique. Compared to
multiattribute utility analysis, SMART
makes simplifying assumptions for the purpose of enabling quick assessment
techniques.
SMART recommends a multistep 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 nexttoleastimportant dimension is assigned an
importance weight representing the ratio of its relative importance to that
of the leastimportant 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 zeroto100 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. SMART’s common applications are in environmental,
construction, transportation, military, manufacturing and assembly
problems. Its ease of use helps in situations where a fair amount of
information is available and access to decisionmakers is easy to obtain.
Its simplicity is what keeps this method fairly popular. Several tools for
project portfolio management use SMART to
prioritize projects.


software suite

A collection of software applications with related
functionality typically sharing a moreorless common interface and ability
to exchange data with each other.


source code

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 humanreadable 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.


SQL

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.


standard deviation

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:
In the equation, the x_{i} are the various
data values, n is the number of values, and x_{Avg}
is the average value of the x_{i}. 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.


standard gamble invariance

An independence condition relevant to
multiattribute utility analysis. If the condition applies in the presence of a
zero condition attribute, the
utility function must have a multiplicative form.
An attribute X_{i} satisfies standard gamble invariance if the
certain equivalent of twooutcome gambles over X_{i}
does not vary depending on the levels of the other attributes, provided that none of the zero condition attributes are at
their zero levels.
Medical decision making is a field where recognizing standard gamble invariance can be useful.
For example, suppose as a result of an injury a woman is experiencing severe, chronic back pain and is, therefore, considering
a risky operation that may eliminate or reduce her pain but may also impact her remaining life expectance. Suppose two attributes
characterize the outcome of the decision: X_{P} the level of back pain following the operation
and X_{T}, the number of remaining years she will. The attribute X_{T}
is a zero condition attribute because, if X_{T} = 0 (the patient dies during the
operation), she will obviously be indifferent regarding the outcome of X_{P}, the level of pain she would have had if
she survived.
To check for standard gamble
invariance, the patient imagines a specific outcome for X_{P}, for example, "mild back pain," and then estimates the
certain equivalent for a gamble over the number of years she will continue to live. For example, suppose she is indifferent between the
certain outcome of living 15 years with mild back pain and a 50/50 gamble of 35 years with mild back pain versus 1 year of mild back pain.
If, when she assumes a
different outcome for X_{P}, say, no back pain, the certain equivalent remains the same—15 years of no back is
the certain equivalent of a 50/50 gamble of 35 years of no back pain versus 1 year of no back pain—then standard gamble invariance
applies. The utility function in that case will have a multiplicative form. If U_{P}(X_{P}) is the patient's utility function
for different levels of back pain and U_{T}(X_{T}) is the patients utility function over number of years of remaining life
(with U_{T}(0) = 0),
then the multiattribute utility function for level of pain and years of remaining life will have the form
U(X_{P},X_{T}) = U_{P}(X_{P}) × U_{T}(X_{T}).


stochastic

Random or randomly determined. Typically applied to
describe a model or method of analysis whose outputs account for
uncertainties and their probabilities. Probabilistic is another term used in
this context with essentially the same meaning, except that the term
stochastic is more often used in contexts where probabilities are derived
through the statistical analysis of data. Compare with deterministic.


stochastic multicriteria acceptability analysis (SMAA)

A family of multicriteria
analysis (MCA) methods and associated software intended to support groups deciding among discrete alternatives.
With SMAA, the decision makers need not express their preferences explicitly. Instead the method is based on
exploring the space of possible weights in order to describe the weights and
valuations that would make each alternative the preferred one. SMAA, in effect, solves a problem that is the
inverse of that addressed by most MCA methods. Rather than identify alternatives that are best given a
decision maker's preferences, SMAA looks for the preferences that are best aligned with the choice of specific alternatives.
SMAA calculations are based on the assumptions that decisionmaking preferences may be described by an
additive
value model with specified criteria. Weights are assumed to be uncertain. The values that would be assigned to each
alternatives performance relative to each criterion are likewise assumed to be uncertain. SMAA allows for defining constraints
over the nature of these uncertainties.
In general, if there are N criteria for selecting alternatives, there will be an Ndimensional space of
possible weights. Weights are assumed to be normalized so that they sum to one. Thus, if there are three criteria there will
be three weights and the space of possible weights would be as illustrated in the figure below.
The decision maker's weights are assumed to be represented
by a joint probability distribution defined in the feasible weight space.
Uncertainties over the value
assignments are likewise assumed to be represented by probability
distributions.
However, instead of eliciting probability
distributions over weights and values from decision makers, SMAA allows for
specifying constraints for defining weights. For example, assuming complete uncertainty, the probability distribution
over weights is assumed to be described by a
uniform distribution over the restricted weight space. For example,
in the 3criteria case, a constraint might be that
W_{1}>W_{2}, and W_{2}>W_{3}. With
these constraints, the restricted weight space is as shown below.
.
The types of constraints allowed by the software include
intervals for weights, intervals for weight ratios (tradeoffs), linear or
nonlinear inequality constraints for weights, and a partial or complete
ranking of the weights.
The probability distributions assigned to weights and the
uncertain criteria values are used to compute confidence factors describing
the reliability of the analysis. The original version of SMAA allowed
computing three such measures: the acceptability index, the central weight
vector, and the confidence factor.
The rank acceptability index describes the share of
different weights and criteria measurements ranking an alternative at a
specified rank. For example, if the top ranking is selected, the
acceptability index describes the share of different weight valuations
making an alternative the most preferred one. Acceptability indices can be
used for classifying the alternatives into stochastically efficient A zero
acceptability index means that an alternative is never considered the best
with the assumed preference model. The acceptability index of the original
SMAA method was not designed for ranking of the alternatives, but instead
for classifying them as more and less acceptable ones, from which the
earlier ones should be taken into future consideration.
The central weight vectors represent the typical
preference favoring each alternative, and the confidence factors measures
whether the criteria measurements are sufficiently accurate for making an
informed choice. The CW's are used for inverse approach: instead of asking
preferences and giving results, answers the question, "Which preferences
support an alternative to be the most preferred one?" The central weight
vector describes the preferences of a typical decision maker supporting this
alternative with the assumed preference model. By presenting the central
weight vectors to the decision makers, an inverse approach for decision support can be
applied: instead of eliciting preferences and building a solution to the
problem, the decision makers can learn what kind of preferences lead into which actions
without providing any preference information.
The confidence factor is the probability for an
alternative to be the preferred one with the preferences expressed by its
central weight vector. CF measures whether the criteria measurements are
accurate enough to discern the efficient alternatives. The central weight
vector & confidence factor describes the preferences of a typical decision maker
supporting this alternative with the assumed preference Model. The
confidence factor is defined as the probability for an alternative to be
the preferred one with the preferences expressed by its central weight
vector. The confidence factors measure whether the criteria measurements
are accurate enough to discern the efficient alternatives.
The various versions of SMAA have added output metrics.
For example,SMAA2 provides probabilities for alternatives to obtain
certain ranks. SMAA2 extends SMAA by taking into account all ranks and
provides five new descriptive measures: the These new measures provide decision makers
with more insight with the decision making problem. One of the advantages
of SMAA over most other MCDA methodologies is that it can be used without
any preference information if such is not available. Flexibility and
maturity of the SMAA methodology allows \out of box" thinking and
application of MCDA in new disciplines {most of reallife decision problems
are multicriteria by nature SMAA methods have been successfully applied in
various reallife MCDA problems with imprecise criteria measurements and
partial/missing preference information Crossplatform, open source
implementation of SMAA methods is available from www.smaa.fi.
The usual approach to apply SMAA in real applications is
to use it repetitively with more and more accurate information until the
information is sufficient for making a decision. Between the analyses,
information can be added by making more accurate criteria measurements, or
assessing the decision maker's preferences more accurately in terms of the
various preference parameters. SMAA methods have proven most useful for
aiding group decision making. It is well suited to any situation where when
decision makers do not want to reveal their true preferences.


stochastic programming

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.


strategic alignment

Also called strategic fit, a measure of the extent
to which an organization's operational decisions are consistent with and
implement its strategy. The logic is as follows. Organizations define their
missions or visions based on their fundamental goals. They then define
strategies for achieving their organizations' fundamental goals. For a
strategy to be successful two things must occur. First, the strategy must
provide the organization with a plan that will achieve its goals given the
realities of the organization's resources, strengths and weaknesses, and
the business environment within which it operates. Second, the daytoday
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
(PPM). However, as often happens with fuzzy concepts, the logic fails
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 most highly aligned with strategy necessarily create
the most value for the organization? Projects should contribute to the
implementation of effective strategy. However, just because a set of
projects is highly 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 scorecard composed of metrics 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 lowcost 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 potential acquirers. The scores indicate
the scorer's 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 judgment of the relative importance of the
various strategy elements, for example, how critical each element is to
achieving the organization's stated vision. (See the description of the
typical strategic alignment
process.)
Although the above application of the balanced scorecard
approach for strategic alignment might provide useful insights on projects
and strategy, it would be of no use for project prioritization. The main goal of PPM is
to identify the portfolio of projects that creates maximum value. The value
created per dollar spent ("bang for the buck," see Mathematical Theory) is often a reasonable metric
for ranking projects. Alignment scores have nothing to do with bang for the
buck. 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.


strategic business unit (SBU)

A company division, product line, single product, or
company brand that has a mission separate from other company businesses and
that can be planned independently from the other businesses. The concept is
that strategic business units can be managed autonomously. Organizing the
enterprise into smaller business unit enhances flexibility, enabling a
large firm to react more quickly to changing market and economic
conditions.


submodel

A component or part of a larger model. A submodel typically analyzes some portion of the problem
addressed by the model or provides a portion of the outputs available from the larger model.


swing weight

A weight (scaling
constant) indicating a decision maker's judgment about how valuable it is
to obtain a specified improvement in performance ("swing") against one
criterion relative to specified
performance improvements against other criteria. Swing weights are
typically assessed using the swing
weight method.


swing weight method

One of the available methods for eliciting weights for
the various criteria defined for
multicriteria analysis. The swing weight
method requires specifying hypothetical changes (swings) in the level of
performance against different objectives and then obtaining judgments of
the relative preferences for obtaining those swings, typically using a
0to100 scale. For example, if the most desirable swing is given a swing
weight of 100 points, how many points would be assigned to obtaining the
next most desirable swing? Although the swing weight method is not
necessarily the most accurate method for eliciting weights, it provides
much more reliable results than assigning weights based on abstract
"importance" of each criterion. A strength of the swing weight method is
that most people find it relatively quick and easy. Some project portfolio management tools include
routines and aids for applying the swing weight method.


SWOT analysis

A decision aid wherein the strengths, weaknesses,
opportunities, and threats associated with a proposed project or other business decision are
systematically identified and examined.

