Technical Terms Used in Project Portfolio Management (Continued)

	Term	Explanation
	risk tolerance	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.
S
	SaaS	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.
	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.
	scaling function	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 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 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.
	scorecard	A table, displayed on single page or screen, that summarizes the results of applying a scoring model. The purpose is to quickly convey information conveying judged performance (e.g., project performance) relative to various dimensions or objectives of interest.
	scoring	An assessment of performance that involves assigning a score 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, multi-criteria decision models that involve scoring, including AHP, ELECTRE, goal programming, and PROMETHEE, the term scoring model typically refers to the least complicated type of multi-criteria 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, un-weighted 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 un-weighted, 0/1 factor model. An un-weighted 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 5-point 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 multi-attribute utility analysis). Such complications, though needed for accuracy, eliminate the simplicity of design that is the main attraction of scoring models.
	scenario	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.
	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.
	shadow price	A quantify that may be computed using some project portfolio management tools. 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. More generally, the shadow price on a constraint defined for an optimization is the amount by which the objective function would increase if the constraint were relaxed by one unit. Capability to compute shadow prices for resource constraints can help organizations identify resources that they may want to increase.
	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 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. An efficient simulation captures only those factors and influences that are most important.
	Six Sigma	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.
	slope	The steepness of a curve at some designated point. The slope of a curve or line indicates how much change in the dependent y-variable occurs when the independent x-variable changes one unit. A horizontal line has a slope of zero. A line that makes a 45 degree angle with the x-axis has a slope of one.
	SMART	A multi-criteria 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 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.
	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 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.
	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.
	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 the same meaning. Compare with deterministic.
	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.

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