Lee Merkhofer Consulting Priority Systems

Technical Terms Used in Project Portfolio Management (Continued)






























A user interface for a software package that, like a dashboard on an automobile, organizes and presents information in a way that is intended to be easy to read and absorb. Most modern software tools employ user interfaces that resemble dashboards, but vendors of project portfolio management (PPM) tools often use the term to ensure that potential customers recognize the similarity. Typically, and unlike most automobile dashboards, a PPM dashboard is interactive — if the user clicks on an item, more detailed information is provided. However, whether or not the information displayed by a PPM dashboard is "real time," as it is with an automobile dashboard, depends on the tool and the type of information presented.


Illustrative PPM dashboard

data mining

The process of extracting from a (usually large) database information useful for decision making. Typically, data mining utilizes software to identify statistical patterns or relationships in data that may be commercially useful. Information obtained in this way is often used to help organizations obtain improved understanding of customers and to aid decisions involving product design, marketing, and customer support.

de Bono Six Hats

A thinking tool for group and individual decision making. The method is designed to help people make better decisions by looking at the choice from different perspectives, thereby producing a more comprehensive understanding of considerations and issues. The method is described in the book Six Thinking Hats by Edward de Bono.

decision analysis (DA)

A body of knowledge and related analytic techniques for helping decision makers choose among alternatives, taking into account the consequences of choosing each alternative and the decision maker's preferences. Decision analysis (DA) is concerned with finding effective and practical ways to implement decision theory.

DA provides methods and aids for addressing essentially all the steps involved in formal decision making, including problem definition, information collection, risk assessment, the identification and screening of alternatives, the evaluation and selection of alternatives, and the communication and implementation of decisions. I like to describe DA as a "megatool" for decision making—unlike most other decision tools, DA is more like a tool box than a tool. DA is relevant to project portfolio management because it provides a framework for analyzing project selection decisions as well as specific methods for quantifying project value and addressing project risk.

DA was initially developed in the 1960s and 1970s at Harvard, Stanford, MIT, Michigan, and other major universities. The term "decision analysis" was coined in 1964 by Ron Howard, a professor at Stanford University. DA is generally considered a branch of the field of operations research, but also has links to management science, economics, systems analysis, psychology and statistical decision theory.

Although DA has continued to advance over the past decades, the basic principles of the field as first articulated in the 1970's remain relevant. For example, when DA was being practiced mainly at the Stanford Research Institute (SRI), it was described as being conducted according to a Decision Analysis Cycle, depicted below.

Decision cycle

The Decision Analysis Cycle

In the first (deterministic) phase, it was advised that analysis begin by identifying the variables affecting decision outcomes judged to be important by the decision maker. Values are assigned, and the sensitivity of decision outcomes to the variables is measured via sensitivity analysis without consideration of uncertainty.

The second (probabilistic) phase) was described as starting with the encoding of probabilities from knowledgeable experts. This phase also includes the assessment of risk preference, which defines the best course of action in the face of uncertainty.

In the third (informational) phase), the results of the first two phases are synthesized through a calculation of the value of eliminating uncertainty over each of the important, uncertain variables. The results of the informational phase show what it is worth in dollar terms to have perfect information. Comparing the value of information with its cost determines whether additional information should be collected prior to committing to a course of action.

In more recent years the Strategic Decision Group (SDG) and other management consulting firms have recommended that DA be conducted as a collaborative process called the Dialogue Decision Process, illustrated below. DA has continued to be an area of consulting specialty, and there are journals and professional societies devoted to the field.

Decision dialogue

The Dialogue Decision Process

According to DA, a good decision is one that (1) considers the full range of alternatives that are available to the decision maker, (2) accounts for what the decision maker believes will be the potential consequences of choosing each alternative, and (3) is consistent with the decision-makers preferences for those possible decision consequences. In other words, making good decisions requires knowing what you can do, what you believe, and what you want.

DA employs various procedures and tools for understanding how the actions taken in a decision determine the consequences that may result, as well as the significance of those consequences relative to the decision-maker's objectives. To provide this understanding, an analytic model of the decision is constructed. This decision model is typically composed of two parts, a consequence model that simulates decision outcomes and a value model for measuring the decision-maker's preferences for those outcomes. Probabilistic reasoning is used to quantify risk and determine whether additional information should be collected before committing to a course of action. The models allow sensitivity analysis, a process that identifies the issues that make the most difference and helps decision makers avoid "paralysis by analysis."

DA is generally focused on three different (but overlapping) types of decisions: (1) one-time decisions where alternatives must achieve multiple, competing objectives, (2) sequential decisions where uncertainties and learning play an important role, and (3) portfolio decisions where the goal is to provide a model and process for selecting a portfolio of related alternatives. The latter application is sometimes termed portfolio decision analysis (PDA), and is the approach to project portfolio management that is described and recommended in this website.

Regardless of the type of decision being analyzed, a major task for the decision analyst is constructing a value model that allows the overall desirability of alternatives to be computed based on how they perform relative to a set of evaluation measures, or attributes. Multi-attribute utility analysis (MUA) is often used to construct the value model. To represent decision timing and uncertainty, sequential decisions are analyzed using decision trees and influence diagrams. Decision analyses of project decisions often include calculations of the expected net present value (ENPV) of candidate projects and may include valuations based on real options analysis.

decision expert (DEX)

A multi-criteria analysis (MCA) decision-making method implemented as an interactive computer program that runs under the MS-DOS operating system on IBM-compatible computers. The underlying methods were developed in the early 1980's by a research team led by M. Bohanec, I. Bratko, and V. Rajkovic.

DEX works with utility functions defined by users of the software. The approach involves decomposing a decision problem into a hierarchy of smaller sub-problems represented in a tree-like structure.

DEX tree-like structure

Illustrative attribute structure defined in DEX

DEX differs from other MCA decision support systems in that it uses qualitative, symbolic attributes instead of quantitative ones. Aggregation equations in DEX (e.g., value functions) are defined by "if-then" decision rules rather than numerically (e.g.,as a weighted scoring model). Fuzzy logic and probability distributions can be defined to accommodate uncertainty and incomplete information.

decision maker

A person who decides things, especially at a high level in an organization.

In the context of multi-criteria analysis, the decision maker is one of the two somewhat idealized actors, the other being the analyst, who are assigned specific responsibilities for the application of a formal approach for solving decision problems. The decision maker is the individual or group of individuals assumed to have responsibility for identifying the need to make a decision and to have authority for making choices. The decision maker is also assumed to have responsibility for identifying decision objectives and for directly or indirectly providing the preference structure needed by the analyst to evaluate and rank decision alternatives. The analyst, on the other hand, is responsible for conducting a wide range of activities directed toward problem formulation and the quantitative and qualitative analysis of the problem The analyst constructs a mathematical model of the decision problem, and, depending on the solution approach used by the analyst, may encode the decision maker's preference structure as a multi-attribute utility function. The analyst is responsible for communicating the results of the analysis to the decision maker and the decision maker must then decide the acceptability of that solution.

decision matrix

A prioritization matrix that is used for the purpose of aiding the selection of an alternative rather than for the purpose of prioritizing alternatives (i.e., the alternative in the matrix with the highest weighted-total score would be recommended for selection).

Sample decision matrix

A sample decision matrix

decision model

An analytic representation (model) of a choice among possible actions or alternatives. A decision model represents the factors relevant to decision making and their relationships.

Decision models fall into two categories: descriptive and prescriptive (a prescriptive decision model is also called a "normative" model). A descriptive decision model describes how people typically make decisions and seeks to explain how various factors influence decision-making behavior, including why people often make sub-optimal decisions.

In the context of project portfolio management, the term decision model usually refers to a prescriptive decision model designed to indicate how people or organizations should choose based on principles of logic and rationality. For example, a decision model based on decision analysis seeks to identify choices that are logically consistent with the available alternatives, preferences, and beliefs of the decision maker. Such a model would include performance measures that indicate the degree to which the possible outcomes of choosing each alternative would achieve each decision objective, and a value model indicating the relative preference the decision maker assigns to such performance. In addition to helping decision makers make good choices, decision models can often be "mined" to provide additional information and insights, including sensitivities and value of information (VoI).

decision science

A broad spectrum of related concepts and techniques used for the systematic study of the process of decision making, including the study of how decision should rationally be made. Decision science draws from numerous fields of study, including economics, decision theory, decision analysis, negotiation analysis, management science, and operations research.

decision support system (DSS)

A computer software program and associated database intended to aid managers in making decisions. A DSS may include a decision model, simulation programs, and algorithms. Alternatively, a DSS may merely provide information useful for supporting decisions. The term is an old one that is not used much now, as its definition is so broad as to not be particularly useful. A project portfolio management tool is an example of a DSS.

decision theory

Typically means utility theory. Sometimes, however, the term refers more broadly to theory relevant both to how decision ought to be made (normative decision theory ) and how they actually are made (descriptive decision theory.

decision tree

A graphic representation of a decision problem wherein the alternatives to decisions and possible outcomes to uncertainties are represented sequentially by nodes and branches in a tree-like diagram. Many software tools are available for constructing and analyzing decision trees.

The sample decision tree below is a popular text book example for a problem that might be faced by an oil wildcatter (a person who drills exploration oil wells in areas not known to be oil fields).

Decision tree

An oil wildcatter's decision tree

According to the software tool used to construct this tree decision nodes are shown as yellow squares, uncertainty or "chance" nodes are shown as green circles, and terminal nodes are represented by blue triangles. The initial decision, shown at the left of the diagram, is whether or not to conduct a seismic test on the land prior to deciding whether or not to drill. If the seismic test is conducted it will indicate whether the site has (a) no structure-bad, (b) open structure-so-so, or (c) closed structure-best. Regardless of whether a seismic test is conducted, the wildcatter must decide whether or not to drill. If a well is drilled, it will come up dry, wet, or soaking in oil. Each path through the tree corresponds to a different sequence of choices and uncertainties and leads to different profit or loss and, perhaps, other outcomes important to the wildcatter.

Because decision trees can be very large, for display purposes it is common to use shorthand ways to avoid having to draw duplicate parts of the tree. In the case of the above tree the node "a" in the lower part of the tree is used to indicate that the nodes and branches beyond this point are exactly the same as those beyond the node in the upper portion of the tree labeled "a".

A decision tree can be quantified by associating probabilities to the uncertainties and computing the value of the outcomes associated with each path through the tree. To illustrate, the tree below is an expanded version of the wildcatter's decision tree that explicitly shows all of the possible paths through the tree. By convention, probabilities are placed under the branches of the chance nodes and the equivalent monetary values corresponding to the various paths through the tree are placed under the terminal nodes. The monetary values may be determined by discounting the cashflows or, if non-financial impacts are to be considered, a method such as multi-attribute utility analysis may be used to compute an equivalent monetary worth that combines both financial and non-financial project outcomes.

Quantified decision tree

Expanded oil wildcatter's decision tree

When a decision tree is quantified by associating probabilities with chance nodes and values to terminal nodes, the tree becomes a stochastic decision model that can be solved using a computational approach known as tree rollback. Tree rollback is based on dynamic programming and indicates the optimal choices contingent on the outcome of each uncertainty. The rollback process begins with the values associated with the terminal nodes of the tree and works backward to the initial decision node. Rollback values are calculated and placed in brackets besides the corresponding nodes. Depending on the node type, rollback values are determined as follows:

  • At a terminal node, the rollback value is simply the value associated with the terminal node.
  • At a chance node, the rollback value is the expected value (the probability-weighted average) of the values on the branches emanating from the node.
  • At a decision node, the rollback value is set equal to the highest rollback value (or minimum cost, if negative) on the branches emanating from the node.

To illustrate, the monetary values on the 3 top terminal nodes are as follows: -$70,000 (if the well is dry), $50,123.4 (if the well is wet), and $200,037 (if the well is soaking). Using the probabilities shown under the branches, the expected value obtained from rollback is $20,044.5 = 0.5 * -$70,000 + 0.3 * $50,123.4 + 0.2 * $200,037). This is the value shown in the brackets to the left of the top-most green node. Rollback values are repeatedly computed in this way until you reach the first node in the tree. As shown, the rollback value for the "no seismic test" option ($20,044.5) is less than the value of doing the test ($22,586.9), so the value-maximizing choice is to begin by conducting the test. The tree also shows that if the test indicates no structure, the optimal decision is not to drill. Conversely, if the test shows open or closed structure, the optimal decision is to drill.

Decision trees provide an additional advantage that they can be analyzed to provide value of information calculations that indicate the worth of obtaining additional information that would help to resolve uncertainties before committing to a decision.

Decision trees are used in some project portfolio management tools, especially tools aimed at pharmaceutical product development and other sorts of R&D, as a means for addressing risk.

decision unit

Also called a decision variable, a variable in a decision model representing the choice that is to be made in the context of a decision problem. As with other decision models, the decision units for a project priority system should be defined as part of the framing process. Among other things, framing requires requires determining which quantities are to be treated as decision variables and which ones are to be taken as fixed. The quantities whose values are fixed (or relatively fixed) are typically called parameters.

Decision units are important because they determine the granularity of the analysis, including spatial, temporal, and intensity assumptions. When shopping for project portfolio management tools, it is very important to understand the restrictions that are placed on the definition of decision units. For example, if you need a tool to help you prioritize capital projects, it might be reasonable to consider one that evaluates "fund" versus "don't fund" options for each project. However, such a tool might be useless for evaluating maintenance projects if the appropriate decision unit is the choice among alternative 5-year spending plans for programs consisting of groups of similar assets.

delphi method

A method used by a group of people to reduce biases in estimates made by the group or other group judgments. The Delphi method is typically conducted as an iterative process wherein a facilitator repeatedly obtains judgments from each member of the group interspersed with feedback of group responses and opinions. Group members typically remain anonymous with regard to the opinions expressed and interact through the facilitator in order to reduce biases in the estimates produced.

delta property

A condition that, if satisfied, ensures that the utility function describing a person's preferences among alternatives with uncertain outcomes will have either a linear or exponential form. The required condition is that if any arbitrary amount D is added to the value of each possible outcomes to an alternative, then the certain equivalent (what the uncertain gamble is worth to the individual) will increase by exactly that amount D.

Dempster-Shafer (D-S) theory

A theory for reasoning about uncertainty similar to, but different than probability theory. D-S theory shows how evidence from different sources can be combined to arrive at a degree of belief represented by a mathematical function called a belief function.

A belief function, like a probability function, assigns a number between zero and one to propositions. However, while a probability indicates the likelihood of the proposition, a degree of belief assigned by a belief function indicates a degree of support provided by the evidence for the proposition. A belief of 0 means no support for the proposition, a belief of 1 means full support. The assigned numbers differ from probability in that belief in a proposition (the proposition is true) and its negation (the proposition is false) need not sum to 1, and both values can be 0 (meaning no evidence for or against the proposition). The mathematics provided by the theory for combining evidence and aggregating beliefs can be regarded as a generalization Bayes theorem for updating subjective probability distributions based on new information. The major difference between the two theories is that the D-S theory is capable of combining evidence and dealing with ignorance in the evidence combination process

The theory owes its name to work by Arthur Dempster and his student, Glenn Shafer. Dempster developed an initial version of the theory in 1967 in the context of statistical inference as a means for combining evidence from different sources to arrive at a degree of belief. Shafer then expanded the theory into a general framework for representing epistemic uncertainty (uncertainty due to lack of knowledge). Since then, numerous researchers, especially in the field of artificial intelligence, have continued to expand and refine the theory.

Belief functions are most often used for combining expert opinions, since experts often differ in their opinions with differing degrees of credibility. Applications include auditing, medical diagnoses, and other applications where information is gathered from semi-reliable sources.

dependency matrix

Also called a dependency structure matrix (DSM), a method for documenting and analyzing the interdependencies between programs, projects, or project tasks. The capability for constructing a dependency matrix is provided by some project management tools and some project portfolio management (PPM) tools . A few PPM tools take into account the interdependencies expressed in a dependency matrix when computing optimal project portfolios.

The rows and columns in a dependency matrix are labeled with projects and/or programs and the cells represent dependencies. A number placed in a cell indicates an estimated degree of dependency between the project represented by the column and the project represented by the row.

Dependency matrix

Dependency matrix

The precise meaning of the numbers entered into the cells is not standardized and differs depending on the application and the nature of the dependencies. For example, in some versions, a zero or one is written into each cell—zero indicates there is no connection between the projects, one indicates that the project corresponding to the column cannot be conducted unless the project corresponding to the row is completed. In a version of the tool for projects to create new products, users indicate in the diagonal cells the amount of revenue produced if the corresponding projects are conducted alone; the numbers in the other cells in the column indicate the amount of revenue produced if the column project and the corresponding row projects are jointly conducted. In another version, users indicate in the cell corresponding to a column and row the "impact," expressed as a percent, that the row project has on the column project, where impact could relate to impact on benefit, cost, schedule, or risk.

Some PPM tools use a dependency matrix for portfolio monitoring. For example, "traffic light" icons may be associated with projects. A red light may indicate that a problem is forecast for a project because a problem has occurred for one of the projects on which it is dependent. A green light may indicate that a project is dependent on other projects, but that so far no problems have been encountered by those projects.

derivative project

A project with objectives or deliverables that are only slightly or incrementally different from those provided by the organization's other projects. The term is typically used for projects intended to modify existing products or services, or that create new products that derive naturally from the firms other product offerings. Projects that provide an add-ons, new packaging, or manufacturing efficiencies are typically viewed as derivative projects.

desktop application

A software application that runs stand alone on a desktop or laptop computer..


Non random. Typically applied to describe a model or method of analysis whose outputs are fully determined by its inputs, with no uncertainty or possibility of an alternative outcome. Compare with stochastic.

difference independence

The independence condition that, together with preferential independence ensures that a cardinal value function can be constructed with the additive form.

Attribute X is said to be difference independent of other attributes if the preference difference between any two attribute bundles that differ only in the level of X does not depend on the common levels of the remaining attributes. In the context of choosing among job offers, for example, suppose location and salary are relevant attributes. If you prefer working in New York city twice as much as working in San Antonio at a salary of $50K, then you must continue to maintain that to same preference ratio (prefer New York twice as much as San Antonio) if the salary offer is $80k.

discounted cash flow (DCF)

See net present value (NPV).

discount rate

A rate, expressed as a percentage, used to compare the value of obtaining future versus present outcomes. The discount rate appears in the formula for net present value, where it is denoted r. The discount rate indicates time preference in that it specifies the return that would be required to make a decision maker indifferent to delaying an outcome (e.g., If you could earn a 10% return, would you be willing to postpone receiving your paycheck for a year?).

In business settings where a business has the opportunity to borrow funds, the The discount rate is typically chosen to be the the average amount that the organization must pay to obtain funds (i.e., opportunity cost associated with making the investment)—If I can earn a return r per year from investing, then I won't be willing to accept less than r if my current investment delays my cash flow by a year. Discount rates are important to project portfolio management because they provide a means for comparing projects that produce delayed costs and benefits.

discrete risk, discrete uncertainty

An event, circumstance or condition that may or may not occur, which would a project or its outcomes (e.g., technology failure, strike, discovery of unexpected hazardous conditions, labor strike). A discrete uncertainty may be characterized by the probability of occurrence and the estimated consequences should the event occur. For comparison, see continuous risk.

discrete scale

A measurement scale that allows for the assignment of only a finite number of possible values, for example, a one-to-ten scale that does not allow assigning non-integer values. For comparison, see continuous scale.


To approximate a continuous variable (one that could take on an infinite number of possible values defined over some range) by a finite number of possibilities. A common application is discretizing a continuous probability distribution so that it can be represented by a small number of branches in a decision tree. If the variable is uncertain and characterized by a continuous cumulative probability distribution, denoted F(x), the continuous distribution is approximated by a discrete distribution P(x) (a probability mass function) that assigns a probability to each of the finite number of possible outcomes. Typically, the discrete approximation is chosen to have the same mean, standard deviation, and skew as the continuous distribution.

Discretizing a probability distribution

Discretizing a continuous probability distribution

Continuous variables are typically discretized for the purpose of simplifying analyses, and many project portfolio management tools use the technique, for example, when discrete scales are used to define the outcomes of variables that are in reality continuous.


The inclusion of different types of investments within a portfolio. Diversification is commonly used in financial investing to reduce risk. Similarly, diversification tends to reduce uncertainty over the total return generated by a portfolio of risky projects. Thus, project diversification is often good for project portfolios. However, diversification is not as effective when uncertain project returns are highly correlated. For example, if many projects would be similarly and significantly affected by the same uncertainty (e.g., an economic recession or a change in currency exchange rates), diversification will not be as effective at reducing portfolio risk.

dominance method

Also called dominance rule, a multi criteria decision making method wherein dominated alternatives are identified and eliminated from further consideration. The method proceeds by comparing the first two alternatives. If one of the alternatives performs as well or better than the other on every criterion, it is said to dominate the other alternative, and the dominated alternative is discarded. Next, the undiscarded alternatives are compared against the third alternative. If any of the alternatives are found to be dominated, they are discarded. The method proceeds until all dominated alternatives have been eliminated.

The dominance method typically results in multiple non-dominated alternatives. Therefore, so, if a ranking of the undiscarded alternatives is required, a compensatory method is typically applied for this purpose.

dominated alternative

A alternative to a decision that is inferior to at least one other alternative with respect to every relevant decision criterion. A dominated alternative has disadvantages without any advantages. A decision matrix is useful for identifying dominated alternatives. Eliminating dominated alternatives is a typical screening step prior to more involved, multi-criteria analysis (MCA).

do minimum scenario

The baseline scenario against which the additional benefits and costs of the with project scenario are measured (often a synonym for the ‘without project’ scenario). The do minimum scenario is project option that includes all the necessary realistic level of maintenance costs and a minimum amount of investment costs or necessary improvements needed in order to avoid or delay serious deterioration of the organization's assets or to comply with safety standards. Do

downside risk

The maximum amount of possible loss in a given decision or situation

drill down

A term used to describe the action of moving from summary level information to the more detailed information on which the summary is based. Tools for project portfolio management (PPM) are often advertised as providing dashboards with drill down capability—clicking on summary information on the dashboard causes the user to navigate to a more detailed level or record. Drill down capability is made possible by arranging data in hierarchies that start with general information and encompass increasing levels of detail.

due diligence

Refers to the level of care and analysis that should be reasonably conducted prior to making business investment decisions. The term is most often applied in the context of venture capital investment and business mergers and acquisitions, where due diligence is regarded as the essential means for preventing avoidable harm to the investing parties. However, the concept applies to any important decision. Before investing scarce resources to conduct a major project, a deliberate, documented process should be undertaken to uncover and understand all of the information relevant to the choice.

Due diligence is not just important for making good decisions; it is also important for the defense of those decisions. In legal disputes involving situations where projects have gone badly or created significant health, environmental, or economic losses, due diligence has been established as a legal obligation, and demonstrating due diligence represents an important legal defense. Courts, however, have held that due diligence requires not merely showing that the standard of care for the decision was normal for the industry, but proving that what was done is what a "reasonable and prudent" professional within the area would do. Failure to meet this standard can give rise to civil and criminal liability

In the context of project portfolio management, due diligence means that organizations should institute a deliberate, documented, quality process for making major project investment decisions. The process should include and ultimately be based on an evaluation of the consequences of doing versus not doing the project and the risks involved. Due diligence for project selection doesn't just improve decision quality, it ensures that tough choices can be defended in hindsight. Even if a simplistic project evaluation technique, such as strategic alignment, could be shown to be the "normal approach," organizations (especially those that depend on projects for success or that operate assets or sell products that produce public risks) would be well advised to utilize better and more defensible project evaluation methods.

dynamic programming

A type of mathematical solution technique wherein a complex problem is broken into a series of interconnected and similarly-structured sub-problems in such a way that the sequential solution of the sub-problems results in the solution to the complex problem. Dynamic programming is used as a solution technique in some project portfolio management tools, especially tools designed for projects requiring a series of decisions, such as new product development projects. When applicable, dynamic programming is a divide-and-conquer approach that can efficiently generate solutions to complex problems.