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Term
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Explanation
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lean
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As in lean project management, lean enterprise, lean production, etc., a business practice that considers the expenditure of resources for any
goal other than the creation of value for end customers (or in some uses, for the enterprise itself) wasteful, and, therefore, a target for elimination. Toyota developed and applied the
concept to manufacturing in the 1990's, and the auto manufacturer's success during this era helped popularize the practice. Project portfolio management
(PPM) applied with the goal of selecting value-maximizing project portfolios can be regarded as consistent with the principles of lean operation, and some PPM tool providers have
used the phrase lean project portfolio management to describe their offerings.
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life cycle portfolio matrix
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Also called the product life cycle portfolio matrix and the ADL matrix, a simple tool developed in the 1980's by the professional services firm Arthur D.
Little intended to help a company manage its collection of product businesses as a portfolio. The key concept is consideration of where each product is within its business life
cycle.
Like other portfolio planning matrices, the ADL matrix represents a company's various businesses in a 2-dimensional
matrix. In this case, the columns of the matrix represents the growth stage of the business product (embryonic, growth, mature, or aging) and the rows represents the product's
competitive position in the marketplace (dominant, strong, favorable, tenable, or weak or nonviable). This results in a 4 by 5 matrix with 20 cells. The company's various product
businesses are placed within the matrix, and the positions are associated with logical business strategies as shown below:
Life cycle portfolio planning matrix
The distribution and trajectory of the businesses across the matrix helps indicate whether the firm's product mix is well balanced now and in the future. For example,
the company will need to maintain a continuing set of mature businesses in order to generate cash to support new embryonic and growth operations.
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linear programming
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A mathematical method for finding the maximum or minimum solution to a problem where the objective function is a linear
combination of the decision variables, for example,
ax1 + bx2 + cx3 ...
and where some or all of those variables are subject to linear constraints, for example,
Ax1 + Bx2 + Cx3 ≤ N or Ax1 +
Bx2 + Cx3 ≥ N
Linear programming has been long applied to resource allocation problems. Typically, the decision variables represent the amount of various resources allocated to
various purposes and the constraints specify how much of each type of resource is available. So long as the objective function is a linear function of the amounts allocated, the
solution can be found using linear programming.
The importance of linear programming derives in part from the efficiency of the algorithm, known as the Simplex Method, by which a linear program may be solved.
Linear programming can handle very large numbers of variables and constraints. Some applications, for example, have involved millions of variables and hundreds of thousands of
constraints.
The main disadvantage of linear programming, of course, is that it requires that the optimization be conducted based on a single, linear objective function. Project selection decisions, as well as most other decision problems, require multiple, generally non-linear, objectives to be simultaneously optimized. (Goal programming and multiobjective linear
programming are variations of linear programming that attempt to account for multiple objectives.) With linear programming you cannot, for example, account for project start-up
costs, efficiencies of scale, and other considerations that commonly cause the relationship between project costs and project benefits to be
non-linear.
Also, linear programming assumes that any solutions within a continuum of possible values that satisfy the constraints are possible (for contrast, see integer programming). In the real world, most often you must choose whether to fund or not fund a project. A linear programming solution that told you to fund
1/5 of the project might not be very useful.
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linear regression
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The relation between variables in a regression analysis when the regression equation is linear, e.g., y =
ax + b.
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M
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mandatory project
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Also called a mandated project, a project deemed a "must do." A common example is a project necessary to comply with
regulatory requirements (for instance, the requirement to conduct an environmental impact analysis prior to doing construction on federal land). A project may or may not be considered
mandatory based on the opinions of the organization's senior executives (depending on whether such opinions are sufficient to determine the organization's project choices). The criteria
for labeling a project mandatory should be carefully designed, as labeling too many projects mandatory reduces flexibility and may result in insufficient resources for undertaking some
high-value, but discretionary projects.. Mandatory projects may or may not be formally evaluated within a Project portfolio management (PPM)
process. Regardless, project portfolio management tools typically provide capability to force mandatory projects into project portfolios.
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market risk
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Also called systemic risk, the risk that a project or other investment will decline in value due to factors that are external to the investment and tend to
impact the market or business as a whole. Common examples of market risk factors for financial investments (stocks, bonds, etc.) include interest rates, foreign exchange rates, and
commodity prices. Such factors are also often important for project investments, but there are many other external risks (e.g., a labor strike, severe weather, breakdown in corporate
governance) that could simultaneously and adversely impact many unrelated projects and would, therefore, be categorized as market or systemic risks. Because market risks tend to affect
many projects, they can have a major impact on the risk of project portfolios. To adequately quantify project portfolio risks, it is frequently necessary to estimate and quantify the
impact of market risks on portfolio value.
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mean
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The average value of a set of numbers.
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measurable value function
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See value function.
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methodology
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A collection of related methods, concepts, and procedures relevant to some field or technical topic, such as the valuation of
project investments.
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metric
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A measure for quantifying some aspect of business or organizational performance, for example cost, percent sales returned, market share, and return on investment. Metrics may be used both to assess previous performance (e.g., What were our monthly sales over the past 6 months?) and as a vehicle for
forecasting future performance (e.g., What is our projection of sales for next month?). To address uncertainty over future performance, rather than provide a point estimate, a range or probability distribution might be assigned to a metric.
Metrics for forecasting play a critical role in project prioritization because knowing which projects would have the greatest positive impact on organizational performance is key to deciding the projects to conduct. Although there is not universal
agreement over terminology, the term performance measure is used here and elsewhere to describe a metric with characteristics that make
it well-suited for use in a project selection decision model.
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Microsoft Project (MP)
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Also referred to as Microsoft Office Project (MOP), a popular PC-based software application sold by Microsoft for supporting project
management. MP is designed to help project managers develop project plans, create schedules, assign resources to tasks, track progress, manage the budget, analyze workloads, and
create reports. The user can create Gannt charts, PERT charts, and project network diagrams; identify critical paths; and perform earned value analysis. The software has been distributed in various editions corresponding to different years of release and in Standard and Professional
versions. A wizard is provided that walks users through the process of project creation, from assigning tasks and resources to reporting results. Project files are in a proprietary file
format with extension .mpp. MP is the dominant project management software application in the PC space. Many project portfolio management tools allow
project data to be transferred to and from MP.
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mission statement
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A (typically) brief, formal statement of the goals of an organization. Many organizations develop a mission statement to help guide decision making and to communicate
to stakeholders the organization's agreed upon aim and purpose. The organization's mission statement is often a good source of fundamental objectives for creating an objectives hierarchy, a useful step for developing performance measures for
evaluating and prioritizing projects.
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mixed integer programming
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See integer programming.
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modern portfolio theory (MPT)
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A method developed by Nobel Prize winner Harry Markowitz for finding "efficient portfolios," portfolios that have the minimum possible risk for a given expected
return. Also called portfolio management theory (or, more simply, portfolio theory), MPT provides a relationship between the market price of an investment, the
investment's expected return, and the risk of that investment relative to the market as a whole. The relationship demonstrates that diversification—including within the portfolio different types of investments— often reduces risk. Although questions have been raised
about some assumptions underlying MPT, the theory is often used by financial investment managers to help make investment allocation decisions.
From the standpoint of project prioritization, MPT has a major limitation—The theory is designed to be used for
optimizing portfolios of financial securities, such as stocks and bonds, not projects. There are important differences between financial and project
investments. With a stock portfolio, for example, an investor can choose any level of investment in each security. Project portfolios, on the other hand, typically require the
organization to choose to do, or not to do, each project. Tool providers that claim their tools are based on MPT are likely being disingenuous—Any techniques provided for
optimizing project portfolios probably have nothing to do with MPT. However, there are some situations where MPT can appropriately be applied to value projects and optimize project
portfolios (see below).
In MPT, the uncertain return from an investment is represented as a random variable characterized by its mean (average or expected
value) and standard deviation (a measure of variability about the mean). If the investment is a financial security (e.g., a stock) these
statistics can be estimated from historical data. The standard deviation is interpreted as a measure of the risk associated with the investment. The theory assumes that, among those
portfolios with a given expected return, the most attractive is the one having the least risk.
MPT shows that a key determinant of the risk of a portfolio is the degree of correlation among the individual investments;
that is, the extent to which their prices (or returns) tend to move together. For example, an up tick in oil prices is often good for oil companies, but bad for airlines. Thus, oil
stocks and airline stocks tend to be negatively correlated. Diversifying by including negatively correlated (or even uncorrelated) investments in a portfolio tends to decrease portfolio
risk.
The risk that cannot be avoided, no matter how much you diversify, is referred to as systematic or market risk. Market risk
stems from correlations among securities that arise because there are economy-wide perils that impact all businesses. MPT shows that the contribution of an investment to the risk of a
well-diversified portfolio is determined not by its riskiness in isolation, but rather by its market risk (measured using a coefficient called beta). A related result is that, on average, the market provides higher expected returns for investments with higher market risks. Note that the way that
the market increases the return on riskier assets is for the asset to trade at a lower price than does a similar but lower risk asset.
One situation in which MPT could be applied to projects is the following. Suppose a project would create a factory for producing some commodity traded in the
marketplace, such as corn or gasoline. Commodities are similar in some respects to financial instruments; their prices are determined by large numbers of buyers and sellers, and
historical data is available for estimating the statistics needed for MPT. The value of the proposed factory will largely be determined by the prices for the commodity that prevail
during the lifetime of the factory. MPT could be used to understand how market risks impact the value and risk of the project and project portfolios that include the project. As another
potential application, MPT can sometimes be used to compute a risk-adjusted discount rate for net present value (NPV) analysis or other techniques,
such as economic value added (EVA), that account for the cost of capital.
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motivation biases
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A category of related judgmental biases with the common characteristic that they tend to promote behavior consistent with one's incentives and motivations. Motivation
biases include the tendency of people to behave differently when they think they are being observed, to unconsciously distort judgment to "look good" and "get ahead", and to remember
their decisions as being better than they were. Many comfort zone biases are also categorized as motivation biases, since behaving in ways
consistent with one's motivations and incentives typically feels comfortable.
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multi-attribute utility analysis (MUA)
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Also called multi-attribute utility theory (MAUT), multi-attribute decision analysis (MADA), multi-objective decision analysis (MODA) and
multi-criteria decision analysis (MCDA), MUA is a decision analysis method for quantifying the value of something (e.g., a project) based on its characteristics, impacts, and other relevant "attributes." MUA is useful for project portfolio management because it provides a highly defensible way to quantify project value, including non-financial
(or "intangible") components of value.
The term MUA derives as follows. In economics, "utility" is a measure of the value or satisfaction derived from something. Sometimes, utility depends on a single
attribute. For example, the utility to me of the cash in my wallet depends on one attribute; namely, the number of dollars. The more dollars I have, the greater the utility of those
dollars. Mostly, though, the utility of something depends on more than one attribute. For example, if I travel to Europe, the utility of the cash in my wallet would depend on two
attributes—the number of dollars and whether those dollars are US or Euros. "Multi-attribute utility" is a measure of value that depends on or is determined by more than one
attribute of the thing being valued.
More precisely, MUA is an approach for deriving a "utility function" (a decision
model) that, according to decision theory, quantifies a decision maker's preferences over the available alternatives to a decision. The
utility function, U, is such that the best alternative is the one that maximizes U. Thus, if we could determine the function U, we could calculate which of the alternatives to a
decision is the most desirable. MUA is a step-by-step process for determining U, a process that is efficient in the common case where multiple criteria (attributes) determine the
desirability of alternatives.
Utility functions for evaluating projects are typically multi-attribute because the desirability of a project depends not just on the dollars the project returns (one
attribute), but also on various additional criteria that capture other types of benefits that may accrue from the project (e.g., improved safety, increased knowledge, etc.). The concept
of MUA is that the correct, multi-attribute function U will be much easier to find if it can be written as some simple combination of single-attribute functions (e.g., as a sum of a
utility function describing the relative desirability of various levels of financial performance and another utility function describing the desirability of various levels of safety).
If the utility function separates in this way, then, deriving the function is easier because each single-attribute function can be assessed without reference to the other
attributes.
MUA provides a step-by-step process for deriving a multi-attribute utility function as a combination of single-attribute functions. The first step is to structure the
objectives of the decision into a hierarchy in such a way as to meet certain criteria, including preferential independence, that ensure that the utility function will separate into single attribute functions. The next step is to define
attributes or performance measures that quantify the achievement of each objective and to develop a single-attribute utility function
(often called a scaling function) for each measure. Tests are performed on the measures to determine how the single-attribute utility
functions should be combined (e.g., added, multiplied, etc.). Since the objectives are not necessarily equally important, weights must be assigned, and techniques (e.g., the swing weight method) are provided to correctly set the weights based on the decision maker's answers to questions indicating willingness to trade off
various levels of performance on the different measures.
MUA can be easily integrated with more traditional financial measures of project value. For example, within MUA, net present value
(NPV) can be used as a performance measure for the project's direct financial value. Likewise, MUA can be used with expected net present value
(ENPV) to explicitly address project uncertainties.
MUA has been applied to many different types of decision problems and there is a vast literature on the topic. The major benefit of the approach is that it produces a
single number, expressible in equivalent dollars, that measures the overall value (utility) of a project. Since this number is derived through a step-by-step process beginning with the
specification of objectives, the logic is open and explicit, can be reviewed, and may be changed if any assumption made at any step is judged to be inappropriate. Scores and weights are
also explicit and are developed according to established techniques and can often be cross-referenced to other sources of information on relative values, such as results from contingent valuations and dollar tradeoffs used in government cost benefit analyses. Because MUA provides a
detailed "audit trail" of assumptions for reviewers, it has been used by government agencies to help make controversial public policy
decisions.
A key characteristic of MUA is its explicit and extensive reliance on judgment. The necessary judgments include value judgments provided by policy makers (e.g., Which
do we want more: a project that would generate $2 million or a project that would reduce the average number of annual worker injuries by 25%?) and technical judgments provided by
specialists (e.g., How do we estimate the impact of a project on the annual number of worker injuries?). This reliance on subjective judgment is sometimes interpreted as a weakness, as
applications may appear overly subjective. Judgments, however, are required for virtually all important decisions. The fact that MUA makes those judgments explicit is an advantage.
Since the judgments and assumptions are represented as inputs to a decision model, interested parties can explore via sensitivity
analysis whether changes would alter conclusions.
A limitation of MUA is that it is not easy to apply correctly. Meeting the technical requirements necessary to satisfy the assumptions of the approach requires skill,
and applications generally must be guided by experienced specialists in the field. Furthermore, like all other decision tools, the decision model produced by MUA will necessarily
involve simplifications that may introduce errors into recommendations.
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