TCMFramework_SM.gif (5258 bytes)TCM Framework: An Integrated Approach to Portfolio, Program and Project Management
(Rev. 2012-01-09)



3.3 Investment Decision Making

3.3.1 Description

    In TCM, investment decision making is a process to analyze investment alternatives and determine whether, how, and when to allocate the enterprise’s limited resources to them. While this section specifically addresses investment decisions during enterprise planning (e.g., capital planning and budgeting), the general process is applicable to other decisions that may be made in any process described in the TCM Framework. The primary input to the decision making process is the scope description for one or more asset solutions that satisfy requirements (see Section 3.2). The output of the decision making process is a defined scope of the selected alternative and the assumptions (i.e., business decision basis or business case) upon which the investment decision was made. This output information is the basis of the project implementation process (Section 4.1) as well as the basis for asset performance measurement and assessment (Chapters 5 and 6). For non-project alternatives, the output is the basis for implementing asset changes (e.g., a change to process activities, etc.).

    This section focuses on for-profit corporations as the enterprise context and on maximizing wealth creation as the dominant objective. Despite this focus, the discussion applies to techniques useful for evaluations in business, not-for-profit organizations, government, and personal lives.

    Asset planning, including investment decision making, addresses any of three types of problems: choosing the best alternative; assessing cost or value; and optimization. All three problem types are addressed essentially by the same decision analysis approach: choosing the highest-value (or lowest-cost) alternative. Optimization analyses are among the most interesting to perform, and these include engineering design, competitive bidding, and activity scheduling.

    Decision analysis (DA) is the foremost process for helping decision makers choose wisely under uncertainty. DA involves concepts borrowed from probability theory, statistics, psychology, finance, and operations research. The formal discipline is called decision science, a subset of operations research (management science). The essence of DA involves (1) capturing judgments about risks and uncertainty[23] as probability distributions, (2) having a single value measure of the quality of the outcome, and (3) putting these together in expected value calculations. An expected value (EV) is the probability-weighted outcome, and this is synonymous with the mean statistic.

    The core analysis technique in this section, DA, is a type of economic analysis. Decision and economic analysis consider economic costs. Economic costs is a relative view of costs, rather than an absolute measure of money. That is, it recognizes that costs represent opportunities lost (i.e., opportunity costs) and that the value of money is relative to the time, currency, and context (e.g., how it is accounted for and taxed) in which it is expended and the definition of "value" that is applied in the valuation process.

    The purpose of the decision process is making good decisions. A good decision is one that is logical and consistent with the strategy and objectives of the enterprise (represented in the organization’s decision policy) and consistent with the information available at the time. Because of risks and uncertainties, a good decision does not guarantee a good outcome. However, making good decisions over the long term can be expected to maximize the enterprise’s progress toward its objectives.

.1 Decision Policy

    The investment decision making process is closely integrated with the requirements elicitation and analysis and asset planning processes (Sections 3.1 and 3.2, respectively). Decisions should always be made in the context of the whole enterprise, including portfolios for assets, resources, and projects. Ideally, all decisions would be optimized in the context of a whole enterprise model. However, it is impractical to solve an all encompassing model for other than the most significant decisions. The practical approach is to first optimize parameters of decision policy using the whole enterprise model. The policy, which is practical to use, then guides day-to-day decisions. The decision policy is, in effect, a requirement for the decision making process. The decision policy aligns the investments with the strategic plan and objectives.

    Decision policy is customarily decomposed along three dimensions:

    In most circumstances, the outcome quality can be measured in monetary equivalents and risk attitude adjustments are unimportant.[24] In most economic evaluations, the outcome measure is net present value (NPV) of the future net cashflow stream. When the project model incorporates judgments about risks and uncertainties, the single-point forecast is the expected value NPV. In a maximization problem (usually involving revenues), the expected value is called the expected monetary value (EMV = EV NPV). In a cost minimization problem, the single-point forecast is expected value cost (abbreviated EV cost).[25] Though usually implicit, the cost should be a present value cost. In best practice, NPV analysis is supplemented by efforts to forecast company bottom-line net cashflow impact, including inflation and tax effects.

    The oft-cited "three pillars" of project management are cost, schedule, and quality. Decision makers must make trade-offs between these criteria and other requirements. Consistent investment decisions require either (a) a multi-criteria decision policy or (b) monetary-equivalents decision policy (i.e., putting schedule and quality into cost terms). Most practitioners find the monetary-equivalents approach more workable. This applies both to the investment decision and to project management decisions. This is because (1) it uses the familiar time value of money concept to reflect time preference, and (2) the asset life-cycle analysis that was used to approve the investment is the basis for assessing cost-equivalents for project schedule and quality changes.

    In many enterprises, other decision making criteria may be used to supplement or substitute for EMV or NPV. These other criteria or measures include payout (PO), payback period, internal rate of return (IRR), discounted return on investment (DROI or ROI) and others. For logical, consistent decisions, these should not be the basis of the enterprise’s decision policy because they do not work well with probabilities or measure value. However, these other criteria may be useful in helping the decision maker better understand the investment. ROI-like measures are especially useful in enterprises operating under a capital constraint assumption.

.2 Model-Centric Analysis

    Forecasting is a critical process within strategic asset planning. Central to almost any planning and assessment analysis is the forecasting model. Models help structure thinking, handle much more detail than possible with the unaided human brain, and help avoid the biases so common in judgment.

    A company’s value is based upon the expectation that it can generate free cashflow available to distribute to investors or attractively reinvest in the enterprise. Forecasting future net cashflow is therefore central to investment economic evaluations. The appropriate model detail depends upon the decisions to be made at various stages of the asset’s life cycle. More modeling effort is suitable when any of these factors is present: high investment, high risks and uncertainties, design to be optimized, and competing good alternatives.

    An asset life-cycle model is the basis for the feasibility analysis (see Section 3.2). This involves forecasting investment, revenues, and costs. Physical operations drive the business, such as production from a factory or an oil reservoir. Industry, economy, and other external factors are also part of the model. Important uncertain input variables to the model can be described as probability distributions. The model is then solved in a way that propagates probability distributions through the calculations. The workhorse methods in decision analysis (DA) are decision trees and Monte Carlo simulation. With probability distributions for some inputs, the output of the models used in these methods is a distribution. Most often, the primary output is a distribution of NPV, and the mean of this distribution is expected monetary value (EMV).

    Evaluation models in DA are called probabilistic or stochastic models. In addition to representing the range of possible outcomes, the stochastic model provides a more accurate value assessment. Because of the way probability distributions propagate through the calculations, there is often a substantial improvement in accuracy. Sometimes, both decision tree analysis and Monte Carlo simulation are used in the same analysis. Decision trees are better suited for problems with low probabilities or subsequent decision points (value of information problems). Monte Carlo simulation is better when there are continuous probability distributions (and there are lots of them in typical project models and design optimization problems).

    Most often, the project model used during project execution will be much more detailed than the feasibility analysis model. In project management decisions, including value analysis and engineering, the asset model is the source of trade-off values. As examples:

    Project risk management (see Section 7.6) addresses uncertainty in both the asset life-cycle and project execution. Threats and opportunities, once identified, should be the basis for brainstorming candidate actions. An action (singly or in combination) is usually appropriate when the expected value cost improvement is greater than the cost of the action. The typical risk is modeled as a binary event (it either happens or not), with a distribution of impact (schedule or cost) if the event happens. Most often, these risk events are embedded as simple tree logic in the Monte Carlo simulation project model.

    For very large projects, there is often a need to plan in greater detail than is suitable for a stochastic project model. A detailed work breakdown structure for a large project might contain hundreds or thousands of activities. In this situation, two project models might be the best approach: a stochastic project model (e.g., 50-100 input variables) used for important project decisions and high-level project risk management; and a deterministic project model (e.g., 500+ input variables) for detailed project planning and management.

    After project completion, the feasibility model remains part of the full company model, perhaps moving from an investment or project portfolio to an asset portfolio. The model should be maintained though the remaining life and is used for budgeting, and strategic planning. The model will be useful for decisions about reinvestments and ultimate abandonment, disposition, or conversion.

3.3.2 Process Map for Investment Decision Making

    Figure 3.3-1 illustrates the process map for investment decision making. As was mentioned, the process centers on decision analysis or evaluation that is based on a decision model. The investment decision making process acts as a gate, through which an alternative from asset planning is either approved and passes through for implementation or is recycled for further elaboration of requirements or planning or other disposition as appropriate.

Figure3.3-1.jpg (101415 bytes)

Figure 3.3-1. Process Map for Investment Decision Making

    The following sections briefly describe the steps or sub-processes in the investment decision making process.

.1 Plan for Investment Decision Making

    Strategic asset planning (requirements elicitation and analysis, asset planning, and investment decision making) should be planned as an integrated process. For example, the asset planning or evaluation team (see Section 3.2) may be the same team that conducts the decision analysis, and the resources for this effort may have been planned well before any investment decisions need to be made.

    The key inputs to planning for investment decision making are the scope description of one or more asset solutions that satisfy requirements, the business strategy and objectives, and the business decision making policy of the enterprise.

    Based on an assessment of the process inputs and the decision criteria established, the planning team further identifies specific activities, resources, and tools for the effort at hand. Specialized analysis skills are applied during decision analysis; some of these skills may have to be acquired from outside of the enterprise. Often, existing models are suited to the present project and may be reused. If the project requires a custom model, the modeling effort is usually considerable. Learning new software tools and developing the model(s) may require significant resources and time that need to be planned for.

    The output of the planning step is documentation of the scope of the decision analysis effort, as reviewed and agreed to by the planning team and business leadership as appropriate. The scope describes the basis of the decision analysis (e.g., objective, criteria, methods, assumptions, etc.); any special concerns about stakeholder interests, strategy, resources, or other issues that are not addressed by the company’s decision policy; and what is or is not included in the analysis and what the team is or is not able to control or change.

    The planning activities may be fairly routine for asset investments for which the company has well established practices. It may be clear to the team that the best decision is based upon experience. If so, the process can proceed quickly to making, documenting, and communicating the decision. Decision analysis only adds value to the process if it has the potential to alter what the decision maker is otherwise going to do (however, if the decision policy is not clear, what might alter the decision maker’s actions may not be clear).

.2 Develop the Decision Model

    A decision model is the central framework for the evaluation team’s assessment of the project alternatives. This is based around a deterministic cashflow projection model. Key inputs to the model are risks or uncertainties that will be judged as probabilities or probability distributions. Including probabilities in the model converts it to a stochastic model: If just one input to the model is a distribution, the outcome (e.g., NPV) will be a distribution.

    The decision model is most often developed as a graphic representation of the key elements in the evaluation process. In particular, chance variables (e.g., sales will be low, medium, or high) and decision alternatives (e.g., build, rent, or do nothing) are organized in sequence. Decision trees, influence diagrams, and flow diagrams are the most popular formats for decision modeling. A script outline is an alternative, and some software programs use a structured word syntax.

    The most important part of the decision analysis is developing a well-formed decision model. Once the evaluation team agrees on the model, this is a good basis for reviewing the assessment approach with the decision maker. Once the decision model is approved, the rest of the evaluation process is fairly routine. Although the initial decision model may hold up, it is common for new project insights to initiate model revisions.

    While best practice is to a use single well-formed decision policy based on optimizing an expected value, a reality is that there will often be lack of consensus. Sensitivity analysis, forecasting supplemental criteria, and testing alternative decision policies may be useful. A multi-criteria value function provides a visible way to combine different criteria and weights. In the monetary-equivalents policy approach, decision makers can adjust the trade-off values.

.3 Quantify Value and Risk

    The decision model describes possible scenarios (realizations) for alternative investment outcomes, as combinations of chance variable outcomes and decision alternatives. Every path in a decision tree represents a unique scenario, and the analysis needs a corresponding net cash flow projection to calculate NPV (assuming this is the relevant value measure). There are infinite possible outcomes with continuous variables.

    Often, investment alternatives are supported by well-established cashflow models. In other cases, a custom cashflow model is required and this represents a substantial part of the analysis effort. Usually the physical processes are modeled first, including project execution or development. Then revenues and costs are added in. Conservation concepts familiar to engineers have analogs in modeling: conservation of physical quantities, cash, and account balancing. Model definition, documentation, review walk-through, and validation methods are especially important with custom models. Embellishing the scope definition for each alternative becomes part of the model documentation.

    The deterministic cashflow model will be used to produce many investment alternative realizations (outcome scenarios). Its inputs will include the outcomes of every relevant chance variable and decision variable. A robust model is one that produces valid projections for all reasonable combinations of input variables. Many decision analysis tools will interface with cashflow models in existing spreadsheets. Care must be taken in developing the cashflow model so that the input value cells are well segregated and so that the model is robust. After meeting these requirements, only a few minutes are required to convert the traditional deterministic spreadsheet model into a stochastic decision analysis model.

    Sensitivity analysis is an important way to ensure that the evaluation team focuses on the drivers of outcome value uncertainty. The traditional methods change one variable at a time, holding all other variables at their base case values. Spider and tornado diagrams are popular presentation formats. When a Monte Carlo simulation model is available, a popular sensitivity method is to compute correlation coefficients between the stochastic (input) variables and the outcome value; prioritization is usually displayed by ranking correlation coefficient magnitudes in a tornado diagram.

    For those uncertain input values found to be most important, the evaluation team asks experts to provide their experienced judgments. Judgments will be expressed as probability distributions that are best elicited through an interview process. Usually the best available expert is chosen to provide a judgment. This often requires going outside the evaluation team or even outside the enterprise. Risk analysis is a term often used for the process of assessing a probability distribution. Although the TCM Framework describes risk management as a separate process (see Section 7.6), working with risks and uncertainties is pervasive in the modeling and evaluation effort.

    We only need to model in detail sufficient to make an informed decision. If doubting whether additional analysis or information is worth the cost, a value of imperfect information analysis can evaluate the option of additional data collection and evaluation effort.

    The models used for decision analysis are not static. For projects using phased planning, the level of detail in the decision model changes during the project life cycle. An asset life-cycle model is the basis of the feasibility analysis. In this model, the project development is represented at a high level. Upon approval, typically, a detailed stochastic project model forms the basis for decisions during project execution (e.g., change management per Section 10.3). Detailed project planning will often result in updates to the asset life-cycle model. After project completion, the asset life-cycle model is again updated to reflect the asset in service through the end of its life.

.4 Evaluate the Decision Model and Recommend Action

    Once the life-cycle cashflow model is ready and the evaluation team has the judgments about uncertain inputs, the team is ready to calculate the expected monetary value (EMV) for each alternative. Payoff tables work well for very simple problems. Decision trees are usually best when there are subsequent decision points. Monte Carlo simulation is usually required for optimization and for problems having many stochastic variables.

    Before recommending the apparent best alternative, the evaluation team should review the analysis. In particular, new insights might suggest other investment alternatives. Would acquiring additional information provide an EMV improvement? Are there cost-effective actions, not previously considered, to mitigate threats or exploit opportunities? The team should perform any analysis rework as needed.

    The evaluation team then prepares and communicates the recommendation for the alternative that maximizes value (usually EMV). Many enterprises have standard procedures and tools for documenting the basis and results of the decision analysis (i.e., the "business case") and summarizing the investment recommendation. The business case describes the investment scope considered, the decision analysis methods used, constraints and assumptions, and then summarizes the decision analysis results and the recommendation for action. If the decision policy is working, the evaluation team will be confident its recommendation will be accepted by the decision makers.

.5 Make, Document, and Communicate Decision

    Upon receiving the recommended action, the decision making authorities should verify that the recommended investment meets the documented requirements, including the decision policy, while ensuring that the asset planning process is still working on the right business problem. Generally, the decision makers meet with the key evaluation team members to ensure that everyone has a good understanding of the business case and recommendation.

    Sometimes the decision analysis result (e.g., EMV) is marginal, and a strong case cannot be made for either implementing or dropping. Management may then decide based upon strategic and portfolio concerns. Management might also direct the evaluation team to rework the analysis with a different scope context (e.g., bringing in a partner) or modified alternatives (e.g., delaying or stretching the project timeline).

    If the investment is not approved and marginal, it remains a candidate and the updated scope remains in the pool of investment candidates to revisit another time. The asset management team will generally reexamine the various portfolios each budget cycle and when the business environment or company circumstances change significantly.

    In some cases, particularly for major capital investments, there may be multiple tiers of analysis, review, and decision making. For example, as part of a capital budgeting process, each organization in the enterprise may decide which strategic investments best address its objectives. Then, on an overall portfolio basis, enterprise management analyzes, reviews, and decides on the competing recommendations from the various organizations. Optimally, the organizations are working within a strategic asset management framework. A common decision policy ensures that they are all working toward the same business objectives and making consistent decisions.

    Making timely decisions can be a challenge because of indecisiveness (e.g., always wanting more information or analysis paralysis), busy decision maker schedules, or conflicts between stakeholders or organizations. Generally, asset planning or project teams are resourced and ready to begin the next phase of scope development, and if decision making is not timely, resources may have to be reassigned, greatly disrupting progress and detracting from performance. Well defined and managed processes and decision policy and thorough analysis help address this challenge.

    The final output is a well documented business decision basis or business case. This is the basis for implementing projects (see Section 4.1), implementing non-project asset changes (e.g., a change to process activities, etc.), or further asset planning elaboration. The cost attributes of the selected alternative are also inputs to the business capital and non-capital control budgets. The business case and budgets, as well as key performance indicators (KPIs) and other targets, are inputs to the performance measurement and assessment processes (Chapters 5 and 6, respectively) in which measures such ROI are assessed on an ongoing basis.

    If a project is implemented, it is common to have a kick-off meeting that helps ensure that the project leadership team understands the decision analysis and basis of decision. The asset life-cycle model remains relevant and is the basis for making tradeoffs among cost, schedule, and performance during project execution.

    Finally, the analysis is documented for organizational learning and captured in a historical database (see Section 6.3). The team should organize the information and documents in way that will be easily retrievable for post-project review and for consideration in future strategic asset planning.

3.3.3 Inputs to Investment Decision Making

.1 Enterprise Decision Policy. This incorporates considerations about business strategy and objectives. For day-to-day decisions, portfolio considerations (such as any capital constraint) are embedded in the parameters of decision policy.

.2 Requirements (see Section 3.1). Establishes the conditions or capabilities that must be met or possessed by a strategic asset (i.e., product, process, etc.) and may include requirements for the decision making process itself (decision policy is a particular type of requirement).

.3 Alternative Investment Scope (see Section 3.2). The initial scope definition to be used by the asset planning or decision evaluation team in developing and appraising alternatives.

.4 Risk Factors (see Section 7.6). Effective decision models identify risk factors (i.e., chance events) for which methods from the risk management process apply.

.5 Alternative Investment Planning Valuations (see Section 3.2). The asset alternative’s cost, schedule, risk, and value attributes (generally in terms of cash flows) are used to quantify value and uncertainty in the decision model(s).

.6 Business Valuations. Decision models require information about revenues. Non-financial criteria or measures are also considered (and are usually monetized).

.7 Stakeholder Input. While requirements should reflect stakeholder needs and wants, their further input may be needed to support review when there are unresolved conflicts or marginal analysis.

.8 Historical Information (see Section 6.3). A data and knowledge base of historical information supports tools development, model building, and so on. Prior investment evaluations may provide data sources, models, risk lists, and other pertinent information.

.9 Domain Knowledge. Experience among evaluation team members and other persons tapped for their special expertise. Usually the best available expert is asked to judge probabilities for chance variables. Custom cashflow modeling should be performed by someone experienced in that area.

3.3.4 Outputs from Investment Decision Making

.1 Project Business Decision Basis. A decision as to whether, when, and how to do the project is communicated to the project team (see Section 4.1). The basis includes the updated scope definition and a description of the decision model, including the assumptions upon which the investment decision was made. Embedded in this model is a high-level scheduling and cost model for project execution. The model will be useful for risk and assumption monitoring through project execution.

.2 Asset Life-Cycle Forecast. The cost and other information used in asset planning and decision making are the basis for performance measurement and assessment. (see Sections 5.1, 5.2, and 6.1).

.3 Historical Information. The project evaluation itself is a learning process. The lessons learned here should be available as part of the enterprise’s knowledge base (see Section 6.3). Historical data are also used for post-implementation review of performance.

3.3.5 Key Concepts and Terminology for Investment Decision Making

The following concepts and terminology described in this chapter are particularly important to understanding the investment decision making process:

.1 Economic Analysis (sometimes referred to as profitability analysis). (See Section 3.3.1).

.2 Economic Costs. (See Section 3.3.1).

.3 Expected Value. (See Section

.4 Decision Policy. (See Section

.5 Decision Criteria. (See Section

.6 Multi-Criteria Decision Making. (See Section

.7 Expected Monetary Value (EMV). (See Section

.8 Monetary Equivalents. (See Section

.9 Profitability. (See Section 6.1).

.10 Decision Modeling. (See Section

.11 Feasibility Analysis. (See Sections 3.2 and

.12 Decision Tree. (See Section

.13 Sensitivity Analysis and Monte Carlo Simulation. (See Section

.14 Value. (See Sections 7.5 and

.15 Uncertainty and Risk. (See Sections 7.6 and

.16 Risk Policy. (See Section

.17 Net Present Value (NPV). (See Section

.18 Rate of Return. (See Section

.19 Business Decision Basis or Business Case. (See Section

.20 Capital Budgeting. (See Section

.21 Portfolio Management. (See Section

Further Readings and Sources

    Decision making is a concept applicable to many areas of practice beyond asset and project management. From a TCM perspective, texts that cover decision making related to capital investment planning and budgeting are most applicable. Increasingly, the economic analysis is done in the context of decision analysis. The following references provide basic information and lead to more detailed treatments.


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