• an increased scrutiny on spending, hence the need for an audit trail,
• the need for a thorough and quantified treatment of risk in the prioritization process,
• the importance of a tool that levels the play field and allows the comparison of “apples and oranges” across business units,
• the value of thinking about capital budgeting as more than just go/no-go decisions.

However, PPM offers another advantage that at times can be extremely useful: when appropriately implemented, PPM can intelligently drive transformational change. In other words, if your business is currently at Point A and you wish to get to Point B, PPM can help ensure that you follow a reasoned and effective path to your destination. A timely example is provided by electric utilities that are developing PPM approaches that will help them create the smart grid.

Smart Grid

Smart grid is, of course, the term used to describe the envisioned implementation of an ever-expanding set of computing and communication technologies for automating and improving the control of electricity distribution. Many see smart grid as the key to national efforts to improve reliability and efficiency, accommodate renewable energy sources, further energy independence, and reduce greenhouse gases.

Smart grid is an explicit goal of U.S. energy policy.  Thirty states have adopted renewable energy standards, which require a pre-determined amount of a state’s energy mix (up to 20%) to come from renewable sources by as early as 2010. Cap and trade legislation, if passed, will increase the costs of traditional energy. The Department of Energy has announced the availability of $3.9 billion in grants for modernizing the electric grid. Clearly, there is plenty of top-down momentum for smart grid.

States with renewable or alternative energy standards (as of July 29, 2009). Source: Pew Center on Global Climate Change

Utility Choices

But smart grid will need to be constructed from the ground up. Utilities have been criticized for “dragging their feet” with regard to smart grid, but it makes sense for utilities to be cautious when it comes to investing limited resources into new and unproven technologies. Utilities must be able to demonstrate to investors and regulators that the projects they undertake produce benefits that justify costs.

A challenge for utilities considering smart grid projects is that the main benefits of smart grid investments are customer and societal benefits, benefits that aren’t reflected on utility balance sheets. Given economic realities and lack of assurance that utilities will be able to recover investment costs, it may be tough for utilities to make a business case for many socially desirable smart grid investments.

Customer and Societal Benefits

Suppose, for example, that a proposed smart grid project would reduce the frequency or duration of outages. Conducting that project would enable the utility to avoid the revenue loss associated with failing to deliver the expected power, but this benefit to the utility is much less than the larger benefit to customers who, for example, might lose perishables in their refrigerators if an outage is prolonged. Similarly, a smart grid project that would lessen electrical demand would reduce the amount of pollutants discharged from petroleum-fueled generation plants. This, in turn, could produce environmental and health benefits for society at large, but the main impact to the utility would be reduced revenue.

Prioritizing Smart Grid Projects

Recognizing the bias inherent in traditional utility-centric, project evaluation methods, a growing number of utilities, including Washington State’s Puget Sound Energy (PSE) and Pennsylvania’s PPL, are adopting PPM approaches that explicitly quantify customer and societal benefits. For example, Martha Dodge, PPL’s Senior Director for Planning and Engineering, states, “Our prioritization model values projects from the customer’s perspective — How much benefit will the customer derive from the proposed project?” Customer benefits that are quantified include customer cost savings and the value to customers of improved electric service reliability.

The models or tools that these utilities are using value projects in two steps. First, templates are provided to facilitate the estimation of the impacts of proposed projects on customers and others. For example, if a project improves reliability, how many customers will be impacted and by how much will the frequency and/or duration of outages experienced be reduced? If a project reduces emissions from burning fossil fuels, how much less fuel will be required?

The second step is to translate customer and societal impacts into equivalent dollar values. Although the dollarization of benefits is difficult, utilities can leverage results from academic research, other industries, and the government. For example, numerous studies have quantified the value to customers of avoiding outages, government agencies have recommended values to place on health and safety, and the auto industry has developed assumptions for quantifying the social benefits of reducing pollution.

For completeness, the models also include other hard-to-quantify but potentially important benefits. For example, projects can impact the stakeholder perceptions of the utility and create goodwill in the marketplace. Such perceptions are important because they can affect funding in equity and credit markets and impact the utility’s ability to obtain necessary project approvals. Likewise the models typically account for risks and allow project value to be adjusted based on the utility’s risk tolerance. Smart grid risks that concern utilities include network security breech, infrastructure obsolescence and communication reliability.

Finally, since there are often interdependencies among proposed smart grid investments (obtaining full value requires implementing a suite of related projects), the models allow scenarios of phased investments to be investigated to support long-term strategy choices.

The models for valuing projects should be designed collaboratively by a team composed of individuals whose understanding encompasses all of the diverse needs that motivate projects and the wide range of impacts that project decisions have on the business. As Mark Velicer, PPL’s Senior Director of Finance and Regulation, notes, “It is important to engage stakeholders in the design of the model, to establish buy-in and ownership, and to ensure that all sources of project value are represented.”

Source: PPM Maturity of Senior Managers, Center for Business Practices Research

Enabling Change

Including customer and societal benefits, as well as risk, in project valuations allows utilities to prioritize smart grid projects together with more traditional, low-tech capital and maintenance projects. But utilities that are taking this course are realizing another advantage—they are finding that the process of designing their PPM approaches, particularly the step of agreeing on the logic for evaluating and prioritizing projects, promotes culture change that will help them to play the role of gatekeeper for many smart grid projects. Specifically, when executives back a project evaluation process that puts substantial weight on creating value for customers and society, they provide a clear message to their managers and staff about where effort should be focused.

Also, utilities are finding that the adoption of an explicit logic for valuing and prioritizing projects helps them respond to external demands to explain their choices, especially important if rate increases are needed to make smart grid investments viable. Including customer and societal benefits when valuing projects is attractive to regulators and consumer advocates.

Lastly, the approach provides a pragmatic means for encouraging the implementation of smart grid projects that make the most sense for specific utility situations. When a utility identifies a project that, although not economically attractive to the utility, would generate high customer or social value, the utility can alert regulators and government agencies and seek help in the form of pricing flexibility, cost recovery, or subsidies to make the socially desirable project financially viable for the utility. If regulators disagree with the logic used by the utility to value customer and societal benefits, they can suggest changes to the value models. Regardless, if PPM can assist regulators and utilities in obtaining agreement over the value and priority of proposed projects, it will certainly facilitate the development of the smart grid.

In conclusion, PPM provides a pragmatic, bottom up perspective to complement the top-down push for smart grid. It will help utilities and regulators maintain a clear vision of the desired destination and ensure that the path forward will realize value at every step along the way.

• Hard dependencies: project A is required for project B to be funded. This is typically the case of infrastructure projects. For example, project A might represent an investment in a new data center (IT), the acquisition of a protein crystallography capability to improve drug discovery (biotech), or the implementation of a pilot smart meter project with a view to a broader smart grid implementation later (utilities). In all of these cases, project A by itself is likely to have a very poor benefit-to-cost ratio. Its sole raison d’etre is to enable other projects — the prizes on which we truly have our eyes.
• Soft dependencies: project A affects the value of project B. This dependency can be positive or negative, and occur on the benefit side or the cost side. For example, there has been much talk recently about whether Apple’s new iPhone 3GS cannibalizes the market share of the iPod Touch (an unfavorable benefit dependency). Conversely, the launch of a new line of product leveraging some of the supply chain elements of an existing offering should create cost-of-goods-sold benefits (a favorable cost dependency).

In this post, we only concern ourselves with hard dependencies. The wrench that foundation projects throw into a portfolio optimization effort is this: how do we prioritize projects that seem to provide little intrinsic benefit, yet are indispensable for “juicier” projects to be available? A simple examination of their benefit-to-cost ratio is clearly inadequate if by benefit we only mean their intrinsic benefit. But is there a possibility of simply redefining what benefit means for such foundation projects? Is it possible to somehow augment the benefit of these foundation projects with the benefit of the projects they enable? Unfortunately, the answer is no: things get more complicated in the presence of multiple dependencies. The following example gives us a window into why.

Illustrative Example

Consider the following mock portfolio. We simplify the presentation by reducing each project to two numbers: its benefit and its cost (we won’t go into the details of how we boiled down the benefit to a single value, here). An arrow represents a requirement: the project at the origin of the arrow is required for the project at the end of the arrow to be available.

The following table tells the story of what the optimal portfolio looks like for an increasingly higher budget. (The reader could easily verify this by hand.)

This table showcases some of the subtleties associated with the valuation of a foundation project. If somebody were to ask, what is the “full benefit” of project #2 (not just its intrinsic benefit), the answer would be very different (if at all defensible) depending on the line we examine in this table.

At the heart of this difficulty lies the following fact: in the presence of dependencies no easy ranking method allows one to decide how or when to fund certain foundation projects. Instead, a full-blown optimization is required, taking into account the entire universe of projects. We have already discussed this topic in this previous post.

Folio Priority System automatically handles such complex dependencies in a seamless and user-friendly way.

Ask The Tough Questions

Most PPM software evaluation guides recommend that you ask questions that play to the vendors’ strengths: they encourage you to discriminate various features and capabilities that support good project management. But, PPM is about selecting the right projects, not just doing projects right. Here are some important, but often neglected, questions that can help you assess whether a vendor’s product will actually help you achieve the primary goal of PPM; namely, to identify and select the project portfolio that will deliver the greatest possible value to your organization.

1. Does the vendor provide a product that is specifically designed for your industry and the types of projects you conduct?
   The value of a project is the worth, to the organization, of the consequences that would occur if the project is conducted. Obviously, the intended consequences that motivate projects differ greatly depending on the industry and type of project (e.g., the consequences desired from a pharmaceutical project to create a new drug are very different than the consequences desired from a utility project to upgrade a transformer). If a vendor’s tool is not specifically designed for your industry, you can be sure that it will not evaluate projects based on their consequences, and, therefore, will be incapable of identifying projects that create the consequences that you most want. Many PPM tools evaluate projects based on some sort of point scoring system that the vendor has selected as a lowest-common-denominator approach applicable to the widest possible customer set. Such tools will not help you make value-maximizing project decisions.
2. Does the software quantify and optimize the value of the project portfolio, with project and portfolio value measured in dollars?
   The ultimate goal of PPM is to enable the organization to select and manage projects so as to derive maximum value. Many tools do not measure project value in dollars (because they lack the necessary methods and analytic rigor for translating “soft benefits” to equivalent dollar values). If non-financial project benefits are not expressed in dollar terms, how can a tool combine the financial benefits expected from projects (e.g., decreases in costs, increases in revenue) with non-financial benefits (e.g., improved corporate image, client service, or learning)? How can it determine whether the benefits to be derived from a project justify its costs if benefits are not measured in dollars? Be sure the system incorporates appropriate methodology for quantifying project value in dollar units.
3. Does the software contain a “configurable model” or a fully-flexible platform for constructing project valuation models?
   If the project value model is hard-wired in source code, the vendor will typically refer to it as a “configurable model.” Configurable models are characterized by a limited number of parameters that can be set to help “fit” the model to the needs of individual customers. For example, the weights that are assigned to represent the relative importance of various criteria are common model parameters that can be chosen by the user. Although parameters are easy to set, changing a configurable model beyond the limits of its parameters is difficult or impossible, as it requires reprogramming source code. A few PPM tools take a different approach. They contain an internal platform upon which virtually any project value model can be constructed. The platform is similar to Excel in that it allows equations or algorithms to be defined or modified without the need to change source code. If you choose a tool with an internal modeling platform you can be assured that the software will provide flexibility to refine your model as needed to incorporate new understanding or to address additional types of projects.
4. Is the software able to address and optimize real project choices?
   To some tools, project choices are all-or-nothing decisions: projects are ranked, and, if the project falls above the funding cutoff, it’s a “go,” if not, it is a “no go.” In the real world, decisions are more complex. Sometimes, the choice is among alternative versions of a project (e.g., a minimum cost, minimum scope version versus one or more expanded or enhanced project solutions). Sometimes it is not meaningful to evaluate a single year of spending in isolation of what happens in subsequent years (e.g., one year of asset maintenance in isolation of the level of maintenance planned for future years). Sometimes, there are interdependencies among projects: the costs and benefits of doing a project depend on the other projects that are in the project portfolio. Find out whether the software merely ranks projects or if it can truly optimize the project portfolio.
5. Does the software provide features that help ensure the quality of data inputs?
   Garbage in means garbage out. Experience shows that features that help promote accurate inputs and quality assurance are essential to obtaining accuracy in project recommendations. One useful (but not always easy to support) feature is the capability to provide users immediate feedback on the implications of project inputs in terms that are meaningful and easily understood. Tools that require the user to toggle between data entry and analysis mode in order to see how project inputs affect project evaluations can be cumbersome and more prone to data entry errors. Evaluating projects based on the consequences of doing versus not doing the project helps promote data quality, since users can rely on professional experience to assess whether consequence estimates for a specified project are reasonable. Likewise, a tool that values a project in terms of dollars provides an objective basis for assessing the results, “Would the organization really be willing to pay the indicated amount to obtain the project benefits?” Another useful feature is allowing users to analyze project concepts prior to “publishing” results for others to see. Check whether the system provides a place for colleagues to explore possibilities and share draft materials without committing inputs to the central database. A tool that allows project proponents to investigate the attractiveness of project proposals can help them to design better project alternatives. However, ask the vendor how the tool and application process counter “gaming” in the specification of inputs.
6. Are you communicating with someone with real expertise and experience?
   PPM is a hot topic, and consultants and vendors are reinventing themselves in their rush to offer tools for the job. Also, some large software vendors with established project management tools have created PPM tools by merely adding cross-project data rollup to their older tools. Such tools do not provide true assistance for optimizing project portfolios. The real opportunity for benefiting from PPM comes from enhanced ability to select the right projects, not from managing your existing projects more effectively. Make sure your candidate supplier provides evidence that they are truly leaders in the field of the “portfolio part” of project portfolio management.

However, newcomers to portfolio optimization sometimes misunderstand what we mean by “efficient frontier.” We do not blame them: the expression is used in different ways in different disciplines. In this article, we contrast efficient frontiers in finance theory and in project portfolio management.

Efficient Frontiers in Finance

In Markowitz’s modern portfolio theory, the efficient frontier is obtained by plotting, for each feasible portfolio of financial instruments, its expected return (i.e., its probability-weighted average return over all risk scenarios) against its risk exposure (i.e., the standard deviation or variance of this return).

It is beyond the scope of this short post to explain the underlying math. We will simply point out that it is the existence of correlation between individual financial assets (e.g., stocks) that allows us to combine them into portfolios yielding a higher return for the same amount of risk.

Ultimately, the math acrobatics are meant to address this question: how can I allocate 100% of my capital amongst various financial instruments so as to achieve the highest possible return for a given risk level I am willing to accept?

Any such allocation is deemed to be an efficient portfolio, in the sense that it is impossible to achieve a greater return without taking on more risk. The collection of all these efficient portfolios then constitute the efficient frontier (the upper part of the blue curve below).

Return vs. risk: the efficient frontier as defined in finance theory. Source: Bob Taylor.

A number of results, most notably Sharpe and Lintner’s Capital Asset Pricing Model (CAPM), further build upon this concept by making equilibrium assumptions about the market players.

For those wishing to drill deeper, here is a very concise technical introduction to modern portfolio theory.

Efficient Frontiers in Project Portfolio Management (PPM)

PPM also makes heavy use of the concept of efficient frontier, but it has a different meaning than in finance theory.

First of all, it addresses a different question, namely: to what extent does a higher investment level enable me to create more value by funding more (or different) projects?

By plotting the value created by a portfolio against the investment necessary to fund it, or, as it is often summarized, its total benefit against its total cost, we answer a question similar to, albeit different from, the one posed in finance. For a given cost level what is the maximum total value I can create by picking the right combination of projects?

Portfolio benefit vs. cost: the efficient frontier as defined in PPM. Generated by Folio Priority System.

From here, cost benefit analysis tells us how we can generate (an approximation of) the efficient frontier by ranking candidate projects by decreasing benefit-to-cost ratio — a method we can intuitively link to the decreasing slope of the red curve.

For a more rigorous treatment, we know that ranking alone is insufficient in the presence of complicating issues such as multiple funding levels, disparate project sizes, or project interdependencies, many of which occur in real-life applications.

Does This Mean PPM Ignores Risk?

No, it certainly doesn’t.

Managing portfolio risk is a critical component of success for organizations seeking to implement portfolio optimization. It raises a number of questions: What is the nature of the portfolio risks? Can they be diversified away? How do systemic risks affect the risk of the portfolio? How much risk can my organization tolerate?

One way to address at least project-specific risk is to penalize risky projects by considering their risk-adjusted benefit rather than their expected benefit when creating the efficient frontier.

Portfolio-level risks such as energy or commodity prices present another level of complexity which must be addressed separately from individual project adjustments.

In this post, we just wanted to lay out the possibly-confusing dual terminology. We will address risk more extensively in a future post.

The following table illustrates where to draw the funding line, assuming a $10 million budget.

Notice in passing that we would not want to go below a B/C ratio of 1, since funding such a project would yield less benefit than it would cost.

What are the problems with ranking?

Ranking furnishes a fine first cut at an optimal portfolio, but fails to fully address the following situations — all encountered by our clients.

• Ranking cannot handle interdependencies. If project X is required before project Y can truly yield benefits, or even be undertaken at all, clearly looking at the B/C ratio of X alone is inadequate. A mesh of interdependencies is common with complex infrastructure or IT projects, for examples. In that case, a comprehensive optimization of this network of projects is indispensable.
• More commonly, ranking fails to consider alternative funding levels. Imagine that each of the projects on the list, instead of an all-or-nothing choice, could be funded in various cheaper alternatives than the all-out version, featuring a spectrum of lower costs and lower benefits. Conventional ranking would simply pick, for each project, the one alternative with the highest B/C ratio. However, it can sometimes be optimal to try to cut costs on one project to enable another project to be funded in a more expensive alternative.
• Finally, ranking ignores fine-tuning, i.e., the so-called knapsack problem. The knapsack problem is often perceived to be an academic and unnecessary complication for real-life portfolio optimization. In the remainder of this article, we go through a real-life case study to evaluate the validity of this perception.

What is the knapsack problem?

Bear with us as we indulge in one short theoretical paragraph. The knapsack problem goes as follows: imagine you have a bunch of objects of various values and weights, from which you have to select any number to fit into a knapsack. Your goal is to create the most valuable knapsack possible, without of course exceeding the allowable weight capacity.

Source: Dake under Creative CommonsAttribution-Share Alike 2.5 Generic license.

The parallel with portfolio optimization is obvious: value is the benefit, weight is the cost, and the weight capacity of the knapsack is your budget constraint.

A ranking approach to this problem, therefore, would order the objects by decreasing density: the yellow object right underneath the pack has the highest “value density” — i.e., B/C ratio — (2.5$/kg) and will go in first: it adds a lot of value relative to a small weight consumed. The green box at the top left will go last, if at all: its value density is the lowest (.33$/kg).

Now imagine we start fitting these objects into the knapsack by picking the highest-density objects first, very much like we ranked projects by B/C ratio in the table above and started funding them, until we hit our limit.

As many of us know from experience, we might then decide to make some changes “on the margin”: even though object 26 was the last one to go in, it leaves a lot of unused room in the knapsack. It turns out if I removed object 23 and placed object 27 instead, I would fill the entire sack, even though I have sacrificed a higher B/C object for a lower one. The degree of “shuffling on the margin” is difficult to build a good intuition for. Many practitioners dismiss this phenomenon as, precisely, marginal.

We wanted to put this question to the test: is ranking really insufficient? Do we really need to optimize and deal with the nagging knapsack problem, or can we be content with the simpler ranking method?

Does optimization really matter in real life?

Optimization really does better than ranking in “real life.” The following example is taken from a portfolio of 901 real client projects (the data has been disguised by linear rescaling, but the shape of the curves and the conclusions are strictly identical).

The red curve represents the efficient frontier of the simple ranking method. The blue curve represents the efficient frontier of a full-blown optimization.

Notice how an optimization is able to extract more value for certain budget levels than a straightforward ranking. Why is that? There are two main reasons why optimization fares better:

• Optimization switches alternative funding levels in and out as appropriate. Even though a given project may have one clear winner funding level, featuring a higher B/C ratio than other funding level alternatives for this project, it is sometimes appropriate to be less “greedy” and fund a less costly version so as to enable another high-yielding project to be funded. This is the dominant effect in area C of the curve, where optimization systematically beats ranking by about $8 million.There are actually very few projects featuring multiple, alternative funding levels in this database.
• Optimization can handle big discrepancies in project spending. Of course, the most dramatic benefit of optimization over ranking can be seen at A. A single, very expensive project “holds up” other projects from being funded in the ranking method because the expensive project still comes first due to its high B/C ratio. In the optimization solution, rather than holding up the budget gap, other, lower B/C ratios are “filled into the knapsack” until such time as the big project can be funded. At that point, the red curve abruptly catches up with the blue one. The same phenomenon happens at B with another project, and, to a smaller extent, in other places along the curve where kinks are visible. The more heterogeneous the project sizes, the more value a straight ranking method is likely to leave on the table. At its worst discrepancy ca. a $46 million budget, 19% of the value is missed by the ranking method.

This case study should not be interpreted as an indictment of B/C-ranking. But it clearly illustrates one of the advantages of optimization in the presence of multiple funding levels and heterogeneously sized projects.