Portfolio Value Management by Chris Trayhorn, Publisher of mThink Blue Book, April 1, 2003 To position their companies for success in the energy industry of the future, utility executives must have a clear vision of the future of the industry and the operating model they believe will be a winning strategy. They must be able to rapidly assess the effect on the enterprise as a whole on the performance of each asset and the impact of asset acquisitions, mergers, divestitures, or other major capital decisions. This has led to the need for portfolio value management (PVM), which recognizes that any company has flexibility in how it operates, maintains, acquires, and disposes of its assets. PVM recognizes that even if there are no obvious business connections among assets, investing in one project or business may affect investment in others. Executives today face an incredibly complex and constantly evolving industry structure, with innumerable unknown variables, complexities, and potential variations of operating models. How, in the face of all this change and uncertainty, can any executive hope to effectively manage and optimize the value to shareholders of a portfolio of assets? Well, reaching into a desk drawer and grabbing a couple of rubber bands, a couple of pieces of cardboard, and a handful of fasteners would be an excellent start. Zeeman’s Machine These materials are required to build an instructive toy called Zeeman’s Machine, developed by Dr. E.C. Zeeman in the 1970s. It is nothing more than a cardboard disk fastened at the center to a large base, and at one point on its periphery to two rubber bands. One of the rubber bands is secured to the base a fixed distance from the center of the disk, and the other band is moved freely by hand from position to position. When the free end is moved, the disk at the center of the machine moves smoothly and predictably in response to steady movement of the rubber band that controls the machine’s motion for a while, then jumps unexpectedly to a new position in response to a very slight additional movement — not unlike sudden jumps or drops in revenue, stock price, or profitability, some might say. While they seem random, these sudden, dramatic changes in the state of the system can actually be predicted, and their occurrence controlled, if the drivers of the machine are understood. To fully understand the behavior of Zeeman’s Machine, one would have to be versed in the principles of a complex branch of mathematics named (with unintentionally negative connotations) catastrophe theory. Mercifully, understanding and using catastrophe theory isn’t about to be portrayed as a quantitative technique for energy company PVM. But the mathematical theory is symbolically very meaningful. It provides an excellent framework to explain why PVM is such an effective technique for shareholder value optimization. System Variables Contrary to the images it first may bring up, catastrophe theory has nothing to do with predicting or explaining things like Chernobyl, Enron, or the California market meltdown. A simplified version of its basic premise can be expressed as follows: In most cases, the state of a complex system of interdependent differential equations can at any time be completely specified by the values of a very large but finite number of system variables. However, if a relatively small number of control variables can be defined, the final configuration determining the performance of the system can be specified as one of a small number of defined variations, and the ability to predict its behavior depends not on the huge number of system variables, but on the much smaller number of control variables for that variation. Certainly, it is true that the financial performance of any diversified energy company is driven by a very large but finite number of system variables (a staggering array of factors such as heat rates, fuel costs, temperatures, transmission line losses, regional business growth rates, market pricing, interest rates, etc.) and the performance of a complex system of interdependent business operations. Many compelling arguments have been made over the last few years that there is likely to be a small number of defined variations of end-state structures for utility companies — Moody’s May 2002 five-business model layout for the merchant sector and our own four-model Vision of the Energy and Utilities Industry Circa 2007, published last October, being two recent examples. So if you accept those premises, then all that is missing to make sense of the developing business outlook is the identification of the control variables that will drive the systems under each variation. Defining the Control Variables In the context of the type of business model within which the utility is working, value-based management (VBM) must serve as the underpinning for PVM. Formally, VBM is a process for establishing performance goals for key metrics (our control variables) in all business units, evaluating new opportunities, and implementing them in a way that allows for sustained growth. The shaping of the control variables begins with the financial markets, which set the value of each business continually. A company, presumably, has chosen the type of company it wants to become, whether that be a retail, distribution, transmission, or merchant energy company, or some viable combination. Periodically, key strategic objectives are set by executives who recognize that the value of one business is influenced directly by its own performance and indirectly by the performance of certain others — interdependencies similar to those mentioned in the catastrophe theory analogy. They are fully aware that specific new business units may bring experience, operational flexibility, or customer base to the entire company that improves the performance of other units. Each of the strategic objectives set by the executive team has one or more critical success factors. Each of these factors, in turn, is dependent on key value drivers. And it is precisely these key value drivers that become the small number of control variables that will become the focus of PVM. Making the Right Decisions Utility companies have historically tended to value acquisition targets based on simple lifecycle performance. For example, a power plant that operates an average of 3,500 hours a year and produces mid-load power will produce a certain amount of revenue based on specific price projections. Subtracting the plant’s operating costs yields its annual value to the company, and extending the same algorithm over the expected lifespan yields its lifecycle performance. Adjusting this for inflation and other factors provides a basic appraisal of net present value. For the utility or integrated energy company (IEC) of the future, however, such a simple approach to valuation is inadequate because it fails to account for the plant’s role as part of the overall strategy for the variation under which it operates. Under PVM, the executive team works within the proper strategy for the variation of company it aims to become and implements specific initiatives that can be measured quantitatively. Targets for each of the selected value drivers — our control variables — are set by management. The right control variables of shareholder value for different companies are those value drivers which give executives and line management levers they can directly control that they know ultimately affect the company’s market valuation. This process helps ensure that there is a distinct, well-specified path that guides the decision-making process from the requirements of the financial markets that set the basis for valuation under VBM through these interdependencies to the ultimate actions of managers and employees. Communicating the Variables In order to make PVM work, however, the information required to assess the level of the control variable and how specific actions have changed it must be communicated regularly throughout the organization. That way, the entire flow of information set into motion by the initial VBM process — from the financial markets to the individuals who can move the drivers — is complete. Some companies are already using Web-based portals to communicate this information on a real-time basis to executives across the company, and tying compensation incentives to the direction in which these drivers move over time. One large IEC is using executive portals to deliver strategic performance measures to top executives. Measures used in each portal are designed to help executives monitor how the company is performing relative to its targets and how it is perceived by key constituents. The variety of measures and display format will enable management to make more timely and informed decisions. Financial and non-financial performance measures linked to the company’s strategic initiatives are displayed in a balanced-scorecard format and are updated monthly. Financial measures include total shareholder return (TSR) and return on equity (ROE), while non-financial metrics include measures to gauge leadership development, safety, and customer satisfaction. For several measures, targets are tied directly to the IEC’s executive incentive compensation program. These include ROE, TSR, and safety, with incentive targets presented directly alongside each measure. Analysis and alerts are also provided for key measures to provide perspective and explanation for recent trends and to highlight performance issues. In this way, PVM can detect trends earlier, avoid unexpected problems, and ultimately increase shareholder value by enabling better-informed portfolio decisions. How PVM Will Work Ten years ago, utilities were conglomerates of regulated businesses that could be evaluated on identical metrics (ROE, TSR, etc.). That’s no longer the case. Because of the differences in the businesses being combined today and the complexity of intertwining their goals and performance measures, PVM is emerging as an important philosophy for integrated energy company executives to embrace. As companies have spun off, acquired, and merged business units in response to the shifting requirements of increasingly competitive markets and changing regulatory structures, the natural reflex has been to pull focus down to the financial results at business unit level. However, as real changes begin to take hold, the focus will have to be brought back to the integrated enterprise level. And that’s exactly what PVM helps utilities and IECs do. PVM recognizes that nearly every asset will have some optionality. The key in extracting the greatest value from the portfolio management approach will be the willingness to value assets holistically and objectively, and to take action based on how those valuations compare to established criteria and constraints. The potential for great value in application of PVM is becoming apparent today. For example, in looking at merchant power asset planning and budgeting, we see that this is where the commodity is actually being produced, and the assets themselves are the most mobile — that is, they are easy to buy, sell, and integrate into a system. Here, an effective PVM program can help decision-makers view the benefits and drawbacks of ownership and various operational strategy variations for generation assets, and plan their investment and operational budgeting activities in a way that exploits the form of that variation. Distribution System Value While a distribution system, on the other hand, tends to be a very large and geographically dispersed asset that is not easily parsed into smaller units, its management can also benefit from implementation of PVM. For example, to the degree smaller distribution systems exist as islands within larger ones, or adjacent systems have overlapping or checkerboard areas, properties may be swapped to achieve efficiencies for both systems. As distribution continues evolving horizontally — with regional and global consolidation to achieve scale economies — distribution companies will pursue a portfolio approach in acquiring and liquidating properties, as well as making substantial investments to improve performance. Companies might seek to acquire poorly performing properties with an eye toward improving them with new technology and management practices. Then they can either sell such properties or continue to operate them, as best fits their strategic objectives. This does not imply that companies will haphazardly buy and sell major fixed assets as if they were porcelain clowns on eBay, however. Recognizing that power plants and distribution grids are not exactly liquid assets, they will focus on a longer horizon and a strategic bigger picture than, say, a financial portfolio manager might. An integrated energy company might bank certain properties because they offer site, market, or risk mitigation characteristics that are expected to be more valuable under alternative scenarios and conditions imposed as part of the PVM assessment. On the other hand, if the value of a certain business is less to one company than what it may be worth in another company’s portfolio, then the portfolio manager may wish to sell the business and reinvest the proceeds into other assets. A truly effective portfolio manager should be willing to part with an asset if the price is greater than the value to the portfolio. The PVM Toolkit In order to make such complex decisions, however, the decision-maker must have the tools to do this consistently and effectively. While Zeeman’s Machine is certainly nice to have to play with when stress levels increase, it’s not fundamentally useful to the company’s steward of PVM activities. However, useful tools do exist. There is a fairly substantial experience base in other industries in making PVM work, and the tools used to perform the analysis required have generally fallen into one of four categories: Scenario Analysis This is usually the starting point, as it is the most qualitative and, hence, conceptually the easiest to grasp. However, even by itself, when done thoroughly, it can be a powerful tool for evaluating strategic options. One large company with whom IBM has worked held a series of workshops to do scenario planning in an attempt to assess enterprise performance improvement opportunities. Their first set of meetings was designed around understanding their competitive position in the markets in which they operated. Another set of meetings was set up to brainstorm and develop a wide variety of scenarios for future industry developments. These were then narrowed down to a set of the most likely scenarios, and the steps required to make improvements that would position the company to take advantage of each scenario were laid out. By looking at commonalities among the steps and the relative likelihood of each scenario, a well-defined, economically supportable strategy for moving forward is now being developed; even though the future is still uncertain, this plan will position the company to be ready to capitalize on whatever comes. Financial Portfolio Analysis Techniques Standard financial portfolio analysis tools can be used successfully to screen potential investments and measure projects against established strategic objectives. For example, efficient frontier analysis considers the balance between value and risk in the selection of optimal portfolios. The theory behind the efficient frontier is that there is not one optimal portfolio, but many different portfolios based on different levels of risk. A portfolio is considered efficient if no other portfolio has greater value for the same level or less risk. Similarly, a portfolio is efficient if no other portfolio has less risk for the same or more value. This theory is translated into practice by first determining those projects or initiatives that could potentially be part of a forward-looking capital investment strategy. The company would evaluate its overall corporate strategy to determine basic elements such as life extension goals, cash flow requirements, total generating or transmission capacity growth needs, and so on. Viable groups of projects within budgetary constraints would be assembled to create individual portfolios, each of which would be plotted in the context of risk versus total return. Based on this, an optimal corporate portfolio along the efficient frontier could be selected based on a chosen level of risk and used as a basis for further decision-making. The goal is to evaluate the impact on total portfolio value and overall risk of any major investment proposed, which, due to project interdependencies, may result in changes greater or less than the value of the investment on its own. Real Options Modeling Real options modeling techniques look at each asset in the portfolio not as a static revenue generator with set associated costs, but as a financial instrument that allows flexibility in its use in terms of the overall portfolio. A single asset or project in the portfolio can be deferred, abandoned, expanded, contracted, or otherwise altered in response to changing market conditions. Real options modeling allows for quantitative assessment of this optionality and inclusion of its value in the assessment of portfolio change options over time. Integer Programming Algorithms Integer programming algorithms dynamically evaluate many different combinations of operational and project options under specific financial, resource requirement, regulatory, or other constraints. The reason integer programming is usually used is that each project or asset can be included or excluded (in the algorithm, assigned an integer value of 1 or 0) from the portfolio of interest, and then IP algorithms can be developed to efficiently iterate over the possible combinations to provide a menu of best-choice portfolios. Conclusion Over the next decade, some utility companies will experience sudden, startling jumps in profitability and industry position (positive or negative), just as the disk in Zeeman’s Machine undergoes abrupt changes in position from time to time. Many executives will continue to see these changes as random, just like the first-time observer of Zeeman’s Machine tends to do. By not understanding their company’s variation of business model, they will spend too much time on analysis of the vast quantity of system variables they glean from performance reports, and will fail to make decisions that will have a substantial impact on the direction of the company. The winners in this new environment, however, will understand their company’s business model variation, focus on making the changes that positively affect its own specific control variables, and exploit the interdependent nature of the portfolio of businesses under their direction. By using PVM tools, they will have the potential to distinguish between decisions that will lead to ordinary (or worse) performance and those which will cause quantum leaps in profitability and industry status. These executives will be the ones who lead their companies to jumps in performance that will soon put them among the industry’s elite, to the surprise of many — but not to themselves. After all, these random changes were predictable and controllable, weren’t they? Dr. Zeeman would agree. Filed under: White Papers Tagged under: Utilities About the Author Chris Trayhorn, Publisher of mThink Blue Book Chris Trayhorn is the Chairman of the Performance Marketing Industry Blue Ribbon Panel and the CEO of mThink.com, a leading online and content marketing agency. He has founded four successful marketing companies in London and San Francisco in the last 15 years, and is currently the founder and publisher of Revenue+Performance magazine, the magazine of the performance marketing industry since 2002.