Summary Chapter 6 looks to the future, a future in which direct and indirect project delivery and finance methods are simultaneously available to governments and to private sector owners of infrastructure collections.1 The focus now turns to very practical questions: how to configure portfolios of infrastructure projects so that different mixes of projects, delivery methods, and financing approaches can be evaluated to positively change portfolio quality, revenues, expense, and technology. Political bodies, elected officials, and public officials will not use the delivery methods of Quadrants I, II, and IV unless they are reasonably sure that their choice of delivery method produces real improvement in infrastructure quality, level of service, cost performance, or timeliness. Similarly, corporate officers, corporate infrastructure managers, and corporate boards of directors need to know, not guess, that proposed infrastructure expenditures will produce incremental improvement in corporate infrastructure performance. Chapters 2 through 5 demonstrate that choice of delivery method, source of financing (owner, producer, users, or combinations thereof), level of investment (“pace”), and level of service (cost and quality) are variables that public and private CLIENTS will continue to use across an infrastructure portfolio. “Optimization” is not a viable approach2 because many of the variables that decision-makers apply can only be subjectively measured. CLIENTS will find themselves selecting one among many configurations for the portfolio, each of which attempts to maximize portfolio performance while using all available financial resources. Chapter 6 begins with a look at current budget and accounting practices in several small communities in New England. The public setting is chosen because data is not only easy to acquire, but the data itself is public information. In each of the jurisdictions described in this chapter, historical sources and uses of all funds available to each community were tracked in a database generated from audited financial statements and locally adopted budgets. All the data collected complied with standard accounting and budgeting practices. Each of these communities is well run, by highly motivated elected and appointed officials. The recorded financial data is detailed and accurate. Yet, this data is not very useful: (a) for projecting future repair and replacement obligations, (b) for establishing the current condition of existing infrastructure assets, or (c) for making reliable predictions of the effect of an infrastructure project on the sources and uses of funds for the entire portfolio of infrastructure assets. Each of these three deficiencies puts practical pressure on public and private sector decision-makers not to use the project delivery alternatives strategically, that is, for the overall gain of the entire portfolio.3 The most visible effect of the current deficiencies in public financial data (and that of many private firms) is the confusion generated from abstract and futile arguments over the “best” project delivery and finance method throughout North America. Chapter 6 explores structural problems that arise from these deficiencies through a more detailed look at capital programming in the City of Medford, Massachusetts. Again, this review is not to criticize, but rather to characterize the difficult challenges facing public CLIENTS trying to manage existing assets and improve and expand future services, all in competition with other government activities and with constrained budgets.4 A new methodology is presented that combines project delivery alternatives with condition assessment and activity cost principles into a single comprehensive approach to infrastructure asset management. The new approach has two key components: the first is a substantial upgrade in the capacity of owners to dynamically describe the current condition of their infrastructure collection. The second component of the new approach is a scenario-based approach to the simultaneous application of multiple delivery methods across a collection of infrastructure projects. A prototype of this decision support system — called CHOICES, OMIT 1998 and 1999 — has already been built at MIT. The model is positioned so that key attributes of each project in the portfolio — such as start date(s), duration(s), multiple project delivery methods, and various project finance structures — can be quickly input for each project in the portfolio. The analysis can quickly access each of the different feasible delivery strategies for each project, so different scenarios for the entire collection of projects can quickly be assembled. CHOICES allows decision-makers to compare and contrast these scenarios with a very clear understanding of how each scenario allocates constrained resources differently across the entire collection. Scenario analysis allows engineers and planners to use financial constraints as the common denominator to choose one configuration from among many and to put the owner at the resource constraint “most effectively.” The scenario approach gives public and private owners (legislatures, secretaries of transportation, councils, boards of directors) real choices at the portfolio level. In its simplest form, each and every portfolio configuration is constructed to expend all available resources, i.e. each configuration represents a different combination of projects, start dates, schedule duration, and project delivery and finance structures. All of these configurations are at the “resource frontier” — i.e., there are no further resources available if any of the scenarios are adopted. This puts complex numerical analysis out of the way, so that decision makers have a clear look at how different projects, start dates, delivery methods, and financial structure affect the quality, cost, and timing of infrastructure services throughout the portfolio. With fixed financial constraints, the scenario approach allows owners to focus on picking projects and configuring the portfolio to produce the greatest benefit at the resource constraint. The question is reduced to a simple question. Which combination (or “scenario” or “configuration’) of projects draws the greatest support among decision-makers? Inevitably, the selection of one scenario means that some projects are not authorized, some are deferred, while other projects are accelerated. In the real world, although financial resources are constrained, we know that some constraints are adjustable.5 Mid-term adjustments in the level of investment in infrastructure are common; indeed, such adjustments are a routine part of capital programming. Even in this dynamic environment, the scenario approach is still preferable as the level of financial investment varies. Now, the decision-making process has two steps. The first step is the same, except that rather than fixing the level of investment, the analysis begins with an assumed level of investment. The second step in the process is to change the initial assumption to different level(s) of investment — either higher or lower, or with a different distribution of investment over time. A second scenario building effort is now possible. For example, how does a change in investment level affect the number, timing, and quality of infrastructure services that will be delivered at the newly assumed financial frontier? New questions emerge, and new scenarios are possible. What would the preferred configuration of projects look like if resources were held constant over the next ten-year period? What would the preferred configuration of projects look like if resources were increased at 3% per year? How would financial constraints change if tax rates, user fees, water rates, sewer rates, or tolls were restructured (up or down)? The CHOICES methodology was applied on a test basis to the City of Medford, Massachusetts, a small city with infrastructure assets valued at approximately $500 million. The Medford case points to the direction of future research in capital programming, project delivery, and project finance. Condition assessment tools and cost accounting records need to be substantially improved so that repair projects and new projects are always considered together in capital programming scenarios. Repair projects must compete with new projects for scarce financial resources, and the scenario approach allows this competition to be conducted by comparing the effects of any project on current and future cash flow at the “resource frontier.”