Service Value and Componentized Accounting of Infrastructure Assets
Publication: Journal of Infrastructure Systems
Volume 25, Issue 3
Abstract
Asset management is a strategic tool for maintaining the value of an asset at a level that is satisfactory for users, asset owners, and taxpayers. This paper introduces the concept of service value. The concept is applied following the recommendations of the Organisation for Economic Co-operation and Development (OECD): service value equals the value of economic benefits generated by an asset. It is shown how alternative value accounting methods result in different asset values. The present value method, as recommended by accounting practitioners and in line with the concept of service value, is more appropriate for asset management purposes than the perpetual inventory method (PIM), despite the fact that the latter is more widely used. The impact of correct asset and service value accounting extends well beyond project and investment program management levels to the national accounting system of fixed capital. Incorrect asset management accounting may lead to serious under- or overestimation of investments and repair debt.
Introduction
The challenge of asset management is duly recognized by all infrastructure wners. However, the tools and practices used for managing assets efficiently and sustainably are often oversimplified, because they focus only on particular types or specific components of assets. One reason for this unsatisfactory situation is an absence of data (Crist et al. 2013). Transport networks, for example, are one of the primary asset systems owned and managed by national and regional governments and, in some instances, by private operators (e.g., concessionaires and single-project companies). However, there is a paucity of adequate data available regarding the condition and value of assets. In addition, there is an urgent need for technical and economic assessment tools to translate data into optimal maintenance decisions. As a result, deriving an assessment of an asset’s (e.g., a road network) condition and determining the optimal investment in maintenance, upgrading, and complete renewal, comprises the typical asset management problem. This problem is not exclusive to road or transport infrastructure assets but also applies to most asset types that require management throughout their life cycle (Di Sivo and Ladiana 2011; Kirkwood et al. 2016).
Asset management can be used by infrastructure owners as a strategy to align their resources with the level of service value that is ensued users and taxpayers throughout an asset’s life cycle. According to Leviäkangas and Michaelides (2014), a holistic perspective is needed in this instance, which not only considers optimizing an asset’s value but also accommodates issues of resilience and reliability. The corollary is that the task of asset management is to prolong an asset’s service life and minimize the negative impacts of maintenance operations (e.g., unscheduled downtime) (Fuggini et al. 2016; Clements and Mancarella 2018).
Under budgetary constraints, infrastructure owners are often tempted to prioritize the short-term optimum. The rationale for this choice is often based upon the assumption that, because the service life of engineering assets can extend over several decades, the shortcomings and shortcuts in daily or annual maintenance are not necessarily immediately observed or measurable. Moreover, the asset may well continue to serve the end users without any failures or service breaks over a long period of time. In the case of roads and rail, for example, even if an asset is inadequately managed, service—with regard to connectivity—may remain intact, despite the fact that under the surface and hidden from the eye, future costs are accumulating (Qiao et al. 2015).
Infrastructure assets are of considerable value to an economy, both to its people and its industries. The European Union Road Federation estimates that 5.5 million km of roads, worth an estimated 8,000 billion EUR, are managed under local, regional, and national governments. Typically, road infrastructure is considered to be the most important of all public assets. These assets are considered to be in need of more funding in terms of both maintenance and new investments (European Union Road Federation 2013). Infrastructure asset funding gaps are commonplace throughout the world (OECD 2016), which has led to increasing incorporation of private financing (Della Croce and Yermo 2013). Thus, a key issue that needs to be addressed in economically sustainable asset management pertains to the decision-making processes that are followed to determine how to optimize asset management. There are two subquestions related to this problem.
The first is how to make the right maintenance and renewal decisions to maximize an asset’s service value over its life cycle. This includes issues related to maintenance, the replacement of some components, and the replacement of the entire asset. The notion of componentization also needs to be incorporated in order to correctly assess the value of assets. Componentization is undertaken to devise optimized strategies to maintain and upgrade components that form integral elements of the asset system.
The UK Highways Agency (2011) has identified 25 asset components, which are divided into seven asset subclasses to meet users’ needs. These are used in risk assessment and resilience planning to reduce service failures. By contrast, the Finnish Transport Agency (FTA), for accounting purposes, uses only two asset classes for roads: 50 years for subcourses, bridges, and tunnels; and 10 years for other structures (pavements, etc.) (FTA 2016). In consideration of the first question, the rationale for the differences is somewhat unclear. The UK Highways Agency’s system is probably aimed primarily at higher-resolution asset management, whereas the Finnish system serves the FTA’s annual reporting system and national accounting. Accounting for assets is essentially a management function that provides information for decision-making. The Certified Public Accountants of Australia (CPA 2013), for example, have stated that incorrect value assessment (depreciation) can lead to biased decisions being made regarding asset values and service capability. These biases may in turn increase costs due to suboptimal asset management strategies.
Usually, componentization and the underlying dynamics between the components have been excluded in research, and the analysis has focused on one particular asset component, such as pavements (e.g., Yu et al. 2008; Gopalakrishnan et al. 2009; Gopalakrishnan 2012; Bianchini 2014; Anastasopoulos and Mannering 2014; Gayah and Madanat 2017), or on a particular deterioration phenomenon (e.g., Shekarchi et al. 2011; Sæther 2011). However, accounting for infrastructure as a capital good and maintenance of infrastructure have been subjects of considerable debate in the literature (Pallot 1990, 1997; Lapsley et al. 2009).
The second question is concerned with the attributes of an asset’s value and capability of delivering a service. In the case of private assets, the issue of value is explicit as it relates to the maximization of their holding value (shareholding value) measured in cash terms. But with public assets, the determination of value is complex, as trade-offs typically occur between investment and maintenance budgets. Essentially, an owner needs to ensure that there is a balance between end user needs and fiscal constraints. The end user group may also comprise different segments with various priorities, such as passengers and industrial freighters. In addition to fiscal and end user preferences, value should also be considered from a technical perspective, because the technical parameters of an asset, by and large, dictate its service capability and, hence, its value to users. Therefore, the valuation of an asset also functions as a consensus-building process. The Federal Highway Administration has emphasized that there is a need to effectively communicate the current condition of highway assets in order to justify long-ranging decisions on asset management (FHA 2017).
The Organisation for Economic Co-operation and Development (OECD 2001) reviewed the road asset management practices in different countries and listed the most commonly used. Most often, road asset management is based on historical costs and straight-line depreciation of road assets. At the time of the OECD’s (2001) review, sophisticated accounting methods were rarely, if at all, used. In addition, the link between measured performance indicators and asset values was absent. Yet there is no sign of less need for road funding, according to the OECD’s International Transport Forum (ITF 2013), and, in general, budgets are becoming ever more constrained. Later, the OECD and ITF (2013) identified two alternatives for asset valuation: (1) present the value of the future economic benefits expected from the asset; and (2) the perpetual inventory method (PIM).
In the present value method, the asset contains all the economic benefits that it is expected to deliver. It becomes, in other words, a stock of wealth that is consumed during its service life. In the case of the PIM, the asset portfolio forms a capital stock that is continuously updated by accumulating acquisitions (investments) and deducting fully depreciated assets. It is evident that there is a potential conflict between the two approaches. While the present value of future economic benefits represents a user-oriented view of asset value, the PIM method clearly adopts the perspective of the asset owner or the accountant. Usually, the PIM is the preferred method, because it can be more easily applied to national accounting systems. As investments are made, it is straightforward to add the investment to the capital stock and depreciate it according to the applied practice.
CPA (2013) recommends the method of future benefits—interchangeably using the term service potential—as a preferred approach. This method is, in principle, the same as the present value method. However, there may be technicalities that differ, such as the pricing methods, because there is no definite standard. In valuing future economic benefits, pricing can be carried out by several pricing methods, such as market or shadow prices, whereas PIM applies the historical cost at which an asset was acquired.
Research Aim and Objectives
There is an abundance of literature, including international guidelines, that have demonstrated how different deterioration models and life cycle approaches can be used in asset management and in the assessment of asset value (CPA 2013; Crist et al. 2013; European Union Road Federation 2013; OECD 2001; OECD and ITF 2013). This paper demonstrates that there is an apparent additional element due to the dynamic interdependency between the components of a road asset; this has an immediate impact on the accounting value. There are four types of dependencies between components: (1) economic; (2) structural; (3) stochastic; and (4) resource dependence (Keizer et al. 2017).
Economic dependence is present when maintaining individual components is more or less expensive than maintaining the components in a lump. Structural dependence relates to the repair or renewal of a component that requires others to be dismantled as well. Stochastic dependence refers to the deterioration process of a component that is dependent on the states and deterioration processes of one or several other components. Resource dependence occurs when tools, manpower, space, and so forth set dependences between the components. A recent review on economic and resource dependence maintenance for multicomponent systems can be found in Pargar et al. (2017). When the components of an asset system deteriorate at different, yet interdependent, rates and interact with one another, affecting the value of the components and, thereby, the value of the entire asset, at least stochastic dependences are present. The valuation of the entire system can be undertaken when all the relevant components are assessed and accounted for in a dynamic interdependent framework.
It is clear that understanding the aforementioned stochastic processes requires compromises with regard to the level of componentization and the modeling of the dynamics between the components. This paper uses constructive modeling and an example case to demonstrate the application of componentized asset accounting. It also demonstrates the implications that componentization and stochastic dependences between the components would bring forth at the agency level. In constructive research approach, a construct, a model, is being proposed. This construct/model may not have been empirically tested, particualrly if the proposed construct is novel. However, several papers can be found in which noncomponentized assets were studied (Van den Boomen et al. 2018; Zavadskas et al. 2017). An example case with the Finnish Transport Agency and real-world data approximates provides a first-hand test and allows formulation of propositions that can be used as the building blocks for wider postulates. The aim of this paper is to examine the issues surrounding service value with specific objectives to (a) demonstrate how a system-dynamics framework can be devised for an exemplary asset system (in this paper, the example is a road); (b) show the long-term implications that this approach to asset management would have on the financial statements of the asset owner; and (c) discuss the consequences of biased asset-value accounting in an asset management context that extends from project management all the way to national accounting for infrastructure assets.
The demonstration is narrowed down to one reference case (asset accounting for the Finnish Transport Agency) and one asset class (roads), but the implications are much wider: the principles of componentization, value assessment, and the dynamics between components are generic in the asset management context. The contribution is meant to be primarily practice and application oriented, but there are implications for the empirical testing of structures and the devising of more holistic asset management strategies, pointing to pathways for future research. The paper is oriented toward the accounting and value analysis rather than the engineering optimization of assets, although there is an obvious need for both aspects.
The paper also contributes to managerial decision-making and the management accounting of assets. Therefore, the direct beneficiaries of this work are asset owners and managers. The system dynamics that are introduced are simplistic and easily understandable and can be used as a starting point on the way toward a more integrated and holistic view of asset management. The real-world reference case makes the analysis concrete and translates the results into a language that can be readily understood by practitioners. The demonstration of how the proposed approach affects asset accounting and annual statements explicitly shows asset managers how a more refined asset management approach can improve their decision-making base.
The research construct relies mainly on the literature but is also based on previous work assigned by the FTA (Mild et al. 2011; Leviäkangas et al. 2017). The cost data was obtained from the FTA through their records, internal documents, or expert estimates from 2016–2017. One relevant source was the pavement asset management report and plan that the FTA was using for its long-term pavement asset management (Junes and Suikki 2017). The FTA’s annual report for 2015 serves as an important benchmark, because the implications for annual reporting are shown through this document (FTA 2016). The FTA commenced using standard accrual-based annual reporting from 2014.
Road Asset System and Its Valuation
A simplified road structure system is presented in Fig. 1. There is a pavement (assumed to be asphalt), a bearing course that assumes the load on the road, and a subcourse that works as a bed for the bearing course. The drainage system comprises ditches, culverts, drainage pipes, and so forth, depending on the particular road structure in question. The illustration is simplified and by no means attempts to present all the details that may be part of a road structure. The life cycles are also rough approximations, which may vary significantly depending on the type of road and the deterioration and preservation factors present. These life cycles are akin to those applied by the FTA in technical assessments. However, the FTA’s actual accounting for these assets is different (explained in “Accounting Bias”).
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Simplified Road Structure System Model
The reference road structure model is shown in Fig. 2. There are different service lives for various components: (1) pavement, 10 years; (2) bearing course, 20 years; (3) subcourse, 40 years; and (4) drainage system, 30 years. These service lives are approximations to demonstrate the effects of componentization in asset management. There are dynamics and interdependencies between the structural components of a road asset, and the condition and performance of each component has an impact on other components. For example, if a drainage system fails to keep road structures dry, water may infiltrate into the structures from the sides and weaken their bearing capacity. This in turn leads to cracking of the pavement, which allows more water to infiltrate and results in an accelerating deterioration spiral of the entire system. This is a very typical problem, encountered especially in the northern hemisphere’s cold climate (Saarenketo et al. 2012), but excessive water has always negative effects on road structures. Dawson (2008) studied the effect of water on bearing capacity and pavement performance.
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There are some necessary assumptions that are made as the analysis proceeds: (1) the service life of the components is known; (2) the structure, factors of deterioration, and stochastic and structural interdependencies between the components are uniform across the network considered; (3) depreciation starts from the first year after the completion of the structure (or component); and (4) deterioration is deterministic, with no uncertainty.
Service Value and Deterioration Model
The modeling of the deterioration process of components and systems is an important input for infrastructure asset management (Van Horenbeek et al. 2010). Deterioration models can be divided into two main families: (1) deterministic models, that is, deterioration curves; and (2) probabilistic models, often based on Markov processes with discrete probabilities.
Deterministic models describe the relationships between the factors affecting infrastructure deterioration using statistical calculations. By contrast, probabilistic models treat the infrastructure deterioration process as consisting of one or more random variables in order to capture the uncertainty and randomness of the process (Agrawal et al. 2010). The Markov chain approach is the most common probabilistic technique used for modeling the deterioration rates of infrastructures with several degradation states. The literature on deterministic models is replete, but, for example, Van den Boomen et al. (2018) and Thoft-Christensen (2009) present cash-flow-based model applications for noncomponentized assets; Frangopol (2011) and Sharabah et al. (2006) are examples of probabilistic approaches. Frangopol and Soliman (2016) offer a general overview of asset deterioration modeling and bring additional viewpoints to the discussion, providing an extensive review of the extant literature.
In deterministic models, asset condition is predicted as a precise value on the basis of mathematical functions of observed or measured variables, such as subgrade strength, axle load applications, pavement layer thicknesses and properties, and environmental factors and their interactions (Robinson et al. 1998). In deterministic models, it is typical to present deterioration in the form of a declining curve in which the units are asset value in monetary terms, a technical indicator, or a composite condition index. In probabilistic models, the changing of an asset or asset component to another condition class (getting worse, in deterioration modeling) is based on either heuristic or empirical probabilities. On a chronological scale, subsequent probabilistic events form a Markov chain. Both methods can be used side by side. The choice of model is partly contextual, depending on the available data, the object of application, and the preferences of the analyst. However, if data are available, probabilistic empirical models give more accurate predictions (Sirvio 2017). The OECD manual for measuring capital (OECD 2009) recommends “the use of geometric patterns of depreciation because they tend to be empirically supported, conceptually correct and easy to implement.”
In a straight-line depreciation case, the annual depreciation (deterioration) is , where is the expected service life. When we assume that the service value of an asset is an accumulated stock of expected benefits that are consumed or depreciated year by year as the asset is being used, we need to deduct the consumed value from the stock. The rate of return of the stock determines the amount of stock to be consumed. The OECD (2009) guidelines refer to “storage of wealth.” This is identical to the concept specified by CPA (2013), that “future economic benefits are synonymous to service potential.”
We use a generic model incorporating the following variables. This model was applied to a study initiated by VTT Technical Research Centre of Finland Ltd. and funded by the Finnish Transport Agency (Leviäkangas et al. 2017). The variables are:
•
= service life of an asset in years; without maintenance the asset will be totally consumed by year ;
•
; indicating the year between 0 and final year ;
•
= selected time value of money; interest rate; the rate refers to the annual rate of return generated by an asset;
•
, = investment made in year 0 and improvements and additional investments in year ;
•
= total future net benefits, generated by an asset over its entire service life; and
•
SV = service value of an asset (expressed as a percentage 0% … 100%).
Investment generates total net benefits after investment outlay (here equal to 1, and no additional investments ), when the annual return on a compounding basis is
This is the stock that is accumulated in the future. The total benefits are cumulatively consumed each year until and the entire asset is consumed. Then, the service value can be defined as the accumulated net benefits (i.e., the benefits less the investment) minus the consumed benefits. Hence, the remaining service value for the asset is as follows:
The calculus is exemplified in Fig. 3, when and . The benefit–cost ratio of this investment for ten years is 159%:100% = 1.59.
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Different rates of return result in different SV decline curves. It should be noted that the deterioration in the service value may not be exactly the same as the deterioration in the technical or historical value. The service value depends on the chronological distribution of the benefits generated by the investment and may take any form, not only that of a compounding return. The higher an asset’s return on investment, the higher the future benefits and their present value. Yet service value decline is slower, ceteris paribus, because a high return maintains a longer service value, as there are more future benefits. Discounting is implicitly assumed to have been taken into account, because the rate of return may be regarded in real terms after adjusting for time value (e.g., inflation). If one needs to include discounting in the calculus, then different rates can be applied. When the discounting rate is higher than the return on an asset, then the investment in the asset will never be profitable, because the required return (discounting rate) exceeds the available return (the return on the asset).
When repairs, renewals, and upgrading are undertaken, an asset’s service life can be extended and more future benefits generated. This should be taken into account by asset management and accounting systems. In this case, the service value formula can be written as
This is illustrated in Fig. 4, in which = time at which an additional investment, repair, or upgrade is made (Year 5 in Fig. 4); = extension of service life (in the example, from 10 years to 15 years, starting from Year 5); and = additional investment as a fraction of initial (in Year 5, 20% of the initial ).
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Componentization, System Service Value, and Investments
The four components of a road structure are assumed to be (1) pavement, (2) bearing course, (3) subcourse, and (4) drainage. These have service lives of 10, 20, 40, and 30 years (Fig. 5).
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The investment needed to build the road can be divided between the components. How the investment cost is distributed between the components depends on a number of factors, but we assume the following cost distribution: (1) pavement, 20%; (2) bearing course, 25%; (3) drainage, 10%; and (4) subcourse (including all preparatory earthwork), 45%. Hence, the total investment cost is 100%. After 10 years, the pavement must be renewed, having reached the end of its service life. Similarly, each component needs to be renewed at the end of its service life. This is defined as base case asset management strategy. In a simplistic case, the life cycle of a road is presented in Fig. 6. The weighted average service value (WASV) for the entire road cross-section, weighed by the cost share of each component, takes the form:where WASV is in year ; = relative share of component of the total procurement cost of the asset, expressed as percentage; when there are components to be considered; ; = service life of component ; and when component is replaced by a new component or is renewed for a second life cycle, is changed to (the third cycle starts with , and so forth; alternatively, can be adjusted for the cycles of each component).
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In reality, the renewal of the components is bundled so that synergies and economies of scale can be achieved. For example, pavement is inevitably renewed when the lower structures are renewed. In Fig. 6, Year 40 is the time for reconstruction of the whole road, probably including the drainage, which may have been renewed in Year 30. These interdependencies make asset management of the whole system much more complex and the optimization of renewal and maintenance operations a multiobjective task. The dynamic relationships between the components point to the fact that their service lives are interrelated. By maintaining pavements in better condition, for instance, the service lives of the substructures may be extended. Likewise, by investing in drainage maintenance, the life expectancy of the whole road structure will be extended.
The cumulative investments in the Base Case strategy are shown in Table 1. The asset management cycle commences with the initial investment, comprising the building of the components and adding up to 100% of the initial cost outlay. The subsequent renewals are added to the initial investment so that cumulative investment value is summed. Inflation does not play a role here, because we are using only relative figures (percentages), as we do with service values. Furthermore, we are not discounting any projected cash flows to the present.
| Component | Year | ||||||
|---|---|---|---|---|---|---|---|
| 0–9 | 10–19 | 20–29 | 30–39 | 40–49 | 50–59 | 60 | |
| Pavement (%) | 20 | 20 | 20 | 20 | 20 | 20 | 20 |
| Bearing course | 25% | — | 25% | — | 25% | — | 25% |
| Drainage | 10% | — | — | 10% | — | — | 10% |
| Subcourse | 45% | — | — | — | 45% | — | — |
| Total (%) | 100 | 20 | 45 | 30 | 90 | 20 | 55 |
| Cumulative investment (%) | 100 | 120 | 165 | 195 | 285 | 305 | 360 |
The best asset management strategy is one that maximizes SV in relation to cumulative investment (CI) and provides value for money. It is straightforward to calculate WASV per CI. This is shown in Fig. 7. Alternative asset management strategies can be compared now with WASV:CI curves to determine the strategies that generate the most value per money spent. The Base Case strategy does not take into account the minimum level of service value that needs to be in place. It only serves as a benchmark for alternative asset management strategies. For alternative strategies, the relevant change is to take into account the system dynamic behavior of the asset and observe what the impact of those dynamics is in terms of their provision of service value.
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Accounting for Service Value of an Asset System
When service value is defined in engineering terms, we usually refer to its technical parameters and perhaps to some indicator-based service level. Thus, engineers tend to know the right time to fix an asset or its components by preventive repair or replacement. Accountants, however, define service value in economic and monetary terms, usually through financial statements and management reports. The challenge for accountants is to link technical value parameters with economic and financial parameters. The links should be explicit and logical in order to manage assets correctly both in a technical sense and in an economic and financial sense. The statements and reports should reflect an asset’s true value with respect its ability to serve users.
The accounting problem becomes evident when oversimplified methods are used. For example, if pavements are renewed in every 5 years instead of every 10 years and this extends the service life of the entire road from 50 years to 70 years, the extended service value should be reflected in financial statements and asset management reports. Using straight-line depreciation in an asset management strategy, which is used to proactively extend service life and enhance service value quickly, will generate a bias in statements and reports and provide an erroneous picture of the true state. In the aforementioned example, simplified depreciation overestimates the decline of the service value and may even report investment and repair debt in cases when it does not exist. In addition, when maintenance and repairs are deferred to save money in the short term (for example, by repaving in every 13 years instead of every 10 years), there is an accelerated decline in the value of the other components. The true repair debt can be much higher than the accounting system suggests. Such a problem may accumulate even at the level of national accounting systems as the systemic bias compounds. With this in mind, these consequences are demonstrated in more detail in the next section of this paper.
Implications of Different Asset Management Strategies
Investing in Critical Components
We make the following assumptions regarding the deterioration of service value in an alternative scenario, defined as Enhanced:
•
Renewing pavements more frequently, that is, every 5 years instead of at 10-year intervals, results in the service lives of the bearing course and subcourse being prolonged for 2 years each time the pavement is renewed.
•
The drainage system’s maintenance is enhanced so that every five years an additional 2% (as a share of the initial total investment expense) is invested in this area. This extends the service life of the initial drainage system from 30 years to 50 years. In addition, due to less flooding, the service lives of the bearing course and subcourse are extended by 10 additional years.
The scenario is, of course, hypothetical, because we do not have specific data about the road components’ behavior. In practice, it is very difficult to assess the effect of maintaining one component while extending the life cycle of another (Kobbacy and Murthy 2008). This alternative asset management scenario (Enhanced) is shown in Fig. 8 in terms of the CI and WASV. As is predictable from the assumptions, the Enhanced scenario produces more stable investment expenses and service value curves. This is due to its frequent 5-year-interval repaving and enhanced drainage maintenance, which extend the service lives of the asset components and, hence, the entire asset. One could characterize the scenarios as almost extreme alternatives; in the Base Case, some deterioration is allowed to take place, and expenses are observably reduced (saved) for a substantial period. However, in the Enhanced scenario, the asset owner (or manager or steward) continuously invests in maintenance and service life extension and in keeping the service value level higher and more stable.
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The comparison becomes even more interesting when we show service value per CI and the net cumulative sum of the two ratios. This demonstrates the strategy that provides more service value per money spent as well as how the two scenarios differ over time. Clearly there is a difference between very long-term (Enhanced) and long-term (Base Case) strategies. This is visible in Fig. 9, which shows that the Enhanced strategy outperforms the Base Case strategy after only 35 years or so. Under budget constraints, it is easy to guess what decision makers will prioritize.
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Accounting Bias
Quite often, asset managers choose to account their assets based on some standard depreciation method (e.g., straight line or annuity depreciation). Most often for public, built-environment assets, a straight line method is assumed. For example, the FTA uses straight line depreciation of 10 years for pavements and miscellaneous light road structures and 50 years for tunnels, bridges, and heavy road structures (bearing course and subcourse included) (FTA 2016). Different countries apply different methods of accounting and depreciation of assets (OECD 2009), so there is no one best method for undertaking this exercise.
If the straight line method is used, there is a risk of over- or underdepreciation if the real deterioration of service value does not follow the depreciation line. For the demonstration road structure system, straight line depreciation and the Base Case scenario already differ substantially from each other. Fig. 10 illustrates the differences in current FTA practice (10-year straight line depreciation for pavements; 50 years for other assets) and alternative asset management strategies (Base Case and Enhanced). There is a considerable bias in the cumulative service value deterioration (depreciation) when a more realistic Base Case deterioration model is assumed instead of the straight line mechanistic approach. After approximately 30 years, the cumulative difference is 200%. In this case, the straight line method overestimates the deterioration in service values by three times the real deterioration rate. In a worst-case scenario, this can lead to misinformed managerial decision-making (e.g., concerning repair and investment debt, which have frequently raised concerns in many countries). Moreover, managers may be subject to decision-making and judgment bias when the Enhanced asset management strategy is applied using current accounting practices. This may lead to misallocations of resources and investment budgets. Preventive maintenance and service-life-prolonging asset management strategies may have radical impacts on depreciation accounting. If there is a mismatch between accounting and real service values, there is a risk of considerable decision-making bias.
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In the FTA’s annual report for 2015 (FTA 2016), the balance sheet value for national roads was 13.8 billion EUR out of a total of about 18 billion EUR for all national infrastructure. The depreciation of assets, which for the most part consisted of transport infrastructure, was 808 million EUR. The asset base comprised roads worth 13.8 billion EUR, railways worth 3.9 billion EUR, and the rest of the assets included waterways and other structures. It is likely that straight-line accounting depreciation is overestimating the investment debt, because the real decline in service values is probably less. However, if investment in day-to-day maintenance and small repairs has been neglected, there is a possibility that deterioration has been faster than a straight line decline.
Table 2 provides an example of the national accounts and those of the FTA that would appear if alternative asset accounting methods were adopted (i.e., assuming a homogenous marginal project). Note that the percentages in Table 2 are rounded to the nearest integer. Table 2 only presents the first 10 years in order to demonstrate the differences between the national accounts and those of the FTA. During the first seven years, an 11% cumulative difference materializes in the accounting system. This may not initially appear to be an issue, but when it keeps repeating and accumulating over the years, the difference begins to have an impact, as is shown in Fig. 10.
| Year | Asset’s service value using straight line depreciation (PIM) | Asset’s service value using componentized accounting based on future benefits (Base Case scenario) | Cumulative difference of (A)—(B) (%) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Pavement (10 years) (%) | Bearing course (50 years) (%) | Drainage (50 years) (%) | Subcourse (50 years) (%) | WASV (%) | Annual depr. cost (A) (%) | Pavement (10 years) (%) | Bearing course (20 years) (%) | Drainage (30 years) (%) | Subcourse (40 years) (%) | WASV (%) | Annual depr. cost (B) (%) | ||
| 0 | 100 | 100 | 100 | 100 | 100 | 0 | 100 | 100 | 100 | 100 | 100 | 0 | 0 |
| 1 | 90 | 98 | 98 | 98 | 96 | 4 | 94 | 98 | 99 | 100 | 98 | 2 | 2 |
| 2 | 80 | 96 | 96 | 96 | 93 | 4 | 87 | 96 | 99 | 100 | 96 | 2 | 4 |
| 3 | 70 | 94 | 94 | 94 | 89 | 4 | 79 | 94 | 98 | 99 | 94 | 2 | 6 |
| 4 | 60 | 92 | 92 | 92 | 86 | 4 | 71 | 92 | 97 | 99 | 91 | 2 | 8 |
| 5 | 50 | 90 | 90 | 90 | 82 | 4 | 62 | 89 | 96 | 99 | 89 | 3 | 9 |
| 6 | 40 | 88 | 88 | 88 | 78 | 4 | 52 | 87 | 95 | 98 | 86 | 3 | 10 |
| 7 | 30 | 86 | 86 | 86 | 75 | 4 | 40 | 83 | 94 | 98 | 82 | 3 | 11 |
| 8 | 20 | 84 | 84 | 84 | 71 | 4 | 28 | 80 | 93 | 97 | 79 | 4 | 11 |
| 9 | 10 | 82 | 82 | 82 | 68 | 4 | 15 | 76 | 92 | 97 | 75 | 4 | 11 |
| 10 | 0 | 80 | 80 | 80 | 64 | 4 | 0 | 72 | 90 | 96 | 70 | 4 | 11 |
Note: Type of asset: road; annual return (benefit): 10%.
Investment Project Selection
In standard benefit–cost calculus, the present value of future benefits equals the investment or acquisition cost when the benefit-to-cost ratio equals one. Whether the benefit–cost ratio equals one or deviates from it does not make any difference in the post-construction situation. The benefit–cost rule is applied only to a project-selection situation. However, different benefit–cost ratios affect the decline rate of the service value, because the rate of return on the investment is, in fact, the same as the stock’s return rate—both reflect the value of future benefits. For example, for the FTA, the required return on investment is 3.5%. This means that if an investment in an asset is providing this return, the investment’s benefit–cost ratio equals one.
The concept of service value and the approach of valuing an asset equal to the future benefits do not alter the project selection criteria from the traditional. Marginal benefits in relation to costs still can and, in fact, must be used as project-selection criteria. The value of each project is the present value of future benefits regardless of the asset management strategy that is applied after the project has been built or assembled. A benefit–cost analysis that maximizes benefits under budget constraints results in the best outcome in the long term. After a project asset is in operation, it matters whether its components are maintained, repaired, and renewed in a way that continues to maximize the future benefits generated by the asset system—under given budget constraints, of course.
The context becomes more complex when there is uncertainty associated with the future benefits; uncertainty associated with the costs and timing of future upgrades, repairs, and renewals; and uncertainty associated with the decline rate of service value.
In such cases, the possibilities for rational decision-making require (1) deterministic (predecided) probability assessments and simulation of different scenarios, after which rational decision-making paths can be chosen based on the risk averseness of the investor; (2) application of option theory so that alternative decision-making paths can be valued accordingly (see, for example, Leviäkangas and Lähesmaa 2002); or (3) application of an appropriate multicriteria decision-making framework (Kabir et al. 2014). The treatment of uncertainties requires in-depth analysis and modeling, and in many cases, likely, some form of system dynamic simulation.
Discussion
There is a limited understanding of the importance and dynamic interdependencies that exist between asset components and optimized methods used for asset management, especially considering the maturity of infrastructure engineering systems that have been developed (e.g., roads, railways, and pipelines). The understanding, however, can be improved by conducting experiments and simulations using system dynamics. In a best-case scenario, system dynamic modeling is empirical, and asset management scenarios can be built on reliable behavioral dynamics of the asset and its components.
The simplified examples presented in this paper are only the tip of the iceberg when it comes to the application areas of asset componentization, dynamics, and service value considerations. Fig. 1 showed the preservation and deterioration factors for a road asset. There is much research to be done in these areas before we gain a more reliable understanding of the importance of both sets of factors. For example, as climate change progresses and more frequent extreme weather phenomena are expected, we have limited knowledge regarding the way in which these factors should be accounted for as design and engineering parameters and as deterioration-accelerating parameters. This also applies to preservation factors. If we, for instance, use more advanced technology in asset condition monitoring, how much will such solutions extend the service life of assets? A wide range of solutions for real-time condition monitoring are available, although large-scale implementation still faces many issues and applications are mainly in trials or in the form of concepts (e.g., Känsälä et al. 2017; Bergqvist and Söderholm 2015; Rio 2015).
Also, improved quality assurance procedures and more sophisticated contracts may be a part of the solution for engendering sustainable asset management. Today, much maintenance and repair is done by private contractors and maintenance service providers. Having the right processes and incentives in place, service values can be ensured without having to overinvest in asset quality. In fact, such approaches have already been called for by international organizations, such as the OECD, which is concerned with the fact that infrastructure needs to be better and more efficiently managed (Crist et al. 2013).
If the proposed thinking is to be exercised in asset management practice, it is evident that innovative tools such as decision-support systems and automated expert systems that help in managerial decision-making are needed. The application of technologies such as big data analytics and artificial intelligence are strategic approaches that have been advocated. The multitude of prospective data, integrating and translating these data into meaningful information for decision-makers, and interfacing this information with humans so that decisions are informed—these issues pose a set of additional challenges on top of the technological challenges. In addition to better and novel methods to engage data analytics, different valuation techniques may be considered. For example, Leviäkangas and Lähesmaa (2002) demonstrated the value of postponing major investments in road management programs when the future is uncertain. These techniques are, unfortunately, usually complex, and their applicability in the accounting of assets needs to be carefully considered and tested.
Conclusion
This paper investigated the asset management problem from the perspective of asset valuation in a context in which the dynamics between the asset components that comprise the asset system were assumed. The example asset system was a road that was divided into four components: pavement, bearing course, subcourse, and a drainage system. Based on expert interviews, experience, and a limited number of references utilized in a prior FTA study (Leviäkangas et al. 2017), the dynamics between the components was assumed so that if one component (in this particular case, pavement and drainage) was strengthened or improved, it affected all the other components and, thus, the whole asset system. The effect was operationalized into a service value estimate that declined over time until the asset was entirely consumed, upgraded, or renewed. The purpose was to show how different valuation techniques, combined with different depreciation methods, will have an impact on an asset’s service value.
Two different methods of asset accounting were applied, the perpetual inventory method used generally by infrastructure managers and authorities and the present value of future benefits (service value). For PIM, the straight line depreciation method was applied. The present value method was then applied to different asset management strategies, with and without componentization, using the concept of service value. It was shown that overlooking componentization and system dynamics in asset management and accounting may lead to serious bias in perceiving the true value of assets and their service potential. Managerial decision-making must be based on unbiased assessments of asset values and communicated via asset management reports and financial and economic statements of the infrastructure owner. One of the possible consequences would be an overestimation of investment and repair debt, an issue that has gained much attention in the infrastructure, asset management, and public policy community. In Finland, where the PIM method is used, the estimated repair and investment debt for the entire public transport infrastructure was estimated to be 2.5 billion EUR in 2016 (FTA 2016), equaling more than 10% of the existing infrastructure asset value. This leads to the conclusion that the impact of correct asset and service value accounting is of such magnitude that it will be visible in the system of national accounts. Hence, the impact will be definitely macroeconomic and not limited to project- or program-management-level planning and decisions.
Acknowledgments
The Finnish Transport Agency is acknowledged for providing support for the initial stage of this study in 2017.
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Journal of Infrastructure Systems
Volume 25 • Issue 3 • September 2019
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This work is made available under the terms of the Creative Commons Attribution 4.0 International license, http://creativecommons.org/licenses/by/4.0/.
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Received: Mar 1, 2018
Accepted: Jan 7, 2019
Published online: Jul 2, 2019
Published in print: Sep 1, 2019
Discussion open until: Dec 2, 2019
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