In a world of aging assets and limited financial and human resources, companies often struggle to decide which asset-related projects should get attention first. Managers are required to compare highly divergent project justifications, and must somehow decide which projects bring most value to the company. Even within one project, it might be difficult to select the right option: is it better to change an asset's maintenance regime, plan a major refurbishment, or replace it altogether? The PAS-55 specification and the new ISO 55000 standard clarify the principles, but do not help with selecting or applying a methodology. This paper explores how combining asset failure risk evaluations with a well-defined corporate value function can lead to optimal decision making, and how the resulting decisions should be tracked and adjusted over the lifetime of the underlying projects or programs to maximize the execution rate.


Making decisions around asset investments, replacements, refurbishments and retirements is complex. Many factors need to be considered, and significant financial and resource constraints often limit the leeway of decision-makers. Yet decisions must be made every day, often on the basis of scarce information. This has led industry experts worldwide to start collecting and promoting sound asset management best practice, culminating with the recently released ISO 55000 standard. One of the key themes is the need to elevate asset management to a company-wide challenge and discipline. Strong organizational alignment is required for a holistic approach where multiple stakeholders contribute to better asset management and decision-making.

The various internal stakeholders in a corporation look at asset management from the perspective of their own needs, using their own data, processes and systems. Yet no single department or system has access to all the data and knowledge necessary to make effective asset investment decisions. This means that, in addition to functional and data alignment, consideration needs to be given to integrating all the IT systems, which each hold some of the decision-making data. Effective decision support solutions draw on data from multiple existing departmental and enterprise IT systems to develop a full picture of all assets, of the challenges attached to them and the financial consequences of each decision (or lack thereof).

Such solutions are increasingly being referred to as Asset Investment Planning solutions, or, if they include a continuing performance management component, Asset Investment Planning and Management (AIPM) solutions.


ISO 55000 is based on the need to manage the whole life cycle of an asset. In this paper the focus is on the productive component of an asset's life, which can be broken into three phases each triggering different AIPM actions and illustrated in Figure 1:
  • For most of its life the asset performs its intended function, supported by an adequate maintenance plan. Ideally, its condition and the degradation over time of its uptime, maintainability and efficiency are tracked, together with the increase in the risks tied to its potential failure or inability to meet its expected level of service. This is the ‘asset analytics’ phase, where predictive analytics are used to determine how long satisfactory asset operation will continue without further investment.

  • When the asset's condition has degraded so far that non-routine action is required, a business plan is developed for the capital investment needed to restore it, e.g., by replacement, refurbishment, etc. This is the investment decision optimization phase, and is often the base for 3- or 5- year corporate plans.

  • Once the capital investment is approved and under way, its progress is tracked, with all plan changes and decisions recorded to ensure that resource use is optimized, lessons are learnt from actions, and all decisions can be justified. This is known as performance management.

Figure 1

Asset Investment Planning & Management.

Figure 1

Asset Investment Planning & Management.

Both PAS-55 and ISO 55000 stress the importance of asset-related risks as key factors in decision-making, complementing traditional life-cycle cost analysis. As an asset's condition degrades, the probability of failure and associated risks increase. The risks should typically be mitigated before they become unacceptable. If the asset is already subject to an appropriate maintenance regime, this means that it needs replacing or refurbishing, or that some other redesign action must be undertaken to reset its condition to a higher level and reduce the probability of failure.

Most corporations mitigate risk by investing in assets presenting unacceptable levels of risks or degrading general performance in the short term – e.g., within 3 to 5 years. Investment in this case could mean asset replacement, redesign, partial change or refurbishment, an asset swap, or ‘soft’ investments such as retraining staff to operate the asset differently.

Asset Analytics

To build a truly sustainable plan, the long term evolution of all assets must be predicted and investments planned to maintain all assets at acceptable risk and performance levels. This is done using predictive analytics as described in Figure 2. It would be unreasonable to expect staff to develop fully documented business investment plans stretching 20 or even 50 years into the future, so asset models are used to predict the most likely behavior of each asset class over its life cycle.
Figure 2

Using asset condition and risk to predict investment needs.

Figure 2

Using asset condition and risk to predict investment needs.

Automatic routines can be used to create suggested investment lists so that all assets remain in optimal condition. Financial and resource constraints can be added to these routines, to test the actual ability of the corporation to make the predicted investments. This often leads to the identification of ‘bow waves’ or ‘walls’ of sustainment investment needs in the future, which will trigger consideration of increases in capital and resources, or the redistribution of predicted investments over time as shown in Figure 3. The latter could lead to a need for some investments to be made earlier than strictly necessary for risk mitigation.
Figure 3

Using Asset Analytics to predict capital and resource needs. A constraint will often expose unacceptable risk and/or a need for higher capital budgets in future.

Figure 3

Using Asset Analytics to predict capital and resource needs. A constraint will often expose unacceptable risk and/or a need for higher capital budgets in future.

Investment decision optimization

As specific assets come closer to the need for investments, their owners convert the suggested investments resulting from Asset Analytics into full business cases. These are usually only part of the set competing for funds and resources. There might also be investment needs arising from maintenance, operating or regulatory requirements, or from growth objectives or function changes for particular assets. Prioritization becomes the key when too many investments compete for limited resources (finance, labor, tools, inventory, etc.).

Optimization of a portfolio of competing investments is typically achieved by evaluating the contribution of each candidate investment, using value functions. These are meant to reflect the asset-owner's strategic objectives and to evaluate the various investments’ benefits using specific metrics aligned with corporate goals (e.g. safety, uptime, ROI).


Once optimization is understood and implemented fully, different scenarios can be evaluated – e.g., on the basis of financial constraints set by management. Different scenarios could be run using relaxed or reduced budgets, and sensitivity analyses performed. This might trigger budget constraint revisions based on the potentially better outcomes under specific scenarios. Scenarios also offer a convenient mechanism to track changes over time: a first scenario might reflect the original budget, a later, updated version, might be produced based on revised costs and/or financial constraints. If such scenarios are recorded they can be recalled and compared later – enabling variance analysis, for instance. Ideally all options and scenarios are logged so that it can be shown that due diligence was properly performed at every stage of budgeting and execution.

Continuous optimization

Typical projects are changed during execution. More urgent work might require resource redirection, materials might be delivered late, budgets might change, etc. Often, however, budgeting and project approval are so complex that investment portfolios are not reviewed outside the formal, annual budget review. The accumulation of changes and slippages caused by this can lead to under-execution, often reducing the original investment execution target by as much as half.

By regularly combining and analyzing all variances in a portfolio, it is often possible to reallocate funds or resources, and improve on the project execution rate. Monthly or quarterly portfolio review meetings, where variances are analyzed and plans adjusted, take advantage of changes from the original plan. This enables execution rates to be improved quite significantly. In effect, the asset investment plans are systematically adjusted to the actual situation, and thus become more useful and credible.

Performance management

When an investment is fully executed, it is important to understand what really happened as opposed to what was planned and promised. This is true for individual investments and full portfolios. Regulators, investors and other stakeholders want to know if resources were optimally applied and if results are in line with what was ‘sold’ to them. All decisions and changes must, therefore, be tracked, enabling full audits, strengthening internal and external credibility, and allowing the lessons learned to be applied in the next budgetary cycle.


Application of the AIPM methodology yields a holistic and dynamic asset investment plan covering all time frames. AIPM enables the development and maintenance of a defensible, well documented asset investment plan built around actual asset, risk and financial data that can be used confidently with internal and external stakeholders.

If well executed, such a solution offers a clear vision between strategic objectives and imperatives, and asset condition, the risks attached to each asset and the investments planned to mitigate risks, and asset degradation. It helps in the maximization of asset value and corporate alignment with asset management standards like ISO 55000.