New Approach to Capital-Allocation Modeling Improves Performance Assessment
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More than 57% of US industry megaprojects exceed their original budget and delivery timeline; this figure grows to more than 80% for megaprojects worldwide. These overruns translate into direct, significant reduction of capital-investment capability, often not realized until the capital is committed irreversibly. This paper presents a conceptual framework for capital-allocation management. This new framework, known as advanced risk and capital-allocation management (ARCAM), aims to synchronize risk, strategy, and capital decision modeling to provide better visibility of future performance of capital-investment opportunities.
Challenges and Analysis
Boardroom Discussions. Because long-term production outlook shapes capital investment, executives are cognizant of changes that may affect capital expenditure (CAPEX) and financial capability. In current context, capital investment refers to capital allocations within projects and any capital relative to the sale of certain divisions or the purchasing of assets.
Now that companies have become leaner, strategy is shifting toward the ability to meet expected demand at a competitive cost. This can take the form of acquisition of distress assets, mergers, and targeted investments that permit flexibility in capital spending. The ability to retain control over capital requirements is paramount, and persistent cost overruns within projects become a concern. This difficulty is compounded by delays in project completion. Companies invest in producing assets to meet short- and long-term production outlook, seeking flexibility to ensure that the only capital spent is that which will work toward meeting production requirements at the lowest cost in time to meet the forecasted demand. Although an oil company’s ability to influence oil pricing may be minimal, its entire competitive position will depend on the performance of its capital portfolio.
Capital Projects and Opportunity Assessment. From discovery of prospect wells to first oil, hydrocarbon fields have a long life cycle marked by increasing commitment of capital. At every investment milestone, a best-effort decision accounts for a probability of success, as with any business investment. At the corporate level, each entity develops a framework for assessing the economic viability of its capital-investment opportunities. Still, project-performance data reflect a wide discrepancy between funding-investment decision estimates and delivery in terms of cost and schedule.
The author’s observation of project-performance data and various project-opportunity-realization frameworks arrives at the conclusion that modifications are necessary to enhance objectivity, consistency, and continuity of opportunity analysis. These modifications form the basis of the ARCAM framework.
Concepts Underlying ARCAM. Advanced risk implementations (ARIs) are one component of an ARCAM framework. ARIs are attributed to superior performance of project portfolios and represent a mathematical decision-modeling process that is intended to capture, quantify, and mitigate risks and uncertainties. This is different from traditional project-risk and -mitigation systems found in most oil corporations.
In any project-driven industry, risks and variation leading to overruns are prime contributors of margin erosion. Whereas an ARI is a single component aiding in on-time and on-budget delivery of projects, ARCAM is the larger framework that incorporates ARI mathematics into the decision metrics of capital-opportunity assessment and realization.
To ensure continuity, quantitative modeling in the ARCAM framework accounts for project risk and uncertainty at all decision points directly; risk assessment and mitigation provide economic feedback for feasibility assessments. A measurable cost benefit is attributed to every risk and mitigation effort. Moreover, capital commitment, including the effect of portfolio-risk exposure, is readily displayed with statistical-control limits synonymous with process controls.
ARCAM Characteristics. Objectivity. Decision modeling drives objectivity. Models are dependent on the quality of the input. Input includes facts and assumptions that capture the status quo of projects. Risks and variations affecting the cost and schedule of each project contribute to the overall performance of a capital-project-investment portfolio. Through an ARI system, the models connect to the performance of invested capital directly.
Unlike traditional assessment methodology, this evaluation of opportunities takes into account a continuous distribution of possible outcomes instead of best, worst, and most-likely scenarios. Like the design of games in a casino, this ultimately allows decision executives to see if the odds are in their favor overall, not merely a P10/P50/P90 performance snapshot (P10/P50/P90 project-delivery dates refer to the dates at which 10, 50, and 90% of all possible outcomes for project delivery fall on or before, respectively). Moreover, an ARI system highlights the root cause of such variation. This allows continuous improvement and revaluation, allowing managers to address and target upcoming hurdles actively within schedule or cost.
Visibility of the overall company risk exposure vs. its ability to handle risk (risk capacity) is the hallmark of a strong ARI. This permits executives to predict the amount of capital committed and the spread of its final performance (i.e., the spread of its capital efficiency) reliably.
Consistency. Different stages of opportunity realization necessitate a different level of risk assessment; different risks are more pertinent at different stages, and scope ambiguity varies. However, the assessment methodology should remain consistent (evaluation, quantification, and modeling of risk) across approval stages and managed by the same team. This allows an ARI model to tie back directly to the capital-investment premise and to track performance through execution.
Because risk modeling is a building block of opportunity evaluation, how an individual models risk can influence return-on-investment (ROI) metrics greatly. ROI metrics remain consistent across stages, as should the building blocks involved in calculating these metrics. This would not be possible in the majority of current risk-management systems in which statistical modeling of risk is remedial and, more importantly, does not tie back to ROI.
Continuity. Continuity refers to applying this type of objective assessment throughout the entire opportunity-realization process with the same risk team. Currently, each decision stage gate involves a different team assessing risks. More often than not, the business case and feasibility study also are performed under a different process internally than project definition. Additionally, typical ARIs are implemented within the project-assurance department (or project controls) and track project opportunities through execution, leading to an inconsistent method of risk assessment.
Transition and handover processes are not the same as active management. Active management of risk exposure throughout the opportunity-realization cycle necessitates the risk department taking a standing role in the capital-management process. If taking risks truly is synonymous with creating value, then managing capital performance means objective, consistent, and continual active management of risk through all the stages of an opportunity.
ARCAM Analysis: Effect of Performance Trends on ROI. To demonstrate the effect of overruns on a typical upstream project, the author analyzed a $3-billion subsea field development. Original project feasibility studies were based on 300 million bbl of reserves with an estimated capital-investment cost of $3 billion and an operating cost of $8/bbl. In addition to best-case, most-likely, and worst-case scenario analysis, a simulated P10/P50/P90 outcome is prevalent in most opportunity-assessment processes. ARCAM modeling was used to cast a wide spread of higher and lower West Texas Intermediate (WTI) prices. The ROI was further stress-tested by taking into account 10% CAPEX increases up to a 110% increase in CAPEX (developing cost). In ARCAM modeling, cost and time overruns can be linked to risks and uncertainties associated with cost items and schedule activities. For the purposes of this model, cost was escalated in increments of 10%. A second stress test accounted for a 20% delay in project delivery up to a 120% delay, also spanning higher and lower WTI prices. Finally, both stress tests reflected historical industry-performance metrics to highlight the most-likely ROI curve at any price.
Cost and time overruns affect the trajectory of the ROI curve directly, making it increasingly difficult to achieve a positive ROI at any oil price point. This underscores the fact that a company’s competitive position is rooted in its ability to manage its capital portfolio, specifically with regard to on-time and on-budget delivery.
In isolation, within project execution, ARIs contribute significantly to the accurate assessment and reduction of project risks through project and portfolio modeling. Moreover, ARI systems expand mitigation options. When implemented as part of a larger ARCAM framework, ARIs can set the stage for significant competitive advantage and provide additional avenues for risk mitigation.
Because all market participants are subject to the same market conditions, true long-term competitive advantage can be built only by addressing challenges in opportunity-assessment and execution strategies. A prolonged low-oil-price environment tests an organization’s willingness to adapt as historical project-performance data point to consistent overruns in both cost and delivery. Top performers must be open to exploring improvements within capital due-diligence processes. Project performance affects the profitability and financial capability of oil corporations more than any other metric. As project-performance trends shed light on the persistent frequency and magnitude of cost and time overruns, every change in the price basis of a project has a much larger effect on the financial health of the company at large.
New Approach to Capital-Allocation Modeling Improves Performance Assessment
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