A variety of methodologies commonly known as business process models are widespread in systems engineering and diverse technology systems. Business process modeling encourages uniform documentation of who generates what information, products, services; for whom; how; and why; and with what authorization. However, no known business process models have the capability to describe sources of risk: that is, what can go wrong in the business process. The goal of this paper is to demonstrate the integration of risk identification into a single existing methodology of business process modeling. The paper thus develops an extension of the integrated definition for function (IDEF) modeling that supports describing sources of programmatic risk in business processes. This is demonstrated in an application to planning and programming $1.5 billion per year of highway construction. Identifying the sources of risk is supported by existing methodologies such as hierarchical holographic modeling (HHM) and failure modes, effects, and criticality analysis (FMECA). The results of this paper will emphasize the importance of explicitly introducing the sources of risk in IDEF and other types of business process modeling. © 2006 Wiley Periodicals, Inc. Syst Eng 9: 187–198, 2006

Allocating resources to competing large-scale infrastructure projects involves multiple objectives. Traditional decision-aiding methodologies focus on the tradeoffs among performance and resource objectives. Existing methodologies may fail to account for unknown and emergent risks that are typical of large-scale infrastructure investment allocation problems. In modern portfolio theory, it is well known that a diversified portfolio can be very effective to reduce nonsystematic risks. The approach of diversification is equally important in choosing robust portfolios of infrastructure projects that may be subject to emergent and unknown risks. In this paper, we demonstrate a methodology to analyze and compare the diversification of portfolios of large-scale infrastructure projects. We classify and explore several metrics of diversification and integrate them with risk and other performance objectives in a multiobjective approach. We test the new metrics and the methodology in a case study of hundreds of millions of dollars of infrastructure investments. The results suggest that the solutions that consider diversification are more robust to emergent risks, thus, identifying an opportunity to incorporate diversification-based optimization methodologies to support a variety of problems involving large-scale infrastructure investments.

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