Modeling the Convergence of Collaborative Systems of Systems: A Quantitative Case Study

Abstract

A system of systems SoSs provides functionality beyond that offered by its constituent systems. The functionality emerges over time, and there is a need to predict and affect the system's convergence toward the desired functionality. The convergence of a collaborative SoS is dependent on political, economic, societal, and technological PEST factors. In this paper, the authors propose a model for predicting and analyzing the convergence of SoSs. We use the United States smart grid, a collaborative SoS, to demonstrate the power of the developed model. The United States smart grid's convergence depends on factors such as energy policies, roadmaps, grants, cost recovery, consumer support, and sufficiency of technological solutions. We constructed a dynamic Bayesian network to predict the convergence and then assess the influence of each of the PEST factors. The output of the convergence model can be used to predict and communicate technical progress to stakeholders and to analyze and optimize the investment of resources to affect the PEST factors. Understanding the convergence of SoSs is important for making sound business and technology investment decisions. This approach of predicting and analyzing convergence should be extended to other SoSs.