https://onlinelibrary.wiley.com/doi/pdf/10.1002/sys.21592

MBSE delivers significant return on investment in evolutionary development of complex SoS

In the modern world, the amount of information stored in modern technology has been exponentially increasing. Access to vast amounts of information has changed how governments, institutions, organi...

Electronic records management systems and the competency of educational institutions: Evidence from Yemen:

What Is Systems Thinking?: Learning, Design, and Technology: An International Compendium of Theory, Research, Practice, and Policy

What Is Systems Thinking?

Assessing the impact of natural disasters and manmade incidents against critical infrastructure systems is important and challenging. As critical infrastructure sectors become more interdependent, a method is needed to assess how disruptions to one sector affect other sectors. This need is particularly significant in the case of critical national supply chains which have not received the same attention as other critical infrastructure systems. This research develops a methodology for performing supply chain impact assessments by integrating model‐based systems engineering (MBSE) with discrete event simulation (DES). SysML models capture the supply chain structure and critical infrastructure interdependencies. Simulation of the supply chain model subjected to external constraints identifies the impacts from critical infrastructure disruptions. This approach enables researchers and public policy planners to assess critical supply chain risks associated with current infrastructure implementations, and to conduct what‐if analyses on alternative solutions. Application of this methodology to assess the natural disaster impacts on an urban food supply system demonstrates the effectiveness of modeling and simulation for evaluating the impacts of critical infrastructure disruptions on a wide range of critical supply chains. Future research should apply this methodology to different national supply chains and investigate additional critical infrastructure interdependency linkage mechanisms.

A resilience systemic model for assessing critical supply chain disruptions

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.

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

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