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Organizations use command and control (C2) systems to collect, organize, and disseminate information in order to make decisions, impart instructions, and manage resources to accomplish a mission. C2 agility and robustness are critical to ensure that a C2 system can perform well in a variety of environments. One system design principle, decentralization, has been closely linked to desirable system characteristics such as agility and adaptability, but its relationship to system performance robustness is not well‐established. In this study, we explore C2 system architectures—ranging from fully centralized to fully decentralized archetypes—to assess their performance and robustness characteristics across a spectrum of operating environments. While the centralized archetype achieves high performance in favorable environmental conditions, its performance quickly degrades when conditions worsen, hindering its overall robustness. Conversely, the decentralized archetype achieves a lower but more stable performance profile resulting in more robustness when performance requirements are lower. Finally, we explore alternative, hybrid architectures with varying degrees of centralized and decentralized decision‐making. We find that by centralizing only certain system‐consequential functions such as resource allocation, and decentralizing more focused decision functions which can be performed suitably with only local information and resources, system performance and robustness are improved beyond that of the simple archetypes. [ABSTRACT FROM AUTHOR]

Disaster response is a challenging environment for data‐driven decision support, because of its rarity, uniqueness, and high stakes. This article develops and demonstrates practical decision support approaches for an important problem faced by the U.S. Federal Emergency Management Agency (FEMA), identifies challenges and successes during their partial implementation, and explores how such approaches can encourage the adoption of data‐driven decision‐making in crisis situations. Specifically, this article develops approaches for locating and staffing temporary Disaster Recovery Centers (DRCs), which provide in‐person service to disaster‐affected communities. Working with FEMA, we developed two models that aim to improve service to survivors and minimize costs. One model fits easily into FEMA's current decision‐making process, while the other further improves service by challenging some extant assumptions. By testing the models using data from three past disasters, we find that this decision support can result in cost savings of 75% on average by eliminating unnecessary DRCs and over‐staffing and, at the same time, maintain or reduce the maximum travel time required for the disaster‐affected population to access DRCs. Aspects of the models have already been used during disaster operations. FEMA's experience highlights the potential for data‐ and model‐driven analyses to improve resource allocation and demonstrates an approach to improve organizational decisions by developing models that either align with or challenge the decision‐making culture. [ABSTRACT FROM AUTHOR]

When dealing with urgent, ill-defined problems, such as rapidly evolving emergency situations, operations managers have little time for problem formulation or solution. While the mechanisms by which humans formulate and solve problems have been described, mechanisms for rapid, concurrent formulating and solving are not well understood. This study investigates these mechanisms through a field study of transportation planning in a humanitarian response setting. The findings show that the problem is solved through greedy search and formulated through sensemaking, in which search enables updates to an evolving problem formulation, and the formulation directs and limits the search process. This study explores the implications of these findings for the development of better problem formulation processes and problem-solving strategies for urgent and ill-defined operations management problems. [ABSTRACT FROM AUTHOR]