Collaboration–competition dilemma in flattening the COVID‐19 curve

Millions of workers in India who migrated to cities for employment have transited back to seek refuge in their home villages, causing disruptions in both cities and villages. This type of mass flow of migrants in transit represents a humanitarian crisis. Understanding migrant flow patterns and ways to ameliorate the conditions for migrants in transit is critical to managing the humanitarian crisis. In this study, we develop a model that examines the influence of migrant networks, inter‐organizational collaboration, and environmental uncertainty on locational advantage, which, in turn, predicts migrant flow patterns. This study contributes to the humanitarian operations management and migration literatures by uncovering how migrant networks and inter‐organizational collaboration help provide access to humanitarian resources. Additional new findings of this study include uncovering different classes of migrants with their respective flow patterns and the role of collaboration along migration paths. The study also uncovers how travel constraints increase the duration of transit and the importance of point‐to‐point transfers to avoid congregation at transit hubs. Furthermore, findings from this research provide insights on how long‐term humanitarian support to migrants through inter‐organizational collaboration morphs to short‐term aid in the event of a crisis.

Section: Methodsmentioning

confidence: 99%

Migrant flows: Humanitarian operational aspects of people in transit

Prasad

Borra²,

Woldt

et al. 2023

“…Van Oorschot et al. (2023) examine the risks to countries that help others by reallocating their excess inventory of diagnostic tests. Tests are scarce and finite resources, so there can be abundance in one country while shortages exist in others, creating a competitive landscape.…”

Section: Public Policies/government Interventionsmentioning

confidence: 99%

“…For operations, the natural research questions for government decision makers are (1) what policies/interventions the government (central/regional) should take and (2) the efficacy of alternative or implemented policies and interventions. The special issue contains four papers focusing on public policies and government interventions: "Pandemic lockdown, isolation, and exit policies based on machine learning predictions" (Evgeniou et al, 2023); "Model-informed health system reorganization during emergencies" (Goncalves et al, 2023); "Collaborationcompetition dilemma in flattening the COVID-19 curve" (Van Oorschot et al, 2023); and "Managing disease containment measures during a pandemic" (Shahmanzari et al, 2023). Evgeniou et al (2023) ask how well a rapidly evolving pandemic can be managed when leveraging machine learning approaches to inform medical procedures that otherwise lack essential data during rapidly evolving epidemics to inform medical procedures.…”

Section: Public Policies/government Interventionsmentioning

confidence: 99%

Managing pandemics: A POM perspective and directions for future research

Anderson

Gupta

Joglekar

et al. 2023

COVID‐19 pandemic has revealed how unprepared operations and supply chain professionals are for “abnormal” conditions. Understanding how the production and operations management field can affect the trajectory and especially the remediation of pandemics is a critical, but understudied, area of research from descriptive, predictive, and prescriptive perspectives. Fourteen research articles in this special issue have attempted to fill this gap with rigor. We first summarize these articles in six categories, (1) public policies and government interventions, (2) hospital capacity, (3) propagation of pandemics, (4) humanitarian operations, (5) private partnerships, and (6) vaccine production, by drawing out the themes addressed. As we look ahead at pandemics yet to come, we note there is still much research needed and conclude by discussing emergent interest in promising themes for studying pre‐pandemic, during pandemic, and post‐pandemic operations.

“…For example, SEIR models have been developed to both illustrate the spread of COVID‐19 and account for more behavioral responses such as public self‐isolation measures when the death rate is high (Fiddaman, 2020; Rahmandad et al., 2020; Struben, 2020). Other studies have also included increased hospital capacity (Goncalves et al., 2021) and inventory trading between countries (Van Oorschot et al., 2023). However, few if any of these studies have linked the contagion model to the economic model to study linkages between the two.…”

Section: Literature Review and Open Questions By Clustermentioning

confidence: 99%

Opportunities for system dynamics research in operations management for public policy

Anderson

Keith²,

López³

2023

Operations management (OM) in the public policy context is extremely complex with many mutually interacting factors characterized by feedback loops, delays and nonlinearities, as well as multiple stakeholders pursuing divergent objectives. Prior researchers have called for a systems approach in these contexts, arguing that standard OM methodologies such as mathematical programming, and queuing theory often cannot fully address these problems. Researchers have employed one such systems approach, system dynamics, successfully for decades for studying OM problems in public policy because it can address such complexity and can also integrate disciplines from outside OM such as political science, epidemiology, ecology, etc. In this paper, we create a roadmap for researchers—both those who are familiar with systems dynamics and those who are not—for the expanded use of system dynamics studying public policy‐related OM problems. We review and organize relevant system dynamics literature in both traditional operations management venues as well as public policy venues unfamiliar to OM audiences. We then identify a set of interesting open questions and potential system dynamics building blocks for answering them by topic. Leveraging this review, we describe under what conditions system dynamics is most appropriate. We then identify several overarching methodological and domain gaps for future research. Finally, we build on previous work to extend a process for using system dynamics with traditional operations management methodologies. It separates model building into two sequential phases: consensus‐building models and detailed operational models. It also incorporates scenario planning and feedback from implementation outcomes.