Opportunities for system dynamics research in operations management for public policy

• 成果类型: Article
• 署名作者: Anderson, Edward G. G.; Keith, David R. R.; Lopez, Jose
• 署名单位: University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13947
• 发表日期: 2023
• 页码: 1895-1920
• 关键词: computer simulation; Literature review; operations management; public policy; scenario planning; system dynamics

摘要

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.