Making Operations Research More Accessible: Insights from the Rise of Machine Learning.

This article focuses on the current challenges and future opportunities for operations research (OR), a quantitative discipline dedicated to transforming data into insights for better decision making, in light of the rapid rise and widespread adoption of machine learning (ML). Despite OR’s extensive history, broad applications across industries such as transportation, healthcare, and logistics, and steady employment growth, it remains less visible and accessible compared to ML, which benefits from open-source tools, interdisciplinary appeal, and substantial funding. The authors propose a comprehensive action plan consisting of 10 targeted initiatives across community awareness, educational outreach, and research and technology to modernize OR’s outreach, increase public engagement, and foster integration with ML. Key recommendations include launching an open-access OR knowledge hub, promoting OR in K-12 and university curricula, encouraging interdisciplinary research and publications bridging OR and ML, and advocating for increased federal funding. The paper emphasizes that enhancing OR’s accessibility, visibility, and collaboration with ML is essential for its sustained relevance and broader impact in solving complex, real-world problems.