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

Operations research (OR) has evolved over the past 50 years into a versatile field with broad applications. However, its growth has been overshadowed by the rapid rise of machine learning (ML), which has seen widespread industry adoption and integration into numerous academic programs. Despite its powerful decision-making capabilities, OR is often perceived as a niche discipline, with accessibility challenges limiting its broader adoption. This paper explores how the field can reach a wider audience by drawing lessons from ML’s global success. We propose a set of recommendations to modernize outreach, increase public awareness, and refine research and technology strategies. Our action plan outlines 10 targeted initiatives to enhance visibility and engagement. By adopting these recommendations, stakeholders can help revitalize OR, ensuring its continued growth and relevance. History: Yu Ding served as the senior editor for this article. Funding: L. A. Albert was supported in part by the National Science Foundation [Grant 1935550]. T. V. Le was supported in part by the National Science Foundation [Grant 2423909].