Breaking Boundaries, Building Bridges

One of the things I love most about editing OR/MS Today is the sheer range of stories that come across my desk. At first glance, they might seem worlds apart – an Olympic cycling team’s gold medal, the optimization of consumer supply chains in the age of generative AI or a student-led forecasting project in Colombia. But somehow, I’m always able to find a unifying thread. This time? The creative, often unexpected, ways the OR/MS community takes mathematical models, algorithms and analytics out into the world to solve problems that matter.

This issue reinforces the importance of industry-academia collaboration in guiding the trajectory of the field in the age of artificial intelligence, machine learning and quantum optimization.

We’ll start with Warren Powell’s “Working with Industry: From the Laboratory to the Field and Back,” a reminder that ideas don’t always thrive where they’re born. Research, no matter how brilliant, can’t make an impact unless it’s tested in the messy, unpredictable environment of the real world – and O.R. researchers are notorious for working on problems that don’t fit in a lab. In this feature, you’ll see how industry-academic collaborations act as a two-way street: industry gains innovative, data-driven solutions, and academics get feedback that sharpens their work for the next cycle of discovery. This give-and-take can be the lifeblood of progress in this field, and one of the main reasons for INFORMS’ existence as your forever professional home.

The laboratory-to-field journey also underpins “Commercializing Optimization Research,” the origin story of Opturion – a university spinoff formed to commercialize the results of constraint programming research, and has been further developing and applying artificial intelligence (AI)-based optimization to problems in transport, logistics and supply chain. The authors assure that a spinoff is not the end of collaboration with academia; it is just the beginning. The benefits of ongoing collaboration are manifold. The Opturion story is one of tackling difficult problems and finding innovative solutions.

From there, we leap into the generative AI (GenAI) revolution. In “GenAI Revolution in Modern Consumer Supply Chains,” we see how generative AI is not just a buzzword – it’s a force reshaping everything from demand forecasting to logistics planning. It’s easy to think of GenAI as something confined to text or image generation, but it has deeper capabilities: synthesizing insights from massive, messy datasets, simulating complex supply chain disruptions and uncovering efficiencies that may not have been spotted otherwise. What’s most exciting is that GenAI isn’t replacing the human expertise of O.R. and analytics professionals – it’s amplifying it.

But not every revolution is technological. Sometimes, it’s personal. In “Lessons from a Career in Analytics: Bridging Academia, Industry and Society,” we hear from Tilburg University colleagues Melvin Drent and Hein Fleuren for a look inside a decades-long career journey, showing how the roles we take on – researcher, consultant, educator – aren’t boxes to be checked but perspectives to be integrated. The wisdom gleaned here is that analytics is not confined to one domain. The same rigor that drives a breakthrough in a university lab can also help a city government tackle congestion, or a nonprofit distribute aid more effectively. Bridging those worlds is not only possible, but also essential.

Speaking of bridging worlds, “Forecasting Social Impact: How Georgia Tech Students are Helping Comfama Predict the Future of the Middle Class in Colombia” reminds us that analytics can illuminate pathways for communities, not just corporations. Working with Comfama, a Colombian social enterprise, Georgia Tech students applied forecasting methods to anticipate how economic shifts could affect the country’s middle class. The stakes here may be different from those on the Olympic track, but no less urgent: understanding the pressures that can cause families to rise – or fall – on the economic ladder. This is analytics in service of equity, long-term planning and human dignity.

From human dignity to human performance, the 2025 Edelman Award winner USA Cycling is featured in “From Underdogs to Gold Medalists: How USA Cycling Pedaled Predictive Analytics to the Podium.” USA Cycling’s Women’s Pursuit Team didn’t just pedal harder, they pedaled smarter. Predictive models shaped training regimens, optimized race strategies and informed every decision leading up to Paris 2024. In a sport where margins are measured in thousandths of a second, analytics became a competitive edge as real as the athletes’ grit. It’s a story that’s equal parts engineering, coaching and mathematics, and a perfect example of how high stakes and high data can combine for unforgettable results – and gold across the board.

When you put all of these stories together, you start to see a pattern. Operations research and analytics are not single-destination disciplines. They are methods and mindsets that can be applied to almost anything: a freight network, a race track, a city street, a rural clinic. The tools are versatile not because they are simple, but because they are adaptable – shaped by the domain they serve.

In working with the INFORMS Committee on Industry-Academia Collaborations (CIAC), I’ve noticed a shift in how the community sees itself – moving from “solvers of technical problems” to “partners in shaping systems.” That’s a powerful evolution, because systems thinking naturally connects the dots between sectors, geographies and even generations.

It’s also why, in one magazine issue, we can just as easily be talking about commercializing an optimization algorithm as we can be celebrating students tackling social mobility in South America. Why we can examine the cutting edge of generative AI one moment and cheer on cyclists the next. Why we understand that industry collaboration isn’t just a pragmatic necessity, but the crucible where the best ideas are tested and refined.

I hope this issue leaves you with the same sense of energy and curiosity it gave me while assembling it. If there’s one thing OR/MS proves over and over, it’s that solutions are rarely born in isolation. They’re forged in collaboration, in experimentation and sometimes in a sprint toward a finish line.

And the best part? The road ahead is wide open.