📈 #55 Operations Research reimagined (Part V): practicality
Let's address the practical issue
Today’s post is sponsored by…
The DecisionOps platform helping optimization teams build more decision models instead of more decision tools.
👉 Do you want to sponsor a post in Feasible? Just reply to this email.
I occasionally watch Operations Research and Artificial Intelligence videos on YouTube.
Yes, I’m that kind of nerd 🤓.
There’s a clear difference between the two fields. You can spot it in the thumbnails. What are you more likely to watch?
1️⃣ These videos with annotated LaTeX presentations, math-filled blackboards, and 80s aesthetics...
2️⃣ Or these videos of people explaining complex things, how the technology will transform the world, and familiar tools?
Also as a nerd 🤓, I sometimes read research papers, the source of truth. What are you more likely to read?
2️⃣ “Attention Is All You Need”
I come from the Operations Research side, and I need to read the first title several times to get a probably mistaken intuition about what the authors want to tell me. The second one sparks my curiosity from the start.
This is a big issue. Today in Feasible I want you to explore:
📚 The theoretical orientation of OR
🔁 The academic-industry divide: misaligned incentive structures
🔧 Practical transformation & pioneers of practical OR
This is the fifth post in a six-part series on the issues that have prevented OR from reaching its full potential and help you avoid these patterns. If you missed them, read Part I, Part II, Part III, and Part IV.
🎧 Prefer to listen? Check out the podcast-like version at the top!
→ Ready? Let’s dive in! 🪂
📚 The theoretical orientation of OR
Operations Research emerged from practical problem-solving during World War II, where scientists optimized military operations under high-stakes conditions.
After WWII, the field quickly transitioned from wartime pragmatism to academia, bringing changes like:
🧑🏫 Military-trained researchers entered academia
🧮 Mathematical techniques became more abstract
🎓 Funding shifted from military to academic institutions
📝 Focus shifted from immediate problem-solving to theoretical refinement
This transition turned OR into a double-edged sword:
Empowering: a powerful, systematic problem-solving toolkit
Constraining: a barrier due to its complexity and lack of accessibility
How can mathematics simultaneously empower and constrain OR?
✨ Enabling capabilities
OR’s greatest strength is mathematical models 🦸♀️. They:
Provide precise problem representations
Enable systematic, scalable problem-solving
Enable algorithms for non-linear optimization or large-scale decisions
This rigor makes OR tools elegant and efficient for solving complex challenges.
🚧 Complexity limitations
However, this sophistication has a cost:
1️⃣ Implementation challenges
Complex models are hard to translate into actionable solutions.
They often require high computational power.
2️⃣ Communication barriers
Math can intimidate non-specialists, creating a challenging learning process.
Researchers often struggle to explain OR’s value to stakeholders in simple terms.
🔁 The academic-industry divide: misaligned incentive structures
The incentive ecosystem of academia and industry is misaligned, hindering OR’s practical application.
The disconnection between academic and industry approaches is a complex systemic issue. This issue is rooted in fundamentally different evaluation metrics, success definitions, time horizons, and value creation mechanisms.
Academic incentives:
📚 Publication pressure distorts the focus on real-world implementation in the publish-or-perish paradigm. It rewards complexity over practicality, incentivizing incremental theoretical contributions. Academic success is often decoupled from practical impact.
🚀 Career advancement is valued through mathematical sophistication, citation indices, and research grant acquisition. This creates a feedback loop that marginalizes researchers focusing on implementation, disconnecting research from real-world problem-solving.
Industry incentives:
⚙️ Immediate problem solving: the industry seeks practical, implementable solutions with measurable outcomes, so simplicity is a core value.
⏱️ Quick return on investment (ROI): minimal delay between solution design and implementation so OR practitioners create a quantifiable business impact.
I see those differences as:
Where:
↔️ Horizontal axis: theory vs practice. Further left: more theoretical abstraction and mathematical sophistication. Further right: more practical implementation and actionable solutions.
↕️ Vertical axis: value creation mechanisms. The further down, the more short-term and immediate value and ROI. The further up, the more long-term and potential transformative impact.
This difference creates four quadrants:
Theoretical potential: deeply abstract mathematical models with unrealized transformative potential. Researchers prioritize intellectual rigor, mathematical elegance, and foundational knowledge, often working years ahead of practical application. An example is a research center dedicated to complex systems.
Innovative pragmatism: applied research with a visionary long-term perspective, bridging theoretical insight with practical potential. This quadrant represents the ideal point of academic-industry collaboration, where sophisticated theoretical frameworks are designed with future real-world applications in mind, like an MIT-IBM Watson AI Lab collaboration.
Theoretical exploration: speculative academic research focused on immediate theoretical insights with limited practical application. This quadrant captures narrowly focused studies that solve specific academic puzzles, generate new mathematical techniques, or explore complex theoretical constructs, like the Operations Research department at Stanford University.
Immediate solutions: practical, rapidly implementable techniques addressing current operational challenges. This quadrant represents industry-driven approaches prioritizing quick, measurable results, minimal complexity, and immediate improvements. Practitioners develop straightforward, robust solutions for quick deployment, like Amazon’s Operations Research team.
There are several ways to reconcile positions by considering collaborative strategies or realigning incentives.
→ The goal shouldn’t be to eliminate mathematical rigor but to make mathematical techniques more accessible, develop intuitive interfaces, create translation mechanisms, and emphasize practical application. This will return to the original roots of OR.
🔧 Practical transformation & pioneers of practical OR
I see some key strategies to follow in the field for this to happen:
🏫 Practical curriculum design: shift from theory-heavy to application-focused education
🛠️ Tool development: create user-friendly, intuitive optimization platforms
📖 Storytelling: develop compelling use cases that demonstrate real-world impact
🤝 Cross-disciplinary collaboration: integrate OR with ML techniques
In addition to the key strategies, OR practitioners must develop strong communication and storytelling skills.
OR professionals can break down barriers, engage stakeholders, and drive real-world impact by crafting compelling narratives and visualizations that make complex concepts accessible.
We need to communicate OR effectively, or we won’t transform complexity into clarity.
🌟 Leaders who demonstrate rather than just speak stand out
I see at least 4 people actively sharing their thoughts on LinkedIn. They have some things in common:
🎥 Visualization over abstraction: they use storytelling to explain complex concepts.
🚀 Practical demonstration: they focus on solving real-world problems, providing immediate, understandable value.
🌍 Community engagement: they encourage knowledge sharing and create supportive ecosystems for OR practitioners.
Their approach to communicating OR-related stuff works because they follow one simple rule:
Show, don’t tell.
This removes the intimidation of mathematical complexity, demonstrates OR’s practical value, creates accessible and engaging content, and bridges theory and practice.
👨🏫 So... Who are they?
Dr. Oskar Schneider creates content around specific use cases, highlighting the practical benefits of optimization techniques and creating a narrative around OR beyond mathematics. I love his mini-interviews with top OR professionals.
Mehdi Nourinejad develops interactive planning and operations tools at interactive-or.net. He is a true driver of transforming theoretical concepts into tangible, visual experiences for users to explore optimization techniques hands-on.
Philip Welch develops Open Door Logistics, a tool for solving Vehicle Routing Problems. He creates user-friendly interfaces for complex optimization challenges. His tool and content demonstrate how OR solves real-world logistical issues.
Geoffrey De Smet: as the CTO of Timefold, a tool for solving complex planning problems. His posts focus on user-friendly optimization solutions. He connects theoretical optimization with practical application.
These practitioners represent a new wave of Operations Research professionals who understand that:
🚫 Complexity isn’t a virtue
🌟 Accessibility drives adoption and understanding
✅ Practical impact matters more than mathematical elegance
The OR community can transform from an arcane academic discipline to a dynamic, problem-solving powerhouse by following their lead.
🏁 Some conclusions
The future of Operations Research lies not in mathematical complexity, but in simply and effectively solving real-world problems.
OR can transform from a theoretical discipline to a practical problem-solving powerhouse by embracing practicality, developing accessible tools, and bridging academic-industry divides.
These theoretical sophistication and practical utility are not mutually exclusive but can be complementary with the right mindset and tools.
Complexity impedes action. The ultimate sophistication is simplicity.
Let’s keep optimizing,
Borja.
PS: Who else do you follow on LinkedIn that spreads the OR word?
How efficiently you actualize the value of optimization into a live business process matters just as much as the modeling tools and solvers you choose.
Nextmv is a DecisionOps platform that simplifies the path from research mode to real-world impact:
👟 Prototype and productionalize models faster with integrations (HiGHS, Pyomo, OR-Tools, Hexaly, AMPL, Gurobi, scikit-learn) and a common API layer
🧪 Innovate, iterate, and manage rollout risk with a suite of model testing tools and results visualizations
🙌 Manage team capacity and stakeholders by increasing transparency into model development to build confidence
👀 Leverage turnkey infrastructure and monitoring that scales with your needs and provides performance observability
Bring your custom model code to Nextmv or explore with a quickstart template.
