📈 #74 The engineering side of Operations Research: tactics to amplify your modeling value through better software practices

Ensuring reliability, maintainability, and business impact

How many times have you read that Operations Research has three pillars?

You name them: optimization knowledge, software engineering skills, and business acumen.

I’ve repeated this multiple times.

But I never provided more details.

So today I want to focus on engineering and how learning from software developers can make you a faster, more reliable OR professional.

We’ll cover:

• 🔧 Why OR needs better engineering

• 📐 3 Software Engineering strategies for OR Engineers

• 🔭 Beyond code: becoming better engineers

Are you ready? Let’s dive in… 🪂

🔧 Why OR needs better engineering

Many OR projects succeed in the mathematical modeling phase but fail in implementation.

This is usually because they're treated as academic exercises rather than software products.

The mathematical model might be elegant and theoretically sound, but without proper software engineering practices, it remains trapped on a researcher's laptop… Inaccessible to actual business users and unable to handle real-world data variations.

This fact leads us to the works on my laptop syndrome.

When an optimization model only "works on your laptop", several critical issues arise:

• Knowledge transfer is difficult when the original developer leaves

• Updates require manual intervention from the original developer

• No automated testing exists to verify results with new data

• Business users can't access the solution independently

• Models aren't connected to production data systems

• Scaling to larger datasets becomes problematic

Thus, your work becomes a proof of concept instead of a business asset.

And there are hidden costs of poor software practices in OR, like technical debt (messy, unstructured code that’s hard to update), fragile models (one change breaks everything), maintenance overhead (only the original author can debug it), and loss of trust (stakeholders lose confidence when bugs happen).

In short:

A brilliant model with bad code dies quietly.

Operations Research isn't just about solving hard problems, it's about solving the right problems reliably under practical constraints.

The difference between a good OR solution and a great one isn't just mathematical elegance but the ability to consistently deliver value in production environments with changing data, evolving business needs, and multiple stakeholders.

Reliability and usability often matter as much as theoretical performance.

Traditionally, OR Engineers were modelers working in isolation or research teams, while Software Engineers handled deployment, systems, and tools.

The gap between modeling and software caused friction: models were hard to integrate, and engineers struggled to understand solver logic.

But now OR Engineers are expected to bridge both worlds and speak both math and code.

How can we, as OR Engineers, improve our value delivery?

📐 3 Software Engineering strategies for OR Engineers

I’m sure there are more strategies, but I wanted to highlight three.

Embrace them to be ahead of 90% of OR Engineers: