Top 5 Pitfalls When Bringing OR Models into Production Software

Operations Research (OR) models are powerful. They can unlock massive efficiency gains, uncover hidden value, and guide better decisions. Yet many promising prototypes never make it into real-world use. Why? Because turning a mathematical model into production software is full of pitfalls.

Here are the top five challenges I've seen in practice:

1. Scaling from Prototype to Reality

A model that runs well on small test data may fail spectacularly on production data.

Issue: Exponential growth in variables and constraints can make runtimes balloon.
Example: A scheduling prototype works on 50 jobs but grinds to a halt at 5,000.
Fix: Use scalable formulations, decomposition techniques, or heuristics for large instances. Always benchmark on realistic datasets early.

2. Maintainability Gets Overlooked

Many OR models live in notebooks or scripts - fine for research, fragile for production.

Issue: Hard-coded assumptions, undocumented parameters, and cryptic solver calls.
Example: “Only the model's author can maintain it.”
Fix: Treat models as software. Apply clean code principles, modularization, and testing. Future developers (and your future self) will thank you.

3. Data Integration Is Harder Than It Looks

Models are only as good as the data they consume.

Issue: Production data is messy - inconsistent IDs, missing fields, changing formats.
Example: A routing model expects clean addresses but receives free-text customer notes.
Fix: Build robust ETL (Extract, Transform, Load) pipelines. Expect errors. Add validation. Data engineering is half the battle.

4. Runtime Unpredictability

Optimization solvers don't always play nicely in production.

Issue: A model might solve in seconds one day, hours the next, depending on data.
Example: Users can't wait for a truck schedule that may or may not finish by tomorrow.
Fix: Add runtime limits, fallback heuristics, and pre-computed solutions. Predictability matters more than squeezing out every last percent of optimality.

5. Ignoring the Human Factor

Even the best optimization won't succeed if people don't trust or adopt it.

Issue: Solutions look great mathematically but feel unrealistic to planners.
Example: “The model says we can run production 24/7 - but operators need breaks.”
Fix: Incorporate business rules, soft constraints, and transparency. Show not just what the solution is, but why.

Closing Thought

Building OR models is one thing. Turning them into production-ready, reliable software is another. Success comes from combining mathematical rigor with software engineering discipline and a deep understanding of business context. Avoiding these five pitfalls is a big step toward solutions that not only optimize on paper, but also deliver real impact.