The gap between theory and practice in supply chain optimisation

Just read a fascinating academic paper from Chalmers University that dives deep into supply chain optimization challenges. While the research is highly technical, it reinforces something we see daily in our work with global logistics companies.

The paper highlights how complex mathematical models can solve optimization problems, but here's what it doesn't address: the real-world gap between theoretical solutions and practical implementation.

In our experience working with Fortune 500 manufacturers and logistics giants, the biggest challenge isn't finding the optimal solution — it's getting accurate, real-time data to feed into these models. You can have the most sophisticated optimization algorithm in the world, but if your inventory data is off by even 10%, your results become meaningless.

This is where technologies like computer vision are game-changing. Instead of relying on manual counts or outdated systems, companies can now capture precise dimensional and inventory data automatically. The result? Those beautiful optimization models actually work in practice, not just on paper.

The research community is pushing the boundaries of what's mathematically possible. Now it's up to us in industry to bridge that gap with practical, implementable solutions.

What's been your experience with implementing optimization models? Where do you see the biggest gaps between theory and practice?