Is Operations Research living up to its promise?

A bold but valid question to kick off the second edition of Planned. OR has long been celebrated as a powerful space for tackling some of the toughest planning problems, like optimizing resource allocation, scheduling shifts, and routing deliveries.

Its potential is clear: better planning can cut costs, boost efficiency, and even improve lives. Yet, despite decades of development, OR remains underutilized. The reason? Persistent gaps that make it difficult to translate theory into real-world impact.

Let’s unpack these challenges and explore why OR hasn’t fully delivered on its promise.

The complexity barrier: slow, pricey, risky

Applying traditional OR expertise requires advanced mathematics, constraint programming, and algorithmic know-how. This is often limited to a handful of specialists.

For businesses, this means lengthy, costly projects with no guaranteed payoff.

The gamble: invest heavily in talent and time, only to face uncertainty about whether the solution will work. Many organizations don’t consider OR, deterred by its complexity and the perception that the juice isn’t worth the squeeze.

Time-to-Value: A distant horizon

OR projects come with a painfully long time-to-value.

Especially when building and refining models to handle constraints. Businesses wait months or even years to see if the solution works in production. What happens when a model fails to hold up? By then, significant resources are sunk, and the risk of failure becomes all too real.

Scalability: Where theory meets reality

OR’s theoretical elegance often crumbles under the weight of real-world scale.

A model that optimizes 50 deliveries might collapse when tasked with 50,000. The same goes for scheduling shifts or allocating resources across sprawling operations. This scalability gap is a major roadblock to widespread adoption.

Real-time agility: Stuck in the past

AI fever has the world locked into an efficiency craze. The OR space is not exempt from that, as shown by the clamor for planning tools to adapt on the fly. The capability to replan in real-time is a critical weakness in a landscape where agility is quickly becoming a non-negotiable.

Time to rethink the status quo

We’re not the only ones asking hard questions about OR. And we’re glad we’re not. The cracks in the current methods are showing: too complex, too rigid, too slow to keep up with real-world demands.

What’s emerging is promising: smarter, leaner, more scalable ways to plan and optimize. We care deeply about this space, and we talk about it because we believe there is much to improve.

So here’s our open question to you: What’s holding OR back? What would it take to unlock its full potential?

Reply to this email, we'd love to hear your thoughts or have you challenge ours.

AI in EU: How to jump ahead

Geoffrey De Smet on the mainstage of SuperNova Conference

Question: How can Europe catch up to the US and China in the AI space?

Geoffrey's key points:

When people think of AI, they usually picture ChatGPT or voice assistants. These are interfaces powered by LLMs, mostly led by the US and China.

Those models have deeper roots, calling other systems to solve requests behind the curtains.

When it comes to 'invisible AI' like PlanningAI, Europe is not lagging behind at all. Four out of the biggest five players in the OR space are European, and the market is still young.

Despite regulations, fragmented markets and cautious funding, Europe has the brain power, ethics and deep sector knowledge to catch up.

But to jump ahead, execution is needed: product over research, ambition over caution, value over hype.

PlanningAI in motion

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