Why smart warehousing requires more than just a good algorithm

The strategic challenge

Warehouses are under increasing pressure. Volumes fluctuate, labor is scarce, and service levels must not drop. At the same time, costs need to be kept under control.

Many organizations look to automation, new software, or AI for solutions. However, the reality is often more complex: warehouse operations are not merely a collection of isolated processes, but an interconnected system.

That is precisely where the greatest challenge often lies.

Warehouse optimization is more complex than it seems.

For instance, a seemingly minor change in slotting—assigning products to picking locations—can have significant consequences for picking routes, lead times, and congestion in popular aisles. The same applies to decisions regarding order batching, picking zones, or picking sequences. Arranging products from heaviest to lightest, or according to store layout, might make loading roll containers more efficient, yet simultaneously result in longer walking distances or less efficient staff utilization.

Moreover, no two warehouses are alike. This variation means that solutions must always be viewed within the context of the specific system in question.

Models versus reality

When modeling warehouse processes, we combine operational knowledge with data from systems such as a WMS. This data reveals, for example, how picking times actually vary depending on the order, the employee, or the time of day.

In practice, these times depend on a multitude of factors:

Product assortment characteristics

Employee experience

Aisle congestion and busyness

Differences between shifts

Daily fluctuations on the shop floor

Consequently, a model that fails to account for this context may present a distorted picture of what is truly optimal. That is precisely why an understanding of the actual operation is essential for effective optimization.

AI, optimization, and domain expertise

The growing interest in AI within the logistics sector makes this issue even more relevant. While AI can be a valuable asset to daily operations, its full potential cannot be realized without an understanding of processes and deep domain knowledge.

That is exactly why, at CQM, we combine mathematical optimization with data analysis and a profound understanding of logistics in practice. During our training programs, consultants delve not only into the mathematical models and algorithms we implement for clients but also into the unpredictable realities of warehouse operations. That is why, during the second module of the training, we visit a warehouse in Tilburg. Observing operations on the shop floor clarifies exactly where variations, constraints, and dependencies arise. This practical experience is crucial for accurately interpreting data and building models that actually work in the real world.

Why CQM invests in domain knowledge

At CQM, we believe that effective models start with a solid understanding of the operational domain. That is why we deliberately invest in domain training for our consultants. During the Smart Warehousing training, colleagues delve into the processes, terminology, and real-world examples found in logistics operations.

This combination of mathematical modeling and a deep understanding of processes helps us develop solutions that are not only theoretically optimal but also truly effective in practice.

Smart warehousing is not just about algorithms or data. It is about understanding a complex system of processes, people, and constraints.

Anyone who understands that system can improve it. And that is precisely where the power of data-driven optimization in warehousing lies.