Reducing picking time by up to 30% is a significant achievement.

But for many operations, the real challenge isn't optimizing a single route. It's maintaining that optimization as the operation evolves.

Products change locations. Categories are reorganized. Layouts are modified. And each of these changes typically involves manual adjustments, remapping, or new configurations to maintain efficiency.

In practice, a large portion of operational effort ends up being dedicated to keeping order fulfillment logic up-to-date.

With AI Picking Optimizer, we've incorporated a new capability within Janis Commerce that automatically optimizes picking routes based on the actual behavior of each operation.

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The Problem with Static Layouts

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Most traditional picking optimization strategies rely on configurations based on layouts or product location maps.

The challenge is that real-world operations are dynamic.

When product locations change or a category is reorganized, these maps lose accuracy and require maintenance to remain effective.

As operations grow, this reliance can lead to increased complexity, greater operational effort, and a gradual loss of efficiency.

Furthermore, knowledge about the most efficient routes often becomes concentrated among specific operators, making it difficult to onboard new teams and limiting scalability.

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Optimization that Adapts to Your Operation

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AI Picking Optimizer was developed to solve this problem.

Instead of relying exclusively on static configurations, the platform analyzes historical fulfillment behavior to identify more efficient route patterns.

Based on this information, it automatically generates the optimal picking sequence for each order, reducing unnecessary travel and simplifying the operator's task.

All of this happens natively within the Janis Orders App, where the picker receives the suggested picking order before starting the task.

Behind this simple experience lies an intelligent layer that processes multiple operational variables to help make each route more efficient.

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Less Maintenance, More Productivity

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One of the main benefits of AI Picking Optimizer is that it reduces the reliance on constant updates.

When the store layout changes or new picking patterns emerge, the model automatically adjusts the routing logic based on information gathered from the operation itself.

This allows efficiency to be maintained without the need for permanent remapping or frequent technical interventions.

Optimization no longer relies on configuration maintenance and instead evolves with the business.

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Operational Benefits

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Reducing travel time directly impacts team productivity and picking capacity.

Key benefits include:

🔹Up to 30% less picking time.

🔹Increased dispatch capacity with the same resources.

🔹Shorter learning curve for new operators.

🔹Reduced reliance on manual configurations.

🔹Greater agility in response to layout changes.

🔹Lower operating cost per order picked.

The result is a more efficient, more flexible operation, better prepared to absorb changes without losing productivity.

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Designed for constantly evolving operations

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AI Picking Optimizer is specifically designed for retailers, supermarkets, dark stores, and omnichannel operations where changes are part of the daily dynamic.

Because in environments where products, routes, and priorities are constantly changing, optimization cannot rely on a static configuration.

It needs to adapt to the pace of the operation.

With AI Picking Optimizer, Janis Commerce introduces a new capability to help businesses pick more orders, with less operational effort and greater adaptability in the face of today's commerce challenges.

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Let's discuss your operation

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The most efficient operations don't just optimize their processes. They also incorporate capabilities that allow them to continuously adapt to change.

If you want to learn how AI Picking Optimizer can help you reduce picking times, increase fulfillment capacity, and decrease reliance on manual configurations, schedule a meeting with our specialists.

We would be happy to analyze your operation and show you how this new capability can generate a real impact on your productivity metrics.

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