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AI

Background

Robotic fulfillment centers and logistic warehouses have made great strides in the last few years. Among the most significant changes, you’ll find improved workflows and much better usage of AI (Artificial Intelligence).

AI goes hand-in-hand with Machine Learning, and together they are making the biggest leap in the effectiveness of robotic automation. The ability to learn to identify patterns in the system and to use that for warehouse optimization can make all the difference between old-school warehouses and state-of-the-art ones.

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Caja Robotics’ AI

Caja Robotics created a system for easy and intuitive usage by the warehouse workers, so the entire AI process is completely powered by our software on the backend and controlled in the cloud. 

Making the best decisions for the logistic workflow is done by our AI-powered software, which manages all aspects of the fulfillment center, from replenishment through inventory management, warehouse optimization, consolidation, and returns to picking and order distribution.
Caja’s software does not divide fulfillment tasks to be handled separately but looks at the entire picture, using smart algorithms to make the best decisions for the entire warehouse operation.
Using Caja’s AMRs solution, you will be at the top of technological innovation and have the advantage you need to lead in the fulfillment centers field. 

The learning system has many advantages, especially related to future orders prediction. It identifies trends and can handle dynamic changes in the system.

AI Advantages

 
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Future
prediction

When our software plans a mission, it looks further than the current situation. Going through past tasks and general trends, the software learns the future needs of the warehouse. If, for example, the system identifies a pattern of high sales of white t-shirts on Fridays, it will automatically optimize the warehouse and rearrange it to have the product much closer to the picking stations on Fridays, saving time and energy when the items are needed.
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Real-time
definitions

Caja’s system is very dynamic with a unique ability to handle real-time changes in warehouse management. If you have an immediate order need or you wish to boost customer orders, the software will autonomously go through all possible route changes and their future effects on the entire warehouse operation and will choose the option with the best results and minimum effect on other orders.
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Navigation
optimization

The options for navigation are countless. Therefore, our learning system constantly analyzes additional robot routes to find better configurations to optimize navigation decisions.
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Clustering
information

One of the best ways to save time in the outbound process is efficient clustering.
Caja’s AI-powered software dynamically checks the commonality of orders to find the best combination. The best option will be the one creating the most commonality of orders at the picking station throughout the period of operation.
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Physical changes
in the warehouse

Our learning system sees beyond day-to-day operations. Caja’s software keeps learning from each order and from the warehouse’s successful completion of tasks. The data gathered can trigger suggestions for physical changes in the warehouse organization, which can make a real difference in warehouse efficiency.
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Dynamic Map
changes

When needed, Caja’s learning software can choose to close a section of the warehouse map or change it temporarily due to obstacles or temporary changes. This is done after going through countless new route options and choosing the one with the least negative impact on order fulfillment. After fixing the problem, the map will return to its initial condition.
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Physical bins identification

When using a Goods-to-Person bin system, there is much more to the process than bringing the entire rack to the picking station. Our robots go through many checklists in a blink of an eye when handling bins. The process includes a verification that the bin is in the right place. Our AI-powered feature goes through countless images of bins to ‘learn’ the ideal bin settings. Through this process, the system can identify damaged bins and trigger an alert. This is an important element when picking a bin or returning it and seeing if there is already a bin there.
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