Overview
The Client is a company from Israel.
The project offers a robotic mini-load ASRS
for goods-to-person order fulfillment and
robotic warehouse automation.
Challenge
The challenge was to create an adaptive, scalable and responsive
system, using ML.
The robotic system can be installed in almost any warehouse since
it adapts to the warehouse and the existing infrastructure.
We made it possible to easily integrate through documented APIs
with the customer’s Warehouse Management System (WMS) for
the system. The responsive system maximizes throughput by
continuously optimizing the warehouse according to present and
predicted orders. Also, project system can respond to sudden and
unexpected challenges as it can easily be switched to manual
picking and back to robotic picking in a matter of minutes.
Solution
- Fleet management by a unique 4D algorithm;
- Fast robots and fastest paths computed in real-time;
- No local network infrastructure and scalable computational power;
- Advanced trends algorithms for inventory and order management;
- Unique navigation management by ML (machine learning);
- Ensuring integration with some of the leading WMS systems.
Result
Our professional IT team successfully completed the challenge and developed a robotic fulfillment solution for unit picking that increases order picking efficiency and storage capacity of warehouses, whilst improving the work environment of warehouse employees.
The system, due to its highly scalable and rapidly deployable modular architecture, can be gradually expanded and cope with fluctuating demands. This scalability allows customers to change the warehouse layout quickly and easily add additional parts, sections or warehouses as needed.
