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Fetch Robotics teams its warehouse bots with Vargo’s WES software

Combined system can optimize order picking for e-commerce, retail distribution, or omnichannel, partners say.

vargo fetch screen shot

Autonomous mobile robot (AMR) vendor Fetch Robotics will integrate its rolling logistics bots with warehouse execution system (WES) software from material handling solutions provider Vargo in a combination the partners say will enable optimized picking for either each, batch, or case amounts.

The launch is a response to companies’ increasing need to optimize their order fulfillment processes in order to handle rising e-commerce and omnichannel volumes, a trend that has only accelerated during the Covid-19 era, the firms said today.


And that challenge has become even tougher in recent months, because pandemic health restrictions have forced companies to keep employees socially distanced during their shifts in distribution and fulfillment centers, the firms said. In response, many DCs have applied increased automation in their systems and have integrated social distancing into workflows by dynamically reassigning tasks to specific workers and orchestrating AMR movement.

The alliance is the latest linkup in a field that has seen a rising number of integrations between vendors, including Fetch itself and the German supply chain tech provider Körber AG, and between Korber and Vargo. Likewise, a flurry of additional AMR vendors have struck deals with system integrators to build combined solutions to speed fulfillment for spikes in e-commerce orders.

San Jose, California-based Fetch and Hilliard, Ohio-based Vargo say their “integrated fulfillment solution” now offers an answer to those challenges and enables warehouses to maintain both high worker productivity and high worker safety at the same time. To do that, it uses Vargo’s Continuous Order Fulfillment Engine (COFE) software to control DC processes from material handling equipment to devices, people, and processes. By linking that flow to Fetch AMRs, the platform allows workers to spend more time picking as opposed to manually moving material throughout a facility for e-commerce, retail distribution, or omnichannel operations.

“Warehouses today are under more pressure than ever before, both in terms of operational efficiency and worker safety. To contend with steadily growing order volumes and an ongoing labor shortage, distribution and fulfillment centers must embrace smarter technology to keep fulfillment operations running,” Fetch Robotics Chief Product Officer Stefan Nusser said in a release. “COFE’s history of providing system-wide pick optimization and orchestration across different workflows and types of automation equipment makes it a perfect complement to robot-assisted picking.”

Throughout the process, Fetch’s own cloud-based software platform, known as FetchCore, will exchange information with COFE. FetchCore will focus on optimizing a fleet of robots, their behavior within the physical work environment, and their interaction with other devices in the workspace. That process includes constant analysis of sensor data generated by the AMRs—which act as “rolling IoT devices”—to detect features such as forklift near-misses, speeding forklifts, or blocked fire doors, Nusser said in an email.

At the same time, COFE optimizes a broader range of warehouse processes, including warehouse management system (WMS) data, sortation devices, automated storage and retrieval systems (AS/RSs), deliveries, shipping times, and other factors.

That flexible approach reflects Fetch’s strategy of supporting numerous WES products instead of having a robot-specific WES, he said. “Our belief is that there are many different types of companies that will be building WES software each with different specializations and also that many customers purchased or developed their own optimization engines,” Nusser said. “Further, we know that some customers want to leverage optimization with their voice, vision, or RF picking while others want to leverage the screen on the AMR, and we need to support both models.”

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