Discover the best use-case scenarios for Autonomous Mobile Robots

February 27, 2019

Automation is not just a trend, but a “must do” in today’s logistics and manufacturing environments. The sectors face labor shortages and rising wages, as well as changes to work safety regulations, which constrain operations and growth potential. For every 6 supply chain, vacancies there’s only one applicant, with a downward trend.

Gideon Brothers’ pallet handling robot ameliorates this trend by offering a plug-and-play, self-driving alternative to the transfer forklift and similar vehicles that require a driver. Moreover, it does so at a cost that is equal to 69-76% of the cost of a forklift and driver across three shifts, and it offers customers the potential for additional data acquisition opportunities – such as inventory or security – to further offset deployment costs.

Thanks to its visual perception, Gideon Brothers’ robot performs better than those based on LiDAR (laser) sensors for navigation in typical dynamic manufacturing and warehouse environments. Below are the most common use-cases.

Goods-to-Person Automation

Autonomous mobile robots transport goods from their regular storage place to a central commissioning area where the picker extracts the required goods that have been ordered by the customer. Once the goods are extracted, the robot returns the pallet to its storage place.

Who this is for: Companies that want to automate picking, but want to avoid capital-intensive fixed automated storage systems (e.g. carousels or shuttle systems). Most scope for efficiency gains is

Efficiencies gained: Pick time & number of orders processed increased. Eliminating the need to walk from one end of the warehouse to the other in order to fill orders reduces the time required to fulfill those orders – leading to higher volume throughput.

Person-to-Goods Automation

The majority of distribution centers rely on Person-to-Goods picking approaches. In this approach, workers go out onto the floor to collect what they require to fulfill orders. In typical Person-to-Goods scenarios, they spend up to 60% of their time traveling and 40% picking. Improved efficiency is achieved primarily by reducing travel time and increasing order accuracy.

‘Lead me’ approach

The robot fleet is integrated with a customer’s Warehousing Management System (WMS) and receives its picking orders. One robot is assigned to one picker. The robot leads the (human) picker from one pick location to the next. A tablet is affixed to the robot, displaying the goods required to fulfill the order.

Who this is for: Warehouse operations that want to keep the Person to Goods picking system and can integrate their Warehouse Management System with the robotic system. Robots increase efficiency and accuracy and allow the worker free movement, without the need to lug carts or operate forklifts.

Efficiencies gained: Travel time is shortened, especially in operations where performance indicators for travel time between pick locations are below industry benchmarks. Order accuracy is raised. Further time savings are achieved by cutting out the last ‘leg’ of travel in the fulfillment process – once an order is completed, the robot proceeds to the dispatch area by itself, and the picker proceeds with the next robot to fulfill the next robot.

‘Follow me’ approach

A reverse scenario to “Lead Me” is “Follow Me.” In this scenario, the robot follows a picker from one location to another.

Who this is for: Companies that want to continue using their existing order fulfillment system without the need to integrate autonomous robots with Warehouse Management Systems. Robots still increase efficiency, allow tracking of performance indicators, and would allow the worker free movement, without the need to lug carts or operate forklifts.

Efficiencies gained: Time savings are achieved by cutting out the last ‘leg’ of travel in the fulfillment process – once an order is completed, the robot proceeds to the dispatch area by itself, and the picker proceeds with the next robot to fulfill the next robot. Efficiency is further raised indirectly, thanks to better tracking of performance indicators (the robot can record data such as route and travel time for each worker), also unveiling room for further improvements in the entire process.

‘Swarm me’ approach

Pickers are assigned to a warehouse zone and they are detached from mobile robots. The mobile robot is integrated with Warehousing Management System from which it receives orders. The robot travels to pick locations where a nearby picker sees a task on the robot’s screen, picks the good(s) and adds it to its order.

Who this is for: Companies that are looking for ways to cut worker travel to a minimum by placing them in zones.

Efficiencies gained:  Travel time for workers is reduced to a minimum, as each worker stays in a single zone and robots move from one zone to the next. Order accuracy is increased.

‘On demand’ transportation approach

Transportation actions are triggered by a mobile graphical user interface (manually by the human). Autonomous mobile robots transport pallets and other objects from ‘start’ location to ‘end’ location’ (both locations could be defined in mission planner through the mobile GUI).

Who this is for: Companies that identified specific parts of their operations for automation but that do not require (direct) integration with their Warehouse Management Systems.

Efficiencies gained: The need for the human operator is cut entirely for specific tasks.