Autonomous logistics is defined as the use of technology devices to execute supply chain processes or movements of vehicles, freight, equipment, people, information, or resources without direct human control.

Autonomous Logistics vs Automation

Compared to automation, autonomous logistics technology collects data and applies the information in ways that improve the performance or efficiency of the machine activity. In short, autonomous processes become smarter based on information collected during operation.

Automation uses a well-defined, static set of parameters to execute tasks. Some decision-making is supported by predetermined information, but an automated system performs specific tasks based on original inputs and outputs.

Autonomous Vehicles in Logistics Networks

Autonomous or “self-driving” vehicles process large amounts of information to make rapid operational decisions without human involvement. In freight transportation networks, autonomous trucks are being tested in both long, over-the-road journeys and for last-mile routes with multiple, frequent stops. In these cases, human drivers still work as an on-board co-pilot to the autonomous technology.

Autonomous vehicles in supply chain warehouses or other controlled environments execute a variety of tasks. Forklifts, picking and packing processes, and other activities supported by autonomous vehicles and robotics are increasingly used in fulfillment and freight management. Drones operating in an autonomous logistics information system can complete product inventory scans or execute small package deliveries. These are capable of smooth operation without the need for a human driver or operator.

What is autonomous logistics management?

Autonomous logistics management relies on artificial intelligence and machine learning to evolve smart transportation movements and improve supply chain processes.

Autonomous logistics technology adjusts delivery routes and predicted arrival times based on real-time conditions, such as weather, traffic, order cancellations, new orders, or driver exceptions. Subsequently, changes occur for any related automated tasks, such as instant push notifications and delivery alerts. This improves communication among supply chain partners.

Logistics management of automated tasks uses robotic process automation (RPA) to complete manual tasks. For example, automation supports digital freight matching, load management, shipment execution, freight claims management, invoice audit and payment, and other necessary supply chain management functions.

Benefits of Autonomous Logistics Supply Chain

In an autonomous supply chain standardization, connectivity, and intelligence supports the ability to anticipate events, develop plans and improve logistics. Benefits derived from this functionality include:

Save time and increase productivity by eliminating repetitive manual processes through robotic process automation.
Increase supply chain agility by anticipating and adjusting a delivery route, process plan, or other exception before a disruption occurs.
Reduce costs in countless ways:
- Deploy machines for tasks that are routinely plagued by human error.
- Move and deliver goods more quickly and efficiently.
- Optimize routes to limit vehicle miles or avoid time lost in traffic.
Improve planning by leveraging technology to automate supply chain decisions and transportation execution based on real-time data.

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How MercuryGate TMS supports Autonomous Logistics Technology

MercuryGate gives shippers, logistics services providers (LSPs), brokers, and 3PLs the power of autonomous logistics and process automation across their transportation management and logistics supply chain. This improves cost control, optimizes freight movements, and enhances service to end customers.

Robotic Process Automation for Transportation Workflows

Use rules-based processes and dynamic workflows to complete repetitive, manual, and error-prone activities, as well as those that are seasonal or time-critical to save keystrokes, reduce errors and expedite supply chain movements.

Automation in Shipment Execution

Advanced algorithms and rulesets plan loads, direct carrier selection, tender shipments, track charges, manage and store contracts, audit freight invoices, manage returns, and complete claims filing. Combined and supported by human-managed exceptions, automation supports freight cost reduction.

Autonomous Rerouting in Final Mile Delivery

While shipments are in transit, dynamic rerouting and optimized response based on changing weather, traffic, order, or delivery conditions can help avoid service failures and tarnished customer experience.

Dynamic Control and Visibility across Modes

Visibility across the entire supply chain enables proactive freight management. It supports your ability to select and manage the best transportation mode for your service needs, whether it is a truckload or less-than-truckload journey in a driver-operated truck, a freight cargo movement by robot, or a final mile delivery by drone.

Find out how autonomous logistics and routing optimization helps a national parcel delivery service achieve same-day delivery goals, improve on-time delivery performance and exceed customer expectations.