What Is Automation in Logistics?

Build resiliency, efficiency and visibility into logistics processes with automation.

Logistics automation leverages AI, IIoT, robotics and cloud computing technologies to optimize supply chain operations. Electronic Data Interchange (EDI) serves as the foundation for real-time data exchange in critical processes such as barcode printing, goods picking, packaging and storage reallocation. Robotic process automation (RPA) is also utilized to enhance efficiency in automotive assemblies, warehouse operations and supply chain management.

For seamless logistics automation, a scalable integration platform like the SEEBURGER BIS Platform with EDI capabilities is required for coordination between ERP, WMS, TMS and other core systems. BIS becomes the resilient central hub for managing automated processes and data flows across the entire supply chain.

Logistics involves the precise coordination of a company’s supply chain, and logistics automation depends upon digitalization in the logistics industry. By using digital technologies to perform tasks with minimal human intervention, companies can increase efficiency and reduce errors in the movement and storage of goods.

Key technologies for logistics automation include:

Artificial Intelligence (AI) and Machine Learning (ML)

These technologies enable systems to learn from data, make predictions and optimize processes. In logistics, AI and ML are used for demand forecasting, route optimization and predictive maintenance.

Industrial Internet of Things (IIoT)

IIoT devices collect and transmit data from various points in the supply chain, providing real-time visibility and enabling better decision-making. Examples include RFID tags, GPS trackers and environmental sensors.

Robotics

Physical robots are increasingly used in warehouses and distribution centers for tasks such as picking, packing, and sorting. Advanced robots can work alongside humans to increase productivity and safety.

Cloud computing and big data analytics

These technologies enable the processing and analysis of vast amounts of data generated by logistics operations, providing insights for optimization and strategic planning.

The integration of these technologies creates a synergistic effect that accelerates automation in logistics. For example, IIoT devices collect real-time data that is fed into AI and ML systems, leading to better demand forecasting and more efficient routing. At the same time, robots use insights from cloud computing and analytics to minimize downtime. This interconnected system allows information to flow smoothly between the technologies, helping companies adapt to changing market demands. Overall, integrating these technologies with a SaaS integration platform creates a more agile logistics landscape.

Imagine a world where business transactions flow seamlessly, powered by a silent language of numbers and codes. That's the reality of EDI. EDI streamlines business communication—everything from barcode printing to replenishment. Take retail order processing, for example. When an EDI translator receives a purchase order, it's automatically recognized by its unique transaction number (850). This digital code contains the buyer's identity, ordered items and prices all in one message. It’s more than just a data transfer. The EDI system decodes the 850 message to harmonize information across systems and applications.

But EDI's decoding magic doesn't stop there. Its full power is realized during the automated exchange (sending and receiving) of these coded EDI messages. In logistics process automation, EDI supports real-time data exchange, enabling effortless communication between systems, applications and people. Implementing a cloud-based integration platform for digital logistics with EDI capabilities is necessary for achieving high levels of automated speed and accuracy in the following critical logistics processes.

Barcode printing

EDI automation in barcode printing involves the transfer of data between systems to generate accurate and timely barcodes. Here's how it typically works:

Order information is received via EDI from customers or internal systems.
The EDI system translates this data into a format compatible with the warehouse management system (WMS).
The WMS processes the order and generates barcode data.
This data is sent to barcode printers via EDI, ensuring real-time accurate label creation.
Confirmation of successful printing is sent back through the EDI system.

When barcodes are printed accurately and scanned correctly with EDI automation, they ensure that products are tracked in real time for continuous movement through the supply chain. This precision minimizes the risk of costly errors, such as mislabeling or shipping the wrong items, which can lead to delays and customer dissatisfaction. Accurate barcodes also facilitate faster fulfillment processing, reducing customer wait times.

Robotic Process Automation (RPA), which uses bots to automate repetitive tasks, has numerous applications for automation in logistics. These applications include automotive assembly lines, warehouse operations and supply chain management, as follows.

Process optimization: RPA can automate the sequencing of parts delivery to assembly lines, ensuring that the right components are available at the right time.
Quality control: Automated systems can perform visual inspections and data analysis to detect defects early in the production process.
Inventory management: RPA can automatically track parts usage, trigger reorders and optimize stock levels based on production schedules.

Order processing: RPA can automate the extraction of order details from various systems, validating information and initiating fulfillment processes.
Inventory tracking: RPA can assist with the continuous monitoring of stock levels, automating cycle counts and the reconciliation of physical and digital inventories.
Shipping and receiving: RPA can automate the creation of shipping labels, customs documentation and tracking updates across multiple carrier systems.

Demand forecasting: RPA can automate data collection from various sources, apply predictive analytics to identify trends, generate accurate demand forecasts and then continuously update them as new information becomes available.
Supplier management: RPA bots can track vendor performance, manage contracts, automate purchase orders based on inventory levels, process invoices and handle routine supplier communications.
Risk assessment: RPA can continuously monitor and analyze supply chain data to identify potential disruptions, assess their impact and trigger automated alerts or contingency plans.

Automation in the logistics industry is driving the development of new methods of transporting and delivering goods on land. Last-mile deliveries by robots and drones, automated guided vehicles (AGVs) and autonomous trucking highlight the connection between logistics automation and innovation.

Last-mile delivery automation

Last-mile delivery automation, digitally enabled through integration, has transformed the final stage of the logistics process with increased efficiency and cost-effectiveness. For IT professionals in the logistics industry, this represents an exciting opportunity to integrate cutting-edge technologies into existing systems.

Delivery robots and drones are at the forefront of innovation in logistics automation for last-mile deliveries. These autonomous devices require sophisticated software systems for navigation, obstacle avoidance and secure package handling. IT professionals need to focus on developing robust APIs for legacy system integration and order management systems, real-time tracking solutions and customer notification tools.

Key considerations for IT teams include:

Implementing secure communication protocols to protect delivery data
Developing scalable cloud-based solutions to manage fleets of delivery robots and drones
Creating user-friendly interfaces for both operators and end users
Ensuring compliance with local regulations and privacy laws
Implementing AI and machine learning algorithms for route optimization and predictive maintenance

By successfully integrating these technologies, businesses can achieve significant improvements in delivery speed, accuracy and customer satisfaction.

Autonomous trucking

As an emerging technology, fully autonomous trucks are not yet widely deployed. They are still in development and testing phases, with the goal of automating long-haul transportation on public roads. Many trucks in the logistics industry, however, already feature advanced driver assistance systems (ADAS) that automate certain aspects of driving.

Daimler Truck expects to be using autonomous trucks on USA highways by 2027. The company has already successfully been moving freight with autonomous trucks on its test route between Phoenix and Oklahoma City.

Benefits of logistics automation for autonomous trucking:

Increased safety by reducing human error
Improved fuel efficiency through optimal driving patterns
Potential for 24/7 operations, increasing asset utilization

Challenges of logistics automation for autonomous trucking:

Regulatory, legal and security considerations
Public acceptance and trust
Integration with existing infrastructure

Similar to drones and delivery robots, autonomous trucking technology requires integration with existing logistics systems, including transportation management systems (TMS), WMS and other supply chain software for:

Real-time data exchange with location tracking and ETA updates
Integration with load planning and route optimization systems
Automated dispatch and fleet management
Handling exceptions and rerouting in case of unforeseen events

Automated Guided Vehicles

While both AGVs and autonomous trucks represent advancements in logistics automation, they serve different purposes and operate in different environments. AGVs operate in controlled warehouse environments, compared to autonomous trucks that drive on public roads.

AGVs are a mature technology used in industrial settings and warehouses for material handling. AGVs move, tug and tow cartons and pallets from one location to another using sensors that follow a set path. Examples of AGVs include forklift AGVs for moving pallets, tugger AGVs for pulling trailers and carts, and unit load AGVs for transporting heavy loads.

Benefits of logistics automation for AGVs:

Increased efficiency and productivity in material handling
Reduced labor costs and improved safety for workers
Flexible and scalable operations with 24/7 operational capability

Challenges of logistics automation for AGVs:

Complexity in programming and maintaining software systems
Need for standardized communication protocols
Integration with existing warehouse management systems

The common challenge for logistics automation with drones, delivery robots, AGVs and autonomous vehicles is integration with existing infrastructure.

Logistics process automation aims to eliminate manual data entry errors by automating data exchange for critical documents, such as purchase orders and shipment notices, with SEEBURGER B2B/EDI capabilities and prebuilt connectors.

To achieve this, logistics process automation depends upon the coordination of processes between ERP, WMS, TMS and other core logistics systems. This type of coordination requires a platform for scalability, flexibility and seamless integration. Seamless integration involves connecting current logistics systems, data migration strategies and data governance policies to maintain data quality and consistency.

An integration platform enables organizations to:

Conduct thorough risk assessments of automated systems
Implement strong cyber security measures to protect against potential threats
Ensure compliance with industry regulations and data protection laws

SEEBURGER's B2B Solution emphasizes automation and workflow optimization, helping businesses streamline their B2B/EDI processes and reduce manual intervention. The solution includes the SEEBURGER BIS Platform with intelligent workflow management tools that automate repetitive tasks, minimize errors and improve operational efficiency.