RPA in Logistics: Part 1. Business Value and Use Cases

Robotic Process Automation represents one of the fastest-growing segments in the global enterprise software market. More businesses than ever resort to custom AI development to improve their day-to-day operations. The reason for this growth is simple: RPA is quick to implement and delivers significant cost savings and process improvements with minimal lead time.

However, RPA is not a universal solution. It is most effective in environments with high volumes of straightforward, well-structured manual transactions that are not overly complex. Fortunately, many processes within logistics and supply chains fit this description perfectly.

Why Logistics Companies are Embracing RPA

The logistics industry depends on its ability to deliver products at high speeds while maintaining exact precision and operational excellence. Organizations continue to experience workflow delays because they perform numerous repetitive tasks that require human intervention. Standard operational tasks, including order processing and freight documentation management, are prone to human error, leading to substantial operational delays. 

The implementation of Robotic Process Automation (RPA) solves operational problems by creating automated systems that handle rule-based digital tasks, thereby reducing logistics operational complexity. Put simply, RPA logistics means automating rules and repetitive human tasks using software bots that simulate them. These bots can be used for all activities related to order processing, shipment tracking, and billing. They run nonstop, eliminating manual errors and significantly reducing turnaround times.

With automation, business operations become faster and more efficient, and they become second priorities (the first being plans to rectify/solve major problems before they blow up). As a result, using AI in business process automation frees up the customer service team, allows the budget to be reallocated to modernizing warehouse operations, for example, and helps employees do their jobs better. In the end, this leaves time and space to design a business expansion plan that outshines the competition and attracts clients. 

Recent Trends in Robotics

There is a constant flow of news in the field of warehouse automation, and the following speaks volumes to the anticipated direction of automation in fulfillment operations. 

• In the UK, Ocado has developed the On-Grid Robotic Picking (OGRP) system, which integrates AI robotic arms that can adjust in real time to different and fragile goods. These arms have been demonstrating grocery fulfillment productivity. They learn to pick exemplar human demonstrations.
• In 2025, Ocado will deploy the Porter AMR, a novel autonomous pallet-moving robot. The AMR-Porter is designed for flexible automation to alleviate congestion during automated pallet manipulation of bulk containers.
• The Singapore-based Walmart Canada customer, GreyOrange, continues to enhance AI in robotics, particularly for human-robot systems in picking and sorting. Its GreyMatter system coordinates multiple agents to achieve superior dispatch times.
   

Seeing how market leaders are implementing RPA in warehouses helps understand the business value of RPA in warehouses. So, let's unfold RPA's key benefits for businesses. 

Key Business Advantages of RPA in the Logistics Sector

One of the best things about RPA is that organizations can achieve business results with very little upfront investment compared to other digital transformation initiatives. This is because RPA is implemented on top of existing software and processes and doesn't require replacing entire operating models or refactoring legacy systems.

The RPA benefits in logistics will depend on what processes are being automated, but generally, the technology brings the following benefits:

• Supply chain focus shift: Routine supply chain activities can be automated, freeing teams to focus on core strategic objectives in customer service, marketing, and sales.
• Better customer experience: Automation means quicker order completion and delivery. RPA will help you do more than just meet your customers' expectations; it will keep even the most demanding clients happy.
• Employees' output: With RPA, your employees will have more time to realize their potential and contribute to your business. 
• Flawless document processing: Just think how great it would be if you never had typos or other human errors in your paperwork. Software bots usually perform with 100% accuracy when extracting, entering, creating, and processing data.
• Fast development and rollout: Implementing RPA for higher-end processes tends to be significantly faster than building entirely new, standalone RPA platforms. It is because every bot is tailored to a specific task. Gradually, even as your processes grow and your company brings on more bots (some businesses use over 50), the installation is gradual, and you aren't asked to make a significant up-front investment. 

The Role of Robots in Business Process Delivery

First, we'd like to stress that, in logistics, many processes still depend on manual workflows and legacy systems. This resistance to change is a major challenge in the logistics industry. That is why we decided to highlight how advanced tech can help reshape processes into their most effective form.

See, in logistics, "delivery" of a business process is often a complex sequence of data-driven and physical operations: from receiving an order by e-mail to shipping a product. Robots aim to simplify this chain by taking over the repetitive, rules-based portion of the pipeline. 

RPA bots can access data systems, gather data, and execute rules-based transactions with nearly perfect accuracy around the clock. For instance, a bot could process an outbound order without human interaction: it might verify inventory levels in the WMS, update the customer record in the CRM, generate an invoice, and more. Such automation minimizes wasted time on repetitive administrative tasks, reduces the risk of costly mistakes, and paves the way for a more agile, responsive supply chain. 

RPA automation is divided into two distinct workforces:

1. Digital Robots (RPA): Software bots that handle high-volume data, documentation, and communication.
2. Physical Robots: Hardware that handles repetitive physical movement, sorting, and inventory checks.

Robotic Process Automation (RPA): The Digital Workforce

RPA thrives on processes that require no creative decision-making but demand high precision:

 Order Processing & Entry:

1. Manual Process: A human reads a customer email, opens an attachment (PDF/Excel), and manually types the SKU, quantity, and address into the Warehouse Management System (WMS).
2. Robotic Solution: An RPA bot detects the incoming email, extracts data using OCR (Optical Character Recognition), validates it against inventory rules (e.g., "If stock is more than 0, then accept"), and enters it into the WMS in seconds.
3. Impact: Processing time drops from 20 minutes to 2 minutes per order, with 100% data accuracy.​

 Freight Booking & Scheduling:

1. Manual Process: Staff log into multiple carrier portals (FedEx, UPS, DHL) to compare rates and book the cheapest option.
2. Robotic Solution: A bot scrapes real-time pricing from all carrier portals simultaneously, selects the best option based on pre-set rules (price vs. speed), and generates the shipping label.

Invoicing & Billing:

1. Manual Process: Reconciling Proof of Delivery (POD) documents with original Purchase Orders (PO) before issuing an invoice.
2. Robotic Solution: Bots match the digitized POD against the PO. If they match perfectly, the bot issues the invoice automatically. If there is a discrepancy, it flags the exception for human review.
3. Case Study: PITT OHIO reported 100% accurate invoice creation and a 95% increase in productivity among Customer Service Representatives after implementing RPA.​

Managing High-Volume, Repetitive Work

Logistics generates massive data trails, and RPA simplifies data processing by running 24/7:

1. Track and Trace Updates: Instead of a human manually checking carrier websites to update a client, bots scrape carrier sites every hour and push updates directly to the client's portal or via email.
2. Regulatory Compliance: For cross-border shipments, bots can automatically generate Bill of Lading (BOL) and customs declarations by pulling data from the original order, ensuring no field is left blank or incorrect.​

We've summarized our findings on RPA's use cases and potential business value in the table below.

Physical Robotics: The Operational Workforce

While RPA handles the "office" rules, physical robots simplify the "floor" rules. These processes are equally repetitive and rules-driven.
 

Goods-to-Person (GTP) Picking:

1. Simplification: Instead of a human walking 10 miles a day to find items, Autonomous Mobile Robots (AMRs) bring the shelf to the human picker. The "rule" here is simple: "Fetch Shelf X and bring to Station Y." The expansion of smart lockers in Sweden is a great example of how robots can be valuable in a country where out-of-home delivery is extremely popular.
2. Benefit: Eliminates the "walking" time (which is 50% of picking labor), allowing the human to focus solely on the complex task of grabbing and verifying the item.​

 Automated Sorting:

1. Simplification: High-speed arms or conveyor diverters read a barcode and apply a simple rule: "If Zip Code starts with 1, divert left."
2. Benefit: Enables high-volume sorting beyond human cognitive limits.

RPA in Supply Chain Management

RPA has great potential to reduce costs and increase efficiency in supply chain management processes. With routine data input and communication handled, RPA bots enhance convenience, efficiency, and visibility across the supply chain.

Typical use cases for RPA within manufacturing include the following:

• Order fulfillment: A software bot can process a customer's order in minutes, while the same task could take a human several hours. The bot may take the order, verify stock availability, and automatically issue an invoice to the customer. This allows them to handle more orders even when they are not open for business.
• Shipment scheduling and tracking: The entire shipment scheduling process can be automated using RPA. Bots may also complete necessary paperwork, match  orders with carriers, and consistently track shipments, delivering status updates in real time to all parties involved.
• Appointment scheduling: Dock appointments with shippers, carriers, and receivers are often scheduled via a series of emails. RPA can be used in conjunction with TMS or yard management software to correctly schedule appointments, send confirmations, and alert all parties of any status updates.
• Document Handling: Logistics is a paper-heavy industry, where bills of lading (BOLs), proofs of delivery (PODs), and customs documentation are critical. RPA bots can also extract necessary data from such electronic documents, open email attachments, recognize document types, and enter the data into the company database without manual rekeying. 
• Freight audit and payment: Errors in freight invoices can cause shippers to overpay, and it has been reported that, due to such errors, shippers may be paying 10% more than they should. RPA can streamline freight audits by matching invoices to contracts, shipping documents, and tax requirements, speeding up the process and identifying discrepancies for further review. 

Pros and Cons of RPA in Supply Chain Management

While RPA offers transformative benefits, it is crucial to consider both its advantages and its limitations.

End-to-End Supply Chain Automation

End-to-end supply chain automation connects and automates several stages of the supply chain:

• order intake and fulfillment
• transportation
• warehousing
• procurement

RPA acts as glue between processes running on different systems, enabling a more seamless flow of data and tasks without manual intervention. In this way, they can respond more quickly to changes in demand, avoid duplicate work, and achieve end-to-end efficiency across the chain. 

Examples of end-to-end automation include:

• Supplier and Vendor Management: RPA can automate supplier onboarding, monitor performance against service-level agreements (SLAs), and manage contract renewals. Bots can also handle routine communications, such as sending inquiries and tracking responses.
• Freight Management: RPA can automate the entire freight management process, from obtaining quotes from multiple carriers and booking shipments to tracking freight movements and auditing invoices.
• Compliance and Reporting: The logistics industry is subject to numerous regulations. RPA can automate data collection and generate compliance reports, ensuring accuracy and timeliness while reducing administrative burden.

Expo Group's Freight Forwarding Process Automation

A great example is Expo Group's freight forwarding process automation. Expo Group, a diversified logistics conglomerate in Bangladesh, sought to accelerate growth by improving both customer service and process efficiency across its freight forwarding operations. They identified that many internal freight processes were sequential, repetitive, and didn't require human judgment. For example, booking shipments, updating cargo status, and preparing documents were handled manually and took excessive time. 

In 2019, Expo Group implemented an attended RPA bot to automate its end-to-end export freight forwarding workflow. The solution automates steps from receiving a booking request, confirming the booking in the customer's portal, recording cargo receipt, to scheduling and marking the shipment as dispatched – all with minimal human intervention. 

The impact was dramatic: the average handling time for these freight processes dropped from 8.35 hours per day to just 48 minutes, a 87.2% reduction in working hours. This translates to hundreds of hours saved monthly. 

Procter & Gamble (Global) Integrated Supply Chain Automation

P&G has been reinvesting in digital automation tools, including RPA, to streamline processes and cut costs. RPA bots at P&G handle numerous repetitive tasks, including order processing, inventory updates, and financial ledger entries. These bots not only execute tasks faster and with fewer errors, but also help identify redundant process steps, enabling the company to simplify workflows. The measurable benefits have been substantial: by automating these supply chain activities, P&G projected significant cost reductions in logistics, manufacturing, and inventory management. 

In fact, the company announced that RPA and related digital efficiencies would allow a 15% reduction in its non-manufacturing workforce (about 7,000 roles), reflecting the scale of manual work eliminated. Those savings will be redirected to further productivity improvements and are key to P&G's strategy to improve "cash productivity." 

Note: The automation initiative is a key component of P&G's strategy to reduce non-manufacturing costs. CEO Jon Moeller explained that the company is "deploying the freed-up cash to implement robotic process automation, a type of intelligent software that replaces white-collar roles." This strategy demonstrates how RPA can free up capital for reinvestment in further innovation and growth.

RPA in Inventory Management

Effective inventory management is critical for balancing supply with demand, minimizing carrying costs, and avoiding stockouts. However, it often involves a high volume of manual, repetitive tasks that are prone to human error. RPA offers a powerful solution for automating these processes, leading to more accurate data, optimized stock levels, and improved operational efficiency.

RPA bots can be programmed to handle a wide range of inventory-related tasks, including:

• Automated stock monitoring: Bots can continuously monitor inventory levels across various systems and locations. They can automatically generate alerts when stock falls below predefined thresholds, ensuring that replenishment orders are placed promptly.
• Demand and supply forecasting: By integrating with historical sales data, market trends, and other relevant data sources, RPA can help generate more accurate demand forecasts. Using predictive analytics in logistics allows for better planning and reduces the risk of overstocking or understocking.
• Purchase order management: RPA can automate the entire purchase order lifecycle. Bots can generate purchase orders based on stock level alerts, send them to suppliers, track order status, and update inventory records upon receipt of goods.
• Data entry and system updates: Manually updating inventory data across multiple platforms is time-consuming and error-prone. RPA bots can automatically perform these updates, ensuring consistency and accuracy across all systems, including ERPs and warehouse management systems (WMS).

RPA in Shipping and Distribution

Shipping and distribution logistics involve coordinating transportation schedules, tracking shipments, and handling documentation like bills of lading, invoices, and customs forms. RPA automates tracking updates, schedules shipments, generates shipping labels/documents, and reconciles delivery data, accelerating the flow of goods and information. By automating these processes, companies can significantly reduce turnaround times and improve accuracy in the supply chain's execution phase.

DHL's Freight Tracking and Exception Handling

DHL Global Forwarding (DHL's freight division) established a global RPA "Center of Excellence" to streamline internal logistics processes. In a pilot project called "Post Flight," a software robot was built to extract flight status data and reconcile it with DHL's shipment operations system, automatically producing a report of any delays or exceptions. This task was previously handled by a team of 30 employees who manually checked flight arrivals and updated shipment statuses. 

After RPA deployment, 15 of those 30 employees (50%) were reassigned to higher-value work, as the robot now manages the process end-to-end, with human staff handling only the few exceptions that require judgment. This not only improved staff productivity but also enhanced customer service by providing faster, more transparent shipment updates. 

Note: Remarkably, DHL recouped the entire investment in the RPA pilot in just one month. Inspired by this success, DHL Global Forwarding expanded, and within a year, more than 80 RPA bots were managing the equivalent work of 300 full-time employees across multiple finance and logistics activities. This example demonstrates how shipping operations can benefit from RPA through significant efficiency gains, rapid ROI, and the ability to repurpose labor for more value-added endeavours. 

FedEx's Last-Mile Delivery Coordination

Global carrier FedEx has also leveraged RPA to enhance shipping and last-mile delivery services. For example, in Singapore, FedEx introduced RPA-supported self-service lockers and pickup/drop-off points as part of its Delivery Manager program. The RPA bots integrate locker systems with FedEx's backend, automating package collection notifications and managing delivery-point data. This gives customers more pickup flexibility while optimizing FedEx routes. 

In FDX's logistics centers, RPA bots have been deployed to automate repetitive data entry and barcode scanning tasks, improving throughput and reducing the impact of manual errors in the sortation systems. These impacts, together with the improvements enabled by AI-driven robotics, such as the partner-robotic sorters FedEx developed with Berkshire Grey, have led to notable advancements in efficiency and output. 

According to FedEx's Vice President of Operations Science, such automation offers dual benefits: improving operational efficiency by enhancing employee safety and helping ensure the global supply chain continues. While specific data points have not been provided, the improvements noted have been in delivery time, package handling accuracy, and an improved routing system that has reduced emissions.

RPA and Automation in Warehouse Productivity

To appreciate the business value of RPA in warehouses, let's look at how market leaders are using it.

The largest industrial robotics company globally, Amazon, has over 750,000 mobile robots and tens of thousands of robotic arms across its systems worldwide. These include systems such as Cardinal, a robotic arm that collaborates in a warehouse and can pick up boxes of various shapes and sizes. Amazon's new fulfillment center in Shreveport, Louisiana, utilizes 10 times as much space as prior centers and has recorded a 25% reduction in fulfillment costs. Analysts expect continued investment in robotics to save about $10 billion annually by 2030. 

Amazonʼs hybrid approach, which augments human labor with technology, shows how the integration of AI and automation is redefining people's roles in the workplace in conjunction with the business strategy.

In much the same way, Walmart in Canada has invested $118 million in a high-tech fulfillment center outside Calgary. This 430,000-square-foot warehouse, equipped with GreyOrange robotics, processes up to 20 million items a year and delivers to 61% of Canadian households in two days. This process also enables human employees to focus on quality control and exceptions while robots sort, pick, and store at higher speeds and with greater accuracy. 

Automated Order Picking and Sorting

Picking and sorting are vital but challenging parts of warehouse fulfillment, particularly in high-volume operations or those with many SKUs or tight delivery windows. These operations were traditionally manual and fragmented. People walked down aisles to pick items, manually sorted them into different zones by destination or weight, and then visually inspected them for quality. It was a slow process, fraught with mistakes and subject to holdups and high labor costs, particularly during peak seasons when workers were few and far between.

Advanced robotic systems are integrated with ML, computer vision, and adaptive grippers to recognize product shapes, orientations, and packaging information.

Some recent innovations illustrating these advances include:

• DHL and LocusBots have combined for more than 500 million picks, leveraging flexible piece-picking solutions featuring autonomous mobile robots across 35+ facilities, demonstrating the scalability and precision of large-scale automation. 
• Boston Dynamics' Stretch robot, now rolled out by DHL, docks and unloads as many as 700 cases per hour, obliterating dock labor and relieving physical strain on the most taxing tasks, such as trailer unloading. 
• Amazon's Vulcan, a next-generation robot with tactile sensing, can handle about 75% of the company's warehouse inventory, carrying out sensitive handling operations once thought to be far too complex for automation. After processing more than 500,000 orders, Vulcan demonstrates how tactile AI solutions can automate tasks traditionally performed by people. 

Important: these robotic systems are designed for human-robot collaboration. By displacing monotonous manual labor, they free up human work agents to take on supervision, exception handling, and system management roles. This transition enhances safe, ergonomic work while improving job satisfaction, workforce value, and strategic relevance.

Warehouse Layout & Routing Optimization

The operational design and routing of a modern warehouse significantly impact throughput, labor efficiency, operational costs, and overall business efficiency. Many warehouses continue to operate static layout systems and pre-determined fixed routes for pickers, even while business demands are dynamic. This results in significant operational container inefficiencies, including pickers walking excessive distances to access frequently requested orders, congestion in critical operational pathways, busy aisles, and suboptimal use of storage systems. 

Improvements in design and route changes are significant because they enable the use of AI to create adaptable, responsive designs in real time. AI shifts design functions from periodic adjustments (e.g., quarterly, annually) to continuous updates by calibrating storage configurations and path designs to match fluctuations in orders, product velocity, and labor. Artificial Intelligence impacts storage layout and picker routing in measurable ways. Below, we explore the quantifiable impact of Artificial Intelligence on storage layout and picker routing.  

Intelligent Route Optimization for Human and Robotic Pickers

The right layout designs positively impact productivity. But similar to layout designs, the workflows within the picking are just as important. Each system is workstation-based, with the workstation stationary and pickers (human and/or robotic) traveling to it to process items. Pickers have a defined route to follow (either zone picking or a defined path), which is often set and does not change based on current system congestion, where pickers are located, or which tasks are required along their route. 

Routing engines using real-time AI can pull and interpret data (aisle occupancy, item locations, equipment availability) and optimize the most effective route for each picker or AMR. An explainable AI solution can reassign item locations and optimize pick paths, reducing average order-picking time across multiple warehouse zones. 

Real-Time Analytics and Visibility

Cloud-based WMS enabled by IoT, RFID, and autonomous robots lets AI in warehouse management weave together the continuous streams of data from these tracking devices and operational machinery to establish a live operational model of the warehouse, aka the digital twin. This allows real-time tracking of inventory flow, queue lengths, equipment utilization, and labour efficiency.

These methods also encourage immediate, actionable corrections. For example, AI-enabled dashboards can visualize congestion among pickers via heatmaps, highlight downtrends in scanning activity, or notify if processing speeds fall under expectations. This enables supervisors to reassign workers or reroute robots in real time, a game-changer for how warehouses handle dynamism. Studies in the International Journal of Engineering Research & Technology support the growing use of AI systems in warehouse operations. 

Enhanced Safety Monitoring

Traditional "safe warehouse" norms relied on manual oversight, such as managers walking the floors or reviewing recorded CCTV footage. These approaches rarely identify and mitigate risks in real time, and, as a result, problems such as blocked fire exits or poor lifting techniques will persist until they eventually lead to an accident. This reactive approach to safety may lead to more workplace injuries.

AI integration has changed the game when it comes to warehouse safety. With computer vision, machine learning, and wearable sensors, AI-enabled solutions can now continuously monitor workplace activity. They can recognize a wide range of safety breaches, from lifting the wrong way to operating machinery unsafely. When a violation is identified, an instant alert is sent, allowing the issue to be addressed without delay.

The pivot toward predictive safety also reduces regulatory risk and production losses due to injuries. Most importantly, AI-powered safety promotes a positive cultural change, where employees' morale and trust in the organization increase as they feel safer in their work environment.  
 

If you were inspired by the amazing use cases of RPA in logistics and want to implement it in your business processes, part 2 of this article explains how you can do it. Read on!