Unlocking Efficiency: The Role of Robotic Process Automation in Manufacturing

As the manufacturing industry grapples with ongoing labor shortages, high turnover rates, and increasing demand for skilled workers — all presenting obstacles to growth — companies are turning increasingly to smart manufacturing technologies. In fact, the 2024 “State of Smart Manufacturing Report” from Rockwell Automation notes that 95% of survey respondents are already using or evaluating smart manufacturing technologies. That’s up considerably from 84% in 2023. Technology investments, as a whole, are up 30% year over year, according to the report.

Robotic process automation (RPA) is emerging as a key solution to bridge the workforce gap, but to optimize efficiency and precision. It also provides a quick return on investment, falling within the top 10 technologies showing the biggest ROI over the past year, according to the Rockwell Automation report.

RPA in Manufacturing

While traditionally associated with office workflows like payroll, invoicing, and customer service, RPA is increasingly becoming a key component of manufacturing and warehousing environments. Like its back-office predecessor, RPA for manufacturing is well suited to repetitive, rule-based tasks. Its impact in manufacturing is growing in several areas, including supply chain management, quality control, preventive maintenance, and administrative automation.

Unlike traditional industrial robots, which do much of the heavy lifting on the factory floor, RPA has much more to do with the robotic brains than the robotic muscle. The software executes a set of instructions to replicate tasks typically done by humans, enabling manufacturers to streamline operations, reduce costs, and minimize errors. While traditional automation focuses on physical tasks performed by machinery, RPA operates in the digital realm, handling processes like data management, inventory tracking, and compliance monitoring with speed and accuracy.

Benefits of RPA

As in the physical world, the automation of mundane tasks enables companies to free up human workers to focus instead on innovation and problem-solving. Manufacturers can reduce human intervention in repetitive tasks while using workers for more value-added responsibilities.

This leads to increased productivity, significant cost savings, and enhanced decision-making through real-time data insights. Accuracy is improved by automating data entry and reporting. RPA also improves compliance by reducing risks related to audits and regulatory reporting.

RPA is scalable and adaptable, quickly adjusting to handle demand fluctuations and complex supply chains.

RPA serves a varying list of functionalities. From optimizing inventory management to streamlining quality control and regulatory compliance, RPA can touch and enhance nearly every aspect of production. It is also instrumental in managing supplier relationships and integrating systems across production lines, enabling seamless communication and collaboration among teams.

RPA is good for automating workforce-related tasks as well, including onboarding, creating training schedules, certification training, and managing attendance, payroll, and shift planning. It can also streamline safety compliance documentation for employees.

RPA Serves a Need in Supply Chains

Supply chains have faced unprecedented disruptions in recent years, impacting manufacturers and logistics providers alike, along with their customers. Automation is transforming supply chains by enhancing efficiency, accuracy, and resilience in manufacturing operations. Of course, RPA can play a considerable role here.

Several software tools are already widely used for managing and coordinating manufacturing efforts, including manufacturing execution systems (MES) and enterprise resource planning (ERP) software. RPA has its place within those digital realms.

Industrial process automation software can provide end-to-end workflow automation for order processing, inventory management, and compliance reporting. It connects with systems like SAP, Oracle, and Microsoft Dynamics to streamline production processes.

Within supply chain management, RPA can automate procurement, supplier management, inventory tracking, shipment tracking, supplier communications, and more, integrating with ERP systems to reduce manual data entry. This speeds up invoice processing and purchase order approvals, and it optimizes logistics and freight scheduling.

Within order fulfillment, RPA can automate customer support requests and order status updates. Likewise, it can process returns and help manufacturers generate reports and compliance documents.

Like many automation trends, the move to institute more RPA applications throughout the manufacturing space was accelerated by the COVID pandemic and the heightened labor concerns the pandemic created.

Arçelik, a global producer of consumer durables and consumer electronics, leads its home market, Turkey, in white goods, small domestic appliances, built-in appliances, and air-conditioners. With the significant change in consumer purchasing behaviors created by the pandemic, Arçelik began using RPA from UiPath for error handling in ecommerce and marketplace orders. Software robots address errors in both company and third-party systems and prompt the relevant teams to act.

Arçelik recognized the importance of RPA within its supply chain as well. The company began using software robots for a variety of processes, including placing import material purchase requests, customs clearance, and handling invoice processing and cross-trade operations for importing finished goods from the company’s factory in Romania.

Arçelik’s RPA processes 650,000 product transactions and 55,000 invoices annually, giving the company’s supply chain team extra time to conduct more value-added tasks. Mistakes have been reduced, process results have been standardized, and time spent on invoice entry and validation has been reduced by 90%.

RPA in Production Processes

There is much for RPA to get involved with directly on the plant floor as well. On the production line, for example, software bots can integrate with AI-based computer vision to assist in defect detection. They can collect and analyze real-time sensor data from Internet of Things (IoT) devices and flag irregularities for predictive maintenance. Here again, there’s an opportunity to automate compliance reporting and documentation.

In terms of maintenance, bots are useful for tracking machine performance logs, telling teams when preventive maintenance is due, and tracking repairs.

At Hannover Messe a few years ago, Siemens detailed how it put an RPA solution it had developed with Microsoft to work at its plant in Berlin.

Siemens has long used Microsoft Azure as the cloud infrastructure for MindSphere, an open, cloud-based industrial IoT operating system now known as Insights Hub. The two companies built on that partnership — combining Microsoft’s IT expertise with Siemens’ OT expertise — to automate the handling of maintenance tasks.

Handled by a cross-functional team, the pilot project was implemented on a highly automated production line making protection relays and was able to quickly increase productivity by automating the creation of repair notifications.

Traditionally, if a machine breaks down, a technician must go see why it broke down. The technician might head to a terminal, log in, click through the screen to find out what happened, and then trigger a ticket for the maintenance team to resolve the issue.

With the implementation of RPA at Siemens, the machine instead sent a message straight to MindSphere, letting the system know it had broken down and could not continue working. The information was automatically picked up by RPA, which would then automatically generate a message for the maintenance team.

This change provided several benefits, including increased quality and productivity, more flexibility, 24/7 availability, increased process transparency, and fast deployment — all while allowing human workers to focus on more value-added tasks.

With the automated system, there was no risk that the information would get lost and no delay in transmitting information to the maintenance team. The maintenance team was able to react immediately, since it had all the necessary information in hand — the state of the machine when it broke down, what parts they might need to resolve the issue, and so on. The team could resolve the breakdown quickly, reducing downtime.

The Integration of RPA and AI

Add artificial intelligence (AI) and machine learning (ML) into the mix, and RPA can be taken to new heights. Traditional RPA automates rule-based, repetitive tasks, but when combined with AI and ML, it evolves into intelligent automation (IA). This integration allows RPA bots to handle unstructured data, learn from patterns to improve over time and refine accuracy, and make better decisions, expanding automation beyond simple, repetitive processes.

The future of RPA in manufacturing promises deeper integration with IoT devices, automating complex workflows with minimal human intervention, minimizing errors, and further driving innovation and efficiency.

AI-driven predictive maintenance uses machine learning and IoT sensors to detect failures before they happen. RPA bots can use those capabilities to trigger maintenance requests based on real-time sensor data and can also learn patterns from past failures to optimize maintenance schedules. ML models can analyze historical data to predict future outcomes, helping RPA bots to act proactively. Instead of following rigid rules, the bots can adapt based on real-time insights.

AI-powered image recognition can also combine with RPA for improved quality control. For example, automated defect detection involves using computer vision and AI to inspect products in real time. RPA can also be used for part recognition on assembly lines, identifying components and ensuring their correct placement.

Image recognition capabilities enable RPA bots to process images, screenshots, and scanned documents — useful for manufacturing, quality inspection, and logistics. In a warehouse, for example, a vision-enabled RPA bot can scan product labels and update inventory systems automatically.

Using AI-driven optical character recognition (OCR) can help bots read and extract data from documents. ML algorithms improve their accuracy over time by learning from corrections. RPA-based intelligent document processing not only speeds up processing but also reduces manual data entry errors.

Meanwhile, the popularity and development of generative AI (GenAI) have skyrocketed, and the technology has become a key priority for industrial applications. This will drive RPA forward as well. The development of natural language processing enables RPA bots to understand, interpret, and process human language from any number of inputs. Such developments also feed into generative AI models, which are increasingly part of RPA activities. In fact, Gartner has calculated that, by 2025, 90% of RPA vendors will offer automation assisted by generative AI.