RPA is revolutionising manufacturing by automating repetitive tasks and integrating complex data across legacy systems, freeing skilled workers to focus on strategic improvements. As Mark Venables reveals, RPA’s scalable and adaptable solutions are a powerful catalyst for digital transformation in today’s fast-paced industrial landscape.
Robotic Process Automation (RPA) has become a transformative tool in manufacturing, offering a streamlined approach to automating repetitive and rules-based tasks. While the term RPA often brings to mind physical robots on production lines, Sean Bailey, Head of RPA at Ten10, clarifies that it refers to software-based automation. “RPA automates high-volume, repetitive tasks that indirectly influence the manufacturing process, like inventory tracking, procurement, and supply chain management,” he explains. “It operates behind the scenes, allowing manufacturing teams to focus on productivity and continuous improvement.”
For manufacturing executives, RPA offers three primary benefits: cost reduction, revenue impact, and improved compliance and sustainability in terms of cost reduction. “Operational costs increase as manufacturing scales, so companies must find ways to stay competitive,” Bailey adds. “RPA automates routine tasks across procurement and supply chain management, improving efficiency and reducing the need for manual intervention. Manufacturers optimise resource allocation and control costs more effectively by automating these repetitive tasks. RPA’s role extends beyond cost savings, driving revenue growth through streamlined coordination between departments like production and sales, enhancing decision-making and customer insights.”
Beyond ERP and CRM systems: RPA’s unique value
RPA bridges automation gaps in legacy systems where modern ERP and CRM systems often fall short. “ERP and CRM systems may claim automation, but RPA addresses challenges with older systems that many manufacturers still rely on,” Bailey explains. “Legacy systems lack built-in automation capabilities, and RPA enables data migration and real-time task automation within these environments. RPA acts as a stopgap solution for companies undergoing digital transformation, allowing data processing and automation to occur without overhauling the infrastructure entirely. RPA is a flexible tool for managing data efficiently, particularly for companies with deeply embedded legacy systems that don’t have API compatibility.”
Incorporating AI into RPA has led to the development of intelligent automation, opening doors to more complex capabilities. “RPA was originally limited to rule-based tasks, but AI integration now allows RPA to handle sophisticated scenarios, such as invoice processing and natural language analysis,” Bailey explains. “AI is the ‘brain,’ while RPA is the ‘hand’ that carries out tasks. This advanced RPA can now tackle document processing and customer feedback analysis, enhancing customer experience and operational efficiency in manufacturing.”
Scalability and strategic advantage in manufacturing
One of the most significant advantages of RPA in manufacturing is scalability. RPA bots operate around the clock, adapting seamlessly to fluctuations in demand and other operational disruptions. “During high-demand periods, such as Black Friday or in the face of supply chain issues, RPA’s flexibility allows manufacturers to manage inventory, process orders, and handle customer service without ramping up human resources,” Bailey notes. “RPA bots provide an advantage in demand forecasting, analysing seasonal trends and supplier performance to support inventory management.
Additionally, RPA is invaluable in bridging gaps in legacy systems, allowing manufacturers to integrate data without costly and time-consuming manual work.
“Many older systems lack API compatibility, which limits their ability to integrate with modern software. RPA mimics human actions within the user interface, automating tasks on systems that don’t support API connections.” He highlights a case where RPA was deployed to automate data migration for a client using a mainframe-based CRM system. “Instead of hiring a temporary team for manual data entry, RPA allowed us to automate the entire process, saving resources and reducing errors,” he says. “It is an essential bridge between outdated systems and modern operational demands.”
Driving quality and operational agility through predictive maintenance
As RPA capabilities evolve with AI integration, predictive maintenance has emerged as a critical benefit, especially in manufacturing environments. “RPA integrated with AI enables real-time monitoring and action for equipment maintenance,” Bailey continues. “For example, an AI-powered RPA system can detect anomalies on production lines and initiate maintenance scheduling before issues lead to costly downtime. This combination of RPA’s efficiency with AI’s analytical power is reshaping quality control. Predictive capabilities reduce the reliance on periodic maintenance checks and enable a more agile response to issues, which is crucial in a competitive and fast-paced manufacturing landscape.”
Traditional quality control relied on intermittent checks, but today’s AI-enhanced RPA tools continuously monitor production conditions. RPA supports continuous quality improvement by integrating real-time feedback and ensures operational agility. This real-time adaptability allows companies to respond swiftly to changing conditions, minimising disruptions and maximising uptime.
Guidance for executives on RPA and AI integration
Many executives feel pressure to adopt advanced AI solutions, often overlooking RPA’s potential for immediate impact. Bailey recommends a measured approach: “Our advice is to start with simpler RPA implementations,” he says. “By proving the value of technology in manageable, lower-risk processes, organisations can build an automation culture without overhauling complex systems. Many organisations encounter challenges when implementing complex AI systems too soon.” He emphasises that incremental successes with RPA provide a foundation for larger digital transformation projects. “RPA establishes a forward-thinking mindset, preparing teams to adopt and scale AI effectively when the time comes.”
Implementing RPA provides a solid starting point for businesses developing an automation-first approach. Bailey cautions against rushing into full-scale AI without building a robust RPA base. “Success in RPA and AI adoption hinges on a balanced approach,” he says. “Focusing on smaller, incremental wins with RPA fosters a culture of automation, making it easier to adopt more sophisticated AI solutions later.”
RPA in action across manufacturing
A notable example of RPA’s transformative power can be seen in the automotive manufacturing sector, where RPA has streamlined supplier management, inventory tracking, and order processing. With high stakes in quality and time-to-market, automotive manufacturers use RPA bots to process and track supplier documentation in real-time, significantly reducing manual workload and enabling rapid response to production demands. By implementing RPA, automotive companies have optimised their supply chain workflows, improved accuracy and timeliness, and reduced human error.
Furthermore, automotive manufacturers benefit from RPA’s integration capabilities, which bridge multiple systems, making aligning production goals with customer demand easier. Enhancing data visibility across departments, RPA facilitates better coordination and decision-making. These organisations are seeing measurable improvements in efficiency and cost savings, reinforcing RPA’s value as a foundational element of digital transformation in manufacturing.
The future of RPA and AI in manufacturing
The future of RPA lies in deeper integration with AI, creating a hybrid workforce where human expertise and machine precision operate in tandem. “RPA’s scalability will allow it to become embedded in nearly every aspect of manufacturing,” Bailey explains. “As RPA advances, manufacturers can expect its application to expand into more complex areas, such as automated supply chain management and demand forecasting, which rely on extensive data processing and real-time adaptability. The future is about creating a symbiotic relationship between human ingenuity and machine efficiency, where RPA supports and enhances, rather than replaces, human roles.”
Manufacturers who establish a strong RPA foundation today can leverage these future advancements. “As RPA technology evolves, the potential to increase operational resilience, reduce costs, and enhance flexibility will only grow,” Bailey concludes. “The manufacturers who invest in RPA now are building a competitive edge that will serve them well as the digital transformation of manufacturing accelerates.”
