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The future of manufacturing lies in smart, connected factories where humans and machines work together, leveraging the efficiency and precision of digital technologies while recognizing the valuable contribution of skilled workers. By integrating the human workforce with automated processes, companies can increase productivity, flexibility and worker satisfaction, ultimately increasing innovation and competitiveness in the manufacturing sector. The EU-funded SHOP4CF project, coordinated by the Technical University of Munich, has created a marketplace for technologies and tools that are ushering in a new era of human-centric robotics in Europe.
Supporting people in production activities
SHOP4CF’s 25 user-friendly plug-and-play components for robotics and automation are available on the Robotics and Automation Marketplace. These components leverage emerging technologies such as augmented reality, virtual reality, digital twins, machine learning, deep learning, process automation, robotics and IoT. The common architecture is based on RAMI 4.0, a reference architecture model designed to provide a common language and framework for Industry 4.0 applications, and FIWARE open source products and services that support digital transformation. This makes it easy to install and use. Jesus Tapia, Head of IMPACT Accelerator at ISDI, said: “The expectations of companies evaluating components on the market go beyond the capabilities of the individual components. If people interested in the components do not find helpful guidance, the adoption rate drops significantly.” Those interested in SHOP4CF’s components and their use can find 13 open online courses covering topics such as ethics, data, predictive maintenance and even fundraising. “Training is a means to promote adoption,” Tapia adds.
Building a bridge between large companies and developers
SHOP4CF’s team of 20 partners, including large corporations, SMEs, research institutes and university partners, contributed more than just the development of technical solutions. The consortium supported “experiments” selected for an eight-month mentoring program, during which four large corporate partners tested the developed solutions. “The involvement of large, internationally renowned companies has been key to fostering excitement within the SHOP4CF community. Moreover, it is a win-win for the parties involved in the use cases: large companies have tried new solutions with very low costs and risks, and component developers have had a great opportunity to interact with these large companies,” Tapia explains.
Discover the benefits of a world-class factory
Four global manufacturers have applied some of SHOP4CF’s components to help them address real-world challenges: Siemens used a user-friendly interface to improve automatic data capture, storage, and traceability, while also increasing human safety. Automated guided vehicles and digital reality applications helped unskilled workers at the Bosch plant in Madrid properly place electronic components on printed circuit boards. SHOP4CF’s components helped Volkswagen reduce body paint defects by predicting when major machine failures would affect paint adhesion and managing maintenance. Finally, at Arçelik, which has plants in several countries, a team of engineering experts from its headquarters used augmented and virtual reality to remotely support inspections and maintenance at its local plants. “We have a real-world example of how robots and AI support humans rather than replace them, unlocking human potential. By taking over monotonous, repetitive and low value-added tasks, tools enable people to use their talents more effectively than machines. As more and more components are added, more use cases can be solved and more large organizations will be attracted. SHOP4CF has the potential to transform European manufacturing with plug-and-play components for robotics and automation, and to snowball into a big picture,” Tapia concludes.
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SHOP4CF, Robotics, Automation, Manufacturing, Plug and Play Components, Augmented Reality, Virtual Reality, Deep Learning, Digital Twin, IoT, Machine Learning
