20 proof-of-concepts of digitalised manufacturing

The main operational focus in Reboot has been in a creation of a Phase I PoC Portfolio, resulting in 20 entries during the first eighteen months. All of them originated from company needs and utilised by our factory members Nokia, ABB, GE Healthcare, KONGSBERG and Ponsse. In addition, there have been several innovation spill-overs where factories learned and implemented good practices from each other or from research.

• Prospect to project enables better sales prediction to improve production planning, procurement and budgeting. It utilizes several kinds of business data such as CRM reports, sales history and market predictions.
• Supply chain transparency improves response times of supply chain in terms of production planning and material flow. Shorter overall production time gives benefit in the market.
• Extranet development PoC & B2B extranet data transfer PoC reduce manual non-value-adding work and increase information transparency in procurement processes at both factory and its supply chain.
• Digital twin technical development replaces physical sensors with virtual ones in end products, cutting the sensor maintenance costs in very demanding environments such as marine thruster systems.
• Tester predictive maintenance lowers the cost of electronics tester maintenance, improves production efficiency and lowers waste as testing is more reliable and anomalies can be spotted via data analytics. SCALE-UP
• Robotic process automation saves human daily working time by utilizing software robots in low value routine work related to IT-systems such as ERP and excel sheets.
• Value of service enabled by digital solution improves means to communicate the customer value of product digital twin to the potential customer. This gives manufacturer confidence when investing to digital service development.
• Factory acceptance test automates the data collection and reduces manual work in product final testing. It also creates a possibility for external classification agencies to give approvals remotely.
• Mobile robots in material handling reduce manual non-value-adding work and increase productivity in intralogistics. Components are transferred automatically/by-order from supermarket to assembly line with mobile robots.
• Automatic component quality control with machine vision increases capacity of assembly cells as inspection is quicker. It is also more reliable and less demanding in terms of human work.
• Standard robot interface increases the utilization rate of cobots, which are usually statically installed to a single workstation. New technology allows cobots to attach to and detach from individual workstations and to utilize task-specific grippers, significantly shortening the ROI related to cobot investments. SCALE-UP
• Automatic error handling reduces the downtime of robots that have entered in an error state through self-diagnostics based on machine learning from video data. Automatic error recognition also facilitates the development of self-recovery functionality.
• AI foreman PoCs produce automated production personnel allocation proposals based on multiple data sources to support faster day-to-day decision-making regarding production planning and resourcing. SCALE-UP
• VR to achieve more understandable instructions facilitates training of new employees and/or new products on manual assembly work and can also serve as competence development / maintenance tool for senior workers.
• Wearables in industry such as smart watches present new ways to inform employees about forthcoming tasks or alerts in production. Notifications can be tailored to each stakeholder group.
• Well-being at work increases understanding on what affects well-being of employees and how it could be improved. Studies show that productivity goes hand in hand with employee wellness.
• Gamification at factory floor improves work motivation and employee satisfaction. Work instructions or routine reporting can have game-like features such as rewarding trophies or visuals with 3D game engines.
• Legislation brings understanding on what conditions data privacy legislation (such as the GDPR & the Act on the Protection of Privacy in Working Life) sets for personal data gathering and processing within factory employees, as well as for automatic decision making through artificial intelligence.