The World Economic Forum (WEF) has announced nine standout factories ‘implementing technologies of the Fourth Industrial Revolution’.
Labelled ‘lighthouses’, five are in Europe, with the remainder in China and the United States. Selected from 1,000 manufacturing companies worldwide, these ‘manufacturing lighthouses’ will form a new network designed to ‘address problems confronting industries in both advanced and emerging economies when it comes to investing in advanced technologies’.
“Identifying the best manufacturing sites in the world is not only necessary, but responds to the stated need from the Future of Production community to accelerate the adoption and diffusion of Fourth Industrial Revolution technology,” said Helena Leurent, Head of the Shaping the Future of Production System Initiative and Member of the Executive Committee at the World Economic Forum in a WEF news release. “The next step is to enable the lighthouses to take a leadership role in developing the overall production ecosystem to truly reap the benefits we expect.”
The network will launch at the World Economic Forum’s 12th Annual Meeting of the New Champions, which will take place on 18-20 September 2018 in Tianjin, People’s Republic of China.
“The Fourth Industrial Revolution is real. Workers and management equally get augmented with technology. These pioneers have created factories that have 20-50% higher performance and create a competitive edge,” added Enno de Boer, Partner and Global Head of Manufacturing at McKinsey & Company, which collaborated with the Forum on the project. “They have agile teams with domain, analytics, IoT and software development expertise that are rapidly innovating on the shop floor. They have deployed a common data/IoT platform and have up to 15 use cases in action. They are thinking “scale”, acting “agile” and resetting the benchmark.”
- Bayer Biopharmaceutical (Garbagnate, Italy): ‘Using data as an asset’- While most companies use less than 1% of the data they generate, Bayer’s massive data lake has led to a 25% reduction in maintenance costs and 30-40% gains in operational efficiency
- Bosch Automotive (Wuxi, China): ‘Optimizing competitiveness’ – By implementing an ”order-to-make” product customization platform and using remote AI to predict maintenance needs before they occur
- Haier (Qingdao, China): ‘Customer-centric technologies’ – Artificial Intelligence led transformations include an ‘order-to-make’ product customization platform and the use of remote AI to predict maintenance needs before they happen
- Johnson & Johnson Depuy Synthes (Cork, Ireland): ‘Process-driven digital twinning’ – This factory used the internet of things to make old machines talk to one other, resulting in 10% lower operating costs and a 5% reduction in machine downtime
- Phoenix Contact (Bad Pyrmont and Blomberg, Germany): ‘Customer-driven digital twinning’ – By creating digital copies of each customer’s specifications, production time for repairs or replacements has been cut by 30%
- Procter & Gamble (Rakona, Czech Republic): ‘Production agility’ – A click of a button is all it takes production lines in this factory to instantly change the product being manufactured, which has reduced costs by 20% and increased output by 160%
- Schneider Electric (Vaudreuil, France): ‘Factory integration’ – Sharing knowledge and best practices across sites has helped this company make sure all its factory sites enjoy the highest energy and operational efficiencies, reducing energy costs by 10% and maintenance costs by 30%
- Siemens Industrial Automation Products (Chengdu, China): ‘3D simulated production line optimization’ – Using 3D simulation, augmented reality and other techniques to perfect the design and operations of its factory, employees helped increase output by 300% and reduced cycle time
- UPS Fast Radius (Chicago, USA): ‘Balancing capacity with customer demand’ – Meeting increasing consumer demand for fast-turnaround customized products has been made possible through a combination of globally distributed 3D printing centres with real-time manufacturing analytics