How can manufacturers make the critical transition from data to action? How do they effectively navigate the overabundance of data to pick out what’s meaningful? How do they use these insights to drive overall productivity and efficiency in an automotive context?
The answer to these questions involves deploying a three-tiered IIoT approach that provides automotive OEMs and suppliers with a comprehensive digital roadmap for their operations.
Manufacturing in the age of Industry 4.0, digitally connected machines and smart factories require a new breed of engineers who are equipped with a fresh set of skills. That’s why Arizona State University recently launched the School of Manufacturing Systems and Networks.
The current paradigm of car manufacturing, which provides a variety of models, colors and options, is called mass customization. However, mass customization is now transforming into mass individualization. In mass individualization, products are uniquely catered to consumers’ desires. For example, some car buyers may want to replace ordinary car seats with luxury brands, such as Louis Vuitton, to differentiate themselves.
Rotor blades, guide vanes and other components for jet engines are made from costly materials and machined to precise tolerances. Such components are typically made in batches, due to the time involved in setting up machine centers. The process also requires a fair number of people to tend the machines and assist in changeover.
In today’s smart factories, cyber physical systems monitor physical processes, create a virtual representation of the physical world, and even make decisions. The traditional structure of the automation pyramid and the distinction between information and operations technologies are blurring.
If the factory of the future will run on data, new types of hardware will be needed to collect, route, process and display it. That means more than just new PLCs, but gateways, edge computers, tablets and other hardware.