Digital Solutions for Industry 4.0

The rapid development of the digital economy is changing consumer behavior as well as expectations. With easier access to information and numerous options available for every car model, the smart customers of the future are looking for variety before deciding on a car which suits their personal needs. In order to meet modern trends, car manufacturers have to quickly adapt their production and manufacturing processes to the evolving market dynamics. By implementing Industry 4.0 successfully, they are able to create “smart factories” to satisfy the needs of “smart customers”. To do this, car manufacturers must have an overall strategy which aims to integrate digitalization along the entire process chain and involves all activities at all levels.

Industry 4.0 calls for new and innovative solutions, which require the digital transformation of processes. The benefits of digitalization are substantial and many OEMs and suppliers have realized that their competitiveness on the market depends on their ability to adopt digital transformation across the entire product development process chain.

Industry 4.0 encompasses a very broad array of technologies. AutoForm’s expertise over the past 25 years has been applied to support customers in the design and validation of sheet metal forming processes and more recently also in BiW assembly processes. In this context, the Full Process Digitalization of the product development and engineering process has been a focus of AutoForm’s technology development. Extensive collaboration and discussions with customers over the past several years have resulted in the recognition of important prerequisites for the successful implementation of full digitalization. These prerequisites, the Three Digitalization Pillars, are indispensable elements of any digitalization strategy. One of the three pillars, the Digital Process Twin, plays a key role in the implementation of Industry 4.0 strategies for industrial mass production processes. It allows the identification of causalities and the prediction of the production outcomes using simulation technology.