The path to a transparent supply network
The article "Data, cloud and digital services potential for the manufacturing industry" (source: Digital Factory Journal) emphasizes the fact that as part of the digital transformation, companies in the manufacturing industry are called upon to provide more transparency in their supply chain. The use of digital service platforms can help to regulate the exchange of data between stakeholders in the supply networks in a trustworthy, fast, and secure way.
material.one is precisely such a platform that brings together manufacturers, suppliers, and laboratories in a secure and efficient exchange. Its main focus being digital First Article Inspection (FAI) of components: Requirements for components (for example compliance with standards such as IATF 16469, VDA Volume 2, or environmental requirements) are provided digitally so that suppliers can design their product development process according to set standards.Laboratories can also be integrated into the end-to-end process by allowing them to access forms and clearly laid out test plans, and by letting them share their test results over the platform. This way, manufacturers always have an overview of where their parts originate, the standards according to which they were tested, and the degree of carbon emission offset associated with the part.material.one's value-adding application has proven its worth in daily practice - e.g. at Mercedes-Benz. All stakeholders within the network greatly benefit from the digital platform, which provides transparency and traceability.
Other major challenges described in the Digital Factory Journal include
Optimize MES
Existing manufacturing execution systems are reaching their limits due to more and more variations being required among other challenges. The trend is moving away from classic MES to modern cloud-based solutions because they increase productivity.
Leveraging the IoT data treasure trove
In the field of IoT, the potential for digitization lies, in particular, within the possibility of capturing sensor data and using it for analyses. Artificial intelligence and machine learning can help identify patterns and correlations within vast data lakes and data streams to improve the quality and performance of machines, which paves the way for predictive quality analytics and predictive maintenance.
Meet customer-specific requests
Companies need to digitize their internal processes and make them more efficient in order to keep up with the megatrend of "mass customization". This can be achieved through optimized variants management, modular building block systems and configurable products.
Develop new as-a-service business models
Networked machines, carefully prepared data, and interfaces to suppliers and customers are creating new "as-a-service" business models - which can be applied to almost anything.