Siemens and Covestro Deutschland have finalised a joint business development plan to provide further strategic reinforcement of their trust-based partnership, particularly in the field of digitalisation.
strategic partnership
The plan is based on the corporate ethos of the two companies, which is both innovative and geared towards sustainability.
Dr Klaus Schäfer, chief technology officer, member of the executive board, Covestro Deutschland: “This collaboration with Siemens generates added value for both companies. We are combining the innovative forces of both companies to extend our lead in this time of rapid development.”
A focal point of the collaboration is to increase the availability of all assets in a plant and to give personnel on site quick and meaningful decision-making tools for their work. Through doing this the companies hope to make the plants more reliable via proven products and innovative services and to be able to predict developments in the condition of a plant’s assets more reliably.
“I am very pleased with this new stage of our concerted collaboration with Covestro Deutschland,” explained Eckard Eberle, CEO of the Business Unit Process Automation, Siemens. “By exchanging expertise, we can support plant operations and maintenance with the right information at the right time, particularly with smart data for production, plant equipment and instrumentation.”
New analytical methods are now available through the use of Siemens’ open, cloud-based IoT operating system, MindSphere. Significant data and measured values are collated on this platform, put into a meaningful context, and intelligently linked in data models. Smart data applications provide support for both operation and maintenance. They enable potential asset or component defects to be predicted, as well as helping to minimize plant downtime. New options are available through Siemens’ data analytics and simulation methods. On this basis, the partnership will examine various applications and evaluate, for instance, the benefit to be gained from correlating data for production.