Data-based services for industrial companies
|Encouraged by||Bavarian State Ministry of Economic Affairs, Regional Development and Energy|
|Project Sponsor||VDI/VDE Innovation + Technik GmbH|
|Support programme||Information and communication technology|
|Grant Number||IUK-1811-0014// IUK611/001|
|Project Duration:||2 years|
|Contact||Prof. Dr. Björn Häckel|
Within the framework of the research project “Data-Based Services for Industrial Enterprises” (DaSIe), innovative analytics solutions and data-based business model innovations are being developed with the help of an overarching research and development approach. This will enable Bavarian companies to further increase their competitiveness in increasingly digitalized, global value-added networks. Through targeted funding, the highly specialized process know-how of Bavarian companies is used to drive forward innovation potential and approaches to optimize value creation.
The focus is on the development of solutions for the targeted (partially) automated determination of customer needs and semantic data preparation based on artificial intelligence, as well as the development of solutions for autonomous decision support, secure data collection, and for data transmission using innovative connectivity solutions. In addition, innovative IT security concepts are considered and developed from the beginning of the development process.
The comprehensive digitalization of the manufacturing industry offers companies a wide range of potential through developments around the Internet of Things. Innovative digital technologies such as cloud computing, big data analytics, artificial intelligence, and the internet-based networking of intelligent objects such as production facilities and products help companies to make their production processes more efficient and flexible. Thus, they are capable of developing new business models with innovative, digital offerings. However, companies are faced with a wide range of challenges with regard to the development of digital solutions and are at the same time subject to a high pressure to act due to a constantly increasing customer demand for corresponding offers as well as a high pressure to innovate due to competitors (some of whom are from outside the industry).
It is therefore of great importance for companies to gain competitive advantages and open up new markets within the framework of their digitization strategy by acting proactively and developing digital business models with innovative hybrid product-service bundles.
Objective of the project:
The goal of the research project is to develop innovative analytics solutions and data-based business model innovations through a comprehensive research and development approach. In order to enable industrial companies to optimize complex production processes and develop secure, data-based business models, the needs of customers and internal stakeholders are to be determined in a semi-automated manner. Based on this, a targeted derivation of data and analysis requirements as well as the holistic development of innovative analytics solutions will be enabled. For this purpose, the research project addresses the five fields of action: semi-automated analysis of customer needs, semantic data preparation based on artificial intelligence, autonomous decision support systems, connectivity and IT security.
This approach is intended to sustainably increase the international competitiveness of Bavarian companies and thus the attractiveness of Bavaria as a business location in increasingly digitalized, global value-added networks. To this end, the current lead of Bavarian companies in technical process know-how will be used to drive forward innovative business models and approaches to optimized value creation in the long term.
BMK, GROB-WERKE, Günzburger Steigtechnik, RATIONAL, RENK and WashTec are cooperating in this research project. The research project is scientifically supported by the Project Group Business & Information Systems
Engineering of the Fraunhofer FIT and by the Augsburg University of Applied Sciences.