​​​Creating Ecosystem Value with Engineering Data​​

EXECUTIVE SUMMARY 

The digital transformation of industrial manufacturing has significantly increased the relevance of Digital Twins as enablers of interoperability, efficiency, and data-driven value creation. Digital Twins enable the structured exchange of engineering data such as technical specifications, computer-aided design files, and simulation models across organizational boundaries and thereby support increasingly interconnected industrial processes. However, heterogeneous data formats, persistent manual workflows, and insufficient standardization continue to limit their scalable adoption within industrial data ecosystems. Against this background, this project was conducted for Festo SE & Co. KG and focuses on Level 1 and Level 2 Digital Twins, as these represent the foundational layers for automation and digital value creation. 

Goal 

The goal of this project was to identify options for scalable Digital Twin–based business models for Festo in industrial data ecosystems. The analysis examined how standardized Digital Twins can create value for Festo and its ecosystem partners, which roles and incentives characterize the involved stakeholders and what implications arise for monetization in an ecosystem context. 

Methodology 

The project followed a mixed-method research approach that combined secondary research with qualitative expert interviews. The secondary research addressed core concepts related to Digital Twins, the Asset Administration Shell, industrial data ecosystems, and data-based business model archetypes. Building on this foundation, semi-structured expert interviews were conducted with key stakeholders, including component manufacturers, machine builders, factory operators, software providers, and ecosystem actors. The resulting insights were subsequently structured and analyzed using the Build Your Own House framework. 

Results 

The results indicate that standardized Digital Twins create significant value in engineering processes by enabling automation and interoperability. In particular, they reduce manual effort related to data acquisition and maintenance while simultaneously improving data quality. At the same time, clear differences in monetization potential become apparent. Digital Twin Level 1 is largely perceived as a mandatory prerequisite for market participation, whereas Digital Twin Level 2, especially advanced simulation models and digital services, shows a higher willingness to pay when clear operational benefits are provided. Building on these insights, the recommendations were systematically derived and structured using the Build Your Own House framework, highlighting the importance of coordinated implementation, data sovereignty, and shared governance in emerging industrial data ecosystem. 

Cooperation Partner 

• ​​Festo SE & Co. KG 
  Ruiter Straße 82 
  73734 Esslingen 
  Germany 
  www.festo.com​ 

​​Contact Person​ 

• ​​Björn Sautter 
  bjoern.sautter@festo.com​ 

Student Team 

• ​​​Andreev Stoyan Hristov 
  Dobringer Paul 
  Lipianina Anastasiia 
  Natochyi Oleksandr 
  Rauhofer Sebastian​​ 

Project Manager 

• ​​​​Fabian Caroline​ 

• ​​Jeske Sandra​