Data sharing in industrial ecosystems
by Prof. Dr. Boris Otto, Prof. Dr. Nikolaus Mohr, Matthias Roggendorf, PhD and Tobias Guggenberger
Industrial ecosystems for data sharing have the potential to power tremendous growth, helping companies optimize existing processes and making new products and businesses possible. At the same time, potential ecosystem members face a number of obstacles, from concerns about protecting confidential information to technical challenges. While industrial ecosystems are still in their infancy, enough success cases exist to identify both the main stumbling blocks and the factors that make these approaches successful. Companies that take proactive steps now to tap into the power of ecosystems can secure a significant competitive advantage.
Access to data and its systematic collection and processing have become key differentiators for industrial companies. Thanks to advances in connectivity and edge computing, they can use previously siloed data to optimize machine performance and even integrate data further to drive value across entire production lines. However, few players have the in-house skills and resources to systematically uncover such sources of value. As a result, both solutions equipment manufacturers and system integrators have started developing solutions as additional services, an attractive new source of income that helps balance their declining margins in many sectors. In extreme cases, entirely new business models have emerged that retired existing approaches. Rolls Royce’s “power by the hour” model of selling uptime instead of turbines is a good example. Analyzing usage and service data beyond primary purpose of data for direct control enables GE to turn around their entire business model. Another example is Caterpillar’s Cat Connect service, which increases fuel and materials efficiency, productivity, and safety.
But the need to share data beyond organizational boundaries has turned out to be one of the main barriers to full-scale adoption. In a connected world, sharing data can be a powerful enabler for all sides: the parties who supply data and the providers developing new services or even disrupting markets with more attractive offerings. Such innovation often requires data from different sources and, potentially, different organizations. These could include component suppliers, machine integrators, and machine users. In addition, context data (such as information on environmental conditions) might be added from other previously unconsidered sectors. The expected result: ecosystems that create winning situations for all participants. Even competing organizations may be able and willing to contribute to such ecosystems if confidentiality is guaranteed and the value they stand to gain exceeds the required investment.
At the same time, win-win data ecosystems are not feasible in every case. For example, if certain players are extremely dominant, they can dictate not only which data formats smaller players must use but also how they participate in the economic benefits. One example of such a dominant player is Walmart, which manages the sharing of supplier data through Retail Link, its proprietary data platform.
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