Abstract
The collection and analysis of industrial Internet of Things (IIoT) data offer numerous opportunities for value creation, particularly in manufacturing industries. For small and medium-sized enterprises (SMEs), many of those opportunities are inaccessible without cooperation across enterprise borders and the sharing of data, personnel, finances, and IT resources. In this study, we suggest so-called data cooperatives as a novel approach to such settings. A data cooperative is understood as a legal unit owned by an ecosystem of cooperating SMEs and founded for supporting the members of the cooperative. In a series of 22 interviews, we developed a concept for cooperative IIoT ecosystems that we evaluated in four workshops, and we are currently implementing an IIoT ecosystem for the coolant management of a manufacturing environment. We discuss our findings and compare our approach with alternatives and its suitability for the manufacturing domain.
Highlights
As a global network that enables real-world assets to interconnect, intercommunicate, and interact digitally, the Internet of Things (IoT) is of interest for all industrial domains that deal with physical goods, where manufacturing is prime
We focus on industrial Internet of Things (IIoT) ecosystems, which are a subtype of a platform ecosystem [49] in which the member enterprises compete and cooperate based on a common set of IIoT assets that are connected over a platform [5]
For data sharing and joint analysis, we interviewed eight members of IIoT projects that all involved data sharing and data analysis across several enterprises
Summary
As a global network that enables real-world assets to interconnect, intercommunicate, and interact digitally, the Internet of Things (IoT) is of interest for all industrial domains that deal with physical goods, where manufacturing is prime. IoT technologies allow organizations to automatically generate digital representations of physical objects that can capture their real-world identities, locations, and states (digital objects). The collection, integration, and analysis of such data enable companies to implement new types of services [1]. Examples documented in the literature include predictive maintenance services for machinery and equipment, process mining for determining bottlenecks in complex production environments, fine-tuning of recipes and sequences for process manufacturing [2], optimization of plant availability, energy management, monitoring and analysis of production and logistics processes [3], and new approaches to the management of packaging [4]. The necessary data for such services can quickly cross company borders [5]
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