# Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems
## 5 Pilot Implementation in the Realm of Smart Manufacturing  
We are currently implementing our concept in the domains of manufacturing, IIoT financing, and crafts. The objective of all cases is to establish sustainable data cooperatives for sharing and analyzing IIoT data. The manufacturing initiative has progressed the farthest, and we use it to illustrate how our concept can be applied in a real-world setting.  
The manufacturing initiative is focused on data- and analytics-enabled coolant management. It currently comprises a manufacturing company, provider of coolant management equipment, and supplier of cooling lubricants. A common goal is to enable more efficient and effective coolant management. In general, coolant management is not only costly but also directly affects production quality and processes (the production needs to be stopped for coolant replacement), as well as causing potential health hazards (risk of toxic fumes).  
The manufacturer wants to outsource as much of the coolant management as possible (including reporting tasks needed for compliance reasons), a task the lubricant provider is willing to take over as it wants to diversify its services. The equipment provider delivers innovative smart machinery (with a battery of sensors) and hopes to develop both its products and the market. The cooperative value proposition of the data cooperative is enabling a \"cooling lubricant as a service\" setting.  
At the core are both descriptive and prescriptive monitoring and analysis services that are supposed to be managed mostly on a cooperative platform (although there are local \"analytical atoms\" needed as well for questions of the immediate coolant steering). The initiative started with basic reporting scenarios but has already explored more complex IIoT analytics services (e.g., the prediction of the time the coolant becomes problematic, the optimization of coolant recipes, or image analysis for coolant supervision).  
In the current form, the data (including the data transformations and an expandable data model) and the logic and access components run in separate cloud environments for reasons of licensing, but the architecture is not final. All analyses require data from all partners who also provide different real objects: cleaning systems, manufacturing  
Figure 6: Summary of the findings.  
halls, machining centers, lubricants, and tanks. With the maturity of the pilot system, aspects of governance gain relevance; the consortium is currently exploring what level of confidentiality is needed for what data source and how to deal with data quality issues. This also leads to questions of pricing, as we found a plethora of tasks that need to be defined, assigned, and dealt with on the accounting side; for example, the installation and maintenance of the sensors, the ongoing correction of measurement errors, issues of data integration, requirements elicitation, model validation, and more.  
To expand the database, better models and design further-reaching services are planned, as well as manufacturers of production equipment and/or insurance companies in the ecosystem. It is also possible to add further manufacturers and potentially lubricant suppliers, although this would immediately require stricter governance, accounting, and trust-building mechanisms, as the complementary nature of the current consortium would be lost.