The full article was originally published by Daniel Trauth on Medium. Read the full article here.
This use case was created within the project Blockchain Reallabor für das Rheinische Revier, funded by the Ministry of Economics, Innovation, Digitisation and Energy of the State of North Rhine-Westphalia, with the aim of forming a thematic community of interests. If you feel addressed, please contact us (email@example.com) or contact the project directly via firstname.lastname@example.org or https://blockchain-reallabor.de/. The original publication by the Blockchain Reallabor can be found here (german).
Information from data and models is regarded as a driver of digital progress. The trend of industrial companies to extract monetary value from unique data they own or collect is considered necessary for survival in competition because other companies’ data is needed to optimize their own production and realize network effects .
The installed sensors on production machines collect large amounts of data during each production step. Currently, however, only the operators of such machines generate added value from the collected sensor data. Elementary production data (raw data) can be condensed into smart data with the help of artificial intelligence technologies. This data can be used to illustrate relationships regarding materials, machine states, intermediate products, qualities, consumables and environmental parameters, among other things. However, a robust prediction for process optimization using data-driven models requires data of unforeseeable and unexpected machine failures . Such data is usually lacking, especially for small businesses. New data sets are significant even if the process framework conditions (quality characteristics such as processing speed or edge infeed) change, as the standard data are not sufficient to optimize the sequence and control of the processing . In such cases, manufacturers or suppliers of materials, tools or machines try to generate data by means of test runs and attempts to improve their own products or detect errors. Such data usually originates from a test environment far away from regular operation.
B2B data trading usually fails because of the existing mistrust regarding the origin, integrity, quality and validity of production data as well as the underlying intention of the company . Network effects between companies, such as the improvement of their own products, machines, processes or services through information from the upstream or downstream supply chain or through acquired technology data, are therefore not taken into account due to the lack of data exchange. If they were to be exchanged, data analysis and subsequent pattern derivation for learning effects for process optimization would require the expertise in the field of “Artificial Intelligence (AI)” of so-called data scientists. However, these specialists are rare .
The blockchain solution enables all market players (manufacturing companies, suppliers and data scientists) to make the cross-company handling of data goods transparent and comprehensible in order to make data usable in the sense of an economic good. In contrast to edge-based data storage options, which are managed centrally by one entity, blockchain technology is a geographically distributed, practically forgery and manipulation-proof database.