IoT Validation Element Renderer

PRESSIOUS DATA SPACE

Holding a large market share in the Balkan peninsula in the domain of offset printing, Pressious (PRES) is exhibiting a significant environmental footprint, since its manufacturing process involves an extensive usage of raw materials (water, paper, ink, aluminium), where defected products contribute the largest part. Although the environmental impact of the company and the problems associated with the product defects have already been identified and partially addressed, their current management leads to excessive cost. For this purpose, Pressious R&D team has already collaborated with the National and Kapodistrian University of Athens (NKUA) in order to implement Machine Learning (ML) models, taking advantage of the historical data that have been collected by the company’s production line (3-year historical dataset). Based on this sufficient in size and labelled dataset as well as the experience of the CERTH nZEB DIH with tools like the International Data Spaces Connector (IDS Connector) and the open source platform offered by AI Regio, such as the Apache StreamPipes, the targets of the present activity primarily involve the minimization of the production chain defects, the reduction of the company’s environmental footprint and effective cost reduction/production enhancement through the creation of a Manufacturing Data Space (MDS).

In particular, the key objectives of this project include:

  1. the digitalization of the manufacturing processes adopting end-to-end pipelines (Apache Streampipes), increasing the efficiency and control of the production,
  2. the creation of an MDS, following the FAIR principles, utilizing the existing labelled dataset,
  3. the minimization of the product deficiencies that are observed during the final quality control,
  4. the facilitation of circular economy principles, becoming environmentally friendly by minimizing the use of wasted materials (societal objective), (v) the production cost reduction in order to increase human resource efficiency and enhance the production,
  5. the dissemination of the project outcomes and exploitation of the gathered experience (impact creation objective). 

AI REGIO OC-driven EXPERIMENTS EC Project AI REGIO Validation Type Use Case