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Nowadays nearly all manufacturing assets, e.g. machines, sensors, devices, software applications and even employees, are generating zillions of data. This huge amount of data offers a great opportunity to those organisations that are able to understand the data they produce and the benefits they can get by touching some configurations they never thought it would make a real difference.

Many data analytics applications enable the easy exploration of the data ingested into the internal data storages or warehouses by running simple and visual queries and drilling in the graphical reporting offered. More concretely, the ICE Dashboard allows an intuitive way of drilling down to the data, from the top layer (a factory shown on a map) down to the manufacturing asset, i.e. sensors or applications, passing over through different levels and enabling the selection of production lines and machines adapting to the complexity of manufacturing production lines. Once the asset has been selected, several dashboards project the data gathered and conclusions could be inferred by the human operator or the manager.

Figure 1: Visualising Data for one of a CREMA Use Case through ICE Dashboard

Once the main data dashboard is on the screen, several graphs and visualisations should be able to be browsed. Within the ICE Dashboard, there exists the possibility of exploring several assets’ attributes against meaningful and self-defined KPI’s or display live data in several layouts. A functionality included in the ICE Dashboard is linear regression to extract hidden relationships between parameters. These relationships will be unleashed and insights can be extracted to optimise production lines by adjusting the machines’ configurations. Another example is trend lines graphics which will help in analysing how the machines are behaving under certain conditions leading to a full understanding of production lines and manufacturing assets.

Standardising Big Data in the Manufacturing domain

Innovation needs to be carried out to improve the manufacturing industry. The usage of standards is a must to reach such innovation. During the 23rd ICE/IEEE ITMC Conference (Madeira, 27-29 June, 2017) a workshop of the CEN (European Committee for Standardisation) was held to evaluate the technical requirements of three domains through three representative projects: Aquaculture (BigDataOcean), Manufacturing (CREMA), Training (ACACIA) and Ethics (e-SIDES). This workshop worked on the needs from these domains/projects allowing the CEN Big Data community to define some technical requirements that will enable innovation in different sectors, turning the available local and heterogeneous large volumes of data in a universally understandable open repository of data assets. The innovation is coming from these sectors, by addressing the problem of global knowledge access and data exchanges between companies and its related stakeholders. Once the outcomes of this CEN Workshop are analysed by the CEN Community, a roadmap will be drawn to develop a Standard in Big Data.