Author:
Bagozi Ada,Bianchini Devis,Rula Anisa
Abstract
AbstractIn recent years, Cyber Physical Production Systems and Digital Threads opened the vision on the importance of data modelling and management to lead the smart factory towards a full-fledged vertical and horizontal integration. Vertical integration refers to the full connection of smart factory levels from the work centers on the shop floor up to the business layer. Horizontal integration is realised when a single smart factory participates in multiple interleaved supply chains with different roles (e.g., main producer, supplier), sharing data and services and forming a Cyber Physical Production Network. In such an interconnected world, data and services become fundamental elements in the cyberspace to implement advanced data-driven applications such as production scheduling, energy consumption optimisation, anomaly detection, predictive maintenance, change management in Product Lifecycle Management, process monitoring and so forth. In this paper, we propose a methodology that guides the design of a portfolio of data-oriented services in a Cyber Physical Production Network. The methodology starts from the goals of the actors in the network, as well as their requirements on data and functions. Therefore, a data model is designed to represent the information shared across actors according to three interleaved perspectives, namely, product, process and industrial assets. Finally, multi-perspective data-oriented services for collecting, monitoring, dispatching and displaying data are built on top of the data model, according to the three perspectives. The methodology also includes a set of access policies for the actors in order to enable controlled access to data and services. The methodology is tested on a real case study for the production of valves in deep and ultra-deep water applications. Experimental validation in the real case study demonstrates the benefits of providing a methodological support for the design of multi-perspective data-oriented services in Cyber Physical Production Networks, both in terms of usability of the data navigation through the services and in terms of service performances in presence of Big Data.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computational Mechanics
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