MSEE represents an initial but decisive step towards uplifting manufacturing services to a much higher engineering maturity level, by developing new models, processes and tools (called MSEE Bag of Assets), with the final aim of a... more
MSEE represents an initial but decisive step towards uplifting manufacturing services to a much higher engineering maturity level, by developing new models, processes and tools (called MSEE Bag of Assets), with the final aim of a peer-to-peer interconnection between service engineering and product engineering along the whole product-service lifecycle. This research and innovation stream, initiated by MSEE in the Virtual Factories domain, is one of the main pillars of H2020 Factories of the Future research agenda.
ABSTRACT When manufacturers join forces to create, manage, and offer new Product-Services in globalized markets, a huge amount of inter-organizational data on tangible and intangible assets is generated in corporate knowledge bases. This... more
ABSTRACT When manufacturers join forces to create, manage, and offer new Product-Services in globalized markets, a huge amount of inter-organizational data on tangible and intangible assets is generated in corporate knowledge bases. This data implies new economic opportunities as well as barriers. The presented approach depicts how e-business companies can benefit from virtualized assets - namely Assets as a Service - for Business Intelligence (BI) in Manufacturing Service Ecosystems (MSE). Thanks to Assets as a Service, more valuable, reliable, and structured data are available within the MSE, ready to be further evaluated, elaborated, or visualized. In this context, BI techniques can be used to automatically deduce implicit dependencies among assets. The purpose of this paper is to advance the understanding and adoption of BI practices in MSE by applying formal semantics also to collaboratively gather, monitor, and analyze shared data about production assets. Consequently, the findings of this work empower MSE members to take better decisions while managing Product-Service innovations in e-business scenarios, hence to create more value. Results are outlined through the example of an industrial scenario.
ABSTRACT Nowadays service sector is becoming more and more relevant in building successful collaborative economies. In this environment Virtual Enterprises (VEs) are forcing a change in the way traditional manufacturing systems are... more
ABSTRACT Nowadays service sector is becoming more and more relevant in building successful collaborative economies. In this environment Virtual Enterprises (VEs) are forcing a change in the way traditional manufacturing systems are managed. Therefore measuring service performances plays an important role in turning company strategic goals into reality. Performance Indicators (PIs) consist in a supporting tool to assess service efficiency and effectiveness. Consequently, determining the most significant activities which need to be controlled and measured through proper PIs becomes essential for VEs. Within this paper, a PI Toolset is going to be presented and tested through industrial use case. The PI Toolset has been developed to support VEs in the selection of significant ac-tivities, to manage governance processes and to support the design and imple-mentation of specific PIs related to the precise use case objectives. Finally, a lesson learned approach has been adopted so to stress strengths and weakness of both proposed methodology and Tools.