Presentation by Angelo Marguglio Research Area Manager and Head of the “Smart Industry and Agrifood” Unit, Engineering Ingegneria Informatica SpA FIWARE Global Summit 21-22 May 2019 - Genoa, Italy

1. Implementing Data Sovereignty:
a FIWARE-MINDSPHERE Interoperability Scenario
Angelo Marguglio
FIWARE SUMMIT Genoa
Tuesday May 21st 2019

2. The IIOT disruption
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§ Common technology that spans industries brings bold new approaches
and enables fast change
§ The real value is a common framework that connects sensor to cloud,
integrated existing and heterogenous data sources, interoperates
between vendors, and spans industries
INTEROPERABILITY IS THE KEY

3. The context: Open and Commercial Digital Platforms
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IDSA is working to build data-
centric ecosystems and value
networks built on trust, using
standardized interoperability and
promoting security and data
sovereignty. Value added apps and
data markets enable the creation
of new business value for data
MindSphere is the cloud-based,
open IoT operating system from
Siemens that connects your
products, plants, systems, and
machines, enabling you to harness
the wealth of data generated by
the Internet of Things (IoT) with
advanced analytics

4. A Powered by FIWARE solution: FI-MIND Bridge
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Smart Industry DAta Model (SIDAM)
Improving interoperability to support
more and more data to be shared
Data Sovereignty
Ensuring a secure data exchange
among parties

5. FI-MIND Bridge: Main benefits
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Advantage: bringing FIWARE data to MindSphere – Industrial companies use
MindSphere: the FI-MIND allows to bring all the data managed / manageable by a FI-
MIND lane
Several levels of data can be ingested in order to cover information coming from
enterprise systems (ERP systems, legacy systems, etc.), operational systems (assembly
line, sensors systems, etc.) and context sources (open data, environment data, etc.).
Using FIWARE data models (SIDAM – Smart Industry Data Models) allows to increase
the level of interoperability between systems avoiding custom processing related to a
particular business scenario and its pilot.

6. FI-MIND Bridge: Main outcomes
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7. FI-MIND Bridge: Smart Industry DAta Models (SIDAM)
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SCOPE DATA
Specific data for Maintenance,
Planning, Quality, etc.
DATA AT REST
From ERP, PLM and other systems
DATA IN MOTION
From Real World such as Devices,
Machines, etc.
CONTEXT DATA
External sources such as Smart
City, Environment, etc.
A simplified taxonomy (ontology, IS-A hierarchy) of Smart Industry Objects (IoT
Boards, IoT Trackers, Containers, Trucks, …) in the chosen domain

8. Experiment in a Smart Supply Chain Scenario
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§ Problems such as delays can happen during
transportation from supplier plant to
production plant, causing replanning of the
production, with an increase of the costs and
lack of efficiency
§ Real time monitoring
§ Near real time integration and visualization
of transportation data (data in motion) with
data about supply schedule (data at rest)
§ Data storage for analysis of historical
behaviour to allow the prediction of events
§ Reduction of production replanning
§ More efficient supply chain

9. Experiment in a Smart Supply Chain Scenario
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10. www.eng.it
@EngineeringSpa
Engineering Ingegneria Informatica Spa
gruppo.engineering
Angelo Marguglio
Head of «Smart Industry» Unit
angelo.marguglio@eng.it
THANK YOU