A solution for processing supply chain events within ontology-based descriptions

A solution for processing supply chain
events within ontology-based descriptions
Date: October, 2016
Contact information
Tampere University of Technology,
FAST Laboratory,
P.O. Box 600,
FIN-33101 Tampere,
Finland
Email: fast@tut.fi
www.tut.fi/fast
Conference: 42nd Annual Industrial
Electronics Conference (IECON2016).
Florence, Italy – October 24-27, 2016
Title of the paper: A solution for
processing supply chain events within
ontology-based descriptions
Authors: Borja Ramis Ferrer, Wael M.
Mohammed, José L. Martinez Lastra
If you would like to receive a reprint of
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A solution for processing supply
chain events within ontology-based
descriptions
Authors: Borja Ramis Ferrer, Wael M. Mohammed, José L. Martinez Lastra
{borja.ramisferrer, wael.mohammed, jose.lastra}@tut.fi
Tampere University of Technology
Factory Automation Systems and Technology Lab
42nd Annual Industrial Electronics Conference (IECON2016). Florence, Italy –
October 24-27, 2016

Outline
A solution for processing supply chain events within
3
• Introduction
• Motivation
• Related work
• Processing supply chain events within ontology-based
descriptions
• Case study
• Testing the approach
• Conclusions
• Further work

4
Introduction
• Industry is constantly moving towards the research,
implementation and deployment of new solutions that
permit the optimization of processes.
• Current ICT developments permit the collection,
distribution, integration, analysis, and manipulation of
heterogeneous data.
• The C2NET project targets the development of cloud-
enabled tools for supporting the SMEs supply network
optimization of manufacturing and logistic assets.

5
Motivation
• The C2NET platform
needs a solution for
catch, process and react
to events triggered in
different locations of
collaborative
manufacturing networks,
which are endowed with
devices that permits the
integration of Cyber-
Physical Systems (CPS)
VALUE&CHAIN&
OPT& DCF&
Plans&Op5miza5on&
Services&
COT&
C2NET&
Sharing&Distribu5on&Assests&
Sharing&Logis5cs&Assets&
Sharing&&Product&Stocks&
Sharing&&Components&Stocks&
Sharing&Manufacturing&Assests&
Stocks&and&
Manufacturing&Real&
Time&Data&Collec5on&
Suppliers&Events&
Procurement&Plans&
Collabora5ve&
Decision&Making&
Produc5on&Plans&
Collabora5ve&
Decision&Making&
Distribu5on&Plans&
Collabora5ve&
Decision&Making&
Stocks&and&Sales&Real&
Suppliers&Events&
Manufacturing&Real&
Manufacturers&Events&
AUTOMOTIVE&USE&CASE&
METALWORKING&SME’s&NETWORK&USE&CASE&
DERMONCOSMETICS&USE&CASE&
Collabora5on&
Services&
Data&&Collec5on&&&
Storage&Services&
OEM&USE&CASE&
Sharing&Logis5cs&Assets&/&Sharing&&Components&Stocks&

Related work (1/4)
6
• The automation pyramid is constructed mainly by 3 layers,
Enterprise Resource Planning (ERP), Manufacturing
Execution System (MES) and the factory shop floor
• Supply chain represents the relation between suppliers,
manufacturer and customers
• Controlling and processing mechanisms are required due
to the massive amount of events in supply chain.

Related work (2/4)
7
• The C2NET platform aims to provide a media for supply
chain partners to communicate. The C2NET DCF allows
users to collect data regardless the variations in the
technologies which they use http://c2net-project.eu/
• Complex Event Processing (CEP) engine is still required
for process the events. However, CEP engines require a
reasonable time for configurations and rule design

Related work (3/4)
8
• Industry increased the implementation of knowledge-
based systems.
• Ontologies are nowadays employed as a mean to
describe knowledge of the system to be controlled.
• OWL is an RDF-based language that permits the
representation of knowledge of any domain within different
elements as e.g. individuals, classes, relations,
restrictions, functions, axioms or rules.

Related work (4/4)
9
• OWL models can be queried within SPARQL in
order to consult ontological data. Moreover
SPARUL can be used to update RDF graphs.
• On the other hand, reasoning engines (or
reasoners) are capable of analyzing ontologies
and inferencing knowledge.

10
The approach
• This paper presents the architecture and main
functionality of a knowledge-based approach that allows
processing supply chain events handled by CPS devices

11
Processing supply chain events within
ontology-based descriptions (1/7)
• In this research work, all events are considered as self-
described events.
• The events are issued from the WS enabled device once
a logic criterion is matched
• An example:
{
“eventId”: “highCurrent”,
“publisherId”: “robotA”,
“timestamp”: 1462095503510
}

12
• The SECA ontology includes semantic descriptions of
status and event-related information.

13
• High level architecture:

• Execution of the event processing:
14

15
• Query for subscribing to events:
PREFIX seca: http://www.tut.fi/FAST-‐SECAOnt#
SELECT ?events ?eventURL
WHERE
{
?Conclusion seca:needsEvent ?events.
?events seca:hasURL ?eventURL.
}

16
• Query for updating the event status after notification:
PREFIX seca: http://www.tut.fi/FAST-‐SECAOnt#
PREFIX xsd: http://www.w3.org/2001/XMLSchema#
DELETE{
seca:event_A seca:hasStatus ?oldStatus.
}
INSERT{
seca:event_A seca:hasStatus seca:Triggered.
seca:event_A seca:hasTimestamp "1461920085584"^^xsd:int.
}
WHERE{
seca:event_A seca:hasStatus ?oldStatus.
}

17
• Query for processing events:
PREFIX seca: <http://www.tut.fi/FAST-‐SECAOnt#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT ?Conclusion ?Action ?Consumer
WHERE
{
{
SELECT ?Conclusion ?TimeWindowConclusion ?Action
(MAX(?TimestampEvent) AS ?MaxTS)
(MIN(?TimestampEvent) AS ?MinTS)
WHERE {
?Event seca:hasTimestamp ?TimestampEvent.
?Event seca:hasStatus seca:Triggered.
?Conclusion seca:needsEvent ?Event.
?Conclusion seca:hasTimeWindow ?TimeWindowConclusion.
}
GROUP BY ?Conclusion ?TimeWindowConclusion ?Action
}
?Conclusion seca:implies ?Action.
?Action seca:bindedTo ?Consumer
FILTER(?TimeWindowConclusion >= (?MaxTS -‐ ?MinTS))
}

18
Case study
• Testing model instances
6 Events
2 Status
4 Conclusions
4 Actions

19
Testing the approach (1/2)
Timing diagram of
event execution
Conclusion A:
hasTimeWindow 1000
needsEvent A,B,C
implies action_A
Conclusion D:
hasTimeWindow 500
needsEvent B,C
implies action_D

20
Testing the approach (2/2)
• Result of the query execution used for processing
events:

21
Conclusions
• This research work offers a solution that can be
employed by the C2NET platform not only to catch and
process events;; but also to notify linked data consumers
• This is a light alternative to regular CEP solutions that
can be easily integrated with C2NET modules
• This solution abstracts the users from understanding the
insights of implementation

22
Further work
• The solution must be tested in a real case scenario, in
which the ontology will be populated with real data and
service descriptions
• The speed of communication and amount of events to be
handled must be tested before deploying presented
solution in the real platform
• The interface of the KB engine could be further extended
in order to be interoperable with other service types.

Acknowledge
• The research leading to these results has received
funding from the European Union’s Horizon 2020
research and innovation program under grant agreement
n° 636909, correspondent to the project shortly entitled
C2NET, Cloud Collaborative Manufacturing Networks .
23

24
THANK YOU!
Any questions?
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A solution for processing supply chain events within ontology-based descriptions

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