This paper presents solutions that leverage Semantic Web Technologies (SWT) to allow pragmatic traceability in supply-chains, especially for the textile industry. Objectives are the identification of the supply-chain, order management, tracking and problem reporting (such as dangerous substance detection). It is intended to be a generic platform supporting potentially any kind of industrial supply-chain, to be usable in harsh environments (mobile appliances) without any kind of communications possibility and to be fully usable to non-IT people, including for the modeling of the production processes. The developed solutions also allow the consumer to benefit from the traceability through information pages available by scanning the QR codes available on the finished products (clothes, clocks, etc.)
2. Definition
Trace∙a∙bil∙i∙ty
/ˌtreɪsəˈbɪləti / noun
’’ the ability to discover information about where
and how a product was made
Source: http://dictionary.cambridge.org/dictionary/business-english/traceability
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6. Our proposed solution
Fairtrace
General Traceability Framework
Fairtrace is made to be Simple
Fairtrace is made to be Generic
Fairtrace is made to be Adaptable
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7. Fairtrace
Objectives
– Identification of the supply-chain (analysis)
– Order management (dashboard)
– Tracking and problem reporting (validators)
Target audience
– Brands and resellers
– Consumer market
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8. B2B Frontend Operative Frontend
B2B Frontend
Operative Backend
System overview
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9. Infrastructure
Backend VM-Linux Server + BigOWLim 3.5
+ Apache Tomcat 6 + Spring MVC
(3.0)
B2B Frontend Ruby On Rails (Fontend), VM-
Linux Server (HTML5 + JS)
B2C Frontend Print-to-mobile (custom)
Partners One android phone each
… < 10000 triples per order (including the model)
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16. Linking forms and model
Databinding
– Bind a field to a property of the product model
• Example: Process#name
<fpo:Process> <fpos:hasProcessName> ‘value’^^xsd:string
Instantiation
– Assert triples from data in the forms
• Values are instantiated following databinding name
• A dataset links a user, collection point, ts and context
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18. Problems & solutions
Genericity can yield holes in the traceability
chain
– Design smart rules (Order -> Fabric -> Yarn)
Automatic classification of the data
– Simulating class restrictions with
rdfs:subPropertyOf and rdfs:domain
Optimize querying patterns
– Aggregate data at function points
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19. Advantages
Flexibility
– No static schema
– No recompilation
Expressivity
– Using inference rules to achieve desired behaviour
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20. Disadvantages
• Structuration
– Thinking in terms on inferences and not in terms
of structure
• Can be substantially slower
– Uses an underlying DBMS
• Maintaining the coherence of the data
– Easy to create contradicting statements
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21. Current situation
• Prototype tested in India with a real order
– 5 indian partners
– Complete order lifecyle
• Patent pending
• Prototype -> Commercialization process
• Fairtrace SA (Ltd)
• Many companies showed interest (Reizl,
Gantt, Migros, …) and state organisms (Army)
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23. Future developments
• Commercialization process
• Transition model to OWL2 RL (no need for
specific rules)
• Strong validation support
– Inbound rules
– Outbound rules
• Integeration with ERP systems
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24. Bruno Alves
Senior Research Associate
Email: bruno.alves@hevs.ch
Phone: +41276069034
Bruno Alves
Senior Research Associate
Email: bruno.alves@hevs.ch
Phone: +41276069034
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Thank you !
25. Reference
1A Semantic-Web Oriented Traceability Solution
Applied To The Textile Traceability
Bruno Alves, Michael Schumacher, Fabian Cretton, Anne Le Calvé, Gilles
Cherix, David Werlen, Christian Gapany, Bertrand Baeryswil, Doris Gerber,
Philippe Cloux
In 15th International Conference on Enterprise Information
Systems, July 4-7, 2003, Angers, France
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