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Fairtrace - A Semantic-Web Oriented Traceability Solution Applied To The Textile Traceability
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Fairtrace - A Semantic-Web Oriented Traceability Solution Applied To The Textile Traceability

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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 …

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.)

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  • 1. Fairtrace 1A Semantic-Web Oriented Traceability Solution Applied To The Textile Traceability Tracing the textile industry ICEIS2013, Angers 6th July 2013 Bruno Alves
  • 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 ICEIS 2013, Angers, France 2
  • 3. Problem statement Would you believe ?  ICEIS 2013, Angers, France 3
  • 4. Negotiation pattern ICEIS 2013, Angers, France 4
  • 5. The right(eous) way ICEIS 2013, Angers, France 5
  • 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 ICEIS 2013, Angers, France 6
  • 7. Fairtrace Objectives – Identification of the supply-chain (analysis) – Order management (dashboard) – Tracking and problem reporting (validators) Target audience – Brands and resellers – Consumer market ICEIS 2013, Angers, France 7
  • 8. B2B Frontend Operative Frontend B2B Frontend Operative Backend System overview ICEIS 2013, Angers, France 8
  • 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) ICEIS 2013, Angers, France 9
  • 10. Dashboard ICEIS 2013, Angers, France 10
  • 11. Order management & problem tracking ICEIS 2013, Angers, France 11
  • 12. Designing formulars ICEIS 2013, Angers, France 12
  • 13. Exploring data ICEIS 2013, Angers, France 13
  • 14. Modelling methodology 1. Picture analysis 2. Critical traceability path 3. Basic data model 4. Complete data model 5. Ontology engineering 6. Deployment ICEIS 2013, Angers, France 14
  • 15. Ontology ICEIS 2013, Angers, France 15 RDF/s hasProcessName hasFlowStart …. .. Process OWLIM Ruleset (propertyChainAxioms) hasFabricGSM hasYarnCount …. .. Product
  • 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 ICEIS 2013, Angers, France 16
  • 17. Example of databinding ICEIS 2013, Angers, France 17
  • 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 ICEIS 2013, Angers, France 18
  • 19. Advantages Flexibility – No static schema – No recompilation Expressivity – Using inference rules to achieve desired behaviour ICEIS 2013, Angers, France 19
  • 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 ICEIS 2013, Angers, France 20
  • 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) ICEIS 2013, Angers, France 21
  • 22. Fairtrace Promotional Tee-Shirt ICEIS 2013, Angers, France 22
  • 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 ICEIS 2013, Angers, France 23
  • 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 ICEIS 2013, Angers, France 24 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 ICEIS 2013, Angers, France 25