Fairtrace

1A Semantic-Web Oriented Traceability Solution Applied To The Textile Traceability

Tracing the textile industry

ICEIS2013, Angers 6th July 2013

Bruno Alves

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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Problem statement

Would you believe ?

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Negotiation pattern

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The right(eous) way

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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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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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B2B Frontend Operative Frontend

B2B Frontend

Operative Backend

System overview

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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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Dashboard

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Order management & problem tracking

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Designing formulars

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Exploring data

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Modelling methodology

1. Picture analysis

2. Critical traceability path

3. Basic data model

4. Complete data model

5. Ontology engineering

6. Deployment

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Ontology

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RDF/s

hasProcessName hasFlowStart

…. ..

Process

OWLIM Ruleset

(propertyChainAxioms)

hasFabricGSM hasYarnCount

…. ..

Product

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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Example of databinding

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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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Advantages

Flexibility

– No static schema

– No recompilation

Expressivity

– Using inference rules to achieve desired behaviour

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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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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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Fairtrace Promotional Tee-Shirt

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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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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 !

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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