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Semantic Blockchains in the Supply Chain

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We build on past work proposing the use of Linked Pedigrees in a semantic technology framework to propose the use of blockchain technology to solve part of the trust issues in the agri-food supply chain.

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Semantic Blockchains in the Supply Chain

  1. 1. Semantic blockchains in the supply chain Christopher Brewster Aston University C.A.Brewster@aston.ac.uk
  2. 2. Outline • Information integration in the agrifood supply chain • Semantic web technologies • Semantic Web technologies in the Food System: Linked Pedigrees • Some limitations • Blockchain technologies • Integrating Semantics and the Blockchain - some initial thoughts
  3. 3. The Problem
  4. 4. The Food Supply Chain • From Farm to Fork • The agri-food system includes much more • More and more parts of this supply chain and agri-food system are leaving digital trace … in James Scott’s terms becoming more “legible”.
  5. 5. Data - Information - Knowledge • The food supply chain involves hundred of actors, thousands of processes, millions of products and (potentially) billions of data points! • Children believe milk comes from supermarkets! • Too much or too little data? • Why do we need it?
  6. 6. Characteristics of Supply Chain • Large numbers of participants • Heterogeneity of participants • Huge variety in ICT uptake • Poor information flow (need to know attitude) • Only one up, one down data flow • Solved by regulation and certification
  7. 7. Food supply chain is … • A highly heterogenous loosely coupled large-scale network of entities with variable but largely minimal degrees of communication and trust between the actors
  8. 8. Drivers for Data Integration • Need for transparency - tracking and tracing • Desire for food awareness - on the part of consumers, but not only • Regulatory pressure - e.g.EU Regulation 1169/2011 • New business opportunities ….
  9. 9. Food Crises and Scandals • Major driver for greater data integration (whether open or closed). • E. Coli in Germany in 2011 - spanish growers lost over €200M • Horsemeat scandal across Europe in 2013 - impact very great on some supermarkets
  10. 10. Lack of Data Integration • Both scandals suffered from lack of data and data integration • E. Coli - who affected? what purchased? where? when? and who participated in the supply chain • Horsemeat - six months for Irish FSA to map the supply chain network • Need for greater supply chain transparency = need for data integration
  11. 11. Tracking, tracing and Visibility • Core demand is to make tracking and tracing easy, AKA supply chain “visibility” • “Visibility is the ability to know exactly where things are at any point in time, or where they have been, and why” — GS1 • Major challenge http://www.gs1.org/docs/GS1_SupplyChainVisibility_WhitePaper.pdf
  12. 12. Trust - 1
  13. 13. Trust - 2 •More information, more data = more trust
  14. 14. What role semantic technologies?
  15. 15. Key Features of Semantic Technologies • Unique identifiers (URIs) — enables consistency and data accretion • Common vocabularies/ontologies/data schemata — creates a tendency towards standardisation WITHOUT losing flexibility • Linking and mappings - create a natural space for new knowledge and data integration • Logical rigour — rules for validation and quality control can be written
  16. 16. AKTive Food (2005) • A bit of history • Paper on “Semantic Web based knowledge conduits for the Organic Food Industry” • Centred on decision support for a restaurant based on data crawled from semantic web marked up websites of food producers • Nice vision …. but no implementation
  17. 17. Linked Pedigrees • Based on “pedigree” concept common in pharmaceutical industry - an audit trail which record path of ownership • Based on GS1 standards (pedigree standard + EPCIS) • “Linked pedigrees” use semantic web/linked data principles • Involves formalisation of EPCIS standard in two ontologies
  18. 18. Linked Pedigrees • Datasets described and accessed using linked data principles. • Encapsulate the knowledge required to trace and track products in supply chains on a Web scale. • Facilitate the interlinking of a variety of related and relevant data, i.e., GS1 product master data with event data PLUS other data outside the GS1 system. • Based on a domain independent data model for the sharing of knowledge among Semantic Web/Linked data aware systems deployed for the tracking, tracing and data capture. • Product knowledge shared among partners as products physically flow downstream or upstream in the supply chain.
  19. 19. Linked Pedigree Architecture
  20. 20. Linked Pedigrees and EPCIS • Formalisations of: • EEM - The EPCIS Event Model • CBV - Core Business Vocabulary • This allows the representation of EPCIS events on the Web of Data • This enables sharing and traceability of information • Tracking of inconsistencies
  21. 21. Socio-technical Limitations • Heterogeneity of the food system - so many different actors, in size and shape • Continuous changes - actors entering and leaving the market • Lack of trust - actors (farmers, food producers) do not trust overarching systems • Cost - margins in the food system are very tight
  22. 22. Key Problems • Any form of centralised data runs into data control issues. Who know what? (cf. Uber as an example) • If each actor must keep their triple store up and running - data access issues (important in food crises)
  23. 23. What role blockchain technologies?
  24. 24. What is the blockchain? • A file called The Blockchain is spread over millions of machines • Which use proof of work and byzantine consensus • To provide a set of chained hashes and digital signatures • To create an unforgeable record of …. (e.g.) who owns how much bitcoin
  25. 25. Blockchain - another definition • “A blockchain is a magic computer that anyone can upload programs to and leave the programs to self-execute, where the current and all previous states of every program are always publicly visible, and which carries a very strong cryptoeconomically secured guarantee that programs running on the chain will continue to execute in exactly the way that the blockchain protocol specifies.” — Vitalik Buterin (founder of Ethereum)
  26. 26. Blockchain • Developed originally as part of Bitcoin • Provides underlying distributed ledger for Bitcoin • HOWEVER, quite separate from Bitcoin and has potentially many other uses • Lots of eager uptake with many startups being founded around this technology (Ethereum, Bitshares, Helloblock, Ripple Labs etc.)
  27. 27. Blockchain in the supply chain • Not my idea! Other people have thought of this! • Startup provenance.org wants to use the blockchain to “tell a story” about a product from producer to end consumer. Currently focussing on certification data! • Still working on on what data to represent ….
  28. 28. Semantic Blockchains • Concept: Construct an architecture where some or all of the data involved in Linked Pedigree is held on the block chain • Result: • Distributed database would resolve some trust issues • Guarantee of continuous uptime (so if an actor disappears, their data is still accessible) • Rules can be written as to who has access to data using specific governance algorithms
  29. 29. Step 1: Basic Usage Eliminate the problems of data centralisation
  30. 30. Step 2: More advanced Usage Guarantee accessibility of data now and in future
  31. 31. Other potential Consequences • Disintermediation of GS1 for product data • Product data, tracking and tracing and supply chain visibility at very low cost. This could be very important for small scale producers/ developing country producers • Standardising supply chain data schemata/ ontologies by the back door
  32. 32. Conclusions • We have argued for the importance of data integration in the agrifood supply chain • We showed the applicability of semantic technologies in the supply chain and introduced the concept of “linked pedigrees”. • We then suggest that blockchain technologies could further improve this technology stack and solve some problems e.g.lack of trust in centralised data control
  33. 33. Thanks • Monika Solanki for all the technical work on Linked Pedigrees, EEM, CBV and much else • Vinay Gupta for conversations leading to the Semantic Blockchain ideas • Jessie Baker for explaining provenance.org
  34. 34. References • Christopher Brewster, Hugh Glaser, and Barny Haughton. “AKTive Food: Semantic Web based knowledge conduits for the Organic Food Industry.” In Proceedings of the ISWC Worskshop Semantic Web Case Studies and Best Practice for eBusiness (SWCASE 05) , 4th International Semantic Web Conference, 7 November (Galway, Ireland, 2005). URL http:// www.cbrewster.com/papers/Brewster_SWCASE.pdf • Monika Solanki and Christopher Brewster. “OntoPedigree: A content ontology design pattern for traceability knowledge representation in supply chains.” Semantic Web – Interoperability, Usability, Applicability (2015). URL http://goo.gl/OdUPg0 • Monika Solanki and Christopher Brewster. “Enhancing visibility in EPCIS governing Agri-food Supply Chains via Linked Pedigrees.” International Journal on Se- mantic Web and Information Systems 10 (2014). (Impact Factor 0.393), URL http:// www.ijswis.org/?q=node/52 • Monika Solanki and Christopher Brewster. “Consuming Linked data in Supply Chains: Enabling data visibility via Linked Pedigrees.” In Proceedings of the Fourth Inter- national Workshop on Consuming Linked Data (COLD2013), held at the Interna- tional Semantic Web Conference (ISWC 2013), 21-25 October 2013 (2013). URL http://www.cbrewster.com/papers/ Solanki_COLD13.pdf • Monika Solanki and Christopher Brewster. “Representing Supply Chain Events on the Web of Data.” In Proceedings of the 3rd International Workshop on Detection, Represen- tation, and Exploitation of Events in the Semantic Web (DeRiVE 2013), , held at the International Semantic Web Conference (ISWC 2013), 21-25 October 2013 (2013). URL http://www.cbrewster.com/ papers/Solanki_DeRiVE13.pdf • Monika Solanki and Christopher Brewster. “EPCIS event based traceability in pharmaceu- tical supply chains via automated generation of linked pedigrees.” In International Semantic Web Conference 2014 (ISWC 2014) (Rivo di Garda, 2014). (ISWC accep- tance rate: 21.1% 180 full submissions, 29 accepted, 9 conditionally accepted), URL http://www.cbrewster.com/ papers/Solanki_ISWC14.pdf

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