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Achieving Supply Chain Traceability through Data Science
- 1. Achieving Supply Chains Traceability through Data Science Business Problem Presenter: Er Raqabi, El Mehdi Supervisor: Wenkai, Li
- 2. Situation Literature Insight Applications Benefits & Limitations 2 Content Star Conferences 2019
- 3. 3 Introduction Star Conferences 2019 Global Supply Chains Traceability Counterfeiting Data Science ML & AI Products / Services Financial Information Did I get a Fake Product
- 4. 4 Case 1 – Sanlu Milk – Domestic Star Conferences 2019 Melamine Fool Protein Tests 300,000 Sick Babies 6 Babies Died Dairy Industry Devastated
- 5. 5 Case 2 – Australian Beef – Global Star Conferences 2019 Cryovac Bag Different Beef 1Kg Counterfeit vs 1Kg Real Worth Billions of $ Customers Complaints
- 6. 6 Case 3 – Amazon – Online Star Conferences 2019 No Regulation Third Party 90% to 100% Gross Margin Little Differentiation 40% of Paid Items Disappointed Customers Little Incentive for Regulation
- 7. Literature - RFID 7Star Conferences 2019 Radio-Frequency IDentification Product Side -> Counterfeiting Unique Identification Increased Visibility Track & Trace Maintain Data Counterfeit Tags exist across billions of RFID Tags
- 8. Literature - Blockchain 8Star Conferences 2019 Linking Blocks Through Cryptography Information Side -> Traceability Reliability Transparency Disintermediation Durability Redundancy – Signature – Consensus
- 9. Insight 9Star Conferences 2019 1 LEARN 2 IDENTIFY 3 EXECUTE From Supply Chain The Critical ZonesBy Intelligent Agents Information SideProduct Side
- 10. Insight – Information Side 10Star Conferences 2019 Information Side Learn about SC Data Mining Set of Points Identify Critical Points Available Data Data Preparation Data Mining Knowledge ExtractionData Science
- 11. Product Side Insight – Product Side 11Star Conferences 2019 Learn about the Product Machine Learning: Classification, Clustering, Regression Apply within Critical Points Intelligent Agent Checks or Executes Tasks in Critical Points Humans Learn Teach Agents Better Level Performance Risk Control & ReductionML & AI
- 12. Application – Case 1 – Sanlu Milk 12Star Conferences 2019 Issue Between Farm & Factory Samples from Different Points Data Science for Critical Points (Un)Supervised Learning Apply through Domestic SC Traceability
- 13. Application – Case 2 – Australian Beef 13Star Conferences 2019 Teach Agents Data Science for Critical Points Classification or Clustering Apply through Global SC Issue within the Chinese Context Learn Australian Beef Features Regulation
- 14. Application – Case 3 – Amazon 14Star Conferences 2019 Reviews Data Selection NLP Model Classification or Regression Risk Value on each Product 85% Identify Critical 3Ps Peer Review
- 15. Benefits & Limitations 15Star Conferences 2019 Benefits Limitations Both Sides Advantage Humans vs Machines Data Selection Data Availability Current AI & ML State Technological Trend Stakeholders Involvement Investment in Agents
Editor's Notes
- Why this topic is important? First Case – Sanlu: Domestic Case, China, 2008.
- Nearly 40% of paid items from Amazon are from 3P merchants.
- Talk briefly about other methods (ERP, Contracts, Inspection, etc), how to track information or products in a supply chain?
- Slower than Centralized Databases because it carries three additional burdens: Redundancy, Signature Verification, Consensus Mechanisms. -Redundancy: Whereas centralized databases process transations once (or twice), in a blockchain they must be processed independently by every node in the network. -Signature Verification: Every blockchain transaction must be digitally signed using a public-private cryptography scheme. This is necessary because transactions propagate between nodes in a peer-to-peer fashion. -Consensus Mechanisms: In a distributed database such as a blockchain, efforts must be expended in ensuring that nodes in the networ reach consensus.
- Movies reviews using NLP and Tfidf, then I got the idea to Scale it to supply chain. Capstone Project in Bombardier Aerospace: Departments Analysis of tasks execution -> Lazy/Problematic.
- Data Science Part: - Learn about the Supply Chain. - Utilize Data Mining. - Define Supply Chain Set of Points. - Identification of Critical Points with High Counterfeiting Probability or Lack of Traceability such as Packaging, Distribution, etc.
- More into the HOW? I should find more insights. AI Part: - Learn about the Product. - Utilize Supervised/Unsupervised Learning. - Classify Real (1) or Counterfeit (0) Product. - Apply within Supply Chain Critical Points. - Machine check the Human output or execute Human tasks in critical points.
- Can be applied to any online product or service. Peer reviews verification and selection (real ones) and then utilization.