Recommended
More Related Content
What's hot
Similar to Achieving Supply Chain Traceability through Data Science
Similar to Achieving Supply Chain Traceability through Data Science (20)
Recently uploaded
Recently uploaded (20)
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.