Machine Learning and Applications in Supply Chain - VRDS

1. MACHINE LEARNING AND APPLICATIONS IN SUPPLY CHAIN

2. DARIAN RASHID • Lean Six Sigma Master Black Belt • Organizational Transformation Specialist • Over 20 years of experience in organizational transformation and process improvement VRDS, Inc. darian@vrds.com • Cloud Systems Architect 732-219-7001

3. What is Machine Learning OUTLINE What is Predictive Analytics Examples in Supply Chain

4. EXPECTATIONS AND CONSIDERATIONS • This is an introduction for beginners • Ok to have never heard these terms • We will not be taking a deep-dive • Will not differentiate between different aspects (i.e., data science, Artificial Intelligence) • Will not differentiate between supervised and unsupervised

5. MACHINE LEARNING • Machine Learning enables systems to predict outcomes without explicit programming • ML systems “learn” from a set of “correct answers” • Subset of Artificial Intelligence • Works with data so large, it would be impossible, or at-least impractical, for humans to manually acquire meaning – hundreds of variables are not uncommon • “We live in a world of IoT. Data is plentiful. Knowing what to do with it is critical.” – My friend, Bob.

6. PREDICTIVE ANALYTICS • Condensing large datasets into information humans can use • Count, Percent, Mean, median • Try to answer questions about future outcome based on current data • How will sales be impacted by a 20% increase in advertising? • What are customers who bought a Zamboni most-likely to buy next? • Machine Learning is the modern-day evolution of classical Predictive Analytics

7. WARNING: LEAVE IT TO THE PROS

8. ML Systems Are Used Everyday

9. UNDER THE COVERS

10. Is this A or B? ANSWERS FIVE IMPORTANT QUESTIONS Is this strange? How much or how many? How is this organized? What should I do next?

11. • Called “Two-class classification” • Useful when there is just 2 answers • Which item is better to purchase? (Product A or Product B) • Q/A Outcome? (Good/Defect) • Is this product damaged during transport: • Before transport? (Yes/No) • At the hub? (Yes/No) • After transport? (Yes/No) • Is this machine/vehicle likely to break down in a given time period? (Yes/No) WHICH CHOICE IS THIS? A OR B?

12. IS THIS STRANGE? • Called “Anomaly Detection” • Is this engine running too hot? • Is this sensor detecting an at-risk condition? • Which supplier/customer is acting outside their “norm”? • Is this activity wasteful/inefficient? • Did an activity take longer than “usual” (outside of bounds, given various conditions – day, month, season, weather, etc.)

13. HOW MUCH OR HOW MANY • Called “Regression Algorithms” • Useful for numerical predictions • What is the highest and lowest temperature that is likely to be reached during transport? • What is the predicted forecast? • Will seasonal influences change my customer’s demand or my supplier’s ability to deliver?

14. • Called “Clustering” • Useful for looking at “natural clumps” • Ask the machine: “How is my data organized / clustered”? • Sorting items on a conveyer • Clustering of conditions for sales • Finding patterns in suppliers’ quality HOW IS THIS ORGANIZED?

15. WHAT SHOULD I DO NOW? • Called “Reinforcement Learning” • The system learns from outcomes and decides on the next action • Useful when a computer needs to make lots of small decisions without human guidance • Change the route depending on current external factors (sometimes hundreds) • Change the course of action based on end-to-end IoT sensor information in real-time • Robot automation (self-guiding vehicles)

16. Source: Artificial Intelligence in Logistics, 2018, DHL

17. THE MACHINE LEARNING PROCESS Acquire & Explore Data Interpret & Communicate Define Project Objectives Implement, Document & Maintain Model Data

18. Advanced Pattern Recognition: Automated recognition of patterns and regularities in data Natural Language Processing (NLP): The ability for computers to intelligently interface with speech and human language Computer Vision: The ability for computers to perceive and interpret digital images, such as photos and videos TYPES OF MACHINE LEARNING Supervised Learning capabilities: Data mining using labeled training data to detect patterns and structures Unsupervised Learning capabilities: Data mining for finding hidden structures without the use of training data Reinforcement Learning: Area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward Acoustics / Image processing capabilities: Acoustic/vibration sensors for monitoring audio frequency for AI applications / cameras for capturing image and video media for AI applications

19. SUMMARY • Machine Learning enables systems to predict outcomes without explicit programming • Answers 5 questions • Applies to any data: numbers, categories, language, audio and video

20. QUESTIONS?