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Machine Learning and Analytics in Logistics and Supply Chain Presented by: Pavel Gupta Co-Founder, NeenOpal Analytics Bangalore, India 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org Agenda Supply Chain:

  1. Machine Learning and Analytics in Logistics and Supply Chain Presented by: Pavel Gupta Co-Founder, NeenOpal Analytics Bangalore, India 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  2. Agenda • Supply Chain: Challenges and Trends • Introduction: Machine Learning and AI • Case Study – ML and AI in Supply Chain and Logistics • Getting Started with Machine Learning • Conclusion 9 Feb 2011 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  3. Supply Chain Challenges • Lower Prices • Faster Delivery • Higher customer service expectations • Demand volatility • High number of products • Supply complexities • More frequent shipments • Transparency and sustainability “Companies that continue to utilize traditional supply chain models will struggle to remain competitive and deliver orders that are complete, accurate and on-time .” 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  4. A Lot of New Products Today Amazon sells over 480 million products in the USA. Amazon’s product selection has expanded by 235 million in the past 16 months. That’s as average addition of 485,00 new products per day. A typical Amazon fulfilment centre 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  5. A Very Long Tail Demand 0.9 million 1.2 million 1.7 million 6.7 million 24 million 30 million 60 million 96 million 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  6. Machine Learning and AI The Future is Here • The most innovative companies in the world – that have disrupted their respective industries – rely on Machine Learning to drive their business processes and a great customer experience • The future of business innovation has Artificial Intelligence (AI) at its very core • Machine Learning (subfield of AI) is no longer restricted to research labs and is fast becoming the cornerstone of business disruption 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  7. What was before Machine Learning? Humans versus Machine Feedback “All knowing programmer” Data Program Results Deterministic Future Outlook 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  8. Machine Learning in our Business Humans versus Machine Historic Data Learner Data Model Predictions • Manual(query) Decision-Making • Automatic (programmatic) Push decision-making to the edge Probabilistic Future Outlook 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  9. Machine Learning Explained Groundtruth INPUT Square House Square Bedrooms Age School Final footage No. Footage Rating Price W 1 H1 1000 4 3 2 $100,000 H2 800 3 1 4 $90,000 OUTPUT Bed- H3 1200 5 3 5 $125,000 rooms W 2 H4 600 2 5 1 $60,000 Price H5 1500 6 3 3 $150,000 W 3 Age W 4 PRICE(Square Footage, Bedrooms, Age, School Rating) = School Rating w 1 x sf + w 2 x br + w 3 x age + w 4 x sr 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  10. Learning Algorithms Mail Spam Non-Spam Regression Classification Ranking Supervised Unsupervised Reinforcement 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  11. Neural Networks INPUT HIDDEN Square Hidden footage 1 OUTPUT Bed- Hidden rooms 2 Price Hidden Age 3 School Hidden Rating 4 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  12. Deep Learning INPUT Square Hidden Hidden Hidden footage 1.1 2.1 3.1 OUTPUT Bed- Hidden Hidden Hidden rooms 1.2 2.2 3.2 Price Hidden Hidden Hidden Age 1.3 2.3 3.3 School Hidden Hidden Hidden Rating 1.4 2.4 3.4 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  13. Case Study Transforming Supply Chain and Logistics Business Challenge: Develop real-time customer feedback and analysis framework to measure customer satisfaction levels. Situation: • Existing process was not capturing valuable customer data Improving Customer Satisfaction Solution/Approach: for a major Logistics Company • Collect and aggregate the customer data on areas such as billing, complaints, repairs, contracts, social media and contact center calls. • Big data analytics model provides real-time feedback and risk flagging for the customers om the verge of churning Impact: • Reduction in customer complaints & improved customer satisfaction levels 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  14. Getting Started Start small by leveraging the cloud • Low hanging fruit: Business problem – “If we just knew …” • Start Supervised: Historic data with ground truth • Do not start with Big Data • Use cloud-based offerings: – Microsoft Azure Machine Learning – Amazon Machine Learning – Google Cloud Machine Learning – Big ML 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

  15. Thank You Pavel Gupta Co-Founder @ NeenOpal Analytics +91-9910945784 pavel.gupta@neenopal.com 9 th Global Supply Chain and Logistics Summit 16 Nov 2016 www.sclgsummit.org

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