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Predicting Demand In A Diverse Retail Environment - AWS Summit Sydney

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One of the most challenging aspects of managing a supply chain is predicting the future demands for products and services. George Weston Foods (GWF) wanted to reduce the restocking frequency and food wastage for its baked good products in all of its 19,000 wholesale customers in Australia and New Zealand. By leveraging the power of Amazon SageMaker’s DeepAR forecasting model, GWF was able to improve their demand predictions using a single machine learning model for all stores and products. In this talk we will explore how GWF is optimising their supply chain using AWS Glue and Amazon SageMaker, providing a better customer experience, reducing waste, and improving costs. You will leave this presentation with the knowledge you need to get started building your own supply forecasting model relevant to your business.

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Predicting Demand In A Diverse Retail Environment - AWS Summit Sydney

  1. 1. S U M M I T SYDNEY
  2. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Joshua Chalmers Business Systems and Analytics Lead Tip Top Bakeries Predicting demand in a diverse retail environment Jenny Davies Solutions Architect Amazon Web Services
  3. 3. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Put machine learning in the hands of every developer Our mission at AWS
  4. 4. Some of our machine learning customers…
  5. 5. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  6. 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Use cases Product demand Workforce demand Sales forecasting Inventory planning
  7. 7. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  8. 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Terminology Notebooks Features Deep Learning Model Parameters Training Hyperparameters Inference
  9. 9. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker: build, train, and deploy machine learning models at scale 1 2 3
  11. 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 1 2 3 Amazon SageMaker: build, train, and deploy ML Models at scale
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 1 2 3 Amazon SageMaker: build, train, and deploy ML Models at scale
  13. 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 1 2 3 Amazon SageMaker: build, train, and deploy ML Models at scale
  14. 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 1 2 3 Amazon SageMaker: build, train, and deploy ML Models at scale
  15. 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 1 2 3 Amazon SageMaker: build, train, and deploy ML Models at scale
  16. 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker: build, train, and deploy ML Models at scale
  17. 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T ‘Everyday Moments of Goodness’ Tip Top Bakeries supplies over 1,500 products to more than 14,000 locations daily. This requires millions of stocking decisions
  18. 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Availability Close to $US1 trillion dollars of lost sales yearly due to product unavailability Lost customers, reduced loyalty and damage to brand reputation
  19. 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Overstocking Roughly 1/3 of human food produced globally is wasted Food waste costs global economy $US940 billion per year Increased costs to consumers and lost revenue for companies
  20. 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Person & Spreadsheet Rules Based System Analytic Models How to do forecasting?
  21. 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Results (so far) For some categories: • Exact matches improved by up to 22% • Up to 30% reduction in overstocking • Up to 10% reduction in understocking Supermarket A – Product 1 Predicted Order Predicted Order Original Order Store Actual Sales Supermarket A – Product 1
  22. 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Deep AR Forecast Model • Single model for all products and stores • Captures seasonality & regionality • Leverage campaign dates and weather • Forecast new items Forecasting with Amazon SageMaker
  23. 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Stage Standard Features Tune & Train Optimise Deploy Time Series & Related Data Machine learning experience beneficial Forecasting with Amazon SageMaker Generate Forecast
  24. 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Stage Standard Features Tune & Train Optimise Deploy Remove the undifferentiated heavy lifting of machine learning and leverage 5 deep learning algorithms and 3 statistical algorithms. … and with Amazon Forecast Generate Forecast Amazon Forecast Time Series & Related Data
  25. 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Some regions/products need: • More years of data • Regional holiday & campaign calendars • New algorithms (Amazon Forecast) What got swept under the rug?
  26. 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build a machine learning flywheel Integrated System • Data pipelines • Training & deploy CI/CD • Model monitoring • Reusable tools & process • Experimentation culture • Productionise Ashton Frost engine flywheel by Globbet is licensed under Creative Commons Attribution-Share Alike 3.0 Unported
  27. 27. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  28. 28. Launching example notebook in Amazon SageMaker
  29. 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Dream big • Where can you increase availability and reduce wastage? • Provides data driven guidance to making business decisions Start small • AWS Glue enables large scale feature engineering • Amazon SageMaker removes the heavy lifting of machine learning Build fast • https://github.com/awslabs/amazon-sagemaker-examples • https://github.com/aws-samples/amazon-forecast-samples
  30. 30. Thank you! S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Jenny Davies djenny@amazon.com Joshua Chalmers Find me on LinkedIn

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