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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SYDNEY

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Joshua Chalmers
Business Systems and Analytics Lead
Tip Top Bakeries
Predicting demand in a diverse
retail environment
Jenny Davies
Solutions Architect
Amazon Web Services

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Put machine learning in the
hands of every developer
Our mission at AWS

4. Some of our machine learning customers…

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Use cases
Product
demand
Workforce
demand
Sales
forecasting
Inventory
planning

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Terminology
Notebooks
Features
Deep Learning
Model
Parameters
Training
Hyperparameters
Inference

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Amazon SageMaker: build, train, and deploy machine
learning models at scale
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Amazon SageMaker:
build, train, and deploy ML Models at scale

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Amazon SageMaker:
build, train, and deploy ML Models at scale

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Amazon SageMaker:
build, train, and deploy ML Models at scale

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Amazon SageMaker:
build, train, and deploy ML Models at scale

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Amazon SageMaker:
build, train, and deploy ML Models at scale

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Amazon SageMaker:
build, train, and deploy ML Models at scale

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‘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

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Availability
Close to $US1 trillion
dollars of lost sales yearly
due to product unavailability
Lost customers, reduced
loyalty and damage to
brand reputation

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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

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Person &
Spreadsheet
Rules Based
System
Analytic
Models
How to do forecasting?

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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

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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

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Stage Standard Features
Tune &
Train
Optimise Deploy
Time Series &
Related Data
Machine learning
experience beneficial
Forecasting with Amazon SageMaker
Generate
Forecast

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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

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Some regions/products need:
• More years of data
• Regional holiday & campaign calendars
• New algorithms (Amazon Forecast)
What got swept under the rug?

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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

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28. Launching example notebook in Amazon SageMaker

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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. 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