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AI: Bite by bite

Gousto Tech
January 30, 2018

AI: Bite by bite

Presentation slides from the AI congress, London, January 2018

Gousto Tech

January 30, 2018
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  1. AI
    Bite by bite
    Dejan Petelin
    Head of Data Science

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  2. BSc + MSc, Computer Science
    Computer science with focus on algorithms.
    Internship at the national institute.
    2004
    2009
    PhD, Machine Learning
    On-line learning of Bayesian
    kernel methods (Gaussian
    processes) on streaming data.
    S2DS
    Intensive and project-based, five
    weeks data-science training.
    2014
    Gousto
    Applying AI (machine learning and
    optimisation algorithms) to disrupt
    the online grocery market.
    Dejan Petelin
    Head of Data Science

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  3. Simple model
    Leading proposition
    Fueling family life
    through good food
    Use AICONGRESS to get 30% off for the first 3 boxes!
    ● You pick from 24 recipes each week.
    ● We deliver a box of wholesome ingredients in
    exact proportions with step-by-step recipe cards.
    ● No planning, no supermarkets and no food waste!
    ● Most choice (24 is just the start).
    ● Most delivery options (7 days, with am & pm slots)
    ● Best price!

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  4. Exponential growth is fun, but also very challenging. Especially,
    when you are pushing boundaries in customer propositions
    at the same time.
    And of course growing the team and
    keeping your bar high.
    Exciting journey so far
    2018

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  5. Irene
    Data Scientist
    Marc
    Data Scientist
    Manuel
    Machine Learning Eng.
    Catherine
    Data Scientist
    Team
    Diverse team - gender and
    background wise - to cover
    different aspects and views.
    Centralised team - part of the
    tech team, working on various
    cross-functional projects.

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  6. How we do
    AI (data science)
    at Gousto
    How we doubled the
    throughput of our
    automated picking line

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  7. How we do
    AI (data science)
    at Gousto

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  8. 1
    Create focus
    Create a separate,
    dedicated data analytics
    function, all data insight
    queries go there.
    2
    Build what
    customers need
    Use agile product
    management and scrum
    to deliver value early.
    3
    Create production
    ready products
    You build it, you run it.
    4
    Measure
    success
    Make it accountable to the
    investment being made.
    Building innovative data products

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  9. Collect
    Everything
    Store
    Everything
    Expose
    Everything *
    * Whilst maintaining data security
    apps
    web
    microservices
    3rd party
    airflow
    (ETL)
    amazon Redshift
    (data warehouse)
    amazon S3
    (data archive)
    (unified log)
    data scientists
    data products
    business users
    periscope
    (analytics and dashboards)
    Amazon DMS
    (data migration)

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  10. Demand
    Planning
    Personalised
    Choice
    Warehouse
    Optimisation
    historic orders data
    Stock
    Handling
    X total orders
    recipe A 3.4%
    recipe B 2.8%
    recipe C 4.1%

    onions
    chicken
    garlic
    ...
    10,000
    8,000
    7,000
    ...
    orders routes
    10012
    10012
    10012
    10013
    10013
    ...
    S001
    S004
    S020
    S005
    S013
    ...
    R1 R2 R3
    R1
    R2
    R3
    0.8
    0.8
    0.6
    0.6
    0.4
    0.4
    1.0
    1.0
    1.0
    R2
    onion
    R3
    beef
    chicken
    parsley
    R1
    defaults
    choosers
    station picks

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  11. How we doubled the
    throughput of our
    automated picking line

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  12. Automated picking line
    Before the automation we were picking ingredients manually, i.e. ingredients were placed on tables,
    grouped by recipes.
    The automated picking line allows us to be more flexible in terms of customer propositions, yet
    efficient enough to make them viable.

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  13. Specific setup
    Gousto
    ● Fresh items
    ● Product catalogue changing on a weekly basis
    ● Item volumes changing on a daily basis
    Gousto’s setup
    ● Items are placed on different locations every week
    (weekly redesign of our warehouse)
    ● Optimise pickface AND finding shortest path to
    collect items (difficult to predict due to
    completely new setup)
    Typical e-commerce
    ● Items with a long shelf-life
    ● Product catalogue are changing rarely
    ● Item volumes are cyclical (seasonality)
    Typical setup
    ● Items are placed on the same locations
    (no need to redesign the warehouse)
    ● Finding the shortest path to collect all items (can
    be predictable due to historic data)

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  14. Trillions of trillions of combinations
    Thousands of slots ... hundreds of items ... and numerous rules.
    p a l e t fi t
    ...
    c i k r a n g s o l t
    be c lo e c he
    ...
    it n is t e p d o y
    t e h d a

    in d e s l i l ti ,
    s o d a c as s e
    ...

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  15. Genetic algorithms
    Well known optimisation algorithm
    It belongs to the larger class of the evolutionary algorithms,
    mimicking the same mechanism used by mother nature in evolution
    by natural selection, i.e. mutation, crossover & selection.
    Suitable for solving binary problems
    Like - in our case - placing items on slots, where a lot of other
    optimisation algorithms struggle.
    Recently got a lot of attention in Deep Learning
    Uber’s AI lab published a paper Deep Neuroevolution: Genetic
    Algorithms Are a Competitive Alternative for Training Deep Neural
    Networks for Reinforcement Learning.

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  16. Production deployment
    Critical part of the business
    ● There is a running time limit
    ● If delayed or it fails, we are in danger of not being
    able to deliver boxes to our customers
    Not after the ‘perfect’ solution
    ● Parallelisation / batching to speed it up
    ● Trade-off between batch size, number of
    generations and performance of the end output
    forecast
    recipe
    SKUs
    pickface
    optimisation
    pickface
    (WMS)
    orders
    (mon)
    orders
    (tue)
    orders
    (wed)
    ... ... ...
    ………….

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  17. Production deployment
    Critical part of the business
    ● There is a running time limit
    ● If delayed or it fails, we are in danger of not being
    able to deliver boxes to our customers
    Not after the ‘perfect’ solution
    ● Parallelisation / batching to speed it up
    ● Trade-off between batch size, number of
    generations and performance of the end output
    forecast
    recipe
    SKUs
    Monitor and react!
    pickface
    optimisation
    pickface
    (WMS)
    orders
    (mon)
    orders
    (tue)
    orders
    (wed)
    ... ... ...
    ………….

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  18. Double the throughput
    Automated warehouse design
    Minimised manual work and avoided a lot bottlenecks -
    comparing to the initial plan to set up warehouse
    manually (by recipes).
    Overriding orders routing
    Significant reduction of station visits, which lead to 90%
    increase in the throughput, i.e. packed boxes per hour.
    Capacity / capability increase
    It directly improved our margin, but it also allowed us to
    keep growing and to invest into new factory later.
    1
    2
    3
    apr may jun jul aug sep oct nov dec

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  19. Thank You
    (and we’re hiring)
    twitter: @GoustoTech
    blog: techbrunch.gousto.co.uk
    apply: gousto.co.uk/jobs

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