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What are the odds? Transforming forecasts with probabilities

What are the odds? Transforming forecasts with probabilities

This is a more generic meetup version of the talk I've uploaded a few other times on probabilistic forecasting.

Julia Wester

July 06, 2022
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  1. @everydaykanban | @55degreesAB 1
    How well does


    your work
    fl
    ow


    fl
    ow?
    Julia Wester
    /in/juliawester/
    @everydaykanban
    Co-Founder, 55 Degrees AB
    What are the odds?
    Transform your forecasts
    with probabilities

    View full-size slide

  2. @everydaykanban | @55degreesAB
    Follow us on social media
    /company/55degrees
    @55degreesAB
    @55degreesAB
    /55degreesAB
    /c/ActionableAgileAnalytics
    Apps that make it easier
    to get work done
    🎉
    From re
    fi
    ning work, to bringing external data into Jira, to
    improving work
    fl
    ows and creating reliable forecasts, 55
    Degrees has apps to make your work less stressful.

    View full-size slide

  3. 3
    @everydaykanban | @55degreesAB
    Benjamin Franklin


    November 1789
    “…IN THIS WORLD,
    NOTHING IS CERTAIN
    EXCEPT DEATH AND
    TAXES.”

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  4. Often forecasts are
    interpreted as
    certainties


    because we don’t tell
    them otherwise
    4
    @everydaykanban | @55degreesAB
    CC BY-SA 3.0, https://en.wikipedia.org/w/index.php?curid=3422341

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  5. 5
    @everydaykanban | @55degreesAB
    The 3 Gaps
    Outcomes
    Plans
    Actions
    !
    Effects Gap
    Difference between what we
    expect our actions to achieve
    and what they actually achieve
    !
    Knowledge Gap
    Difference between what we
    would like to know and what we
    actually know
    !
    Alignment Gap
    Difference between what we want people
    to do and what they actually do

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  6. 6
    @everydaykanban | @55degreesAB
    Used with permission from Jim Benson
    A predictable
    response

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  7. 7
    @everydaykanban | @55degreesAB
    EE4: Building an SSV
    Timeline
    Total expected hours
    Total hours
    Sam Jansen
    Expected hours
    Total hours
    Alexander Meynen
    Expected hours
    Total hours
    Tinus Michiels
    Expected hours
    Total hours
    Jeroen van Aert
    Expected hours
    Total hours
    Jef Van Den Bergh
    Expected hours
    Total hours
    Bob Verschueren
    Expected hours
    Total hours
    1 2 3 4 5 6 7 8 9 10 11 12 13 14
    Tasks Subtasks wk h h h h h h h h h h h h h h
    Engineering
    a. Case SSV 1 i. Motor/ Solar panel analysis 7 4 3
    ii. Gear ratio 15 2,5 2,5 10 2,5 2,5 2,5 2,5
    iii. Choice of materials 10 5 1 1 1 1 1 5 1
    iv. Frame design 30 15 2 1 1 1 15 1 1
    v. Optimalisation (bisection method) 25 4 15 10 4
    vi. Sankey diagram 10 12 10
    vii. Production parts 20 10 12 10
    viii. Construction 60 10 10 10 10 10 10
    b. Case Simulink i. Simulation solar panel 24 4 4 4 4 5 4 4 10
    ii. Simulation DC-motor 7 4 3
    iii. Simulation race 6 1 1 1 1 1 1
    c. Case SSV 2 i. Slope run test 24 4 4 4 4 4 4
    ii. Simulation drive shaft 7 3 4
    iii. Technical drawings 40 10 30
    iv. Collision analysis 15 10 5
    d. Race day 30 5 5 5 5 5 5
    Enterprising
    e. Wikipage i. Company information 3 3 1
    ii. SSV information 5 5 1
    f. Name & Logo i. Design 2 1 2
    ii. Engraving 6 3 3
    g. Documents i. Plan of approach 4 2 2 2 2
    ii. Work breakdown structure 4 2 2 2 2
    iii. Gantt chart 10 1 5 3 5 3
    iv. Cooperation contract 2 2 2
    v. Case 1 document 45 5 5 15 5 5 15 5 5 15 5
    vi. Simulink document 15 5 10 5 10
    vii. Case 2 document 40 15 5 10 10
    h. Wikipage i. Upload documents 5 5 1
    ii. Blog 5 5 2 2
    iii. Design description 6 3 3
    iv. Final process report 5
    i. Evaluation i. Test 1 30 5,00 5 5 5 5 5
    ii. Test 2 30 5,00 5 5 5 5 5
    iii. Peer assessments 1
    total 548 0 92 27 92 25 92 21 88 22 91 25 90 25
    Legende milestone
    responsibillity
    Gantt chart 1
    Educating
    Weeks
    The workplace version of ruling it harder…
    CC BY-SA 3.0 - https://upload.wikimedia.org/wikipedia/commons/thumb/b/bf/AM4_Gantt.pdf

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  8. Deterministic Forecast
    8
    @everydaykanban | @55degreesAB
    IT WILL BE


    DONE ON


    X DATE
    •Communicates a single
    possible outcome


    •Provides no probability of
    that outcome


    •Appropriate only when
    certainty is assured

    View full-size slide

  9. Probabilistic Forecast
    9
    @everydaykanban | @55degreesAB
    THERE’S AN


    85% CHANCE


    IT WILL BE DONE


    ON OR BEFORE

    X DATE
    •Two components:


    ‣ Range of outcomes


    ‣ Probability you’ll fall into
    that range


    •Appropriate whenever
    uncertainty is present

    View full-size slide

  10. 10
    Let’s create these probabilistic forecasts…
    @everydaykanban | @55degreesAB
    Single Item Forecasts
    Multiple Item Forecasts
    📆


    Fixed Date
    📑


    Fixed Scope
    2 Types

    View full-size slide

  11. For each, we can use the
    same simple data to
    calculate the probabilities
    11
    @everydaykanban | @55degreesAB

    View full-size slide

  12. Single Item Forecasts
    12
    @everydaykanban | @55degreesAB
    How long will it take?
    Finish
    Start
    Cycle Time: The total elapsed time it takes a work item
    to travel from start to
    fi
    nish

    View full-size slide

  13. 13
    @everydaykanban | @55degreesAB
    Finish
    Start
    Cycle Time: The total elapsed time it takes a work item
    to travel from start to
    fi
    nish
    Single Item Forecasts
    (Finish date - start date) + 1

    View full-size slide

  14. 85% of work took
    16 days or less
    Use a Cycle Time Scatterplot to
    calculate probabilities
    14
    @everydaykanban | @55degreesAB
    50% of work took


    7 days or less

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  15. 15
    @everydaykanban | @55degreesAB
    Wrong as much
    as right…
    Contains too
    many outliers
    Just the right
    balance?
    Ask: What is the fallout if we fall
    outside of the probability?
    by understanding your risk tolerance
    Choose a probability

    View full-size slide

  16. 16
    @everydaykanban | @55degreesAB
    85% of the time we finish a

    work item in 16 days or less

    (from the day it starts)
    probabilistically
    Deliver your forecast

    View full-size slide

  17. 17
    @everydaykanban | @55degreesAB
    The underlying rule of thumb

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  18. Multiple Item Forecasts
    18
    @everydaykanban | @55degreesAB
    Finish
    Start
    Throughput: The total count of items that pass the
    de
    fi
    ned
    fi
    nish point in a given unit of time
    When will
    they all be
    done?

    View full-size slide

  19. 19
    @everydaykanban | @55degreesAB
    A Throughput Run Chart will show you the data
    But you need a Monte Carlo Simulation
    to calculate the probabilities

    View full-size slide

  20. 20
    @everydaykanban | @55degreesAB
    For Fixed Scope, use Monte Carlo: When
    There’s an 85% chance that
    we will
    f
    inish these 45 items on
    or before May 25

    View full-size slide

  21. 21
    @everydaykanban | @55degreesAB
    For Fixed Date, use Monte Carlo: How Many
    There’s an 85% chance
    that we can
    f
    inish


    67 or more items by then

    View full-size slide

  22. 22
    @everydaykanban | @55degreesAB
    For an entire portfolio, use Portfolio Forecaster!

    View full-size slide

  23. 23
    @everydaykanban | @55degreesAB
    • When more than one outcome is possible,
    probabilistic forecasts are necessary.


    • Probabilistic forecasts show risk in 2 ways:

    a probability (%) and a range.


    • Choose a % that matches your risk tolerance


    • Tools like ActionableAgile and Portfolio Forecaster
    exist to help you create these forecasts in minutes.
    Summary

    View full-size slide

  24. @everydaykanban | @55degreesAB
    Thanks for
    listening!
    🎉
    Follow us on social media
    /company/55degrees
    @55degreesAB
    @55degreesAB
    /55degreesAB
    /c/ActionableAgileAnalytics

    View full-size slide