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Multi-learning and # Backlogs by Lv Yi

Agile Singapore
April 16, 2019
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Multi-learning and # Backlogs by Lv Yi

In the large-scale product development organization, backlogs are pervasive. Backlogs are the manifestation of various specializations. Why more backlogs? Efficiency is the main driver. What are the consequences? The increased end-to-end cycle time for delivery and the reduced flexibility to maximize value. The number of backlogs is the key lever in achieving agility - fewer backlogs, more agility. Multi-learning means learning across functions, technical domains and customer domains. More multi-learning enables fewer backlogs, while fewer backlogs drive more multi-learning.

Join us for this conversational session with Lv Yi as he explores how large-scale product organization grows rigidity and possible ways to deal with it.

Agile Singapore

April 16, 2019
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  1. #backlogs & multi-learning
    Lv Yi, Odd-e

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  2. dependent backlogs
    Component A
    Tasks
    Component B
    Tasks
    Component C
    Tasks
    Analysis
    Tasks
    Design
    Tasks
    Testing
    Tasks

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  3. multi-learning for
    functions and components
    • real team with one backlog (i.e. cross-functional feature team)

    • specification by example

    • collective code ownership

    • pair/mob programming

    • component mentors

    • current-architecture workshop

    • multi-team design workshop

    • communities of practices

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  4. independent backlogs
    Feature
    Items
    Feature
    Items
    Feature
    Items

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  5. multi-learning for
    customer domains
    • multi-specialized feature teams with one backlog

    • multi-team PBR

    • initial PBR

    • overall PBR

    • one sprint review

    • review bazaar

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  6. summary
    • the more dependent backlogs, the longer cycle time

    • the more independent backlogs, the less adaptiveness

    • The ultimate lever is to reduce #backlogs

    • multi-learning enables fewer backlogs

    • fewer backlogs drives multi-learning

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