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.

Transcript

1. #backlogs & multi-learning Lv Yi, Odd-e
2. dependent backlogs Component A Tasks Component B Tasks Component C Tasks Analysis Tasks Design Tasks Testing Tasks
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
4. independent backlogs Feature Items Feature Items Feature Items
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
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