Post on 29-Nov-2014
description
Cycle time & Automation: hidden value & business cases
Tom Breur Data Warehouse Automation conference
www.dwhautomation.com Amsterdam, 20 September 2012
Little’s Law (1)
where (in Operations Research): L = number of customers in a system λ = average arrival rate W = average time in the system
L = λ × W
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Characteristics of Little’s Law Little’s Law pertains to the system as a
whole, and also its constituent parts No assumption is made with regards to
variable distribution(s) Little’s Law holds for systems in “steady
state”, e.g.: neither starting nor shutting down
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Queuing theory
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Little’s Law (2)
where (in Agile BI): L = number of features/requests WIP λ = average arrival rate W = average time “in the system”
L = λ × W
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What is “time in system”? Sprint length? Time from entry in Product BackLog (PBL)
until delivery? Time from initial feature request until
delivery? Time from information need until delivery?
What cycle are you referring to?
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Development “queue” (=cycle)
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requirements analysis Design Implementation Verification Maintenance
Story prepping Sprint Maintenance
“Zero Sprint work” “Sprint work” “New Sprint work”
(design) (code) (unit test) (system test) (acceptance test)
How can you reduce cycle time? Concurrent development “Swarming” Reduce WIP Automation (and others)
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Concurrent development (1) Waterfall: you can avoid mistakes/rework
by getting good requirements upfront The most costly mistakes arise from
forgetting important elements early on Detailed planning (BDUF) requires:
early (ill informed) decisions uses more time leading to less tangible products to resolve
ambiguity 9 www.xlntconsulting.com vicious cycle
Concurrent development (2) Agile: decide at “last responsible moment”
decisions that haven’t been made, don’t ever need to be reverted
No “free lunch” – deferring decisions requires: anticipating likely change coordination/collaboration within team close contact with customers
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“Swarming” Of all Stories/tasks in
a Sprint, only one lies on the “critical path”
“Impediments” signal completion of a Story is jeopardized
Swarming is (should be) default response
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Reduce Work-in-Progress (WIP) Central idea Lean/Kanban: set WIP limits More WIP leads to longer (and less
predictable) lead times Running our of WIP triggers a standstill –
how can this be beneficial??
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Automation Can take on many different forms: Standardized processes, templates, etc. ETL/DDL generation
Staging hub (for 3-tiered DWH architectures) data marts
Maintenance version control documenting “as built” design
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Business case for automation Little’s Law: L = λ × W Information delivery
as a cycle implies that throughput gains accrue exponentially over time
Gains anywhere along the cycle contribute to productivity
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Conclusion The information value chain has inherent
variation manage “the system” accordingly
Reduce cycle time by “managing” variation working “in parallel” automation
Gains from sustainable shortening of cycle time are exponential
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