Kanban Metrics in practice for leading Continuous Improvement
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Transcript of Kanban Metrics in practice for leading Continuous Improvement
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for leadingContinuous Improvement
Kanban Metricsin practice
@BattistonMattia
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About me
● from Verona, Italy
● software dev & continuous improvement
● Kanban, Lean, Agile “helper”
● Sky Network Services
Mattia Battiston@BattistonMattia
Ciao!
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Why are we here?
OUR EXPERIENCE
WHY
HOW
IMPROVING
LESSONS LEARNT
FORECASTING
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Kan...what?
a little knowledge of Kanban helps(limiting WIP, lead time, value vs waste, queues, batches, etc.)
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Why do we need metrics?
#1: drive continuous improvement #2: forecast the future
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But I thought metrics were bad....
Typical problems:gaming
dysfunctions
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Good vs Bad metrics
● look at improving the whole system ● reward/punish individuals
“95% performance is attributable to the system, 5% to the people”
W. Edwards Deming
● feedback about state of reality ● used as target
● leading (let you change behaviour) ● lagging (tell you about the past)
● all metrics must improve ● local optimisations
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Our system
Iteration-Based
On-demand
Direct
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How do we collect the data?
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The SpreadsheetInputs: story details; start time and duration of each state
Public version: https://goo.gl/0A9QSN
For you to copy, reuse, get inspired, etc.
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All the maths you need
● Min, Max
Normal: data is distributed around a central valuee.g. height of UK population
Skewed: data has a long tail on one side (positive or negative)e.g. income of UK population (positive skew)Lead time of stories follows skewed distribution
● Average (mean)avg(1,2,2,2,3,14) = (1+2+2+2+3+14)/6 = 4
● Median: separates the high half from the low half. Less impacted by outliersmedian(1,2,2,2,3,14) = 2
● Mode: value that occurs more frequentlymode(1,2,2,2,3,14) = 2
● Standard Deviation: measures the amount of dispersion from the average. When high, values are spread over a large range.
stdev(1,2,2,2,3,14) = 4.5; stdev(1,2,2,2,3,5) = 1.2;● Percentile: percentage of elements that fall within a range
50% perc(1,2,2,3,7,8,14) = 3; 80% perc(1,2,2,3,7,8,14) = 7.8;
● Normal Distribution vs Skewed Distribution:
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Cumulative Flow DiagramDescription: Each day shows how many stories are in each state
n. s
torie
s
days
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Cumulative Flow DiagramIdeal CFD: thin lines growing in parallel at a steady rate -> good flow!
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Cumulative Flow Diagram● Objective: retrospect (but needs a good facilitator)
CFD used for Retrospective
● Objective: demonstrate effectiveness of changes
changed WIP limit in DEV from 3 to 2
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Cumulative Flow Diagram
● Objective: decide what you should work on today● Objective: forecasting: rough info about lead time, wip, delivery date (although
they’re easier to use when tracked separately)
WIP
Lead Time
Throughput
Delivery Date
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CFD Patterns
(taken from CFD article by Pawel Brodzinski)
growing lines: indicate large WIP + context switching. action: use WIP limits
stairs: indicates large batches and timeboxesaction: move towards flow (lower WIP,
more releases, cross-functional people)
flat lines: nothing’s moving on the boardaction: investigate blockers, focus on finishing, split in
smaller stories
single flat line: testing bottleneckaction: investigate blockers, pair with testers,
automate more
typical timeboxed iterationdropping lines: items going backaction: improve policies
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metrics forDelivery
Time
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Control ChartDescription: For each story it shows how long it took. Displays Upper and Lower control limits; when a story falls out of limits something went wrong and you should talk about it.
stories
lead
tim
e (d
ays)
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Cycle/Lead Time stats + HistoryDescription: Stats to get to know your cycle time and lead time. They let you predict “how long is the next story likely to take?”. Visualize trends of improvement
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Lead Time distribution
lead time (days)
n. s
torie
s th
at t
ook
that
long
Description: For each lead time bucket (#days), how many stories have taken that long.Useful to show as a percentage to know probability.
WEIBULL DISTRIBUTION
50%
80%
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Story HealthDescription: Indicates if the story is in good health or if we should worry about it. Based on lead time distribution
50-80% >90%80-90%0-50%0-4 gg 5-7 gg 8-10 gg >10 gg
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Cycle Time vs Release Prep. Time
stories
days
Description: For each story shows how long it spent in the iteration and in release preparation (Context specific). Used to discuss cost vs value of release testing.
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metrics forPredictability
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Iteration Throughput
iteration
no. s
torie
s co
mpl
eted
Description: Number of stories that get done for each iteration
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Rolling Wave ForecastingDescription: visualise in the backlog the likelihood of stories getting done in the next 2 weeks
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Arrivals RateDescription: how often a story is started, aka pulled into our system (arrival). This is how often you can change your mind about what to do next
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Points vs Lead Timele
ad t
ime
(day
s)
story points
Description: Shows low correlation between estimated points and actual lead time
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Disney StationsDescription: Like queueing at Disneyland. “How long in here? How long from here?”
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Task TimeDescription: Shows how long tasks usually take (context specific). Gives you an idea of how long a story will take based on n. of tasks
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metrics forQuality
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Bugs percentageDescription: Percentage of bugs over stories. Also expressed as “1 bug every X stories”
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metrics forContinuous
Improvement
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Flow EfficiencyDescription: Shows how long stories have spent in queues - nobody was working on them. Shows how much you could improve if you removed waiting time.
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Time in status
time
spen
t in
sta
te (
days
)
story
Description: for each story visualise how long it spent in each status (absolute and percentage). Shows trends of where stories spend more time
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Retrospective
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ResourcesBooks
Metrics● Data driven coaching - Troy Magennis● Seven Deadly Sins of Agile Measurement - Larry Maccherone● The Impact of Lean and Agile Quantified - Larry Maccherone● Kanban at Scale: A Siemens Success Story - Bennet Vallet● FocusedObjectives@Github - Troy Magennis● Visual feedback brings key Agile principles to life - Bazil Arde
n● How visualisation improves Psychological Safety - Bazil Arden
Forecasting● Cycle Time Analytics - Troy Magennis● Top Ten Data and Forecasting Tips - Troy Magennis● Forecasting Your Oranges - Dan Brown● Using Maths to work out Potentially Deliverable Scope - Ba
zil Arden● Forecasting Cards - Alexei Zheglov
Story Points● Story Points and Velocity: The Good Bits - Pawel Brodzi
nski● No correlation between estimated size and actual time
taken - Ian Carroll
Lead Time● Analyzing the Lead Time Distribution Chart - Alexei Zheglov● Inside a Lead Time Distribution - Alexei Zheglov● Lead Time: what we know about it, how we use it - Alexei Z
heglov● The Economic Impact of Software Development Process Cho
ice - Troy Magennis
More● Flow Efficiency - Julia Wester● Cumulative Flow Diagram - Pawel Brodzinski
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Thank you!
@BattistonMattia
really, really appreciated! Help me improve