kanban metrics in practice for leading continuous improvement
TRANSCRIPT
for leadingContinuous Improvement
Kanban Metricsin practice
@BattistonMattia
About me
● from Verona, Italy
● software dev & continuous improvement
● Kanban, Lean, Agile “helper”
● Sky Network Services
Mattia Battiston@BattistonMattia
Ciao!
Why are we here?
OUR EXPERIENCE
WHY
HOW
IMPROVING
LESSONS LEARNT
FORECASTING
Kan...what?
a little knowledge of Kanban helps(limiting WIP, lead time, value vs waste, queues, batches, etc.)
Why do we need metrics?
#1: drive continuous improvement #2: forecast the future
But I thought metrics were bad....
Typical problems:gaming
dysfunctions
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
Our system
Iteration-Based
On-demand
Direct
How do we collect the data?
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.
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:
Cumulative Flow DiagramDescription: Each day shows how many stories are in each state
n. s
torie
s
days
Cumulative Flow DiagramIdeal CFD: thin lines growing in parallel at a steady rate -> good flow!
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
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
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
metrics forDelivery
Time
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)
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
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%
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
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.
metrics forPredictability
Iteration Throughput
iteration
no. s
torie
s co
mpl
eted
Description: Number of stories that get done for each iteration
Rolling Wave ForecastingDescription: visualise in the backlog the likelihood of stories getting done in the next 2 weeks
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
Points vs Lead Timele
ad t
ime
(day
s)
story points
Description: Shows low correlation between estimated points and actual lead time
Disney StationsDescription: Like queueing at Disneyland. “How long in here? How long from here?”
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
metrics forQuality
Bugs percentageDescription: Percentage of bugs over stories. Also expressed as “1 bug every X stories”
metrics forContinuous
Improvement
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.
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
Retrospective
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
Thank you!
@BattistonMattia
really, really appreciated! Help me improve