Kanban 101 - An Introduction to Planning with Little's Law

KANBAN 101
AT AGILE NEW ENGLAND
Presented by:
Code Genesys
Ajay Reddy
Jack Speranza
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com | @codegenesys

Today’s Agenda
Improved Project / Release Planning
• Introduction & Background
• Planning with Little’s Law
• Project Planning
• Sprint Planning
• Evolutions
• Enhanced Risk Management (work selection & prioritization)
• Modeling & Simulations

Section 1 of 3
Background

Project Planning Process
• Concept is Developed
• Project Parameters are Established
• Minimum Viable Product / Minimum
Marketable Features are Defined
• Features are Prioritized
• Estimates of Required Time and Resources
are Established
Official Licensed Material Copyright CodeGenesys, LLCtraining@codegenesys.com

The Moving Targets
The amount of work requested.
The amount of time needed.
The amount of resources allocated.
The level of quality delivered.

The Challenge
Your mission, should you choose to accept
it…
• Establish a Reliable Delivery Date for
a Given Scope of Work and Set of
Resources (Team Members)
• Establish a Reliable Alternate Date if
Resources were Taken Away / Added
or Scope of Work were Changed
Is this truly possible???

The Headaches
Estimating Skills – some individuals /
teams are better than others at
estimating.
Work Item Variability – the development
process has too many variables that
influence “lead time.” Outliers are
inevitable.
Non-uniform Metrics – the most
performance metrics are rarely apples to
apples comparisons.
Politics – estimates are invariably
perceived as commitments.

Scrum Systems – Estimation & Velocity
• Estimation of hours or
story points is subjective
and imprecise
• Velocity measurements
are only relative and not
comparable across
multiple teams
• Velocity is essentially a
“throughput “-based
measure.

Throughput Focus
Throughput represents the number of work items delivered in a given
time period (such as a Sprint).
-- a decent indicator of how well a team or organization is performing
-- no clear indication of the amount of time required to complete work items…

Kanban Systems – Flow Based
Queuing system design plus granular tracking of work types and
workflow enables probabilistic forecasting.
Example of visualizing a single
workflow through which all
work types flow. Enables
forecasting based upon
objective measurements vs.
subjective sizing.
Example of visualizing
multiple workflows that
relate to specific classes of
service (each of which may
have different rates of flow).
This system typically allows
for more precise forecasting.

Lead Time Focus
Traditional Labels – typically
measured as the amount of time
required to deliver a work item.
-- measured from the date of
commitment through delivery
into the first infinite queue
Alternative Labels – shifts
focus to realization of
business value.
-- “flow time” + “delivery
time” is the cycle relevant to
our forecasting process.

Lead Time Histogram

Section 2 of 3
Planning with Little’s Law

Kanban / Scrumban as a Queuing System
Source: Dimitar Bakardzhiev

Applying Little’s law
Departure rate approximately equals
arrival rate (λ).
No work items get lost and none
remain in the system indefinitely.
Little’s Law holds for queuing
systems where:
training@codegenesys.com Official Licensed Material Copyright CodeGenesys, LLC
Can be applied at both iteration and release
levels...

The Basic Math
Use with care – there are many variables that
influence reliability of any forecast.
Best Use is to attain improved understandings
& to influence expectations vs. making explicit
commitments.

The Math in Action
Proposed Project Parameters
• Web Application using familiar technologies and 2 stable Scrum Teams.
• 50 Features defined.
Historical Team Data
• Average team size = 6
• Average # of Work-in-Progress (WIP) per team member = 2
• Historical density of 13.5 user stories per feature
• Average lead time of .9 weeks per user story
• Average throughput of 28 user stories per 2-week sprint (14 per week)
Release Backlog = 50 Features x 13.5 Stories / Feature
= 675 User Stories
Proposed Project Scope

The Math in Action (cont.)
Forecast Request # 1
• Can you complete the project as defined in 26 weeks using the resources
allocated?
Duration = Number of Stories x (Ave. Lead Time / Ave. WIP)
= 675 x [.9/(12 resources x 2 items each)]
= 25.3 weeks
Forecasted Project Schedule

The Math in Action (cont.)
Forecast Request # 2
• We need to complete this project in 18 weeks. What additional resources do
you need?
Ave. WIP = Ave Lead Time x (Number of Stories / Duration)
= .9 x (675 / 18)
= 33.75
34 user stories / 2 WIP per resource = 17 resources
Forecasted Resource Needs

But Wait… What About the Z-curve?
Little’s Law can only be
reliably applied to work
performed during the 2nd
leg of the project /
release z-curve…

Accounting for
Uncertainty
Natural ebb & flow (associated with the
1st & 3rd legs of the z-curve)
Dark Matter (work expansion that comes
from a better understanding of the work
being undertaken)
Failure Load (defects & technical debt, for
example)
When calculating Project (or Release) lead time,
we need to account for uncertainty in terms of:
Source: Dimitar Bakardzhiev

Managing the
Uncertainty
• 1st & 3rd Legs of Z-curve
• Dark Matter
• Failure Load
• Other Risks?
Calculate and Manage Against a Project
Buffer Accounting for:
Have Team Focus on Median Lead Times
Project Buffer =
Z-curve coefficient
x
(1 + ave. failure load + ave. dark matter)
x
(# of stories / ave. throughput)

Putting it All Together
Monitoring & Managing Projects in Flight
Planned project (Release) lead time is the
sum of the calculated project length and a
project buffer. In our example:
• 2nd Leg of Z-curve: 25.3 weeks
• Project Buffer: 18.8 weeks
• Total: 44.1 weeks
Managing projects in flight requires
evaluating two performance metrics -- the
percentage of the project completed
related to the percentage of the buffer
consumed.
Monitoring this relationship lets managers
see and proactively address potential
problems before they actually manifest
themselves in more obvious ways.

What About Sprint Planning?
The techniques applied in Release Planning can also be employed
at the Sprint Planning level:
• Little's Law provides an additional check and balance on the
correlation between velocity and actual throughput.
• Teams that actively manage risk through embedded Risk
Management techniques (such as Cost of Delay calculations
and / or class of service mechanisms) are enabled to improve
their story selection & prioritization capabilities, improving the
likelihood of meeting their Sprint commitment.

Section 2 of 3
Evolutions

Why Risk is Relevant
• Higher Risk Means Greater Variability in Lead Times
• greater variability means less predictability
• Factors that Influence Risk
• having to interact with other systems to deliver completed work
• having to work with new systems or technologies
• having to learn about new new business domains
• and many more...
• Quantifying, Tracking & Visualizing Risk
• improves shared understandings of project health
• enables early discovery and intervention

The Result – Better Decisions
Which of these projects is more likely to be completed on time?

Back to Sprint Planning
Improving the Likelihood of Meeting Your Sprint Commitment
• 70 User Stories remain in the Product Backlog.
• Risk has been quantified on each on a crude scale:
• High Risk Items: 20 User Stories
• Medium Risk Items: 36 User Stories
• Low Risk Items: 14 User Stories
• Average Lead Time for All Stories: .9 weeks
• Average Lead Time for High Risk Stories: 1.6 weeks
• Average Lead Time for Low Risk Stories: .4 weeks
# stories = duration x (ave. WIP / ave. lead time)
Proposed Sprint Backlog

Sprint Planning (cont.)
# stories = duration x (ave. WIP / ave. lead time)
All Risk Stories (combined):
# stories = 2 weeks x [(12 resources x 2 stories)/ .9 weeks]
= 53.33 user stories
High Risk Stories:
# stories = 2 weeks x [(12 resources x 2 stories)/ 1.6 weeks]
= 30 user stories
Medium Risk Stories:
# stories = 2 weeks x [(12 resources x 2 stories)/ 1 week]
= 48 user stories
Low Risk Stories:
# stories = 2 weeks x [(12 resources x 2 stories)/ .4 weeks]
= 120 user stories
What a Difference Risk Makes
When we don’t account for risk, 50 user
stories may seem like an appropriate
number of stories to select for the Sprint
Backlog.

Modeling & Simulations
• Modeling is used when greater precision is required, or when we need to
isolate and measure the impact of specific risk factors in a more granular
fashion.
• Modeling reality through repetitive simulations produces the rough equivalent of
actual performance data
• Past performance data (or its equivalent) improves our understanding of the
probability of various outcomes that would otherwise remain elusive.
• Elevated levels of uncertainty and risk
means greater difficulty developing
accurate forecasts.
• Poor forecasting erodes trust and sense
of predictability with non-technical
leadership.
• Trust & predictability are essential to
sustained agility and high-performance.

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