London Kanban Coaching Exchange Flow Systems - like developments being run with a process like Scrum or Kanban - provide us a lot of data, and, if we know how to look at it, a lot of information about the health of our processes and projects. Start and end dates for example provide Throughput, Time in Process, Work in Progress as well as more exotic metrics such as Flow Debt, WiP-Aging and Delivery Bias indicators. Adding in Target Dates and historic distribution data for Lead Times provides Buffer Consumption measures, and the simple application of Monte Carlo models (plug it into the spreadsheet!) gives completion probabilities over a range of dates. We'll review what these metrics mean and who's writing about them. Then look at concise, concrete and pragmatic advice for how you can use them on your projects. Whether you've never seen a Control Chart or a Cumulative Flow Diagram - or if you're using flow metrics every day and bring along some diagrams and insights to share, this will be an entertaining evening of the whys and hows of project numbers. About the Speaker Andy Carmichael: Whether as a manager, developer, coach or author, a common theme to what I’ve done throughout my career has been helping teams make “better software… faster”. Working with a wide variety of clients on very small to impossibly large projects, remains my principal source of education - certainly outweighing various degrees and certifications I’ve also picked up along the way! Thinking deeply about business problems and finding the intersection with how people best work together, is where I find the fun - and the value - lies. Twitter: @andycarmich Blog: Improving Projects

1. Exploring Flow
Metrics in Kanban
Systems
AN INTRODUCTION TO KEY FLOW METRICS THAT LOOK INSIDE
PROCESSES AND REVEAL THEIR SECRETS
ANDY CARMICHAEL
@andycarmich ac@openxprocess.com

2. “Doing Agile” versus “Being agile”
 Agile practices
 Pair programming
 Daily stand-ups
 Combining development and
operations processes (DEVOPS)
 Burn-ups, Burn-downs, CFDs
 Continuous Integration / Delivery
 Test-Driven Development
 Automated build and test
 “Sprints” (the cycle between
deliveries / plan changes)
 Story point estimating
 Multi-disciplinary teams
 Retrospectives
 Agility (the quality possessed by
those who are “agile”)
 Ability to change direction (or deliver
change) at speed
 Shorter time from idea to value
 Less waste from a change in the plan
 Limited cascade of change from one
area to another
 Small changes are inexpensive
 Releases are frequent (and
inexpensive)
 Resistance to valuable change is low

3. Kanban Foundational Principles
of change management
1. Start with what you do now
including
 current processes
 current roles and responsibilities
 current job titles
2. Agree to pursue improvement
through evolutionary change
3. Encourage acts of leadership at
every level in your organisation -
from individual contributor to
senior management
of service delivery
1. …
2. …
3. …
Watch David Anderson’s blog for more on
this topic coming soon…

4. See Delivery as a “Flow System”
Pool of
Ideas
Proposals Selected Development Acceptance Complete
Commitment Delivery
Lead Time
Work in
Progress
Delivery Rate
Items per
time period
Work Item

5. Flow Systems follow Little’s Law
In 1961 Dr John Little (studying Queuing
Theory) proved that, in a stationary system:
λ = L / W
 λ is the average arrival rate
 L is the average number of items in the
queue,
 W is the average time in queue
 Subject to similar assumptions we can apply this to
delivery systems:
Throughput = WiP / TiP
 The overline indicates the average (arithmetic mean)
 Throughput (Th) is the rate items depart the system
under consideration. If this is at the Delivery point (and
there are no discards) we call this Delivery Rate
 WiP is the number of items in the system
 TiP is “time in process” for an item from entering to
leaving the system (or part of the system) under
consideration. We call this Lead Time for the time
taken from the Commitment Point to Delivery Point.

6. Little’s Law is a precise relationship
provided the system’s not trending
That is, either:
 The period being averaged is between 2 consecutive points where WiP=0
or
 The system is “stationary”
In a “stationary” system…
 The age of WiP has not changed significantly over the period
 The amount of WiP has not changed significantly over the period
 Every item that arrives, eventually departs
In most Kanban delivery systems neither of these conditions will apply precisely
over typical periods of control (e.g. 1-4 weeks)

7. Exercise – calculate DR, WiP and TiP
from arrival and departure dates
 Then validate your working with Little’s Law
Av DR – WiP/TiP = 0
 And plot Control Chart and
Cumulative Flow Diagram

8. Little’s Law is a fact rather than an aim…
 Variability, batches and iterations are not the enemy
 Remember “value trumps flow trumps waste”
 But
 Predictability is an aim (helped by smooth flow, limited variability, continuous
flow)
 Flow Debt is undesirable (delivering more quickly now…
at the cost of slower times later)
 Indicators:
 Net Flow (Troy Magennis, focusedobjective.com)
 Delivery Bias (xprocess.blogspot.com)
 “TiP Deficit” (Dan Vacanti, Actionable Agile Metrics)
 Age of WiP Indicator (xprocess.blogspot.com)
 Buffer Usage (TOC, Dimiter Bakardzhiev)
 Net Flow (Troy Magennis, focusedobjective.com)

9. Flow Metrics
The basics…
 Delivery Rate
 Work in Progress, WiP
 Time in Process, TiP
(Lead Time if between commitment and delivery)
Other metrics indicating “Flow Debt”…
Net Flow
Delivery Bias
TiP Deficit
Age of WiP Indicator
delivering more
quickly now…
at the cost of
slower times later

10. Net Flow
( DR – λ ) / TargetTh
 Positive if more deliveries than new
items
 Negative if more arriving than being
delivered
 Simple / useful indicator
 Doesn’t look inside the system so not a
predictor of future TiP

11. Delivery Bias
( Th – WiP / TiP ) / TargetTh
 Will be zero in a system which is “stationary”
over the averaging period
 Will be positive if
 Throughput is higher than “balanced” and/or
 WiP is increasing, and/or
 TiP is lower than balanced

12. “TiP Deficit”*
( ExpectedTip - TiP ) / TargetTiP
 Will be zero in a system which is “stationary” over
the averaging period
 Will be positive if
 Older WiP is being delivered ahead of newer WiP
 Age of WiP reducing
 Will be negative if
 Newer WiP is being delivered ahead of older WiP
(expedite lane)
 Age of WiP increasing
* Dan Vacanti’s “Flow Debt” defined in
Actionable Agile Metrics

13. Age of WiP Indicator
( AgeOfWip – TiP/2 ) / TargetTiP
 Possibly best predictor of future TiP
increases
 Age of WiP in a very regular system will
be about half the average TiP
 Normalised with “TargetTiP” so
parameter can be used to compare
different systems

14. Buffer Management
 Based on Takt Time
(a measure of Target Throughput)
 Delivery date includes buffer (time)
 As buffer is used up intervention
may be needed
Source: Dimitar Bakardzhiev
dimiterbak.blogspot.com
Steve Tendon and Wolfram
Müller’s Tame the Flow

15. Probabilistic Forecasting

16. References
Andy Carmichael
@andycarmich
xprocess.blogspot.com
www.actionableagile.com
Thank you!

17. “
”
The worse mistakes are not the
result of wrong answers…
but wrong questions
PETER DRUCKER