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Beyond KanbanManaging Investment in Knowledge Work against Business Risks
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Part 1 - Introduction
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Some Core Kanban Concepts
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Wish to avoid discard after commitment
Commitment is deferred
Backlog
H
E
C A
I
Engin-eeringReady
D
5Ongoing
Development Testing
Done3 3
TestReady
5
PTCs
Commitment point
We are committing to getting started. We are certain we want
to take delivery.
UAT
Deploy-mentReady
∞ ∞
FF
FFF
F F
G
Pull
ChangeRequests
Items in the backlog remain optional and unprioritized
Poolof
Ideas
Backlog
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Discard rates are often high
H
E
C A
I
Engin-eeringReady
D
5Ongoing
Development Testing
Done3 3
TestReady
5
PTCs
UAT
Deploy-mentReady
∞ ∞
FF
F F
G
ChangeRequests
H
Discarded
The discard rate with XIT was 48%. ~50% is commonly observed.
Options have value because the future is uncertain
0% discard rate implies there is no uncertainty about the future
I
Reject
Deferring commitment and avoiding interrupting
workers for estimates makes sense when
discard rates are high!
Poolof
Ideas
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Upstream Kanban Prepares OptionsReady
forEngin-eering
F
H
I
Comm-itted
D
4 Ongoing
Development
Done3
JK
12
Testing
Verification
3
L
Commitment point
4 -
Requi-rementsAnalysis
2412 -
BizCaseDev
4824 -
Poolof
Ideas
∞
Min & Max limitsinsure sufficientoptions are alwaysavailable
Committed WorkOptions
Discarded
O
Reject
P Q
$$$ cost of acquiring options
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FF
FFF
F F
Replenishment Frequency
UAT
H
E
C A
I
Engin-eeringReady
Deploy-mentReady
G
D
5∞
Replenishment
Ongoing
Development Testing
Done3 3
TestReady
5
PTCs
ChangeRequests
∞
Discarded
I
The frequency of system replenishment should reflect
arrival rate of new information and the transaction &
coordination costs of holding a meeting
Pull
Frequent replenishment is more agile.
On-demand replenishment is most
agile!
Poolof
Ideas
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FF
FFF
F F
Delivery Frequency
UAT
H
E
C A
I
Engin-eeringReady
Deploy-mentReady
G
D
5∞
DeliveryOngoing
Development Testing
Done3 3
TestReady
5
PTCs
ChangeRequests
∞
Discarded
I
The frequency of delivery should reflect the transaction &
coordination costs of deployment plus costs &
tolerance of customer to take delivery
Pull Deployment buffer size can reduce as frequency of delivery
increases
Frequent deployment is more agile.
On-demand deployment is most agile!
Poolof
Ideas
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FF
FFF
F F
Specific delivery commitment may be deferred even later
UAT
H
E
C A
I
Engin-eeringReady
Deploy-mentReady
G
D
5∞
Pull
Ongoing
Development Testing
Done3 3
TestReady
5
PTCs
ChangeRequests
2nd
Commitmentpoint*
Kanban uses
2 Phase Commit
∞
Discarded
I
We are now committing to a specific deployment and delivery
date
*This may happen earlier if circumstances demand it
Poolof
Ideas
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FF
FFF
F F
Defining Kanban System Lead Time
UAT
H
E
C A
I
Engin-eeringReady
Deploy-mentReady
G
D
5∞
Pull
Ongoing
Development Testing
Done3 3
TestReady
5
PTCs
ChangeRequests
∞
System Lead Time
The clock starts ticking when we accept the customers order, not
when it is placed!
Until then customer orders are merely available options
Lead time ends when the item
reaches the
first ∞ queue.
Discarded
I
Poolof
Ideas
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Delivery Rate
Lead Time
WIP=
Avg. Lead Time
Avg. Delivery Rate
WIP
Poolof
Ideas
ReadyTo
Deploy
Little’s Law & Cumulative Flow
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Flow Efficiency
Done
Poolof
Ideas
F
H E
C A
I
Engin-eeringReady
Deploy-mentReady
GD
GYPB
DEMN
2 ∞
P1
AB
Lead Time
Ongoing
Development Testing
Done VerificationAcceptance3 3
Flow efficiency measures the percentage of total lead time is spent actually adding value (or
knowledge) versus waiting
Until then customer orders are merely available options
Waiting Waiting WaitingWorking
Flow efficiency = Work Time x 100%
Lead TimeFlow efficiencies of 2% have been reported*. 5% -> 15% is
normal, > 40% is good!
* Zsolt Fabok, Lean Agile Scotland, Sep 2012, Lean Kanban France, Oct 2012
Working
Multitasking means time spent in working columns is often waiting
time
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Observe Lead Time Distribution as an enabler of a Probabilistic Approach to Management
Lead Time Distribution
0
0.5
1
1.5
2
2.5
3
3.5
Days
CR
s &
Bu
gs
SLA expectation of44 days with 85% on-time
Mean of 31 days
SLA expectation of105 days with 98 % on-time
This is multi-modal data!
The work is of two types: Change Requests (new
features); and Production Defects
This is multi-modal data!
The work is of two types: Change Requests (new
features); and Production Defects
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Filter Lead Time data by Type of Work (and Class of Service) to get Single Mode Distributions
85% at10 days
Mean5 days
98% at25 days
Change R
equest
s
Pro
duct
ion D
efe
cts
85% at60 days
Mean 50 days
98% at150 days
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Lead Time
Lead Time
Allocate Capacity to Types of Work
Done
Poolof
Ideas
F
H
E
C
A
I
Engin-eeringReady
Deploy-mentReady
G
D
GY
PBDE
MN
2 ∞
P1
AB
Separate understanding of Lead Time for each type of work
Ongoing
Development Testing
Done VerificationAcceptance3 3
ChangeRequest
s
Production
Defects
Separate understanding ofLead Time for each type of work
4
3
Consistent capacity allocation should bring some consistency to delivery rate of work of each type
Consistent capacity allocation should bring more consistency to delivery rate of work of each type
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FF
FFF
F F
Defining Customer Lead Time
UAT
H
E
CA
I
Engin-eeringReady
Deploy-mentReady
G
D
5∞
Pull
Ongoing
Development Testing
Done3 3
TestReady
5
PTCs
ChangeRequests
∞
Customer Lead Time
The clock still starts ticking when we accept the customers
order, not when it is placed!
Discarded
I
Poolof
Ideas
Done
∞
The frequency of delivery cadence will affect customer lead
time in addition to system capability
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The Optimal Time to Start
impa
ct
When we need it
85th percentile
Ideal StartHere
Commitment point
If we start too early, we forgo the option and opportunity to do something else that may
provide value.
If we start too late we risk incurring the cost of delay
With a 6 in 7 chance of on-time delivery, we can always
expedite to insure on-time delivery
time
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The Key to Governance of Portfolio Risk
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Simplifying Alignment & Corporate Governance
Kanban systems enable a greatly simplified model for management
of risk & corporate governance
Our business has defined promises to our shareholders in terms of the products, services
and markets we operate within and our tolerance to risk.
If we can show that we develop good, innovative ideas within those bounds and that our people are
always working on the best of those available choices, we can claim appropriate use of
shareholders’ funds
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Ideas should reflect opportunities to exploit& be classified by the business risks they address
I
J
K
F
U1
L
P
ST
U
R
Q
Risk #1 Are we creating the right ideas?Ready
forEngin-eering
F
P
I
Comm-itted
D
4 Ongoing
Development
Done3
J
K
12
Testing
Verification
3
L
Commitment point
4 -
Requi-rementsAnalysis
2412 -
BizCaseDev
4824 -
Poolof
Ideas
∞
S
T
U
R
Q
V
YZAA
XW
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Risk #2 What to Pull Next?Ready
forEngin-eering
F
H
I
Comm-itted
D
4 Ongoing
Development
Done3
JK
12
Testing
VerificationAcceptance
3
L
Commitment point
4 -
Requi-rementsAnalysis
2412 -
BizCaseDev
4824 -
Poolof
Ideas
∞
Pull
Replenishment
Pull
PullSelection
I have 4 options, which one should
I choose?Replenishing the system is an act of commitment – selecting items for delivery – for conversion from
options into real value.
Pull Selection is choosing from immediate options – ideally dynamic selection of the item with the most immediate risk attached to it by a suitably
skilled member of the team
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Risk Assessment
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A Lean approach to alignment with business risks uses Qualitative Assessment
But how do we determine the risks in a work item that we must
manage? We need a fast, cheap, accurate, consensus
forming approach to risk assessment. We need Lean Risk Assessment!
The answer is to use a set of qualitative methods to assess different dimensions of risk such as
urgency
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Sketch market payoff functionR
oom
nig
hts
sold
per
day
Actual rooms sold
Cost of delay
Estimated additional rooms sold
When we need it When it arrived
Cost of delay for an online Easter holiday marketing promotion is difference in integral between the two curves
time
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Cost of Delay based on Market Payoff Sketches
Cost of delay function for an online Easter holiday marketing campaign delayed by 1 month from mid-January(based on diff of 2 integrals on previous slide)
Treat as a Standard Class item
time
impa
ct
Total costof delay
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Establish urgency by qualitative matching of cost of delay sketches
time
impa
ct
time
time
time
impa
ctim
pact
impa
ct
time
impa
ct
time
impa
ctim
pact
Expedite – critical and immediate cost of delay; can exceed kanban limits (bumps other work)
Fixed date – cost of delay goes up significantly after deadline; Start early enough & dynamically prioritize to insure on-time delivery
Standard - cost of delay is shallow but accelerates before leveling out; provide a reasonable lead-time expectation
Intangible – cost of delay may be significant but is not incurred until much later; important but not urgent
time
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Cost of Delay has a 2nd Dimension
time
impa
ct
time
impa
ct
time
impa
ctim
pact
Extinction Level Event – a short delay will completely deplete the working capital of the business
Major Capital – the cost of delay is such that a major initiative or project will be lost from next year’s portfolio or additional capital will need to be raised to fund it
Discretionary Spending – departmental budgets may be cut as a result or our business misses its profit forecasts
Intangible – delay causes embarrassment, loss of political capital, affects brand equity, mindshare, customer confidence, etc
time
?
Working capital
Working capital
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3rd Dimension: Shelf-Life Risk
Short(days, weeks,
months)
Medium(months, quarters,
1-2 years)
Long(years, decades)
KnownExpiryDate,
Seasonal(fixed window
ofopportunity)
FashionCrazeFad
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Shelf-Life Risk
Sch
ed
ule
Ris
kShort
(days, weeks,months)
Medium(months, quarters,
1-2 years)
Long(years, decades)
High
Low
KnownExpiryDate,
Seasonal(fixed window
ofopportunity)
FashionCrazeFad
Inn
ovati
on
High
Low
Num
ber
of
Opti
on
s
Many
Few
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Shelf-Life Risk
Diff
ere
nti
ato
r
Short(days, weeks,
months)
Medium(months,quarters,1-2 years)
Long(years,
decades)
Low
Low
KnownExpiryDate,
Seasonal(window of
opportunity)
FashionCrazeFad
Inn
ovati
on
High
Low
Num
ber
of
Opti
on
s
Many
Few
Sp
oiler/
Follow
er
High
Low
Schedule Risk
If we are market leading our innovations are less time critical
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When should we start something?
impa
ct
When we need it
85th percentile
Ideal StartHere
Commitment point
If we start too early, we forgo the option and opportunity to do something else that may
provide value.
If we start too late we risk incurring the cost of delay
With a 6 in 7 chance of on-time delivery, we can always
expedite to insure on-time delivery
time
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Risk is a multi-dimensional problem
So understanding cost of delay enables us to know what to pull
next?Yes, however, it isn’t always relevant! Cost of
delay attaches to a deliverable item. What if that item is large? Whole projects, minimum
marketable features (MMFs) or minimum viable products (MVPs) consist of many smaller items.
We need to understand the risks in those smaller items too, if we are to know how to schedule work,
replenish our system and make pull decisions wisely
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Market Risk of Change
Mark
et
Ris
k
Sch
ed
ulin
g
Highly likely to change
Highly unlikely to
change
StartEarly
StartLate
Differentiators
Spoilers
Table Stakes
Cost Reducers
Potential Value
ProfitsMarket Share
etc
RegulatoryChanges
Buy (COTS)Rent (SaaS)
Build(as rapidly as
possible)
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Product Lifecycle Risk
Pro
du
ct
Ris
k
Investm
en
t
Not well understoodHigh demand for innovation &
experimentation
Well understoodLow demand for
innovation
Low
Low
Innovative/New
Major Growth Market
Cash Cow
GrowthPotential
High
Low
High
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Risk is a multi-dimensional contextual problem
These are just useful examples!
We must develop a set of risk taxonomies that work in context
for a specific business.
We can easily envisage other risk dimensions such as technical risk, vendor dependency risk, organizational maturity risk and so forth.
It may be necessary to run a workshop with stakeholders to explore and expose the real
business risks requiring management
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Risk Management
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Understanding our tolerance to different risks
We need to decide what we value as a business, our strategic position & our go-to-market
strategies
What are our expectations for predictability, business agility, profitability?
Are our current capabilities aligned with our expectations?
Have we a clearly stated strategic position and set of go-to-market strategies?
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Matching Cost of Delay Risk to Capability
Lead T
ime
Short
Long
Deliv
ery
Business Agility
Reple
nis
hm
en
t
Frequent
Seldom
Frequent
Seldom
Pre
dic
tabili
ty
High
Low
Expedite
Fixed Date
Intangible
Standard
PredictabilityNot Applicable
Where does our business currently
rank on these sliders?
If we suffer a lot of expedite demand, strong capability with business agility without a need for predictability will work. However, our business will
be constantly in a reactive mode
If we have many fixed date requirements we need a reasonably strong business agility capability and
a lot of predictability
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Matching Shelf-Life Risk to Capability
Short(days, weeks,
months)
Medium(months,quarters,1-2 years)
Long(years,
decades)
Lead T
ime
Short
Long
Deliv
ery
Business Agility
Reple
nis
hm
en
t
Frequent
Seldom
Frequent
Seldom
Pre
dic
tabili
ty
High
Low
Where does our business currently
rank on these sliders?
Kanban system dynamics
Are our business strategy and expectations aligned with our currently observed capabilities?
If we plan to pursue short shelf-life opportunities, do we have the agility and predictability to pull it off?
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Matching Market Risk of Change to Capability
Lead T
ime
Short
Long
Deliv
ery
Business Agility
Reple
nis
hm
en
t
Frequent
Seldom
Frequent
Seldom
Less
Less
Differentiators
Spoilers
Table Stakes
Cost Reducers
RegulatoryChanges
Pre
dic
tab
ilit
y
More
Where does our business currently
rank on these sliders?
Highly regulated industries require predictability in delivery capability
To pursue a strategy of innovation or fast market following we need a high level of business agility –
fast, frequent delivery
To be innovative or fast following in a highly regulated industry requires us to be both predictable
and exhibit a high level of business agility
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Understanding capability is critical to our risk management strategy
If you cannot assess your current delivery capability and align your
strategy and marketing plans accordingly, then …
You are doomed before you start!
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How much risk do you want to take?
Given our current capabilities, our desired strategic position and go-to-market strategies, how much
risk do you want to take?
We only have capacity to do so much work. How we allocate that capacity across different risk
dimensions will determine how aggressive we are being from a risk management perspective.
The more aggressive we are in allocating capacity to riskier work items the less likely it is that the
outcome will match our expectations
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Hedge Delivery Risk by allocating capacity in the kanban system
Done
FH
E
C
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Engin-eeringReady
Deploy-mentReady
G
D
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PB
MN
2 ∞
P1AB
Ongoing
Development Testing
Done VerificationAcceptance3 3
Expedite 1
3
Fixed Date
Standard
Intangible
2
3DE
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Aligning with Strategic Position or Go-to-Market Strategy
Done
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Engin-eeringReady
Deploy-mentReady
G
D
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PB
MN
2 ∞
P1AB
Ongoing
Development Testing
Done VerificationAcceptance3 3
Table Stakes 3
1
Cost Reducer
s
Spoilers
Differentiators
2
1DE
DA
The concept of a minimum viable product (MVP) will contain the
table stakes for at least 1 market niche
Market segmentation can be used to narrow the necessary table stakes for any given market niche!
Enabling early delivery for narrower markets but potentially including value generating
differentiating features
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Trade off growing market reach against growing share & profit within a niche
Done
F
H
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Engin-eeringReady
Deploy-mentReady
G
D
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PB
MN
2 ∞
P1AB
Ongoing
Development Testing
Done VerificationAcceptance3 3
Table Stakes 3
1
Cost Reducer
s
Spoilers
Differentiators
2
1DE
DA
It is important to define a MVP in terms of table stakes and
differentiators required to enter a specific market segment
Capacity allocated to Table Stakes will determine how fast new niches can be developed.
Allocate more to Table Stakes to speed market reach/breadth.
Allocate more to differentiators to grow market share or profit margins
Allocate more to spoilers to defend market share
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Visualizing Risks on a Kanban Board
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Visualize Risks on the Ticket
Title
Checkboxes… risk 1 risk 2 risk 3 risk 4
req
com
ple
te
Color of the ticket
Typically used to indicated technical or skillset risks
H
Decorators
Letter
SLA orTarget Date
Business risk
visualization highlighted
in green
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Visualize Risks on the Board
Done
FH
E
C
A
I
Engin-eeringReady
Deploy-mentReady
G
D
GY
PB
MN
2 ∞
P1AB
Ongoing
Development Testing
Done VerificationAcceptance3 3
Expedite 1
3
Fixed Date
Standard
Intangible
2
3DE
[email protected], @djaa_dja
Abandon Prioritization. Banish Priority
Prioritization is waste!
Prioritization is an exercise to schedule a sequence of items at a specific point in time. Only at the point of commitment can a proper assessment be made of what to pull next. Filter options based on kanban signals. Select from filtered subset
Priority is a proxy variable for real business risk information.
Do not mask risk behind a proxy. Enable better governance and better decision making by
exposing the business risks under management throughout the workflow
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2 Real Business Problems
Probabilistic Forecasting requires me to trust that future system capability will reflect the (relatively) recent past
capabilityIf given a choice of systems in which to place business, I would like to know the best choice
Lowest priceFastest deliveryMost predictable delivery
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Comparative Assessment
We need a method to monitor the “trustworthiness” of the system that makes
comparative assessment of currently observed capability against historical observations within the same system
Must be a leading indicator
We need a comparative assessment method to assess comparative
capability across different systems
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Comparative Assessment
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Analysis of Cost Distribution
Can you tell which system is better?
200 400 600 800 1000 1200 1400 1600 1800 2000 2400 2800 3200 3600 4600 6400 6600 More0123456789
System A Cost($) Per Item
Frequency
Cost Per Item in $
Freq
uenc
y
200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3600 4000 4200 5400 More02468
101214
System B Cost($) Per Item
Frequency
Cost Per Item in $
Freq
uenc
y
Average cost / item = $2218
Average cost / item = $1723
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Analysis of Throughput Distribution
Can you tell which system is better?
0 1 2 3 4 More02468
1012141618
System B Throughput
Number of Tickets Per Day
0 1 2 3 4 More0
5
10
15
20
25
System A Throughput
Number of Tickets Per Day
00.5
11.5
22.5
33.5
44.5
System A Throughput
1/15/2
013
1/19/2
013
1/23/2
013
1/27/2
013
1/31/2
013
2/4/2
013
2/8/2
013
2/12/2
013
2/16/2
013
2/20/2
013
2/24/2
013
2/28/2
013
3/4/2
013
3/8/2
013
3/12/2
013
3/16/2
0130
0.51
1.52
2.53
3.54
4.5
System B Throughput
Mean 1.00 / day Mean 1.15 / day
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Lead Time Distributions
System B is clearly faster with better due date performance (but are they processing work of similar complexity & size?)
5 10 15 20 25 30 40 45 55 65 More02468
101214
System A
Frequency
Lead Time (Days)
-25 -20 -5 0 5 10 20 30 35 40 More0
2
4
6
8
10
12
System A
Frequency
Lead Time Expectation Spread (Days)
5 10 15 20 25 30 More0
5
10
15
20
25
30
System B
Frequency
Lead Time in Days
-15 -10 -5 0 5 10 15 20 More05
1015202530354045
System B
Frequency
Lead Time Expectation Spread (Days)
Mean 17 days Mean 12 days
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ComparisonSystem A System B
Cost (avg per item) $2218 $1723
Throughput (avg per day) 1.00 1.15
Lead time (mean days) 17 12
Avg system WIP (items) 17 14
Avg. WIP per State 4.25 2.8
Due Date Performance(% within expectation)
42% 75%
• System A costs 29% more per item than System B• System B has 15% higher delivery rate than System A• System B is typically 5 days (or 29%) faster than System A• System B delivers 33% more items within expectation• Total WIP is not significantly different in either system
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Comparative assessment
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Liquidity
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Where is the best place to place a work order to best manage risk?
Investment bankers know how to answer this question! They prefer to place orders in liquid
markets. In a highly liquid market they have trust that an order will be fulfilled accurately, quickly
and at the correct price.
Highly liquid markets are markets with a high level of trust. High liquidity inherently gives us high
confidence in the market.
But can we view kanban systems as markets for software
development?
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Measuring Liquidity
what is required are well matched buyers, sellers and access to capital such as mortgages, bridging
loans or cash buyers injecting capital into the system, to fund the transactions .
when these conditions are present transactions will take place!
The more transactions, the more liquid the market
Market liquidity is measured as transaction volume
[email protected], @djaa_dja
Adverse Market Conditions
In a market with lots of buyers but few well matched sellers, inventory will be scarce, few transactions will occur. When a property comes on the market it could sell quickly but there will be anxiety over the correct price. This may delay the sale or cause the buyer to
overpay through fear of losing the purchase to competitive buyers. In some markets like England,
the seller may refuse to close the transaction in hope of a higher price (gazumping).
Lack of liquidity causes a lack of trust in the system and delays transactions
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More Adverse Market Conditions
In a market with lots of sellers but few well matched buyers inventory will grow and few
transactions will happen. Uncertainty will develop over the correct price. A lack of trust will result in a disparity between asked prices and offered prices. Additional information may be sought to establish
a fair price. Transactions will be delayed
Hence, market liquidity can be measured as rate of transactions
concluded!
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So, how would we measure liquidity?
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Measuring Liquidity
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Measuring Real Liquidity…
If we recall, liquidity is measured as transaction volume in the market. So what are the transactions in a kanban
system?
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Pull Transactions in Kanban
Done
Poolof
Ideas
F
H
E
C A
I
Engin-eeringReady
Deploy-mentReady
GD
2 ∞
No Pull
Ongoing
Development Testing
Done VerificationAcceptance3 3
Work flows through a kanban system when we have well matched work order or items of WIP with
suitable staff to add valuable new knowledge and progress work to completion.
For work to flow freely in a kanban system, we must have work available to pull and suitably
matched workers available to pull it. Hence, the act of pulling is the indicator that an item of work
was matched to available workers and flow happened.
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Variety & Specialization increase WIP
Done
Poolof
Ideas
F
H
E
C A
I
Engin-eeringReady
Deploy-mentReady
GD
4 ∞
No Pull
Ongoing
Development Testing
Done VerificationAcceptance5 4
J
L
K
As a result, there will be a minimum level of WIP required to facilitate flow. For systems with
inherent liquidity problems - lots of heterogeneity in work types or variance in demand for quality
(non-functional requirements) and|or lots of specialists workers, non-instant availability
problems or variability in skill and experience of workers, then the WIP in the system will need to
be larger in order for work to flow freely. The liquidity measure will not rise until the WIP rises.
Pull
∞
More WIP increases liquidity & increase flow!
Pull
And Cost!
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Can you tell which of these systems is more liquid?
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Liquidity is measured as derivative of the rate of pull transactions
To make the measure robust to the number of states in the kanban system, the amount of WIP and the number of
workers involved, we propose the derivative of the rate of pull
transactions as the indicator of liquidity.
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Analysis of Derivative of Pulls
The derivative shows us clearly that System B has smoother flow and is a more liquid system
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Comparative assessment
Assessing the spread of variation in the derivative of pull transactions gives us a metric for assessing the predictability of a kanban system.
A narrow spread in the derivative shows a smooth flowing (laminar)
system that is likely highly predictable.
A wider spread implies turbulent or unpredictable flow implying higher
risk
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Trustworthiness
A trustworthy kanban system will exhibit smooth laminar flow. The
spread in the derivative values will be narrow.
Such systems are most likely to offer the most predictable results.
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Managing Risk with Liquidity Metrics
Probabilistic forecasting in Kanban is based on the assumption that the recent past will reflect the near
future.
We must monitor the derivative of the rate of pull transactions.
Changes in the rate of pull may indicate that the recent past, no longer reflects our current reality.
Hence, probabilistic forecasts are at risk
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Characteristics of Liquid Markets
A liquid financial market would exhibit several characteristics…
Tightness – bid-ask spreadImmediacy - how quick an order is filledBreadth - ability to handle large ordersDepth - processing orders at different pricesResiliency - ability of the market to swing back to normal or adjust after a surge in orders off the market or one large order that moves the price....
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Assessing Liquidity Kanban SystemA liquid kanban system would exhibit these
characteristics…
Tightness – spread in derivative of pull transactions indicating trustworthiness and likelihood of on-time deliveryImmediacy – shape of lead time distribution (mode, median, mean, and tail 85%ile, 98%ile)Breadth – variety of types of work handled (incl size from single requests to large projects)Depth – variety of risks under management (and depth of taxonomies)Resiliency - ability of the system to recover to normal or adjust after a surge in orders breaching WIP constraints or swarming on expedite orders…
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Analysis of Derivative of Pulls
A histogram and run chart of the derivative of pull transactions shows clearly the liquidity in the kanban system
This metric is independent of the number of states in the workflow or the amount of WIP
Some work remains to make it robust to recirculation of tickets on the board
When encountering re-cycling tickets(negative pull) the resulting positive pull should not be counted
And…
Experimentation is needed to determine how far back to sample
the distribution to provide sufficient data to indicate current
normal operating range for liquidity
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Visualizing Liquidity
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SLA Not Met
Large Med Small
Immediacy – Lead Time SLA
Tightness – Derivative of pull transactions
Green arrow pull transaction
Breadth – Types of worked pulled (Size)
SLA Met
Depth – Managed Risk LowMedHigh
Resilience – Recover to normal circumstances after an exceptional period
SLA is affected briefly due to swings in Breadth, Depth and Immediacy, but comes back to normal.
Color of shape represents age
Relatively youngRelatively old
Liquidity Board Deconstructed
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System A – Liquidity Board
• Immediacy – Lead time SLA is met less than 50% of the time and work items stay in a work state for a relatively long period of time
• Tightness – Inconsistent number of pulls through the workflow
• Breadth – Little variance in breadth size of items typically small or medium
• Depth – Little variance in managed risk items are typically low in risk
• Resilience – Once the system has one to two large items come in it really never recovers
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System B – Liquidity Board
• Immediacy – Lead time SLA is met 80 % of the time and work items stay in a work state for a relatively short period of time
• Tightness – Relatively consistent number of pulls through the workflow
• Breadth – large variance in breadth; size of items range from small to large
• Depth – Large variance in managed risk, items range from low to high
• Resilience – Once the system takes on large items and high risk the SLA suffers, but eventually recovers
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Decisions to Route Work
So what conclusions can we draw about how to route work within this organization?
System A should only be given work that is known to be small and is not time critical
System B can be trusted with a greater variety (in size and risk profile) of work and especially time critical work
We should encourage managers in System A to adopt practices and
techniques used in System B
We are likely to place new investment in System B as we
trust the managers better to run a more liquid system
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Summarizing Liquidity
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Liquidity of the system should be considered against observed capability
before placing an orderSome kanban systems may appear faster and cheaper but carry more inherent risk as they have poorer
liquidity, handle less variety, are less resilient (can’t cope with or recover from
burst traffic)
Slightly longer to deliver but with greater certainty may be preferable to a system
with a lower average lead time but poorer liquidity & greater risk
ObservedCapability
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Liquidity is a Good Metric
Our measure of liquidity, as pull transaction volume and the spread of its
derivative, meets the criteria* for a useful metric…
SimpleSelf-generatingRelevantLeading Indicator
ObservedCapability
Liquidity is a global system measure.
Driving it up should not cause local optimization or undesired
consequences!
* Reinertsen, Managing the Design Factory 1997
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Relevance of Liquidity as a MeasureLittle’s Law
Narrow spread of variation in lead time for a fixed WIP means a more
predictable delivery rate. This is turn means greater predictability on
delivery date for a given volume of work and therefore a more accurate
price.
In order to have confidence that our probabilistic forecasts, based on (recent) historical data for the
distribution of lead times, are accurate, we must monitor the derivative of pull transactions as an indicator that the
risk isn’t changing
So our plans carry less buffer for variation
And
Our planning horizons can be shorter!
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Table Stakes 3
1
Cost Reducer
s
Spoilers
Differentiators
2
1
Improving Liquidity through Labor Pool Flexibility
Teams
F
HE
CA
Engin-eeringReady
G
D
GY
PBDE
MN
2
P1
AB
Ongoing
Analysis Testing
Done VerificationAcceptance3 3Ongoing
Development
Done3
Joe
Peter
Steven
Joann
David
Rhonda
Brian
Ashok
TeamLead
Junior who will be rotated through all 4 teams
Generalist or T-shaped people who can move flexibly across rows on the board to keep work
flowing
It’s typical to see splits of fixed team workers versus flexible system
workers of between 40-60%
Roughly half the labor pool are flexible workers
Promotions from junior team member to flexible
worker with an avatar clearly visualize why a
pay rise is justified. Flexible workers help manage liquidity risk
better!
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Conclusions
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Kanban enables new powerful approaches to risk management in
knowledge work
Kanban systems enable us to visualize many dimensions of real business risks.
Hedging risks is possible with capacity allocation in the system
Kanban is enabling the practical application of real option theory and
related concepts like real liquidity
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Qualitative Approaches are Lean
Qualitative approaches to risk assessment are fast, cheap and drive
consensus
There is no crystal ball gazing! Risk analysis is not speculative!
Stop speculating about business value and ROI. Instead assess real risks
and design kanban systems to manage them!
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Quantitative Approaches are also Lean
Quantitative approaches to using historical data and probabilistic
forecasting are viable and cheap and hence Lean!
There is no crystal ball gazing! Risk analysis is not speculative!
Stop speculating about future outcomes. Forcast
probabilistically and monitor the liquidity to
assess trustworthiness of forecasts!
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Don’t Set Yourself Up for Failure
Know your current capabilities!
Lead time distributions & replenishment and delivery
cadence define business agility!
If your strategic plan & marketing objectives are
not aligned with your current capabilities, you many be doomed before
you start!
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Kanban & Qualitative Risk Assessment are powerful in combination
Kanban systems address variability in, and focus attention on improving, flow!
Improved predictability & business agility from Kanban is only
valuable if exploited
Fully exploit Kanban-enabled business agility to delivery better business outcomes
through qualitative & quantitative risk
management
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Thank you!
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About
David Anderson is a thought leader in managing effective software teams. He leads a consulting, training and publishing and event planning business dedicated to developing, promoting and implementing sustainable evolutionary approaches for management of knowledge workers.He has 30 years experience in the high technology industry starting with computer games in the early 1980’s. He has led software teams delivering superior productivity and quality using innovative agile methods at large companies such as Sprint and Motorola.
David is the pioneer of the Kanban Method an agile and evolutionary approach to change. His latest book is published in June 2012, Lessons in Agile Management – On the Road to Kanban.
David is a founder of the Lean Kanban University, a business dedicated to assuring quality of training in Lean and Kanban for knowledge workers throughout the world.
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About
Raymond Keating has 20 years’ experience in the financial and technology industry developing and managing trading systems for major financial institutions such as J.P. Morgan, Republic National Bank, HSBC, NYMEX and the CME Group. Ray is an Accredited Kanban Trainer through the LKU.Currently Raymond is Director of Software Engineering at the CME Group. Functioning as the development manager for the ClearPort trading application and change agent for the group he has evolved the processes and policies by applying the core practices of Kanban. Recently Raymond has been researching applying the characteristics of market liquidity to a Kanban system.
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Acknowledgements
Donald Reinertsen directly influenced the adoption of virtual kanban systems and the assessment of cost of delay & shelf-life as criteria for scheduling work into a kanban system.
Chris Matts & Olav Maassen strongly influenced the concept of options & commitments and the upstream min-max limits is based on an example first presented by Patrick Steyaert.
We’d like to thank Andrew Milne for the animation of liquidity visualization and CME Group for their collaboration and access to data to demonstrate the quantitative theories presented here
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David J Anderson& Associates, Inc.
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Appendix
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