key note - lean kanban north america 2013 - beyond kanban

[email protected], @djaa_dja

Beyond KanbanManaging Investment in Knowledge Work against Business Risks


Part 1 - Introduction


Some Core Kanban Concepts


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


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


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


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


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


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


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


Delivery Rate

Lead Time

WIP=

Avg. Lead Time

Avg. Delivery Rate

WIP

Poolof

Ideas

ReadyTo

Deploy

Little’s Law & Cumulative Flow


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


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


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


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


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


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


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


Part 2Qualitative Risk

Management


The Key to Governance of Portfolio Risk


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


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 -


2412 -

BizCaseDev

4824 -

Poolof

Ideas

∞

S

T

U

R

Q

V

YZAA

XW


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 -


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


Risk Assessment


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


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


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


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


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


3rd Dimension: Shelf-Life Risk

Short(days, weeks,

months)

Medium(months, quarters,

1-2 years)

Long(years, decades)

KnownExpiryDate,

Seasonal(fixed window

ofopportunity)

FashionCrazeFad


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


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


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


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


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)


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


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


Risk Management


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?


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


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



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?


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



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


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!


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


Hedge Delivery Risk by allocating capacity in the kanban system

Done

FH

E

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A

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Engin-eeringReady

Deploy-mentReady

G

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PB

MN

2 ∞

P1AB

Ongoing

Development Testing


Expedite 1

3

Fixed Date

Standard

Intangible

2

3DE


Aligning with Strategic Position or Go-to-Market Strategy

Done

F

H

E

C

A

I

Engin-eeringReady

Deploy-mentReady

G

D

GY

PB

MN

2 ∞

P1AB

Ongoing

Development Testing


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


Trade off growing market reach against growing share & profit within a niche

Done

F

H

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C

A

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Engin-eeringReady

Deploy-mentReady

G

D

GY

PB

MN

2 ∞

P1AB

Ongoing

Development Testing


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


Visualizing Risks on a Kanban Board


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


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


Expedite 1

3

Fixed Date

Standard

Intangible

2

3DE


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


Part 3Quantitative Risk

Management


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


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


Comparative Assessment


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


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


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


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


Comparative assessment


Liquidity


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?


Liquidity in the housing market

Sellers BuyersBank

$100

$100

Cash


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


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


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!


So, how would we measure liquidity?


Measuring Liquidity


Measuring Real Liquidity…

If we recall, liquidity is measured as transaction volume in the market. So what are the transactions in a kanban

system?


Pull Transactions in Kanban

Done

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GD

2 ∞

No Pull

Ongoing

Development Testing


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.


Variety & Specialization increase WIP

Done

Poolof

Ideas

F

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Engin-eeringReady

Deploy-mentReady

GD

4 ∞

No Pull

Ongoing

Development Testing


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!


Can you tell which of these systems is more liquid?


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.


Analysis of Derivative of Pulls

The derivative shows us clearly that System B has smoother flow and is a more liquid system


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


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.


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


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....


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…


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


Visualizing Liquidity


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


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


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


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


Summarizing Liquidity


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


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


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!


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!


Conclusions


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


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!


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!


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!


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


Thank you!


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.


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.


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


David J Anderson& Associates, Inc.


Appendix


Example Distributions

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