Making Better Decisions - understanding "fitness for purpose", matching strategy to objectives

dja@leankanban.com @lkuceo Copyright Lean Kanban Inc.
Presents
Presenter
David J. Anderson
Modern Management
Methods
San Francisco
May 2014
Release 1.1
Making Better Decisions
understanding “fitness for purpose”,
matching capability to strategy & objectives

Blizzard Skis

Mittersil, Austria
Blizzard
Factory

Blizzard is the largest private
sector employer in the Pengau Alps
region of Salzburgerland, AustriaMittersil is a factory town with
over 400 people relying on the
factory either directly or
indirectly for employment

• Since 1945
• Hand-made by craftsman
• Innovator
• Award winner

In 2007, Blizzard, effectively
bankrupt, faced closure from
parent company, Tecnica in ItalyToday Blizzard is the most
effective & efficient ski
manufacturer in the world!

What went wrong at Blizzard, a
proud & leading brand in alpine ski
equipment?And what enabled a remarkable
turnaround, from the brink of
extinction to a return to
innovation & profitability?

Orders & Deliveries of Skis
2006 winter
skis
delivered
to dealers
Timeline for manufacturing, delivery and order placement for ski
industry in northern hemisphere prior to 2007
timeNov Dec Jan
2006
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
2007
Feb Mar Apr
Start
manufacturing
2007 skis
2007 winter
skis
delivered
to dealers
Start
manufacturing
2008 skis
Orders
placed for
2007 winter

2006 was a warm winter and poor
snow conditions badly affected the
ski industry as people stayed home
and didn’t buy new equipment
Blizzard dealers were left
holding a lot of 2006 inventory
that they would hold & later
discount during the 2007 winter

Effects of climate change in the Alps
Glacier
recession is
clearly
visible
1912 1933
2003

2007 was also a warm winter as
climate change began to seriously
affect the Alps
Gun shy from 2 bad winters,
Blizzard dealers delayed
commitment on 2008 orders
until May after the ski season
had finished

In 2006 business risks appear to be low
2006 winter
skis
delivered
to dealers
Changing climate conditions and its affect on ski dealers
dramatically shifts the risk profile of ski manufacturing
timeNov Dec Jan
2006
2007
Feb Mar Apr
Start
manufacturing
2007 skis
2007 winter
skis
delivered
to dealers
Start
manufacturing
2008 skis
Orders
placed for
2007 winter

Previously lead time for delivery is 12 months
2006 winter
skis
delivered
to dealers
Traditionally manufacturers have had a full year to make the
skis for the following winter
timeNov Dec Jan
2006
2007
Feb Mar Apr
Start
manufacturing
2007 skis
2007 winter
skis
delivered
to dealers
Start
manufacturing
2008 skis
Orders
placed for
2007 winter Lead Time
to manufacture
2007 deliveries

By 2007 the risk profile has changed dramatically
2006 winter
skis
delivered
to dealers
Dealers still holding 2006 & 2007 inventory decide to wait until
the end of the 2007 season to place reduced orders for 2008
timeNov Dec Jan
2006
2007
Feb Mar Apr
Start
manufacturing
2007 skis
2007 winter
skis
delivered
to dealers
Start
manufacturing
2008 skis
Orders
placed for
2007 winter
Orders
placed for
2008 winter
Volume is low
due to over-
stocking of
older models
The period of speculative
manufacturing grows from 2
months to 6 months
Time period of
building to
forecast rather
than against
customer orders
Blizzard fail to anticipate falling
demand and over-produce 2008 skis.
Bankruptcy is a serious possibility!

Tecnica Group & Blizzard managers
turn to their business school
education and seek to cut costs by
consolidation & centralization
Centralizing all order
processing through Tecnica HQ
adds 1 month to order times,
increasing speculative build-to-
forecast. As a result costs go up!

Cutting costs will not make Blizzard
“fit for purpose” !!!
What is required to be “fit for
purpose” in a period of climate
change, is to defer manufacturing
until firm orders are placed!
Blizzard need to cut the lead
time to build skis!

By 2010 the market has a new equilibrium
2010 winter
skis
delivered
to dealers
Traditionally manufacturers have had a full year to make the
skis for the following winter
timeNov Dec Jan
2010
2011
Feb Mar Apr
Start
manufacturing
2011 skis
2011 winter
skis
delivered
to dealers
Start
manufacturing
2012 skis
Orders
placed for 2011
winter
Orders
placed for
2012 winter
Volume is low
due to over-
stocking of
older models
Lead Time
to manufacture
2011 deliveries
Lead time to manufacture
skis to order is now 6
months. No speculative build-
to-forecast
To have a viable business Blizzard
need a capability to make skis twice
as fast as before!

In 2010 a Lean initiative was started
in the factory. This was followed
later with a Kanban initiative in IT
and Quality Assurance
Blizzard becomes the first Lean
ski manufacturer in the world!
CIO, Eric-Jan Kaak wins Austrian
CIO of the Year 2013
and is promoted within Tecnica
Group

Meanwhile, Major Ski Resorts Deploy Snow
Cannon to Reduce Risk for Warm Winters

Lesson 1

Blizzard initially made a bad
decision because they didn’t
understand the dynamics of their
external environment
Once they realized that only
manufacturing skis faster &
deferred commitment would
make them “fit for purpose” did
they focus improvement efforts
where they could be most
effective

Understanding
“fitness for purpose”

What makes a pizza delivery service
“fit for purpose” ?
• Fitness criteria are metrics
that measure things
customers value when
selecting a service again &
again
• Delivery time
• Quality
• Predictability
• Safety (or conformance to
regulatory requirements)

Meet Neeta - a project manager
• Neeta’s team are working
late (again)
• Neeta needs to feed them
with pizza
• What attributes do her team
care about in a pizza delivery
service?
• Delivery time =
approximately 1 hour
• Non-functional quality =
tasty & hot
• Functional quality (order accuracy) =
doesn’t matter if small mistakes are
made, geeks will eat any flavor of
pizza
• Predictability =
+/- 30 minutes is acceptable
• Safety =
so long as health & safety in food
preparation is good, it’s fine

Neeta is also a working mom!
• Neeta gets home late.
Her kids are really
hungry and even
though she shouldn’t
she decides to order
pizza for them
• What makes a pizza
delivery service
acceptable to her kids
age 4, 6, 9 & 11 years?
• Delivery time =
20 minutes
• Non-functional quality =
doesn’t matter too much, it’s pizza!!!
• Functional quality (order accuracy) =
it must be cheese pizza! No other flavor is
acceptable! (even if you take the
pepperoni off)
• Predictability =
+/- 5 minutes maximum!!!
• Safety =
only mommy worries about that stuff!

Lesson 2

To be “fit for purpose” there is a
product component & a service
delivery component
We need to offer a selection of
different recipes which are
tasty & popular. However, we
must also deliver with speed &
predictability

Modern creative & knowledge
worker businesses often obsess
with product definition &
strategy
Operational excellence and
service delivery excellence are
often overlooked or treated as
inferior management skills

Lesson 3

Neeta has 2 identities –
Mother and Project Manager
Each of Neeta’s identities
represents a different market
segment for the pizza delivery
service

We need a different set of
thresholds for our fitness
criteria for each market segment
Our business needs the ability to
“sense” changing customer
tastes. As time goes by the
criteria & thresholds for a given
market segment may change

Our pizza delivery service can be
“fitter for purpose”
by offering different classes of
service for each market segment
But, do we have the capability to
deliver on customer
expectations?

Understanding & improving service
delivery capability with Kanban

Test
Ready
F
F
FF
F
F F
Commitment Frequency
E
I
G
D
Replenishment
Discarded
I
Pull
Ideas
Dev
Ready
5
Ongoing
Development Testing
Done
3 35
UAT
Release
Ready
∞ ∞
The frequency of system
replenishment should reflect
arrival rate of new
information and the
transaction & coordination
costs of holding a meeting
Frequent replenishment &
commitment is more agile.
On-demand commitment is
most agile!

Test
Ready
F
F
FF
F
F F
Defining Kanban System Lead Time
E
I
G
D
Pull
System Lead Time
Discarded
I
Ideas
Dev
Ready
5
Ongoing
Development Testing
Done
3 35
UAT
Release
Ready
∞ ∞
The clock starts ticking when
we accept the customers
order, not when it is placed!
Until then customer orders are
merely available options
Kanban
system lead
time ends
when the
item
reaches the
first ∞
queue

Test
Ready
F
F
FF
F
F F
Delivery Frequency
E
I
G
D
Delivery
Discarded
I
Pull
The frequency of delivery
should reflect the transaction
& coordination costs of
deployment plus costs &
tolerance of customer to take
delivery
Ideas
Dev
Ready
5
Ongoing
Development Testing
Done
3 35
UAT
Release
Ready
∞ ∞
Frequent delivery is more
agile.
On-demand delivery is most
agile!

Service Delivery Agility
Service Agility
Commitment
frequency
Lead Time
Delivery
Frequency
LeadTime
Short
Long
Delivery
Service Agility
Commitment
Frequent
Seldom
Frequent
Seldom
More
Agile
Less
Agile
Kanban system dynamics

Understanding
Cost of Delay Risk

Data from Corbis, Seattle, April 2007
Lead times for IT system software change requests
deployed during April 2007
Lead Time Distribution
0
0.5
1
1.5
2
2.5
3
3.5
1
8
15
22
29
36
43
50
57
64
71
78
85
92
99
10
6
11
3
12
0
12
7
13
4
14
1
14
8
Days
CRs&Bugs
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
Lead Time Histogram

85% at
10 days
Mean
5 days
98% at
25 days
ChangeRequests
ProductionDefects
85% at
60 days
Mean
50 days
98% at
150 days
Mode
Median
45 days
Filter by Type or Class to get Single Modal Data

Understanding Cost of DelayRoomnights
soldperday
Actual rooms sold
Cost of delay
Estimated additional
rooms sold
When we need it When it arrived
Cost of delay is difference in integral between the two curves
timeJan Feb Mar Apr
First sketch the market payoff function for the total
lifetime of the opportunity. In this example, a Spring
Break promotion for a hotel chain.

Cost of Delay for a limited shelf-life opportunity
follows an s-curve shape
Cost of delay function for a Spring Break marketing campaign
delayed by 1 month from mid-January based on the difference of
2 integrals on previous slide
time
impact
Total cost
of delay

When should we start something?
impact
When we
need it
85th
percentile
Ideal Start
Here
Commitment point
timeJan
10
Nov
11
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
If we pull the work into our
kanban system on Nov 11 we have a
6 out of 7 chance of on-time
delivery

We can study sensitivity to different start dates
impact
When we
need it
50th percentile
Later
Start
Here
Commitment point
timeJan
10
Nov
25
If we start as late as November 25
we only have a 50% chance of on-
time delivery
However, the cost of delay
incurred if we deliver within 60
days is relatively small. We have
an 85% chance of achieving
delivery with acceptable cost of
delay
85th percentile

What is the latest we could start?
impact
When we
need it
0th percentile
Very late
start
Commitment point
timeJan
10
Dec
19
If we start as late as December 19
we have 0% chance of on-time
delivery
We have about a 10% chance of a
total loss delivering the
promotion beyond the expiry date
of the opportunity
85th percentile
total loss

To be certain of delivery without incurring any
cost of delay is expensive
impact
When we
need it
98th
percentile
Early
Start
Commitment point
timeJan
10
Aug
11
If we are conservative and do not
wish to carry any risk of late
delivery or any risk of incurring
an opportunity cost of delay, then
we must start as early as August
13th.
We must commit to our Spring
Break 2015 promotion during
Summer 2014!!!

Lesson 4

Lead time is perhaps the most
important metric we gain from
kanban systems
Lead time coupled with cost of
delay sensitivity analysis is a
key enabler of deferred
commitment and consensus on
when to make commitments

What if we planned to do a series
of marketing promotions for
seasonal opportunities?
Such as, Halloween, Thanksgiving,
New Year’s, Valentine’s,
Spring Break, Cinco de Mayo
& 4th of July?

How many service teams do we need?
impact
time
Halloween
Thanksgiving
New Year’s
Spring Break
Cinco de Mayo
4th of July
Valentine’s
WIP=4
To avoid risk of late delivery
altogether, we need a WIP of at
least 4.
If current WIP >= 4 then workers
will experience significant
periods of slack and utilization
will be low.
Commitments must be made 150
days in advance of deliver and up
to 240 days prior to the event
Additional WIP beyond 4 must be
of a lower class of service to
insure that these marketing
promotions can be delivered
without incurring a cost of
delay

What if we were willing to accept more risk?
impact
time
Halloween
Thanksgiving
New Year’s
Spring Break
Cinco de Mayo
4th of July
Valentine’s
With a 50% chance of on-time
delivery we only need 3 WIP.
We can take on more lower class
of service work, or we can reduce
the WIP limit and reduce staffing
on the system by as much as 25%.
Staff will still experience
periods of slack if we don’t
introduce lower class of service
work
Commitments need only be made
45 days in advance. A 105 day gain
from the risk averse plan
WIP=3
If we do reduce the WIP limit
and/or adjust staffing we can
expect the lead time
distribution to change –
hopefully left-shifted with a
shorter tail

What happens if we have a much
more agile organization?If observed lead time capability
was significantly shorter and
predictability greater, what
benefits do we gain?

ChangeRequests
85% at
44 days
Mean
33 days
98% at
68 days
Median
26 days
Improved Service Delivery Capability
Shorter tail,
much more
predictable

Now, how many service teams do we need?
impact
time
Halloween
Thanksgiving
New Year’s
Spring Break
Cinco de Mayo
4th of July
Valentine’s
To completely avoid risk of late
delivery we need a WIP of 3.
If WIP >= 3 when data was
collected we have capacity. There
will also be slack for lower
class of service work
Commitments are needed 70 days
in advance.
If we were prepared to reduce
demand by just 1 project, WIP = 2 is
sufficient
WIP=3

What if we were willing to accept more risk?
impact
time
Halloween
Thanksgiving
New Year’s
Spring Break
Cinco de Mayo
4th of July
Valentine’s
With a 50% chance of on-time
delivery we only need WIP = 2.
Potentially allowing us to reduce
staff by 33%.
As a result the lead time
distribution will also be left-
shifted further improving agility.
There will still be some slack
for lower class of service work.
Commitments need only be made
26 days in advance.
WIP=2

Lesson 5

We have to conclude that for a
reasonably acceptable economic
outcome, we must accept some
risk of late delivery & incurred
cost of delay
To guarantee on-time delivery
and no lost opportunity cost
due to delay, we must have 33-
50% more WIP, staff to work it
and accept large amounts of
idle time

Aligning goals with capability

Go on, you can lift it!

Kanban system
dynamics
Shelf-life
(of business opportunities)
Is your service delivery fit for purpose?
Short
(days, weeks,
months)
Medium
(months,
quarters,
1-2 years)
Long
(years,
decades)
LeadTime
Short
Long
Delivery
Service Delivery
Agility
Replenishment
Frequent
Seldom
Frequent
Seldom
Predictability
High
Low
Is your service
delivery
predictability &
agility fit enough for
your business
strategy?If you plan to pursue short shelf-life opportunities,
you must measure predictability, lead time,
replenishment & delivery frequency as fitness
criteria. Does the capability exist to pursue the
chosen strategy effectively?

Lesson 6

Improve your capabilities before
pursuing market segments or
strategies that require levels of
service delivery beyond your
reach
With coaching & incremental
development a child can grow
to dead lift a large bar bell.
Impatience & over-reaching is
likely to end in tears!
With patience, education and a
focus on evolutionary change,
your organization can grow its
service delivery capability

Conclusions

1. Understand your external
environment before deciding what
to change
2. “Fitness for Purpose” has both a
product component & a service
delivery component
3. Each market segment will have its
own fitness criteria and threshold
values

4. Lead time coupled with cost of delay
sensitivity analysis is crucial for
determining start dates
5. For acceptable economic outcomes we
must have a mix of work with different
risks and expect some late delivery &
incurred cost of delay
6. Improve your capabilities before
pursuing market segments or
strategies that require service delivery
you currently cannot achieve

Thank you!

About
David Anderson is a thought
leader in managing effective 21st
Century businesses that employ
creative people who “think for a
living” . He leads a training,
consulting, publishing and event
planning business dedicated to
developing, promoting and
implementing new management
thinking & methods…
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,
published in June 2012, is, Lessons in Agile Management – On
the Road to Kanban.
David is a founder of the Lean Kanban Inc., a business
dedicated to assuring quality of training in the Lean Kanban
Method for managers of those who must “think for a
living.”

I’d like to thank Eric-Jan Kaak and the staff at Blizzard for providing access
to produce the story of their Lean transformation.
Software maintenance lead time data courtesy of Corbis.
Acknowledgements

Making Better Decisions - understanding "fitness for purpose", matching strategy to objectives

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