1.
By the Power of
Metrics
LeanKanban North America 2015 - #LKNA15
Wolfgang Wiedenroth @wwiedenroth

2.
Metrics in the Kanban Practices1.Visualize
2.Limit WIP
3.Manage Flow
4.Make Policies explicit
5.Implement Feedback Loops
6.Improve Collaboratively, Evolve Experimentally
(using models/scientific method)

3.
Metrics in Kanban’s 3 Agendas
•Sustainability
•Service-Oriented
•Survivability

4.
Visualization

5.
20#
25#
30#
35#
40#
45#
50#
55#
60#
65#
70#
75#
80#
85#
90#
95#
100#
105#
110#
115#
1#M
ay#2012#
7#M
ay#2012#
11#M
ay#2012#
17#M
ay#2012#
23#M
ay#2012#
29#M
ay#2012#
4#Jun#2012#
8#Jun#2012#
14#Jun#2012#
20#Jun#2012#
26#Jun#2012#
2#Jul#2012#6#Jul#2012#12#Jul#2012#
18#Jul#2012#
24#Jul#2012#
30#Jul#2012#
3#Aug#2012#
9#Aug#2012#
15#Aug#2012#
21#Aug#2012#
27#Aug#2012#
31#Aug#2012#
6#Sep#2012#
12#Sep#2012#
18#Sep#2012#
24#Sep#2012#
28#Sep#2012#
4#Oct#2012#
10#Oct#2012#
16#Oct#2012#
22#Oct#2012#
26#Oct#2012#
1#Nov#2012#
7#Nov#2012#
13#Nov#2012#
19#Nov#2012#
23#Nov#2012#
29#Nov#2012#
5#Dec#2012#
11#Dec#2012#
17#Dec#2012#
21#Dec#2012#
27#Dec#2012#
2#Jan#2013#
8#Jan#2013#
14#Jan#2013#
18#Jan#2013#
24#Jan#2013#
Analyse# Selected# Planning# Planning#Done# Dev# Dev#Done# TesDng# TesDng#Done/Endgame# to#be#released# Released#
Cumulative Flow Diagram
Work piling up to be analyzed
Arrival Rate
Departure Rate

6.
20#
25#
30#
35#
40#
45#
50#
55#
60#
65#
70#
75#
80#
85#
90#
95#
100#
105#
110#
115#
1#M
ay#2012#
7#M
ay#2012#
11#M
ay#2012#
17#M
ay#2012#
23#M
ay#2012#
29#M
ay#2012#
4#Jun#2012#
8#Jun#2012#
14#Jun#2012#
20#Jun#2012#
26#Jun#2012#
2#Jul#2012#6#Jul#2012#12#Jul#2012#
18#Jul#2012#
24#Jul#2012#
30#Jul#2012#
3#Aug#2012#
9#Aug#2012#
15#Aug#2012#
21#Aug#2012#
27#Aug#2012#
31#Aug#2012#
6#Sep#2012#
12#Sep#2012#
18#Sep#2012#
24#Sep#2012#
28#Sep#2012#
4#Oct#2012#
10#Oct#2012#
16#Oct#2012#
22#Oct#2012#
26#Oct#2012#
1#Nov#2012#
7#Nov#2012#
13#Nov#2012#
19#Nov#2012#
23#Nov#2012#
29#Nov#2012#
5#Dec#2012#
11#Dec#2012#
17#Dec#2012#
21#Dec#2012#
27#Dec#2012#
2#Jan#2013#
8#Jan#2013#
14#Jan#2013#
18#Jan#2013#
24#Jan#2013#
Analyse# Selected# Planning# Planning#Done# Dev# Dev#Done# TesDng# TesDng#Done/Endgame# to#be#released# Released#
Cumulative Flow Diagram
Release Cycle is getting shorter
Daily Deployments
Biweekly Deployments
Weekly Deployments

7.
That’s how Flow looks like
Cumulative Flow Diagram

8.
That’s the opposite of Flow
it’s called Christmas holidays
Cumulative Flow Diagram

9.
0"
2"
4"
6"
8"
10"
12"
14"
16"
18"
20"
1" 2" 3" 4" 5" 6" 7" 8" 9"10"11"12"13"14"15"16"17"18"19"20"21"22"23"24"25"26"27"28"29"30"31"32"33"34"35"36"37"38"39"40"41"42"43"44"45"46"47"48"49"50"51"52"53"54"55"56"57"58"59"60"61"62"63"64"65"66"67"68"69"70"
y = No. of Tickets finished
with lead time x
x = Lead Time in days
Average Lead Time
Lead Time Distribution Chart

10.
0"
1"
2"
3"
4"
5"
6"
6" 7" 8" 9" 10" 11" 12" 13" 14" 15" 16" 17" 18" 19" 20" 21" 22" 23" 24" 25" 26" 27" 28" 29" 30" 31" 32" 33" 34" 35" 36" 37" 38" 39" 40" 41" 42" 43" 44" 45" 46" 47" 48" 49" 50" 51" 52" 1" 2" 3" 4" 5" 6" 7" 8" 9"
MEDIAN"
x = Calendar Weeks
y = No. of tickets finished
in calendar week x
Throughput
Mean

11.
Lean Kanban Central Europe

12.
Visualized metrics let you
see things faster

13.
Visualized metrics let you
identify pattern

14.
Visualized metrics give
everyone the same picture and
raise awareness

15.
Visualized metrics are
great feedback loops

16.
Manage Flow

17.
Demand
Capability

18.
Demand Capability
Flow = Balance of
Demand and Capability

19.
Capability Analysis
Demand Analysis
How much demand
do we have?
What are the
sources of our
demand?
Do we have
seasonal variance
in demand?
What are the risk profiles
that are attached to
different types of work?
What skills are
required for
different types of
demand?
What are our
current lead times?
What is our
delivery rate?
What skills do we
have?

20.
What’s our throughput?
Mean
0"
1"
2"
3"
4"
5"
6"
6" 7" 8" 9" 10" 11" 12" 13" 14" 15" 16" 17" 18" 19" 20" 21" 22" 23" 24" 25" 26" 27" 28" 29" 30" 31" 32" 33" 34" 35" 36" 37" 38" 39" 40" 41" 42" 43" 44" 45" 46" 47" 48" 49" 50" 51" 52" 1" 2" 3" 4" 5" 6" 7" 8" 9"
MEDIAN"

21.
20#
25#
30#
35#
40#
45#
50#
55#
60#
65#
70#
75#
80#
85#
90#
95#
100#
105#
110#
115#
1#M
ay#2012#
7#M
ay#2012#
11#M
ay#2012#
17#M
ay#2012#
23#M
ay#2012#
29#M
ay#2012#
4#Jun#2012#
8#Jun#2012#
14#Jun#2012#
20#Jun#2012#
26#Jun#2012#
2#Jul#2012#6#Jul#2012#12#Jul#2012#
18#Jul#2012#
24#Jul#2012#
30#Jul#2012#
3#Aug#2012#
9#Aug#2012#
15#Aug#2012#
21#Aug#2012#
27#Aug#2012#
31#Aug#2012#
6#Sep#2012#
12#Sep#2012#
18#Sep#2012#
24#Sep#2012#
28#Sep#2012#
4#Oct#2012#
10#Oct#2012#
16#Oct#2012#
22#Oct#2012#
26#Oct#2012#
1#Nov#2012#
7#Nov#2012#
13#Nov#2012#
19#Nov#2012#
23#Nov#2012#
29#Nov#2012#
5#Dec#2012#
11#Dec#2012#
17#Dec#2012#
21#Dec#2012#
27#Dec#2012#
2#Jan#2013#
8#Jan#2013#
14#Jan#2013#
18#Jan#2013#
24#Jan#2013#
Analyse# Selected# Planning# Planning#Done# Dev# Dev#Done# TesDng# TesDng#Done/Endgame# to#be#released# Released#
The CFD also helps
Departure Rate

22.
How fast can we deliver?
0"
2"
4"
6"
8"
10"
12"
14"
16"
18"
20"
1" 2" 3" 4" 5" 6" 7" 8" 9"10"11"12"13"14"15"16"17"18"19"20"21"22"23"24"25"26"27"28"29"30"31"32"33"34"35"36"37"38"39"40"41"42"43"44"45"46"47"48"49"50"51"52"53"54"55"56"57"58"59"60"61"62"63"64"65"66"67"68"69"70"
Mode = most common lead time
Median = 50%
Average = 11 days
80% of all tickets will finish in x
90% of all tickets will finish in x
98% of all tickets will finish in x
Weibull with
shape parameter k = 1.5

23.
Features
0"
1"
2"
3"
4"
5"
6"
7"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10"11"12"13"14"15"16"17"18"19"20"21"22"23"24"25"26"27"28"29"30"31"32"33"34"35"36"37"38"39"40"41"42"43"44"45"46"47"48"49"50"51"52"53"54"55"56"57"58"59"60"61"62"63"64"65"66"67"68"69"70"71"72"73"74"75"
Different types of work?
Bugs
0"
1"
2"
3"
4"
5"
6"
7"
8"
9"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15" 16" 17" 18" 19" 20" 21" 22" 23" 24" 25" 26" 27" 28" 29" 30"
Expedites
0"
1"
2"
3"
4"
5"
6"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15" 16" 17" 18" 19" 20"

24.
0"
1"
2"
3"
4"
5"
6"
7"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10"11"12"13"14"15"16"17"18"19"20"21"22"23"24"25"26"27"28"29"30"31"32"33"34"35"36"37"38"39"40"41"42"43"44"45"46"47"48"49"50"51"52"53"54"55"56"57"58"59"60"61"62"63"64"65"66"67"68"69"70"71"72"73"74"75"
How fast can we deliver features?

25.
Features Q(p;k, λ) = λ( - ln(1 - p))1/k
Number of data points: 59
Shape parameter (k): 1.54
Scale parameter (λ): 12.69
Average: 11.92
0"
1"
2"
3"
4"
5"
6"
7"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10"11"12"13"14"15"16"17"18"19"20"21"22"23"24"25"26"27"28"29"30"31"32"33"34"35"36"37"38"39"40"41"42"43"44"45"46"47"48"49"50"51"52"53"54"55"56"57"58"59"60"61"62"63"64"65"66"67"68"69"70"71"72"73"74"75"
How fast can we deliver features?

26.
0"
1"
2"
3"
4"
5"
6"
7"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10"11"12"13"14"15"16"17"18"19"20"21"22"23"24"25"26"27"28"29"30"31"32"33"34"35"36"37"38"39"40"41"42"43"44"45"46"47"48"49"50"51"52"53"54"55"56"57"58"59"60"61"62"63"64"65"66"67"68"69"70"71"72"73"74"75"
How fast can we deliver features? Weibull with
shape parameter k = 1.5
Mode = most common lead time
Median = 50%
Average = 11 days
80% of all tickets will be finished in around 17 days
90% of all tickets will be finished in around 22 days
98% of all tickets will be finished in around 30 days

27.
How fast can we fix bugs?
0"
1"
1"
2"
2"
3"
3"
4"
4"
5"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15"

28.
Bugs
Number of data points: 8
Shape parameter:
Scale parameter:
Average: 3.88
not enough data points, but visualisation
gives us an idea of the shape
0"
1"
1"
2"
2"
3"
3"
4"
4"
5"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15"
How fast can we fix bugs?
between 1.25 and 1.50

29.
Forecasting Cards to the rescue
k = 0.75
k = 1.25
k = 1.50

30.
Thank you Alexei!
Alexei Zheglov

31.
0"
1"
1"
2"
2"
3"
3"
4"
4"
5"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15"
How fast can we fix bugs?
98% of bugs
are fixed in 12.4 days
Weibull with
shape parameter k = 1.25

32.
Features are expected to be finished in 17 days with probability
of 80%
Bugs are expected to be fixed in between
3 (average) and 12 days (98%)
SLEs you can communicate to your customer

33.
Calculate your project
lead time and budget

34.
Average lead time per ticket
Average WIP
Project Scope (no. of tickets)
Average throughput
What we need

35.
Using Little’s Law
WIP
Lead Time
Throughput =

36.
Project Lead Time = No. of Tickets
Average Lead Time
Average WIP
x450
1.2
15
= 36 weeks
Calculate Project Lead Time

37.
Calculate Project Budget
Average WIP = Average Lead Time
No. of Tickets
Delivery date in weeks
= 1.2
450
36
= 15 WIP

38.
Be careful!
20% 20%60%
Project Scope
End Date
2nd
leg
1st leg
3rd leg
DeliveryRate

39.
Metrics can help you
to better understand your
demand and capability

40.
Metrics can help you
calculate Service Level
Expectations (SLEs)

41.
Metrics can help you
forecast your projects

42.
Metrics to secure survival

43.
Sustainability
Service-Oriented
Survivability
Kanban’s 3 Agendas

44.
Examlpes of services
HR
Marketing
Customer Care
Software Development
Change Management
Problem Management

45.
Survivability
What’s the purpose of
the services we provide?
What do customers using this service
care about?

46.
What do customers using this service
care about?
Make these your fitness criteria!

47.
Fitness Criteria
“Fitness Criteria are metrics that measure things
customer value when selecting a service again and
again.”
- Delivery Time
- Quality
- Predictiability
- Safety (conformance to regulatory requirements)
David J. Anderson

48.
Make it your core metric
you always measure!

49.
Quality
Bugs per Week
0
15
30
45
60
31 32 33 34

50.
Predictability
SLA Compliance in %
0
25
50
75
100
April May June July
13%10%16%
87%90%
100%
84%
Delivered in time SLA not met

51.
Delivery Time
0"
1"
2"
3"
4"
5"
6"
7"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10"11"12"13"14"15"16"17"18"19"20"21"22"23"24"25"26"27"28"29"30"31"32"33"34"35"36"37"38"39"40"41"42"43"44"45"46"47"48"49"50"51"52"53"54"55"56"57"58"59"60"61"62"63"64"65"66"67"68"69"70"71"72"73"74"75"
14 days SLA

52.
Metrics support changes

53.
Metrics for improvements

54.
Metrics for improvements

55.
Metrics for improvements

56.
Metrics can help to distinct between
positive and negative changes from
an objective point of view

57.
Data helps reducing risk and emotions
“gut feeling”Risk
Data

58.
Troy Magennis at LKCE13’s speaker dinner
"Sometimes, you just have to roll
back with your chair to take a
second look from the back and
make a good guess how the curve
will end up."

59.
"We do this only until we have
enough data to provide better
sample."
Troy Magennis at LKCE13’s speaker dinner

60.
Always support change
with measurements!

61.
Example metrics to evaluate change
WIP limit breach
defect rate
customer
satisfaction
employee
satisfaction
number of
blockers
time spent on “real
quick” work
time tickets were
blocked
time waiting for
external suppliers
rework
time spent on
white noise
…
your fitness
criteria

62.
Metrics for improvements
Not like that! Keep it simple!

63.
Metrics for improvements
Creator Markus Beyer - Thank you!

64.
Regularly check your metrics,
whether they have become obsolete!

65.
Wrap up
to check if your service
is fit for purpose
Metrics help you
to evaluate
your changes
to manage your
projects
to manage Flow

66.
Collect data now!
It’s easy as 1-2-3!

67.
Thank you for listening!
Wolfgang Wiedenroth
wolfgang.wiedenroth@it-agile.de
@wwiedenroth
www.agilemanic.com