See how metrics can be used with your Kanban System for managing flow, your project and changes.

1. By the power of metrics
LeanKanban Central Europe 2014 - #LKCE14
Wolfgang Wiedenroth @wwiedenroth

2. Metrics in the Kanban Method
Practices
1.Visualize
2.Limit WIP
3.Manage Flow
4.Make Process Policies Explicit
5.Develop Feedback Loops
6.Improve Collaboratively, Evolve Experimentally
(using models/scientific method)

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

4. Metrics in the Kanban Method
Practices
1.Visualize
2.Limit WIP
3.Manage Flow
4.Make Process Policies Explicit
5.Develop Feedback Loops
6.Improve Collaboratively, Evolve Experimentally
(using models/scientific method)
Kanban’s 3 Agendas
Sustainability
Service-Oriented
Survivability

5. 115#
110#
105#
100#
95#
90#
85#
80#
75#
70#
65#
60#
55#
50#
45#
40#
35#
30#
25#
20#
1#May#2012#
11#May#2012#
7#May#2012#
17#May#2012#
23#May#2012#
29#May#2012#
4#Jun#2012#
14#Jun#2012#
8#Jun#2012#
20#Jun#2012#
26#Jun#2012#
2#Jul#2012#
6#Jul#2012#
12#Jul#2012#
Departure Rate
24#Jul#2012#
18#Jul#2012#
30#Jul#2012#
3#Aug#2012#
Work piling up
15#Aug#2012#
9#Aug#2012#
21#Aug#2012#
27#Aug#2012#
31#Aug#2012#
6#Sep#2012#
12#Sep#2012#
24#Sep#2012#
18#Sep#2012#
28#Sep#2012#
10#Oct#2012#
4#Oct#2012#
16#Oct#2012#
22#Oct#2012#
26#Oct#2012#
1#Nov#2012#
13#Nov#2012#
7#Nov#2012#
19#Nov#2012#
23#Nov#2012#
29#Nov#2012#
5#Dec#2012#
11#Dec#2012#
21#Dec#2012#
17#Dec#2012#
2#Jan#2013#
27#Dec#2012#
8#Jan#2013#
14#Jan#2013#
24#Jan#2013#
18#Jan#2013#
Analyse# Selected# Planning# Planning#Done# Dev# Dev#Done# TesDng# TesDng#Done/Endgame# to#be#released# Released#
Visualize
Cumulative Flow Diagram
y = No. of Tickets
y = Time
Arrival Rate

6. Visualize
Release Cycle is getting shorter
Daily Deployments
Weekly Deployments
Biweekly Deployments

7. Visualize
That’s how Flow looks like

8. Visualize That’s the opposite of Flow!
we call it Christmas holidays

9. Visualize
20"
19"
18"
17"
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14"
13"
12"
11"
10"
9"
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1"
0"
Lead Time Distribution Chart
Average Lead Time
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

10. 6"
5"
4"
3"
2"
1"
0"
MEDIAN"
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"
Visualize
Throughput
x = Calendar Weeks
y = No. of tickets finished
in calendar week x

11. Visualize

12. Visualized metrics let you
see things faster

13. Visualized metrics let you
identify pattern

14. Visualized metrics give
everyone the same picture

15. Visualized metrics are
great feedback loops

16. Manage Flow

17. Manage Flow
Demand
Capability

18. Manage Flow
Demand Capability
Flow = Balance Demand against Capability

19. Manage Flow
Demand Analysis Capability 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. Manage Flow using Weibull
18"
16"
14"
12"
10"
8"
6"
4"
2"
0"
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"

21. Manage Flow using Weibull
18"
16"
14"
12"
10"
8"
6"
4"
2"
0"
Mode = most common lead time
Median = 50%
Average
80% of tickets finish in less time
95% of tickets finish in less time
98% of tickets finish in less time
Weibull with
shape parameter k = 1.5
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"

22. Manage Flow using Weibull
18"
16"
14"
12"
10"
8"
6"
4"
2"
0"
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"
4"
3"
3"
2"
2"
1"
1"
0"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10"
5"
4"
4"
3"
3"
2"
2"
1"
1"
0"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15"
Features
Bugs
Expedites

23. Manage Flow using Weibull
18"
16"
14"
12"
10"
8"
6"
4"
2"
0"
Features Q(p;k, λ) = λ( - ln(1 - p))1/k
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"
Number of data points: 143
Shape parameter (k): 1.64
Scale parameter (λ): 11.64
Average: 10.76

24. Manage Flow using Weibull
18"
16"
14"
12"
10"
8"
6"
4"
2"
0"
85% of tickets finish in 13.2 days
95% of tickets finish in 20.9 days
Q(p;k, λ) = 11.64( - ln(1 - p))1/1.64
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"

25. Manage Flow using Weibull
5"
4"
4"
3"
3"
2"
2"
1"
1"
0"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15"
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

26. Manage Flow using Weibull
5"
4"
4"
3"
3"
2"
2"
1"
1"
0"
1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15"
Bugs
Number of data points: 8
Shape parameter: looks like between 1.25 and 1.50
Scale parameter:
Average: 3.88

27. Manage Flow using Weibull and Forecasting Cards
k = 0.75
k = 1.25
k = 1.50

28. Manage Flow using Weibull and Forecasting Cards
Alexei Zheglov

29. Manage Flow using Weibull and Forecasting Cards
5"
4"
4"
3"
3"
2"
2"
1"
1"
0"
98% of tickets finish in
12.4 days
1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15"

30. Manage Flow using Weibull
Service Level Expectations (SLE) we can communicate
85% of all features can be expected in 13 days
Bugs can be expected to be delivered in between
3 (average) and 12 days (98%)

31. Manage Project Flow
Project Scope
Average Lead Time
Average Throughput
Average WIP

32. Manage Project Flow using Little’s Law
WIP
Lead Time
Throughput =

33. Manage Project Flow using Little’s Law
Calculate Project Lead Time
Project Lead Time = No. of Tickets
Average Lead Time
Average WIP
= 450
1.2
15
= 36 weeks

34. Manage Project Flow using Little’s Law
Calculate Project Budget
Average WIP = Average Lead Time
No. of Tickets
Delivery date in weeks
= 1.2
450
36
= 15 WIP

35. Manage Project Flow using Little’s Law
Project Scope
20% 60% 20%
End Date
2nd leg
1st leg
3rd leg
Delivery Rate

36. Metrics help you
to better understand your
demand and capability

37. Metrics help you
calculate Service Level Expectations (SLE)
for different work items

38. Metrics help you
forecast your projects#
without estimating

39. Metrics to secure survival

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

41. Service-Oriented
Product Development
Maintenance
Online Marketing
Access Management
Change Management
Problem Management

42. Survivability
What’s the purpose of the services we provide?
What criteria need to be satisfied
to call the service fit for this purpose?

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

44. Survivability
Bugs per Week
60
45
30
15
0
31 32 33 34
SLA Compliance in %
100
75
50
25
0
April May June July
Lead Times
20
15
10
5
0
0 5 10 15 20

45. Ask your customer
what they care about!
Make it your core metric#
you always measure!

46. Metrics supporting changes

50. Metrics help us to distinct
between good and bad
changes from an objective
point of view

51. Metrics for improvements
Emotions
Risk
Measuring

52. Metrics for improvements
"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."
- Troy Magennis at LKCE13 reception

53. Metrics for improvements
"We do only this until we have
enough data to provide better
sample."
- Troy Magennis at LKCE13 reception

54. Always support
change with
measurements!

55. Metrics for improvements
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
…

56. Not like that! Keep it simple!

57. Bob from Operations
Wanted Y
Took me 60min
#
Urheber Markus Beyer - Herzlichen Dank!
Joe from Marketing
Wanted Y
Took me 30min
Sue from Product
Wanted Y
Took me 15min
CEO
Wanted Y
Took me 6h
Joe from Marketing
Wanted Y
Took me 45min

58. Regularly check your metrics, whether
they have become Chindōgu!

59. chindōgu are sometimes described
as "unuseless" – that is, they
cannot be regarded as "useless" in
an absolute sense, since they do
actually solve a problem; however,
in practical terms, they cannot
positively be called "useful".
https://en.wikipedia.org/wiki/Chind%C5%8Dgu

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

61. to check if your service
is fit for purpose
Collect metrics now
to evaluate your
changes
to manage your
projects
to manage Flow

62. Thank you!
Wolfgang Wiedenroth
Mail: wolfgang.wiedenroth@it-agile.de
Twitter: @wwiedenroth