By the Power of Metrics - Lean Kanban North America 2015
67
By the Power of Metrics LnKanban North America 2015 - #LKNA15 Wolfgang Wiedenroth @iedenroth
-
Upload
wolfgang-wiedenroth -
Category
Devices & Hardware
-
view
2.066 -
download
0
Transcript of By the Power of Metrics - Lean Kanban North America 2015
By the Power of Metrics
LeanKanban North America 2015 - #LKNA15 Wolfgang Wiedenroth @wwiedenroth
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)
Metrics in Kanban’s 3 Agendas
•Sustainability
•Service-Oriented
•Survivability
Visualization
20#
25#
30#
35#
40#
45#
50#
55#
60#
65#
70#
75#
80#
85#
90#
95#
100#
105#
110#
115#
1#May#2012#
7#May#2012#
11#May#2012#
17#May#2012#
23#May#2012#
29#May#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
20#
25#
30#
35#
40#
45#
50#
55#
60#
65#
70#
75#
80#
85#
90#
95#
100#
105#
110#
115#
1#May#2012#
7#May#2012#
11#May#2012#
17#May#2012#
23#May#2012#
29#May#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 Deploy
ments
Biweekly DeploymentsWeekly Deployments
That’s ho
w Flow looks li
ke
Cumulative Flow Diagram
That’s the opposite of Flow it’s called Christmas holidays
Cumulative Flow Diagram
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
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
Lean Kanban Central Europe
Visualized metrics let you see things faster
Visualized metrics let you identify pattern
Visualized metrics give everyone the same picture and
raise awareness
Visualized metrics are great feedback loops
Manage Flow
Demand
Capability
Demand Capability
Flow = Balance of Demand and Capability
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?
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"
20#
25#
30#
35#
40#
45#
50#
55#
60#
65#
70#
75#
80#
85#
90#
95#
100#
105#
110#
115#
1#May#2012#
7#May#2012#
11#May#2012#
17#May#2012#
23#May#2012#
29#May#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
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 x98% of all tickets will finish in x
Weibull with shape parameter k = 1.5
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?
Bugs0"
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"
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?
Features Q(p;k, λ) = λ( - ln(1 - p))1/k
Number of data points: 59Shape parameter (k): 1.54Scale parameter (λ): 12.69Average: 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?
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 days98% of all tickets will be finished in around 30 days
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"
Bugs
Number of data points: 8Shape 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
Forecasting Cards to the rescue
k = 0.75
k = 1.25
k = 1.50
Thank you Alexei!
Alexei Zheglov
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
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
Calculate your project lead time and budget
Average lead time per ticket
Average WIP
Project Scope (no. of tickets)
Average throughput
What we need
Using Little’s Law
WIPLead Time
Throughput =
Project Lead Time = No. of TicketsAverage Lead TimeAverage WIP
x 450 1.2 15
= 36 weeks
Calculate Project Lead Time
Calculate Project Budget
Average WIP = Average Lead TimeNo. of Tickets
Delivery date in weeks
= 1.2 450 36
= 15 WIP
Be careful!
20% 20%60%
Project ScopeEnd Date
2nd le
g
1st leg
3rd leg
Delivery
Rate
Metrics can help you to better understand your
demand and capability
Metrics can help you calculate Service Level Expectations (SLEs)
Metrics can help you forecast your projects
Metrics to secure survival
Sustainability
Service-Oriented
Survivability
Kanban’s 3 Agendas
Examlpes of services
HRMarketing
Customer Care
Software DevelopmentChange Management
Problem Management
Survivability
What’s the purpose of the services we provide?
What do customers using this service care about?
What do customers using this service care about?
Make these your fitness criteria!
Fitness Criteria“Fitness Criteria are metrics that measure thingscustomer value when selecting a service again and again.”
- Delivery Time- Quality- Predictiability- Safety (conformance to regulatory requirements)
David J. Anderson
Make it your core metric you always measure!
Quality
Bugs per Week
0
15
30
45
60
31 32 33 34
PredictabilitySLA Compliance in %
0
25
50
75
100
April May June July
13%10%16%87%90%
100%
84%
Delivered in time SLA not met
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
Metrics support changes
Metrics for improvements
Metrics for improvements
Metrics for improvements
Metrics can help to distinct between positive and negative changes from
an objective point of view
Data helps reducing risk and emotions
“gut feeling”Risk
Data
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."
"We do this only until we have enough data to provide better sample."
Troy Magennis at LKCE13’s speaker dinner
Always support change with measurements!
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 crit
eria
Metrics for improvements
Not like that! Keep it simple!
Metrics for improvements
Creator Markus Beyer - Thank you!
Regularly check your metrics, whether they have become obsolete!
Wrap upto check if your service is fit for purpose
Metrics help youto evaluate your changes
to manage your projects
to manage Flow
Collect data now! It’s easy as 1-2-3!
