Agile and Continuous Delivery for Audits and Exams - DC Continuous Delivery Meetup
Agile DC Lead Time
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Transcript of Agile DC Lead Time
![Page 1: Agile DC Lead Time](https://reader035.fdocuments.us/reader035/viewer/2022062615/54878ba1b4af9f5f388b599c/html5/thumbnails/1.jpg)
Lead Time:What We Know About ItAnd How It Can Help Forecast Your
Projects
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Alexei Zheglov
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@az1#agiledc
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Goodhart’s Law
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Kanban System Lead Time
DeliveredIdeas AnalysisInputQueue
Ready to
Deliver∞325
Development Test
3
Lead Time
The FirstCommitment
Point
AB
C
Discarded
D
![Page 6: Agile DC Lead Time](https://reader035.fdocuments.us/reader035/viewer/2022062615/54878ba1b4af9f5f388b599c/html5/thumbnails/6.jpg)
Ask Not
DeliveredIdeas AnalysisInputQueue
Ready to
Deliver∞325
Development Test
3
Lead Time
AB
C
Discarded
D
Not “how long will it take?”
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Do Ask
DeliveredIdeas AnalysisInputQueue
Ready to
Deliver∞325
Development Test
3
Lead Time
AB
C
Discarded
D
When should we start?
When do we need it?
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Decide
DeliveredIdeas AnalysisInputQueue
Ready to
Deliver∞325
Development Test
3
Lead Time
AB
C
Discarded
DOne event
precedes (leads) another one
by this much
One eventprecedes (leads) another
oneby this much
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Why?
DeliveredIdeas AnalysisInputQueue
Ready to
Deliver∞325
Development Test
3
Lead Time
The FirstCommitment
PointAB
C
Discarded
D
Includes the time the work item
spent as an option
Depends on the transaction
costs (external to the system)
Measures the true delivery
capability
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Customer Lead Time
DeliveredIdeas Activity 1InputQueue
Output Buffer
∞???
Activity 2 Activity 3
?
Customer Lead Time
AB
Kanban system(s) lead time
+time spent in the
unlimited buffer(s)
C
Discarded
D
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(Local) Cycle Time
DeliveredIdeas Activity 1InputQueue
Output Buffer
∞???
Activity 2 Activity 3
?
AB
C
Discarded
D
Cycle time is always local
Always qualify where it is from
and to
Often depends mainly on the size of the local
effort
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Discussion 1: Gaming Metrics
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Readyto Test
Flow Efficiency
F
Q E
C A
J
GD
GYBG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
IdeasReadyto Dev
5IP
Development Testing
Done3 35
UATReady toDeliver
∞ ∞
Work WaitWork
Official training material, used with permission
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Readyto Test
Flow Efficiency
F
Q E
C A
J
GD
GYBG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
IdeasReadyto Dev
5IP
Development Testing
Done3 35
UATReady toDeliver
∞ ∞
Work WaitWork
Official training material, used with permission
Work is waiting
Work is still waiting!Multitasking creates
hidden queues!
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Readyto Test
Flow Efficiency
F
Q E
C A
J
GD
GYBG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
IdeasReadyto Dev
5IP
Development Testing
Done3 35
UATReady toDeliver
∞ ∞
Work WaitWork
Official training material, used with permission
%100time elapsed
time touchefficiencyflow
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Readyto Test
Measuring Flow Efficiency
F
Q E
C A
J
GD
GYBG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
IdeasReadyto Dev
5IP
Development Testing
Done3 35
UATReady toDeliver
∞ ∞
Work WaitWork
Official training material, used with permission
Timesheets arenot
necessary!
Rough approximations (±5%) are often
sufficient
In Aggregate
Sampling
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Readyto Test
Measuring Flow Efficiency
F
Q E
C A
J
GD
GYBG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
IdeasReadyto Dev
5IP
Development Testing
Done3 35
UATReady toDeliver
∞ ∞
Work WaitWork
The results are often between 1%
and 5%*
*-Zsolt Fabok, Lean Agile Scotland 2012, LKFR12; Hakan Forss, LKFR13
The result is not limited to the number!
What did you decide to do?
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If the Flow Efficiency Is 5%...
If... Before After Improvement
Hire 10x engineers 100 95.5 +4.7%
The task is three times bigger 100 110 -9.1%
The task is three times smaller 100 96.7 +3.4%
Reduce delays by half 100 52.5 +90%
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Consequences of Low Flow Efficiency
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Goodhart’s Law’s Corollary
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Start Measuring?
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Discussion 2: Measuring Lead Time
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Deterministic approachto a probabilistic process?
probabilistic
!!!
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0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 95-99 100-104
0
2
4
6
8
10
12
14
16
18
20
Example
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0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 95-99 100-104
0
2
4
6
8
10
12
14
16
18
20
Example
Best-fit distribution:Weibull with
shape parameter k=1.62
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Heterogeneous Demand
DeliveredIdeas AnalysisInputQueue
Ready to
Deliver∞325
Development Test
3
AB
C
Discarded
D
E
G
F
H
Demand placed upon our system is differentiatedby type of work and risk
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Drill down by project type
0-410-14
20-2430-34
40-4450-54
60-6470-74
80-8495-99
02468
101214161820
0-4 5-9 10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85-89
95-99
100-104
0
2
4
6
8
10
12
14
16
18
20
Mixed data from different types of
projects
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4 types, 4 different distributions
0-410-14
20-2430-34
40-4450-54
60-6470-74
80-8495-99
02468
101214161820
0-410-14
20-2430-34
40-4450-54
60-6470-74
80-8495-99
02468
101214161820
5-910-14
15-1920-24
25-2930-34
35-3940-44
45-4950-54
55-5960-64
65-6975-79
80-8485-89
100-1040
2
4
6
8
10
12
14
16
18
0-4 5-9 10-14
15-19
20-24
25-29
40-44
55-59
60-64
65-69
70-74
75-79
95-99
0
1
2
3
4
5
6
...
...
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Delivery Expectations
0-410-14
20-2430-34
40-4450-54
60-6470-74
80-8495-99
02468
101214161820
0-410-14
20-2430-34
40-4450-54
60-6470-74
80-8495-99
02468
101214161820
Shape Average In 98%
1.62
1.23
1.65
3.22
In 85% of cases
30 d
35 d
40 d
56 d
<51
<63
<68
<78
<83
<112*
<110*
<99
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Delivery Expectations
0-410-14
20-2430-34
40-4450-54
60-6470-74
80-8495-99
02468
101214161820
0-410-14
20-2430-34
40-4450-54
60-6470-74
80-8495-99
02468
101214161820
Shape Average In 98%
1.62
1.23
1.65
3.22
In 85% of cases
30 d
35 d
40 d
56 d
<51
<63
<68
<78
<83
<112*
<110*
<99
The averages are insufficient
to specify delivery capabilities!
The average says nothing about variability!
Needed:the average and a high percentile (usually 80-
99%)
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Another Example
0-2.5 2.5-5 5-7.5 7.5-10 10-12.5 12.5-15 15-17.5 25-27.50
2
4
6
8
10
12
Development
0-3 3-6 6-9 9-12 12-15 15-180
2
4
6
8
10
12
14
Support
Shape: 1.16 Shape: 0.71
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Weibull DistributionsOccur Frequently
Operations, support (k<1)
New product development (k>1)
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Weibull DistributionsOccur Frequently
Operations, support (k<1)
New product development (k>1)
The unique signature of your
process
The unique signature of your
process
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Bias
Feedback
How to “Read” a Distribution
Scale
Control
Expectations
Forecast
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Mode: how we rememberthe “typical” delivered work
item.Trouble: it’s a very low
percentile.18-28% common.
![Page 36: Agile DC Lead Time](https://reader035.fdocuments.us/reader035/viewer/2022062615/54878ba1b4af9f5f388b599c/html5/thumbnails/36.jpg)
Median: 50% more, 50% less.
Perfect for creatingvery short feedback loops
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Average: we need it for Little’s Law
LeadTime
WIPteDeliveryRa
Little’s Law:handle with care
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The 63% percentile isthe best indicator of
scale
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High percentiles (80th-99th):critical to defining
service-level expectations
High percentiles (80th-99th):critical to defining
service-level expectations
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Statistical process control:Sprint duration in iterative
methods,SLAs in Operations, etc.
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Forecasting Cards
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While I Was Preparing This Presentation, Somebody Sent Me This...
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Discussion 3:Probabilistic or Deterministic?
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TestReady
S
RQ
P
ON
F
A Few Words About Projects…
H
E
C
I
G
D
M
DevReady
5Ongoing
Development Testing
Done3 35
UATReleaseReady
∞ ∞
ProjectScope
Official training material, used with permission
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Delivery Rate
Lead Time
WIP=
Applying Little’s Law
From observed capability
Treat as a fixed variable
Targetto
achieve plan
Calculated based on known lead time
capability & required delivery
rate
Determines staffing level
Official training material, used with permission
![Page 46: Agile DC Lead Time](https://reader035.fdocuments.us/reader035/viewer/2022062615/54878ba1b4af9f5f388b599c/html5/thumbnails/46.jpg)
Delivery Rate
Lead Time
WIP=
Applying Little’s Law
From observed capability
Treat as a fixed variable
Targetto
achieve plan
Calculated based on known lead time
capability & required delivery
rate
Determines staffing level
Complicating factors here:
Dark matter“Z-curve effect”
Scope creep
Complicating factors here:Variety of work item types and
risks
![Page 47: Agile DC Lead Time](https://reader035.fdocuments.us/reader035/viewer/2022062615/54878ba1b4af9f5f388b599c/html5/thumbnails/47.jpg)
Delivery Rate
Lead Time
WIP=
Applying Little’s Law
From observed capability
Treat as a fixed variable
Targetto
achieve plan
Calculated based on known lead time
capability & required delivery
rate
Determines staffing level
Complicating factors here:
Dark matter“Z-curve effect”
Scope creep
Complicating factors here:Variety of work item types and
risks
![Page 48: Agile DC Lead Time](https://reader035.fdocuments.us/reader035/viewer/2022062615/54878ba1b4af9f5f388b599c/html5/thumbnails/48.jpg)
TestReady
S
RQ
P
ON
F
A Few Words About Projects…
H
E
C
I
G
D
M
DevReady
5Ongoing
Development Testing
Done3 35
UATReleaseReady
∞ ∞
ProjectScope
Lead time data andobserved/measured delivery
capabilityat the feature/user story level
are critical to forecasting projects
The project initiation phase is a great time to
builda forecasting model and
feedback loops
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New Kanban Book
Mike Burrows
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Influencers
Troy Magennis Dimitar Bakardzhiev David J Anderson
Dan Vacanti Dave White Frank Vega
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Discussion 4: What Now?
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Alexei Zheglov