Lead Time:What We Know About ItAnd How It Can Help Forecast Your
Projects
Alexei Zheglov
@az1#agiledc
Goodhart’s Law
Kanban System Lead Time
DeliveredIdeas AnalysisInputQueue
Ready to
Deliver∞325
Development Test
3
Lead Time
The FirstCommitment
Point
AB
C
Discarded
D
Ask Not
DeliveredIdeas AnalysisInputQueue
Ready to
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Development Test
3
Lead Time
AB
C
Discarded
D
Not “how long will it take?”
Do Ask
DeliveredIdeas AnalysisInputQueue
Ready to
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3
Lead Time
AB
C
Discarded
D
When should we start?
When do we need it?
Decide
DeliveredIdeas AnalysisInputQueue
Ready to
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Development Test
3
Lead Time
AB
C
Discarded
DOne event
precedes (leads) another one
by this much
One eventprecedes (leads) another
oneby this much
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
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
(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
Discussion 1: Gaming Metrics
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
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!
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
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
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?
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%
Consequences of Low Flow Efficiency
Goodhart’s Law’s Corollary
Start Measuring?
Discussion 2: Measuring Lead Time
Deterministic approachto a probabilistic process?
probabilistic
!!!
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
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
Heterogeneous Demand
DeliveredIdeas AnalysisInputQueue
Ready to
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AB
C
Discarded
D
E
G
F
H
Demand placed upon our system is differentiatedby type of work and risk
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
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
...
...
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
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%)
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
Weibull DistributionsOccur Frequently
Operations, support (k<1)
New product development (k>1)
Weibull DistributionsOccur Frequently
Operations, support (k<1)
New product development (k>1)
The unique signature of your
process
The unique signature of your
process
Bias
Feedback
How to “Read” a Distribution
Scale
Control
Expectations
Forecast
Mode: how we rememberthe “typical” delivered work
item.Trouble: it’s a very low
percentile.18-28% common.
Median: 50% more, 50% less.
Perfect for creatingvery short feedback loops
Average: we need it for Little’s Law
LeadTime
WIPteDeliveryRa
Little’s Law:handle with care
The 63% percentile isthe best indicator of
scale
High percentiles (80th-99th):critical to defining
service-level expectations
High percentiles (80th-99th):critical to defining
service-level expectations
Statistical process control:Sprint duration in iterative
methods,SLAs in Operations, etc.
Forecasting Cards
While I Was Preparing This Presentation, Somebody Sent Me This...
Discussion 3:Probabilistic or Deterministic?
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
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
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
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
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
New Kanban Book
Mike Burrows
Influencers
Troy Magennis Dimitar Bakardzhiev David J Anderson
Dan Vacanti Dave White Frank Vega
Discussion 4: What Now?
Alexei Zheglov