The document explores the concept of lead time in project management, outlining its measurement and impact on forecasting delivery capabilities. It discusses the significance of understanding flow efficiency and cycle time in improving project outcomes while highlighting the implications of low flow efficiency and the distinction between deterministic and probabilistic approaches. The document emphasizes the importance of utilizing lead time data for effective project planning and decision-making.

1. Lead Time:
What We Know About It
And How It Can Help Forecast Your Projects
Alexei Zheglov
Lean Kanban Asia-Pacific
Bangalore, December 2014

2. Alexei Zheglov
connected-
knowledge.com
alex@LeanAtoZ.com

3. @az1
#lkapac

4. “When a measure becomes a target,
it ceases to be a good measure.”
Goodhart’s Law

5. Kanban System Lead Time
DeliveredIdeas Analysis
Input
Queue
Ready to
Deliver
∞325
Development Test
3
Lead Time
The First
Commitment
Point
A
B
C
Discarded
D

6. Ask Not
DeliveredIdeas Analysis
Input
Queue
Ready to
Deliver
∞325
Development Test
3
Lead Time
A
B
C
Discarded
D
Not “how long will it take?”

7. Do Ask
DeliveredIdeas Analysis
Input
Queue
Ready to
Deliver
∞325
Development Test
3
Lead Time
A
B
C
Discarded
D
When should we start?
When do we need it?

8. Decide
DeliveredIdeas Analysis
Input
Queue
Ready to
Deliver
∞325
Development Test
3
Lead Time
A
B
C
Discarded
D
One event
precedes (leads) another one
by this much
One event
precedes (leads) another one
by this much

9. Why?
DeliveredIdeas Analysis
Input
Queue
Ready to
Deliver
∞325
Development Test
3
Lead Time
The First
Commitment
Point
A
B
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

10. Customer Lead Time
DeliveredIdeas Activity 1
Input
Queue
Output
Buffer
∞???
Activity 2 Activity 3
?
Customer Lead Time
A
B
Kanban system(s) lead time
+
time spent in the unlimited
buffer(s)
C
Discarded
D

11. (Local) Cycle Time
DeliveredIdeas Activity 1
Input
Queue
Output
Buffer
∞???
Activity 2 Activity 3
?
A
B
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

12. Discussion 1: Gaming Metrics
• Given the goal to reduce the lead time (as we
have just defined it), what would you do?
• What would happen, good and bad?
• How can you game the local cycle time metric?
• Bonus question: if your delivery time metric
included the time before commitment, what
would you be motivated to do?

13. Ready
to Test
Flow Efficiency
F
E
J
G
D
GY
BG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
Ideas
Ready
to Dev
5
IP
Development Testing
Done
3 35
UAT
Ready to
Deliver
∞ ∞
Work WaitWork
Official training material, used with permission

14. Ready
to Test
Flow Efficiency
F
E
J
G
D
GY
BG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
Ideas
Ready
to Dev
5
IP
Development Testing
Done
3 35
UAT
Ready to
Deliver
∞ ∞
Work WaitWork
Official training material, used with permission
Work is waiting
Work is still waiting!
Multitasking creates
hidden queues!

15. Ready
to Test
Flow Efficiency
F
E
J
G
D
GY
BG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
Ideas
Ready
to Dev
5
IP
Development Testing
Done
3 35
UAT
Ready to
Deliver
∞ ∞
Work WaitWork
Official training material, used with permission
%100
timeelapsed
timetouch
efficiencyflow 

16. Ready
to Test
Measuring Flow Efficiency
F
E
J
G
D
GY
BG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
Ideas
Ready
to Dev
5
IP
Development Testing
Done
3 35
UAT
Ready to
Deliver
∞ ∞
Work WaitWork
Official training material, used with permission
Timesheets are
not necessary!
Rough approximations (±5%)
are often sufficient
In Aggregate
Sampling

17. Ready
to Test
Measuring Flow Efficiency
F
E
J
G
D
GY
BG
DE NP
P1
AB
Customer Lead Time
Wait Wait WorkWork
Ideas
Ready
to Dev
5
IP
Development Testing
Done
3 35
UAT
Ready to
Deliver
∞ ∞
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?

18. 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%

19. Discussion 2:
Consequences of Low Flow Efficiency
(all positives, really)
• Why is lead time is hard to fudge?
• Why does lead time improve mostly due to
system-level improvements?
• How likely are the lead time data from your
previous projects to help you plan a new one?

20. Measuring the delivery time
cannot be separated from
understanding commitment.
Goodhart’s Law’s
Corollary

21. Start Measuring?

22. Discussion 3: Measuring Lead Time
• Do you already collect lead time data?
• If not, do you already have these data available
somewhere, waiting for you to discover them?
• If not, would it be difficult or easy to start?
• What would you do differently in your company
with respect to lead time data after this
presentation?

23. Deterministic approach
to a probabilistic process?
probabilistic
!!!

24. 0
2
4
6
8
10
12
14
16
18
20
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
Example

25. 0
2
4
6
8
10
12
14
16
18
20
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
Example
Best-fit distribution:
Weibull with
shape parameter k=1.62

26. Heterogeneous Demand
DeliveredIdeas Analysis
Input
Queue
Ready to
Deliver
∞325
Development Test
3
A
B
C
Discarded
D
E
G
F
H
Demand placed upon our system
is differentiated
by type of work and risk

27. Drill down by project type
0
5
10
15
20
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

28. 4 types, 4 different distributions
0
5
10
15
20
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
5
10
15
20
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
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
75-79
80-84
85-89
100-104
0
1
2
3
4
5
6
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
...
...

29. Delivery Expectations
0
5
10
15
20
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
5
10
15
20
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
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

30. Delivery Expectations
0
5
10
15
20
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
5
10
15
20
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
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%)

31. Another Example
0
2
4
6
8
10
12
0-2.5 2.5-5 5-7.5 7.5-10 10-12.5 12.5-15 15-17.5 25-27.5
Development
0
2
4
6
8
10
12
14
0-3 3-6 6-9 9-12 12-15 15-18
Support
Shape: 1.16 Shape: 0.71

32. Weibull Distributions
Occur Frequently
Operations, support (k<1)New product development
(k>1)

33. Weibull Distributions
Occur Frequently
Operations, support (k<1)New product development
(k>1)
The unique signature
of your process
The unique signature
of your process

34. Bias
Feedback
How to “Read” a Distribution
Scale
Control
Expectations
Forecast

35. Mode: how we remember
the “typical” delivered work item.
Trouble: it’s a very low percentile.
18-28% common.

36. Median: 50% more, 50% less.
Perfect for creating
very short feedback loops

37. Average: we need it
for Little’s Law
LeadTime
WIP
teDeliveryRa 
Little’s Law:
handle with care

38. The 63% percentile is
the best indicator of scale

39. High percentiles (80th-99th):
critical to defining
service-level expectations
High percentiles (80th-99th):
critical to defining
service-level expectations

40. Statistical process control:
Sprint duration in iterative methods,
SLAs in Operations, etc.

41. Forecasting Cards

42. While I Was Preparing This Presentation,
Somebody Sent Me This...

43. Discussion 4:
Probabilistic or Deterministic?
• Would you describe the prevailing approach in
your organization as probabilistic or
deterministic?
• Is the expected answer to “how long will it take?”
a single number?
• Can you instead ask, “when do we need it?” and
“when should we start?”
• Can you make decisions given distributions of
probabilities?

44. Test
Ready
S
R
Q
P
O
N
F
A Few Words About Projects…
E
I
G
D
M
Dev
Ready
5
Ongoing
Development Testing
Done
3 35
UAT
Release
Ready
∞ ∞
Project
Scope
Official training material, used with permission

45. Delivery Rate
Lead Time
WIP
=
Applying Little’s Law
From observed
capability
Treat as a fixed
variable
Target
to
achieve plan
Calculated based on
known lead time
capability & required
delivery rate
Determines
staffing level
Official training material, used with permission

46. Delivery Rate
Lead Time
WIP
=
Applying Little’s Law
From observed
capability
Treat as a fixed
variable
Target
to
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

47. Test
Ready
S
R
Q
P
O
N
F
A Few Words About Projects…
E
I
G
D
M
Dev
Ready
5
Ongoing
Development Testing
Done
3 35
UAT
Release
Ready
∞ ∞
Project
Scope
Lead time data and
observed/measured delivery capability
at the feature/user story level
are critical to forecasting projects
The project initiation phase
is a great time to build
a forecasting model and
feedback loops

48. New Kanban Book
Mike Burrows

49. Influencers
Troy Magennis Dimitar Bakardzhiev David J Anderson
Dan Vacanti Dave White Frank Vega

50. Discussion 5: What Now?
• What new ideas have your learned in this
session today?
• What will you do differently when you return to
your office tomorrow?

51. Alexei Zheglov
connected-knowledge.com (blog)
alex@LeanAtoZ.com
@az1