LKCE 2013 – Modern Management Methods
Cycle Time Analytics
Making decisions using Lead Time and Cycle Time to avoid needing estimates for
every story
Troy Magennis
@t_magennis
Slides at bit.ly/agilesim

2
@t_Magennis slides at bit.ly/agilesim

Q. What is the chance of the 4th sample
being between the range seen after the
first three samples?
Actual Maximum
(no duplicates, uniform distribution, picked at random)
2
4
3
1
Actual Minimum
@t_Magennis slides at bit.ly/agilesim

Q. What is the chance of the 4th sample
being between the range seen after the
first three samples?
Actual Maximum
(no duplicates, uniform distribution, picked at random)
Highest sample
2
?
?
4
3
?
1
?
Lowest sample
Actual Minimum
@t_Magennis slides at bit.ly/agilesim

Q. What is the chance of the 4th sample
being between the range seen after the
first three samples?
Actual Maximum
(no duplicates, uniform distribution, picked at random)
Highest sample
25% chance higher
than highest seen
2
25% lower than highest
and higher than second highest
4
3
25% higher than lowest and
lower than second lowest
1
Lowest sample
Actual Minimum
25% lower than
lowest seen
@t_Magennis slides at bit.ly/agilesim
A. 50%
% = (1 - (1 / n – 1)) * 100

Q. What is the chance of the 12th sample
being between the range seen after the
first three samples?
Actual Maximum
(no duplicates, uniform distribution, picked at random)
Highest sample
2
9
5
5% chance higher than
highest seen
?
3
12
4
10
6
11
?
7
1
8
Lowest sample
Actual Minimum
5% lower than
lowest seen
@t_Magennis slides at bit.ly/agilesim
A. 90%
% = (1 - (1 / n – 1)) * 100

# Prior Samples
3
4
5
6
7
8
9
10
11
12
13
15
17
20
Prediction Next Sample Within Prior Sample Range
50%
67%
75%
80%
83%
86%
88%
89%
90%
91%
92%
93%
94%
95%
@t_Magennis slides at bit.ly/agilesim

Four people arrange a
restaurant booking after work
Q. What is the chance they
arrive on-time to be seated?
8
@t_Magennis slides at bit.ly/agilesim

9
15 TIMES more likely at least on person is late
1 in 16 EVERYONE is ON-TIME
Person 1 Person 2 Person 3 Person 4
Commercial in confidence

10

Estimating the wrong things
and getting a poor result
doesn’t mean we shouldn’t
estimate at all
We just need to estimate
things that matter most
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Commercial in confidence

85%
Forecasts are attempts to
Change of
At Least 2
th August
15answer questions about
Teams
2013
future events. They are an
estimate with a stated
Definitely
Greater
uncertainty
than
$1,000,000
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@t_Magennis slides at bit.ly/agilesim

There is NO single
forecast result
Uncertainty In = Uncertainty Out
There will always be many
possible results, some more likely and this is the
key to proper forecasting
@t_Magennis slides at bit.ly/agilesim

Likelihood
Probabilistic Forecasting combines many uncertain
inputs to find many possible outcomes, and what
outcomes are more likely than others
50%
Possible
Outcomes
50%
Possible
Outcomes
Time to Complete Backlog
18
@t_Magennis slides at bit.ly/agilesim

Likelihood
Did the Obama 2012 Campaign Fund Advertising to
Achieve 50% Chance of Re-election?
85% Possible
Outcomes
15%
Time to Complete Backlog
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@t_Magennis slides at bit.ly/agilesim

Task Uncertainty – Summing Variance
1
2
3
4
Source attribution: Aidan Lyon, Department of Philosophy. University of Maryland, College Park. “Why Normal
Distributions Occur” http://aidanlyon.com/sites/default/files/Lyon-normal_distributions.pdf
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@t_Magennis slides at bit.ly/agilesim

Decision Induced Uncertainty
Every choice we make changes the outcome
Planned / Due Date
July
Cost of Delay
Dev Cost
Staff
Actual Date
August
September
October
Forecast Completion Date
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@t_Magennis slides at bit.ly/agilesim
November
December

What is modelling and how to use cycle time
MODELING AND CYCLE TIME
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A model is a tool used to
mimic a real world process
Models are tools for low-cost
experimentation
@t_Magennis slides at bit.ly/agilesim

Simple
Depth of Forecasting models
Linear Projection
System Cycle Time
Diagnostic
Partitioned Cycle Time
25
Simulated process
Commercial in confidence

Simple Cycle Time Model
Amount of
Work
(# stories)
Lead Time
or Cycle
Time
Random Chance
/ Risk / Stupidity
26
@t_Magennis slides at bit.ly/agilesim
Parallel
Work in Proc.
(WIP)

Capturing Cycle Time and WIP
Story
Start Date
Completed Cycle Time
Date
(days)
1
2
3
1 Jan 2013
5 Jan 2013
5 Jan 2013
15 Jan 2013
12 Jan 2013
4
5
6
7
8
9
27
10
6 Jan 2013
3 Jan 2013
7 Jan 2013
10 Jan 2013
10 Jan 2013
13 Jan 2013
15 Jan 2013
14
Date “Complete” – Date “Started”
7 Jan 2013
18 Feb 2013
22 Jan 2013
18 Jan 2013
26 Jan 2013
Use with attribution

Capturing Cycle Time and WIP
Story
Start Date
Completed Cycle Time
Date
(days)
Date
1 Jan
1
2
3
1 Jan 2013
5 Jan 2013
5 Jan 2013
15 Jan 2013
12 Jan 2013
3 Jan
4 Jan
5 Jan
4
5
6
7
8
9
28
10
6 Jan 2013
3 Jan 2013
7 Jan 2013
10 Jan 2013
10 Jan 2013
13 Jan 2013
15 Jan 2013
7 Jan 2013
18 Feb 2013
22 Jan 2013
Count of Started, but
18 Jan 2013
26 Jan completed
Not 2013
Use with attribution
6 Jan
7 Jan
8 Jan
9 Jan
10 Jan
…
15 Jan
WIP
5

Capturing Cycle Time and WIP
Story
Start Date
Completed Cycle Time
Date
(days)
Date
1 Jan
WIP
1
1
2
3
1 Jan 2013
5 Jan 2013
5 Jan 2013
15 Jan 2013
12 Jan 2013
3 Jan
4 Jan
5 Jan
2
2
3
4
5
6
7
8
9
29
10
6 Jan 2013
3 Jan 2013
7 Jan 2013
10 Jan 2013
10 Jan 2013
13 Jan 2013
15 Jan 2013
6 Jan
7 Jan
8 Jan
9 Jan
10 Jan
…
15 Jan
4
5
5
5
7
…
7
14
7
7 Jan 2013
18 Feb 2013
22 Jan 2013
4
42
12
18 Jan 2013
8
26 Jan 2013
13
Use with attribution

30
Trial 1 Trial 2 Trial 100
9
13 13
5
Sum:
51
1
4
7
5
11
28
…
35
19
5
13
11
83
11
Fancy term for turning a small set
of samples into a larger set:
Bootstrapping
Use with attribution
By repetitively sample
we build trial
hypothetical “project”
completions

Sum Random Numbers
Historical Story Cycle Time Trend
25
11
29
43
34
26
31
45
22
27
More often
Less often
Sum:
31
43
65
45
8
7
34
73
54
48
295
410
…..
Basic Cycle Time Forecast Monte Carlo Process
1. Gather historical story lead-times
2. Build a set of random numbers based on pattern
3. Sum a random number for each remaining story
to build a potential outcome
4. Repeat many times to find the likelihood (odds)
to build a pattern of likelihood outcomes
Days To Complete
19
12
24
27
21
3
9
20
23
29
187

1. Historical Cycle Time
Monte Carlo Analysis =
Process to Combine
Multiple Uncertain
Measurements /
Estimates
6. Phases
2. Planned Resources/ WIP
4. Historical Scope
Creep Rate
3. The Work (Backlog)
Backlog
Feature 1
Feature 2
Feature 3
(optional)
33
5. Historical Defect Rate and Cycle Times
(optional)
@t_Magennis slides at bit.ly/agilesim

34
Commercial in confidence

35
@t_Magennis slides at bit.ly/agilesim

Y-Axis:
Number of
Completed
Stories
Project Complete
Likelihood
Range of complete
stories probability
0 to
50%
X-Axis: Date
36
@t_Magennis slides at bit.ly/agilesim
50 to
75%
>
75%

How certain
based on model
forecast
Further
calculations to
make economic
tradeoffs
37
Commercial in confidence

What is 10% Cycle Time
Reduction Worth?
Baseline
Staff Cost Cost of Delay Total Cost
$912.000 + $190.000 = $1.102.000
Experiment: 10% Cycle Time Reduction
Staff Cost Cost of Delay Total Cost
$883.200 + $177.419 = $1.060.619
Opportunity: $41.381
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What is One Designer Worth?
Baseline
Staff Cost Cost of Delay Total Cost
$912.000 + $190.000 = $1.102.000
Experiment: + 1 Designer
Staff Cost Cost of Delay Total Cost
$610.400 + $5.000
= $615.400
Opportunity: $486.600
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FORECASTING STRATEGIES
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When you have historical data
1. Model Baseline
using historically
known truths
The
Past
2. Test Model
against historically
known truths
3. Forecast
The
Future

Compare Model vs Actual Often
Range of complete
probability
Actual results to compare
if model is predictable
43
@t_Magennis slides at bit.ly/agilesim

When you have no historical data
The
Future
@t_Magennis slides at bit.ly/agilesim

If we understand how cycle time is
statistically distributed, then an
initial guess of maximum allows an
accurate inference to be made
Alternatives • Borrow a similar project’s data
• Borrow industry data
• Fake it until you make it… (AKA guess range)
47
@t_Magennis slides at bit.ly/agilesim

Probability Density Function
1997: Industrial Strength Software 2002: Metrics and Models in
by Lawrence H. Software Quality Engineering
(2nd Edition) [Hardcover]
Putnam , IEEE , Ware Myers
Stephen H. Kan (Author)
0.32
0.28
0.24
0.2
0.16
0.12
0.08
0.04
0
-10
0
10
20
30
40
50
60
70
80
x
Histogram
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Gamma (3P)
Lognormal
Rayleigh
@t_Magennis slides at bit.ly/agilesim
Weibull
90
100
110
120
1

Waterfall
Weibull Shape
Parameter = 2
AKA Rayleigh
49
Commercial in confidence

Agile / Lean / Kanban
Weibull Shape
Parameter = 1.5
50
Commercial in confidence

Typical Operations / Release
Weibull Shape
Parameter = 1
AKA Exponential
51
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Shape – How Fat the
distribution. 1.5 is a
good starting point.
Probability Density Function
0.28
0.24
f(x)
0.2
Scale – How Wide in
Range. Related to the
Upper Bound. *Rough*
Guess: (High – Low) / 4
Location – The
Lower Bound
0.16
0.12
0.08
0.04
0
0
10
20
30
40
50
60
70
x
Histogram
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Weibull
@t_Magennis slides at bit.ly/agilesim
80
90
100
110
120

What Distribution To Use...
• No Data at All, or Less than < 11 Samples (why 11?)
– Uniform Range with Boundaries Guessed (safest)
– Weibull Range with Boundaries Guessed (likely)
• 11 to 30 Samples
– Uniform Range with Boundaries at 5th and 95th CI
– Weibull Range with Boundaries at 5th and 95th CI
• More than 30 Samples
– Use historical data as bootstrap reference
– Curve Fitting software
53
@t_Magennis slides at bit.ly/agilesim

Questions…
• Download the slides (soon) and software at
http://bit.ly/agilesim
• Contact me
– Email: troy.Magennis@focusedobjective.com
– Twitter: @t_Magennis
• Read:
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1. Historical Cycle Time
Design
Develop
Test
Design
Develop
A Process to Combine
Multiple Uncertain
Measurements /
Estimates is Needed
Test
2. Planned Resources/ Effort
4. Historical Scope
Creep Rate
3. The Work (Backlog)
Backlog
Feature 1
Feature 2
Feature 3
(optional)
55
5. Historical Defect Rate & Cycle Times
(optional)