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If you are struggling to forecast project delivery dates and cost, or you want to eliminate the story estimation process because you feel it is waste, or you need to build the business case for hiring more staff, then this session is relevant to you. All estimates have uncertainty, and understanding how multiple uncertain factors compound is the first step to improving project and team predictability. A major benefit of Lean is the low weight capture of cycle time metrics. This session looks at how to use historical cycle time data to answer questions of forecasting and staff skill balancing. This session compares the benefits of using cycle time for analysis over current planning techniques such as velocity, burn-down charts, and cumulative flow diagrams. This session takes you on a journey of what to do after capturing cycle time data or what to do if you have no history to rely upon. Reducing reliance on developer estimation (popularized by the twitter hashtag of #NoEstimates movement) is good general advice, having the tools to plan and manage teams and projects is still important to maintain support at the executive level. This session details the approaches to getting the numbers you need to have whilst minimizing un-necesary overhead and estimating ONLY this factors that matter most.
Citation preview
Cycle Time AnalyticsMaking decisions using Lead Time and Cycle Time to avoid needing estimates for every
story
Troy Magennis @t_magennis
Slides at bit.ly/agilesim
LKCE 2013 – Modern Management Methods
2 @t_Magennis slides at bit.ly/agilesim
Commercial in confidence3
Forecasting Myths
1. You need lots of data to forecast
2. Statistical forecasting requires complex mathematics
3. If we have no data we can’t statistically forecast
False. Everything you see today uses nothing more than the + and /
operations!
False, if we understand the behavior of a domain (cycle time for example)
we can be confident with no initial data
False. Most uncertainty is reduced within the
first few samples
Actual Maximum
Actual Minimum
1
3
2
4
Q. What is the chance of the 4th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random)
@t_Magennis slides at bit.ly/agilesim
Actual Maximum
Actual Minimum
1
3
2
4
Highest sample
Lowest sample
Q. What is the chance of the 4th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random)
?
?
?
?
@t_Magennis slides at bit.ly/agilesim
Actual Maximum
Actual Minimum
1
3
2
4
25% chance higher than highest seen
25% lower than highest and higher than second highest
25% higher than lowest and lower than second lowest
25% lower than lowest seen
Highest sample
Lowest sample
Q. What is the chance of the 4th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random)
A. 50%
% = (1 - (1 / n – 1)) * 100@t_Magennis slides at bit.ly/agilesim
Actual Maximum
Actual Minimum
1
3
2
12
5% chance higher than highest seen
5% lower than lowest seen
Highest sample
Lowest sample
Q. What is the chance of the 12th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random)
?
?
A. 90%
% = (1 - (1 / n – 1)) * 100
4
5
6
7
8
9
10
11
@t_Magennis slides at bit.ly/agilesim
# Prior Samples Prediction Next Sample Within Prior Sample Range
3 50%4 67%5 75%6 80%7 83%8 86%9 88%
10 89%11 90%12 91%13 92%15 93%17 94%20 95%
@t_Magennis slides at bit.ly/agilesim
9
Four people arrange a restaurant booking after work
Q. What is the chance they arrive on-time to be seated?
@t_Magennis slides at bit.ly/agilesim
Commercial in confidence10
Person 1 Person 2 Person 3 Person 41
in 1
6 EV
ERYO
NE
is O
N-T
IME
15 T
IMES
mor
e lik
ely
at le
ast o
n pe
rson
is la
te
11
Commercial in confidence13
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
17
Forecasts are attempts to answer questions about
future events. They are an estimate with a stated
uncertainty
85% Change of
15th August 2013
Definitely Greater
than $1,000,000
At Least 2 Teams
@t_Magennis slides at bit.ly/agilesim
There is NO single forecast result
Uncertainty In = Uncertainty Out
There will always be manypossible results, some more likely and this is the
key to proper forecasting@t_Magennis slides at bit.ly/agilesim
19
Probabilistic Forecasting combines many uncertain inputs to find many possible outcomes, and what
outcomes are more likely than others
Time to Complete Backlog
50% Possible Outcomes
50% Possible Outcomes
Like
lihoo
d
@t_Magennis slides at bit.ly/agilesim
20
Did the Obama 2012 Campaign Fund Advertising to Achieve 50% Chance of Re-election?
Time to Complete Backlog
85% Possible Outcomes
15%
Like
lihoo
d
@t_Magennis slides at bit.ly/agilesim
21
Task Uncertainty – Summing Variance
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
1 2 3 4
@t_Magennis slides at bit.ly/agilesim
22
Staff
Dev
Cos
t
Cost
of D
elay
Decision Induced Uncertainty
Forecast Completion DateJuly August September October November December
Planned / Due Date
Actual Date
Every choice we make changes the outcome
@t_Magennis slides at bit.ly/agilesim
23
MODELING AND CYCLE TIMEWhat is modelling and how to use cycle time
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
Commercial in confidence26
Depth of Forecasting models
Linear Projection
System Cycle Time
Partitioned Cycle Time
Simulated process
Sim
ple
Dia
gnos
tic
27
Simple Cycle Time Model
Amount of Work(# stories)
Lead Time or Cycle Time
ParallelWork in Proc.(WIP)
Random Chance / Risk / Stupidity @t_Magennis slides at bit.ly/agilesim
Use with attribution28
Capturing Cycle Time and WIPStory Start Date Completed
DateCycle Time
(days)1 1 Jan 2013 15 Jan 20132 5 Jan 2013 12 Jan 20133 5 Jan 20134 6 Jan 20135 3 Jan 2013 7 Jan 20136 7 Jan 2013 18 Feb 20137 10 Jan 2013 22 Jan 20138 10 Jan 2013 18 Jan 20139 13 Jan 2013 26 Jan 2013
10 15 Jan 2013
Date “Complete” – Date “Started”
14
Use with attribution29
Capturing Cycle Time and WIPStory Start Date Completed
DateCycle Time
(days)1 1 Jan 2013 15 Jan 20132 5 Jan 2013 12 Jan 20133 5 Jan 20134 6 Jan 20135 3 Jan 2013 7 Jan 20136 7 Jan 2013 18 Feb 20137 10 Jan 2013 22 Jan 20138 10 Jan 2013 18 Jan 20139 13 Jan 2013 26 Jan 2013
10 15 Jan 2013
Date WIP1 Jan3 Jan4 Jan5 Jan6 Jan7 Jan8 Jan9 Jan
10 Jan…
15 JanCount of Started, but Not completed work
5
Use with attribution30
Capturing Cycle Time and WIPStory Start Date Completed
DateCycle Time
(days)1 1 Jan 2013 15 Jan 2013 142 5 Jan 2013 12 Jan 2013 7 3 5 Jan 20134 6 Jan 20135 3 Jan 2013 7 Jan 2013 46 7 Jan 2013 18 Feb 2013 427 10 Jan 2013 22 Jan 2013 128 10 Jan 2013 18 Jan 2013 89 13 Jan 2013 26 Jan 2013 13
10 15 Jan 2013
Date WIP1 Jan 13 Jan 24 Jan 25 Jan 36 Jan 47 Jan 58 Jan 59 Jan 5
10 Jan 7… …
15 Jan 7
9
5
13 13
11
Use with attribution31
9
13
117
5
35
14
4 19
1Sum: 51
14751128
35195131183
Trial 1 Trial 2 Trial 100
…
Fancy term for turning a small set of samples into a larger set:
Bootstrapping
By repetitively sample we build trial
hypothetical “project” completions
Sum Random Numbers
25112943342631452227
31436545
87
34735448
1912242721
39
202329
187410295Sum:
…..
Historical Story Cycle Time Trend
Days To Complete
Basic Cycle Time Forecast Monte Carlo Process
1. Gather historical story lead-times2. Build a set of random numbers based on pattern3. Sum a random number for each remaining story to build a potential outcome4. Repeat many times to find the likelihood (odds) to build a pattern of likelihood outcomes
𝑇𝑜𝑡𝑎𝑙𝐷𝑎𝑦𝑠❑=𝑆𝑢𝑚(𝑆𝑡𝑜𝑟𝑦𝑛×𝑅𝑎𝑛𝑑𝑜𝑚𝑛)
𝐸𝑓𝑓𝑜𝑟𝑡
More often
Less often
34
Backlog
Feature 1
Feature 2
Feature 3
1. Historical Cycle Time
3. The Work (Backlog)
2. Planned Resources/ WIP
Monte Carlo Analysis = Process to Combine Multiple Uncertain Measurements / Estimates
4. Historical Scope Creep Rate
(optional) 5. Historical Defect Rate and Cycle Times(optional)
6. Phases
@t_Magennis slides at bit.ly/agilesim
Commercial in confidence35
36 @t_Magennis slides at bit.ly/agilesim
37
Y-Axis: Number of Completed
Stories
Project Complete Likelihood
Range of complete stories probability
0 to 50%
50 to 75%
>75%X-Axis: Date
@t_Magennis slides at bit.ly/agilesim
Commercial in confidence38
How certain based on model
forecast
Further calculations to
make economic tradeoffs
39
Baseline
Experiment: 10% Cycle Time Reduction
Staff Cost€912.000
Cost of Delay€190.000
Total Cost€1.102.000+ =
Staff Cost€883.200
Cost of Delay€177.419
Total Cost€1.060.619+ =
Opportunity: €41.381
What is 10% Cycle Time Reduction Worth?
40
Backlog
Feature 1
Feature 2
Feature 3
1. Historical Cycle Time
2. Planned Resources/ Effort
3. The Work (Backlog)
5. Historical Defect Rate & Cycle Times
4. Historical Scope Creep Rate
A Process to Combine Multiple Uncertain Measurements / Estimates is Needed
(optional)(optional)
Design Develop Test
Design Develop Test
41
Baseline
Experiment: + 1 Designer
Staff Cost€912.000
Cost of Delay€190.000
Total Cost€1.102.000+ =
Staff Cost€610.400
Cost of Delay€5.000
Total Cost€615.400+ =
Opportunity: €486.600
What is One Designer Worth?
42
FORECASTING STRATEGIES
Janu
ary
Febr
uary
Mar
ch
April
May
June July
Augus
tSe
ptem
ber
The Future
The Past
1. Model Baseline using historically
known truths
2. Test Model against historically
known truths3. Forecast
When you have historical data
45
Compare Model vs Actual Often
@t_Magennis slides at bit.ly/agilesim
Range of complete probability
Actual results to compare if model is predictable
When you have no historical dataJa
nuar
y
Febr
uary
Mar
ch
April
May
June July
Augus
tSe
ptem
ber
The Future
@t_Magennis slides at bit.ly/agilesim
49
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)
@t_Magennis slides at bit.ly/agilesim
50
Probability Density Function
Histogram Gamma (3P) Lognormal Rayleigh Weibull
x1301201101009080706050403020100-10
f(x)
0.32
0.28
0.24
0.2
0.16
0.12
0.08
0.04
0
@t_Magennis slides at bit.ly/agilesim
1997: Industrial Strength Softwareby Lawrence H. Putnam , IEEE ,
Ware Myers
2002: Metrics and Models in Software Quality Engineering
(2nd Edition) [Hardcover]Stephen H. Kan (Author)
Commercial in confidence51
Waterfall
Weibull Shape Parameter = 2AKA Rayleigh
Commercial in confidence52
Agile / Lean / Kanban
Weibull Shape Parameter = 1.5
Commercial in confidence53
Typical Operations / Release
Weibull Shape Parameter = 1AKA Exponential
54
Probability Density Function
Histogram Weibull
x1201101009080706050403020100
f(x)
0.28
0.24
0.2
0.16
0.12
0.08
0.04
0
Scale – How Wide in Range. Related to the
Upper Bound. *Rough* Guess: (High – Low) / 4
Shape – How Fat the distribution. 1.5 is a good starting point.
Location – The Lower Bound
@t_Magennis slides at bit.ly/agilesim
55
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
@t_Magennis slides at bit.ly/agilesim
56
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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