lkce16 - estimation made easy by pawel brodzinski and tomek rusilko
TRANSCRIPT
IF A PROJECT HAS NO RISKS, DON'T DO IT.
Tom DeMarco & Timothy Lister
DON’T DO IT
T. DEMARCO, T. LISTER: WALTZING WITH BEARS
ACCURACY IN ESTIMATING DID NOT IMPROVE AS INFORMATION ACCUMULATED, WHILE CONFIDENCE INCREASED CONSISTENTLY.
Claire Tsai, Joshua Klayman, Reid Hastie
ACCURACY OF ESTIMATION
SOURCE: TSAI, KLAYMAN, HASTIE: EFFECTS OF AMOUNT OF INFORMATION ON JUDGMENT ACCURACY AND CONFIDENCE
SCIENTISTS AND WRITERS ARE NOTORIOUSLY PRONE TO UNDERESTIMATE THE TIME REQUIRED TO COMPLETE A PROJECT, EVEN WHEN THEY HAVE CONSIDERABLE EXPERIENCE OF PAST FAILURES TO LIVE UP TO PLANNED SCHEDULES. A SIMILAR BIAS HAS BEEN DOCUMENTED IN ENGINEERS' ESTIMATES.
Daniel Kahneman, Amos Tversky
ESTIMATION BIAS
COUNTING THE NUMBER OF STORIES METRIC DOESN'T TAKE THE SIZE INTO ACCOUNT. IT TURNS OUT IT DOESN'T MATTER. THE SIZE OF STORIES IS GELLED TO A VERY COMMON SIZE. WE COULD USE THROUGHPUT VERY SUCCESSFULLY WITH THE RESEARCH.
Larry Maccherone
THROUGHPUT VS STORY POINTS
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTE CARLO EXAMPLE
Today
Burn-up
Scope
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
AVERAGE PACE FORECAST - SIMPLE REGRESSION
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Scope
6 iterations
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTE CARLO EXAMPLE: THROUGHPUT SAMPLES
Today
Burn-up
Scope
2
2
5
8
4Throughput
Values
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTE CARLO EXAMPLE: THROUPUT DISTRIBUTION
Today
Burn-up
Scope
2
2
5
8
4Throughput
Values
8 tasks 20 %
5 tasks 20 %
4 tasks 20 %
2 tasks 40 %
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTE CARLO EXAMPLE: UNLUCKY BRIAN
Today
Burn-up
Scope
8 tasks
5 tasks
4 tasks
2 tasks
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTE CARLO EXAMPLE: WORST CASE SCENARIO
Today
Burn-up
Scope
8 tasks
5 tasks
4 tasks
2 tasks
12 iterations
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTE CARLO EXAMPLE: BEST CASE SCENARIO
Today
Burn-up
Scope
8 tasks
5 tasks
4 tasks
2 tasks
12 iterations
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTE CARLO EXAMPLE
Today
Burn-up
Scope
8 tasks
5 tasks
4 tasks
2 tasks
12 iterations3 iterations
MONTE CARLO EXAMPLE
Today
Burn-up
Scope
8 tasks
5 tasks
4 tasks
2 tasks
0
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40
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTE CARLO EXAMPLE
Today
Burn-up
Scope
0
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
8 tasks
5 tasks
4 tasks
2 tasks
MONTE CARLO EXAMPLE
Today
Burn-up
Scope
8 tasks
5 tasks
4 tasks
2 tasks
0
10
20
30
40
50
Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
MONTE CARLO EXAMPLE
Today
Burn-up
Scope
0
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
8 tasks
5 tasks
4 tasks
2 tasks
HISTORICAL DATA: COMPLETED STORIES
start date end date2016-02-01 2016-02-042016-02-01 2016-02-032016-02-05 2016-02-112016-02-04 2016-02-102016-02-08 2016-02-082016-02-09 2016-02-122016-02-09 2016-02-112016-02-12 2016-02-162016-02-11 2016-02-162016-02-19 2016-02-222016-02-18 2016-02-22
start date end date2016-02-19 2016-02-242016-02-17 2016-02-242016-02-23 2016-02-242016-02-23 2016-02-252016-02-25 2016-02-252016-02-25 2016-02-262016-02-26 2016-03-032016-02-26 2016-03-032016-02-29 2016-03-022016-03-01 2016-03-03
HISTORICAL DATA: LEAD TIMES
1 2 3 4 5 8 9 10 11 12 15 164
3 5
5
4
3
1
3
4
17 18 19 22 23 24 25 26 29 1 2 3
3
2 2
4
6
1
2
3 5
5
3
3
HISTORICAL DATA: WORK IN PROGRESS
1 2 3 4 5 8 9 10 11 12 15 16
17 18 19 22 23 24 25 26 29 1 2 3
2 2 2 2 2 3 4 4 4 3
1 2 4 4 4 4 3 3 3 4
2 2
4 3
READY…
4 tasks
3 tasks
2 tasks
1 task6 days
5 days
4 days
3 days
2 days
1 day
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Iteration1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
} 24 tasks to go
6 days
5 days
4 days
3 days
2 days
1 day 3
5
5
4
3
1
34
32
26
1
3
5
5
3
3
3
3 55
5
5
SAMPLING LEAD TIME VALUES
13 14 15 16 17 18 19 20 21 22 23 24
1 2 3 4 5 6 7 8 9 10 11 12
SAMPLING LEAD TIME VALUES
3
5
5
4
3
1
3
4
3
2
2
6
1
3
5
5
3
3
3
3
5
5
5
5
1 2 3 4 5 6 7 8 9 10 11 12
SAMPLING WORK IN PROGRESS
4 tasks
3 tasks
2 tasks
1 task 6 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 66 6
66 6 6 6 6
87
1 2 3 4 5 6 7 8 9 10 11 12
SAMPLING WORK IN PROGRESS
4 tasks
3 tasks
2 tasks
1 task 6 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 6
6
6
6
6 6 6
6
6
6
6
6
6
6
6
6
6
6
66
6
6
6
66
6
6
6
6
6
6
6
66
6
6
6
6
87
1 2 3 4 5 6 7 8 9 10 11 12
13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32 33 34 35 36
66
66
666
6666
66
666
6
6666
6666
6
66
6666
66
66
6
66
66
6
66
6
66
66
6
6 6 6 6
66 6
6
66
6 6 6 6
6
6
66
66
6 6 6 6
6
6
6
6 6 6
6
6 6 6
66
6
Valu
e A
xis
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Workdays
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
Valu
e A
xis
0
400
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1200
1600
Workdays
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
Valu
e A
xis
0
400
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1200
1600
Workdays
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
Valu
e A
xis
0
400
800
1200
1600
Workdays
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
50%
Valu
e A
xis
0
400
800
1200
1600
Workdays
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
Valu
e A
xis
0
400
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Workdays
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
90%
Valu
e A
xis
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1600
Workdays
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
Valu
e A
xis
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Workdays
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
99%
90% CONFIDENCE LEVEL MEANS THAT OUR ESTIMATE IS CORRECT 9 TIMES OUT OF 10. KINDA.
Pawel Brodzinski
90% CONFIDENCE INTERVAL?
ANY PROPOSED FORECASTING METHOD JUST HAS TO BE BETTER THAN WHAT YOU DO NOW, OR AT LEAST LESS EXPENSIVE WITH A SIMILAR RESULT.
Troy Magennis
BE BETTER
SOURCES & RESOURCES
▸ Troy Magennis original work: http://www.lkce13.com/videos/magennis/
▸ http://focusedobjective.com/wp-content/uploads/2013/05/Modeling-and-Simulating-Software-Projects-Troy-Magennis.pdf
▸ http://blog.lunarlogic.io/2016/how-we-estimate-at-lunar-logic/
▸ https://www.chicagobooth.edu/research/workshops/marketing/archive/workshoppapers/s06/tsai.pdf
▸ Planning Fallacy: https://books.google.pl/books?id=R-syxO7M67AC&pg=PA9&q=&redir_esc=y#v=onepage&q&f=false
▸ https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555
▸ Flow efficiency: https://hakanforss.wordpress.com/2014/06/17/flow-thinking-aceconf/
▸ http://zsoltfabok.com/blog/2013/12/flow-efficiency/
▸ https://www.infoq.com/presentations/agile-quantify
▸ http://brodzinski.com/2015/02/story-points-velocity-the-good-bits.html
▸ https://estimation.lunarlogic.io/
▸ https://www.agilealliance.org/estimation-and-forecasting/
▸ Projectr: http://getprojectr.com/