how to bet in montecarlo and end up with some money in your pocket
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HOW TO BET IN MONTECARLO AND END UP WITH SOME MONEY IN YOUR POCKET
@pavleras
www.nasa.gov
www.nasa.gov
PREDICTABILITYWHEN?
HOW MUCH?
INPUT VARIABLES
https://www.flickr.com/photos/czarcats/
UNCERTAININPUTS
PeopleProduct Size
DefectsInterruptions
Time To MarketThroughtput
DATA
https://www.flickr.com/photos/popculturegeek/
RUNTRIPS TO
THE FUTURE
AGGREGATEOUTCOMES
https://www.flickr.com/photos/jeepersmedia/
https://www.flickr.com/photos/jeepersmedia/
#1 REDUCEUNCERTAINTY
NOT REMOVING IT!
MODEL
www.focusedobjective.com
Work days to complete =
(Estimated days of work /
number of developers)
+
((Estimated days of work * Defect rate)
/ number of developers)
MODELING
REJECT ITEMS
#2 DATA SHAPESTHE MODEL
AND MODEL SHAPES
THE DATA
LEARNING LOOP
#3 ONLY WHENMODEL
REFLECTSREALITY…
CYCLE TIME FORECASTING
THROUGHTPUT FORECASTING
www.focusedobjective.com
CYCLE TIME FORECASTING
www.focusedobjective.com
GATHER WORK ITEMS CYCLE TIME
https://www.flickr.com/photos/popculturegeek/
CYCLE TIME
CYCLE TIME
DETOUR
SCATTER PLOTDate
Cycl
e Ti
me
in D
ays
SCATTER PLOT
=PERCENTILE.INC(CYCLETIME,0.5)
11 days
Date
Cycl
e Ti
me
in D
ays
SCATTER PLOT
=PERCENTILE.INC(CYCLETIME,0.75)
18 days
Date
Cycl
e Ti
me
in D
ays
SCATTER PLOT
=PERCENTILE.INC(CYCLETIME,0.85)
20 days
Date
Cycl
e Ti
me
in D
ays
SCATTER PLOT
=PERCENTILE.INC(CYCLETIME,0.95)
29 days
Date
Cycl
e Ti
me
in D
ays
WI 1WI 2WI 3WI 4WI 5WI 6WI 7WI 8…WI 100
BACKLOG????????…?
CYCLE TIME
WI 1WI 2WI 3WI 4WI 5WI 6WI 7WI 8…WI 100
BACKLOG????????…?
CYCLE TIME BUILD A SET OF
RANDOMCYCLE TIME
WI 1WI 2WI 3WI 4WI 5WI 6WI 7WI 8…WI 100
BACKLOG????????…?
CYCLE TIME BUILD A SET OF
HISTORICCYCLE TIME
BOOTSTRAPPING
=INDEX(data,rows(data)+rand()+1,columns(data)+rand()+1)
BOOTSTRAPPING
=INDEX(data,rows(data)+rand()+1,columns(data)+rand()+1)
BOOTSTRAPPING
=INDEX(data,rows(data)+rand()+1,columns(data)+rand()+1)
BOOTSTRAPPING
=INDEX(data,rows(data)+rand()+1,columns(data)+rand()+1)
WARNING
BUILD A SET OF
RANDOMCYCLE TIME
=RANDBETWEEN(botttom,top)N
umbe
r of w
ori i
tem
s
Cycle time in Days
Num
ber o
f wor
k ite
ms
Cycle time in Days
=RANDBETWEEN(botttom,top)
BUILD A SET OF
RANDOMCYCLE TIME
=RANDBETWEEN(botttom,top)
Cycle time in Days
Num
ber o
f wor
i ite
ms
WEIBULLSHAPE = 1.5
connected-knowledge.com/2014/09/08/how-to-match-to-weibull-distribution-without-excel/
WI 1WI 2WI 3WI 4WI 5WI 6WI 7WI 8…WI 100
BACKLOG
SUM A RANDOM
CYCLE TIME
FOR EACH WORK ITEM
WI 1WI 2WI 3WI 4WI 5WI 6WI 7WI 8…WI 100
956 7
10 87
14…6
BACKLOG
SUM A RANDOM
CYCLE TIME
FOR EACH WORK ITEM
SUM470
Trial 1
WI 1WI 2WI 3WI 4WI 5WI 6WI 7WI 8…WI 100
956 7
10 87
14…6
BACKLOG
SUM A RANDOM
CYCLE TIME
FOR EACH WORK ITEM
SUM470
Trial 19858
15 94
13…14
Trial 2
SUM510
WI 1WI 2WI 3WI 4WI 5WI 6WI 7WI 8…WI 100
956 7
10 87
14…6
…
BACKLOG
SUM A RANDOM
CYCLE TIME
FOR EACH WORK ITEM
SUM470
743 56345…13
Trial 1 Trial 2000
SUM336
9858
15 94
13…14
Trial 2
SUM510
DIVIDE BY THE AMOUNT
PARALLEL EFFORT
3/31/16 4/7/16 4/14/16 4/21/16 4/28/16 5/5/16 5/12/16 5/19/16 5/26/16 6/2/16 6/9/16 6/16/16 6/23/160
10
20
30
40
50
60
70
80
90
100
26
35
49
70
9390
65
53
28
8
1 1
Forecast Completed Date (on or before)
Sim
ulat
ed O
ccur
renc
e Fr
eque
ncy
REPEAT MANY TIMES TO
BUILD A PATTERN OF OUTCOMES
3/31/16 4/7/16 4/14/16 4/21/16 4/28/16 5/5/16 5/12/16 5/19/16 5/26/16 6/2/16 6/9/16 6/16/16 6/23/160
10
20
30
40
50
60
70
80
90
100
26
35
49
70
9390
65
53
28
8
1 1
Forecast Completed Date (on or before)
Sim
ulat
ed O
ccur
renc
e Fr
eque
ncy
REPEAT MANY TIMES TO
BUILD A PATTERN OF OUTCOMES
WI 1WI 2WI 3WI 4WI 5WI 6WI 7WI 8…WI 100
956 7
10 87
14…6
…
BACKLOG
SUM470
743 56345…13
Trial 1
SUM336
9858
15 94
13…14
Trial 2
SUM510
THROUGHTPUTFORECASTING
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
1/7/16 1/14/16 1/21/16 1/28/16 2/4/16 2/11/16 2/18/16 2/25/16 3/3/160
20
40
60
80
100
120
140
4
48
95
126114
84
217 2
Simulated Forecast Date Frequency
Forecast Completed Date (on or before)
Sim
ulat
ed O
ccur
renc
e Fr
eque
ncy
12/3/15 12/10/15 12/17/15 12/24/15 12/31/15 1/7/16 1/14/16 1/21/16 1/28/16 2/4/16 2/11/16 2/18/16 2/25/160
20
40
60
80
100
120 Simulated Burn Downs (first 50)
Date
Rem
aini
ng S
torie
s
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
THROUGHTPUT
www.focusedobjective.com
20% 60% 20%
Project Scope
time
Perform
ing
Battlefield
Experiments
1st LEG20%
2nd LEG60%
3rd LEG20%
Performing
Battlefield
Experiments
THROUGHTPUT
www.focusedobjective.com
WHAT MIGHT GO WRONG…
ADDED SCOPE
BLOCKED ITEMS
DATA
https://www.flickr.com/photos/popculturegeek/
#4 WHEN YOU DON’T HAVE COLLECT IT
WHEN YOU HAVE USE IT
#4 NO DATA UNIFORM OR
WEIBULL11 < 30
PERCENTILES> 30
BOOTSTRAPPING
DETOUR
CALIBRATION
90% ConfidenceInterval
https://www.flickr.com/photos/x1brett/
32.5$M
http://jaysonberray.com
#5 UNCERTAINTYREDUCES FASTERTHAN
YOU THINK*
160$
M
50%
Head – HeadHead – TailTail – HeadTail – Tail 25%
(4 + 8) / 26 MONTHS
10 x Head = 0,09%
#6 PLANS BASED ONAVERAGES
ARE WRONGON AVERAGE”
Sensitivity Analysis
https://www.flickr.com/photos/jeepersmedia/
Sensitivity Analysis
Model
Sensitivity Analysis
Forecast
Sensitivity Analysis
Change 1factor
Sensitivity Analysis
Forecast
Sensitivity Analysis
Change 1factor
Sensitivity Analysis
Forecast
Sensitivity Analysis
Order
CONCLUSSIONS
CONCLUSIONS
ww.focusedobjective.comhttps://www.flickr.com/photos/theyoungones/
“All models are wrong.
Some are useful”
“Just has to be better than what is currently used and intuition
alone”
1
“When you don’t have dataCollect it”
“when you have it, use it with care…”
2
“Uncertaintyreduces
faster thankyou
think”
3
Keep researching
Addtional Resourceshttp://bit.ly/SimResources
More info about me
BecomingAgile.wordpress.com@pavleras
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