how to bet in montecarlo and end up with some money in your pocket

HOW TO BET IN MONTECARLO AND END UP WITH SOME MONEY IN YOUR POCKET

@pavleras

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/

#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


CYCLE TIME FORECASTING


GATHER WORK ITEMS 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


18 days

Date

Cycl

e Ti

me

in D

ays

SCATTER PLOT


20 days

Date

Cycl

e Ti

me

in D

ays

SCATTER PLOT


29 days

Date

Cycl

e Ti

me

in D

ays

WI 1WI 2WI 3WI 4WI 5WI 6WI 7WI 8…WI 100

BACKLOG????????…?

CYCLE TIME


BACKLOG????????…?

CYCLE TIME BUILD A SET OF

RANDOMCYCLE TIME


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)

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/


BACKLOG

SUM A RANDOM

CYCLE TIME

FOR EACH WORK ITEM


956 7

10 87

14…6

BACKLOG

SUM A RANDOM

CYCLE TIME

FOR EACH WORK ITEM

SUM470

Trial 1


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


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


956 7

10 87

14…6

…

BACKLOG

SUM470

743 56345…13

Trial 1

SUM336

9858

15 94

13…14

Trial 2

SUM510

THROUGHTPUTFORECASTING


THROUGHTPUT


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


THROUGHTPUT


20% 60% 20%

Project Scope

time

Perform

ing

Battlefield

Experiments

1st LEG20%

2nd LEG60%

3rd LEG20%

Performing

Battlefield

Experiments

THROUGHTPUT


WHAT MIGHT GO WRONG…

ADDED SCOPE

BLOCKED ITEMS

DATA


#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

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/


Model


Forecast


Change 1factor


Forecast


Change 1factor


Forecast


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

http://bit.ly/SimResources




More info about me

BecomingAgile.wordpress.com@pavleras

how to bet in montecarlo and end up with some money in your pocket

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