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ENGR 3360U Winter 2014

Unit 10-4

Quantitative Assessment of Risk

Dr. J. Michael Bennett, P. Eng., PMP,

UOIT,

Version 2014-I-01

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Un t 10 – Pro e!t R s "ana#ement

$%an#e Re!or&

2014-'-01 'nitia( $reation

2014-'-0110-2 )*r+ ,+"+ ennett. P+En#+. P"P ENGR 3360

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2014-'-01 )*r+ ,+"+ ennett. P+En#+. P"P ENGR 336010-3

10+4 Quantitative Risk Ana(/sis

10+4+1 'nuts

+1 EEP

+2 Proe!t !oe tatement

+3 Risk "ana#ement P(an

+4 Risk Re#ister

+ P"P

10+4+2 oo(s an& e!%niues

+1 *ata Gat%erin# 5 Reresentation e!%niues

+2 Quantitative Risk Ana(/sis 5 "o&e(in#10+4+3 ututs

+1 Risk Re#ister U&ates

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ur Risk Para&i#m

$ontro(

'&entif/

Q5Q Ana(/7eP(an

ra!k

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Quantitative

8ere 9e ut num:ers on t%e risks

8ere :e *ra#ons

,ust :e!ause t%ere is a num:er. &oes not make ittrue;

Not ever/t%in# t%at matters. !an :e measure&+

Not ever/t%in# t%at is measure&. matters

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%e '&ea( is to

Quantif/ t%e ossi:(e out!ome

Assess t%e ro:a:i(it/ of a!%ievin# o:e!tives

Use t%e numeri! rankin# to 9at!% t%e %i#%est'&entif/ rea(isti! !osts. s!%e&u(es. s!oe tar#ets

in t%e fa!e of t%e roe!t risks

*etermine t%e :est P" &e!ision 9%en somerisks rear t%eir u#(/ %ea&s

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+1 *ata Gat%erin# 5 Reresentation ek=s

'ntervie9in#

Pro:a:i(it/ &istri:utions

E>ert ument

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Ran#e of $ost Estimates

WE(ement

@o9 @ike(/ 8i#%

*esi#n 4 6 10

ui(& 16 20 3

est 11 1 23

ota(Proe!t

41

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Pro:a:i(it/ *istri:utions

0.25 0.50 0.75 1.000.00 0.25 0.50 0.75 1.000.00

Likely Likely

Beta Triangular

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+2 Quantitative Risk Ana(/sis 5 "o&e(in#

ensitivit/ ana(/sis

E>e!te& monetar/ va(ue

*e!ision tree ana(/sis"o&e(in# an& simu(ation

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Risk Re#ister U&ates

Pro:a:i(isti! ana(/sis of roe!t

Pro:a:i(it/ of a!%ievin# !ost an& time

o:e!tivesPrioriti7e& (ist of uantifie& risks

ren&s in uantitative risk ana(/sis resu(ts

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Ran#e of Estimates

B We !an=t forete(( re!ise(/ t%e !osts an& :enefits for future /ears+

B 't=s more rea(isti! to &es!ri:e a ran#e of ossi:(e va(ues+

– Cor e>am(e a ran#e !ou(& in!(u&eDB timisti!=. most (ike(/=. an& essimisti!=

estimatesB %e ana(/sis !ou(& t%en &etermine 9%et%er t%e

&e!ision is sensitive to t%e ran#e of va(ues+


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%e Ru(e of i>

E>am(es. %o9 (on# &oes it take /ou to

&rive to U'F ou (ive in !ar:eria+


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Pro:a:i(it/

B Pro:a:i(it/ of f(iin# a !oinD – 8ea& H0IJ. ai( H0IJ

B %is is t%e (on# ran#e freuen!/ an& t%e sin#(e tria((ike(i%oo&+

B Pro:a:i(ities !an a(so :e :ase& on ast &ata. e>ert uement. or some !om:ination of :ot%+

B "at%emati!a((/D – et9een 0 an& 1

0 (can never happen) – 0% 1 (will always happen) – 100% 0.5 (hal the ti!e"as in the a#$ve ea!ple $ lipping a c$in"

50%)


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Pro:a:i(it/. !ont=&+

B %e sum of a(( ro:a:i(ities must eua( 1+ – 8ea& H0+J. ai( H0+J

0 KL Pro:a:i(it/ KL 1

∑PHout!ome J L 1. 9%ere t%ere are M out!omes

B As oose& to (ar#e &istri:utions. it is more!ommon in e!onomi! ana(/sis to use 2 to

&is!rete ossi:i(ities+B %is is :e!auseD

B Ea!% out!ome reuires more ana(/sis+

B

E>ert uement s%ou(& :e (imite& for a!!ura!/+2014-'-0110-1 )*r+ ,+"+ ennett. P+En#+. P"P ENGR 3360

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,oint Pro:a:i(it/ *istri:ution

B %e ro&u!t of t9o in&een&ent events

PHA an& J L PHAJ > PHJ

B E>am(eD C(iin# a !oin an& ro((in# a &ie – urnin# u a %ea& an& ro((in# a 4B > 1O6 L 1O12 oint ro:a:i(it/

B ,oint ro:a:i(ities !an in!rease t%e num:erof ossi:i(ities an& :e!ome arit%meti!a((/

:ur&ensome+


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E>e!te& a(ue

B %e E>e!te& a(ueD – Ea!% out!ome is 9ei#%te& :/ its ro:a:i(it/

an& t%e resu(ts are summe&D &pecte' value utc$!e* +(*) , utc$!eB +(B). . . .

– %is is N a sim(e avera#e+

2014-'-0110-1< )*r+ ,+"+ ennett. P+En#+. P"P ENGR 3360

10

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E!onomi! *e!ision rees

A usefu( too( in &e!i&in# a(ternatives

2014-'-01 )*r+ ,+"+ ennett. P+En#+. P"P ENGR 336010-1?

U 10 P R "

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*e!ision rees

B *e!ision rees !an :e !reate& for !om(e>&e!ision makin#D

– /m:o(sD

-ecisi$n $'e /hance $'e

/h$$se a path

+rune

/1

/2/

+1+2

+

/hance event

with pr$# +i


U 10 P R "

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*e!ision rees. !ont=&+


U t 10 P t R " t

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Pro:(em

W%at &e!ision s%ou(& :e ma&e on t%e

!%oi!e of a ne9 ro&u!t as ust &es!ri:e&F

"ARR is 10I+ 'f t%e roe!t is terminate&

after 1 /ear. sa(va#e is 0.000+ 'f it is use&

to ? /ears. 0+


U t 10 P t R " t

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o(ution

No&e < PW L 0000

No&e ? PW is 100000HPOA. 10I.

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No&e PW L -?00M 600MHPFA. 10I.

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*e!ision rees. !ont=&+


Un t 10 Pro e!t R s "ana#ement

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Cinis%

PW No&e 4 L H100M0MJHPOC. 10I.

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Cina( o(ution

+3 $'e 4



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Quantif/in# Risks 9it% Pro:a:i(it/

ev L @ike(i%oo&i > 'ma!ti

Pro:(em is t%at it is %ar& to uantif/ t%ese

Cor e>am(e. 9%at is t%e (ike(i%oo& of as%i %ittin# an i!e:er# in t%e Nort%

At(anti!F


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2014-'-01 )*r+ ,+"+ ennett. P+En#+. P"P ENGR 336010-2?

Prioriti7in# Risks

Use t%e Pareto Ru(e

Pi!k t%e to 10


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Risk E>am(e

uose t%at /ou %ave i&entifie& t%e tri(etD

1+ r 1 %i#% staff turnover

2+ l 1 estimate& at 0+

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Risk E>am(e !ont+

tes to Re&u!e t%e Risk of urnovers mi#%t in!(u&eD

meet 9it% !urrent staff to &etermine !auses of %i#% turnover

!%an#e t%ese :efore roe!t !ommen!es

assume that turnover WILL occur &urin# roe!t an& :ui(& t%at into (an

or#ani7e roe!t teams so t%at information a:out ea!% &eve(oment a!tivit/ is9i&e(/ &iserse&

&efine &o!umentation stan&ar&s

esta:(is% me!%anism so t%at t%ese are &eve(oe& in a time(/ manner

!on&u!t eer revie9s an& 9a(kt%rou#%s for se!on&-sour!in#

i&entif/ critical technologists

&efine a :a!ku erson for ea!% !riti!a( te!%no(o#ist


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2014 ' 0110 31

ur Risk Para&i#m

$ontro(

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Q5Q Ana(/7eP(an

ra!k

3360 unit 10.4 2014-i-01

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