ScienceDi ec
A ailable online a www.sciencedi ec .com
P ocedia Compu e Science 196 (2022) 948–955
1877-0509 © 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS –In e na ional Con e ence on ENTERp ise In o ma ion
Sys ems / P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h and Social Ca e
In o ma ion Sys ems and Technologies 2021
10.1016/j.p ocs.2021.12.096
10.1016/j.p ocs.2021.12.096 1877-0509
© 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS –In e na ional Con e ence on ENTERp ise
In o ma ion Sys ems / P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on
Heal h and Social Ca e In o ma ion Sys ems and Technologies 2021
A ailable online a www.sciencedi ec .com
ScienceDi ec
P ocedia Compu e Science 00 (2021) 000–000
www.else ie .com/loca e/p ocedia
1877-0509 © 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS - In e na ional Con e ence on ENTERp ise In o ma ion Sys ems /
P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h and Social Ca e In o ma ion Sys ems
and Technologies 2021
CENTERIS - In e na ional Con e ence on ENTERp ise In o ma ion Sys ems / P ojMAN -
In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h
and Social Ca e In o ma ion Sys ems and Technologies 2021
A me hod o p ojec po olio isk assessmen conside ing isk
in e dependencies – a ne wo k pe spec i e
Camilo Micana*, Gab iela Fe nandesb, Madalena A aújoc
aUni e si y o Valle, Logis ic and p oduc ion esea ch g oup, Cll 13 # 100-00, Cali 760034, Colombia
bUni e si y o Coimb a, CEMMPRE, Depa men o Mechanical Enginee ing, Polo II, Coimb a, 3030-788, Po ugal
cUni e si y o Minho, Cen e Algo i mi, A enida da Uni e sidade, Guima ães 4800-058, Po ugal
Abs ac
P ojec po olios ep esen he b idge be ween p ojec s and s a egy. Howe e , he inal esul s may no be as expec ed because
ma e ializa ion o isk ac o s. Hence, li e a u e has acknowledged p ojec po olio isk assessmen as an elemen o he p ojec
po olio isk managemen , being he elemen ha p o ides in o ma ion on he impo ance o isk ac o s. Fo ha , some speci ic
cha ac e is ics should be conside ed, such as isk in e dependency in luence and he isk ac o s impac o e po olio highe le els.
Thus, his s udy is ocused on he de elopmen o a me hod o p ojec po olio isk assessmen ha conside s bo h isk ac o
in e dependencies and hei impac on he s a egic objec i es as a ne wo k. In addi ion, he me hod also allows inco po a ing bo h
isk ac o s de i ed om p ojec s and de i ed a p ojec po olio le el. A ep esen a i e example is p o ided o illus a e he
p oposed me hod.
© 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS - In e na ional Con e ence on ENTERp ise
In o ma ion Sys ems / P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h
and Social Ca e In o ma ion Sys ems and Technologies 2021
Keywo ds: P ojec po olio; isk assessmen ; isk ac o s; ne wo k heo y
*Co esponding au ho .
E-mail add ess: camilo.mican@co eouni alle.edu.co
A ailable online a www.sciencedi ec .com
ScienceDi ec
P ocedia Compu e Science 00 (2021) 000–000
www.else ie .com/loca e/p ocedia
1877-0509 © 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS - In e na ional Con e ence on ENTERp ise In o ma ion Sys ems /
P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h and Social Ca e In o ma ion Sys ems
and Technologies 2021
CENTERIS - In e na ional Con e ence on ENTERp ise In o ma ion Sys ems / P ojMAN -
In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h
and Social Ca e In o ma ion Sys ems and Technologies 2021
A me hod o p ojec po olio isk assessmen conside ing isk
in e dependencies – a ne wo k pe spec i e
Camilo Micana*, Gab iela Fe nandesb, Madalena A aújoc
aUni e si y o Valle, Logis ic and p oduc ion esea ch g oup, Cll 13 # 100-00, Cali 760034, Colombia
bUni e si y o Coimb a, CEMMPRE, Depa men o Mechanical Enginee ing, Polo II, Coimb a, 3030-788, Po ugal
cUni e si y o Minho, Cen e Algo i mi, A enida da Uni e sidade, Guima ães 4800-058, Po ugal
Abs ac
P ojec po olios ep esen he b idge be ween p ojec s and s a egy. Howe e , he inal esul s may no be as expec ed because
ma e ializa ion o isk ac o s. Hence, li e a u e has acknowledged p ojec po olio isk assessmen as an elemen o he p ojec
po olio isk managemen , being he elemen ha p o ides in o ma ion on he impo ance o isk ac o s. Fo ha , some speci ic
cha ac e is ics should be conside ed, such as isk in e dependency in luence and he isk ac o s impac o e po olio highe le els.
Thus, his s udy is ocused on he de elopmen o a me hod o p ojec po olio isk assessmen ha conside s bo h isk ac o
in e dependencies and hei impac on he s a egic objec i es as a ne wo k. In addi ion, he me hod also allows inco po a ing bo h
isk ac o s de i ed om p ojec s and de i ed a p ojec po olio le el. A ep esen a i e example is p o ided o illus a e he
p oposed me hod.
© 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS - In e na ional Con e ence on ENTERp ise
In o ma ion Sys ems / P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h
and Social Ca e In o ma ion Sys ems and Technologies 2021
Keywo ds: P ojec po olio; isk assessmen ; isk ac o s; ne wo k heo y
*Co esponding au ho .
E-mail add ess: camilo.mican@co eouni alle.edu.co
2 Au ho name / P ocedia Compu e Science 00 (2021) 000–000
1. In oduc ion
A p ojec po olio is “a collec ion o p ojec s, p og ams, subsidia y po olios, and ope a ions managed as a g oup
o achie e s a egic objec i es” [1, p. 3]. Consequen ly, P ojec Po olio Managemen (PPM) ep esen s coo dina ed
managemen o a se o p ojec s ca ied ou by a speci ic o ganiza ion, which allows s a egic managemen o he
p ojec s. PPM help o c ea e a decision-making p ocess ha adds alue o he o ganiza ion, guiding he po olio o
achie e s a egic bene i s. In ac , PPM is a cen al mechanism o implemen success ully he s a egy [2–5]. Thus, he
impo ance o PPM lies in i s impac on he achie emen o compe i i e ad an ages o he o ganiza ion, and i is
conside ed a s a egic weapon ha ep esen s he in es men p io i ies o he o ganiza ion o achie e i s s a egic
objec i es [4,6,7].
The e ec o isk managemen a he p ojec le el has been s udied, e idencing posi i e impac s on he success o
each indi idual p ojec [8], howe e , managing isks only a he le el o p ojec s is no enough because a s a egic
iew o he p ojec po olio is no conside ed. In his ega d, he li e a u e in he ield o isk managemen has
p og essi ely e ol ed om p ojec isk analysis o P ojec Po olio Risk (PPR) analysis [9,10]. A p ojec po olio
o which he isks a e analyzed, e alua ed, and dis ibu ed ac oss se e al p ojec s, has a be e p obabili y o success
[11,12].
P ojec Po olio Risk Assessmen (PPRA), like PPR iden i ica ion and PPR esponse, is an elemen o p ojec
po olio Risk Managemen . PPRA is o ien ed o p o iding in o ma ion on he impo ance o isks and isk ends,
among o he ac o s, in suppo o isk esponse decisions [1]. In his conce n, he PPRA mus allow o iden i y, quali y,
and quan i y he e ec s o isk ac o s. I would gene a e g ea e app oxima ions o eali y, gi ing he decision-make s
a sys emic and dynamic p ojec po olio iew. PPRA would allow ocusing e o s and esou ces on he ac o s ha
a e ele an , and ha a e ep esen a i e o he p ojec po olio execu ion [13,14].
Among o he s, he li e a u e highligh ed wo componen s which should be conside ed as pa o isk assessmen
om a p ojec po olio pe spec i e. On one hand, he isk impac assessmen o e highe le els such as po olio le el
o s a egic objec i es le el [1,10,14]. On he o he hand, isk in e dependencies quali ica ion and quan i ica ion
[10,13,15]. The e o e, his esea ch deepens in hese wo componen s; and p oposes a me hod o PPRA conside ing
impac s on s a egic objec i es and isk ac o s in e dependencies. The e o e, in his s udy isk was concep ualized
wi h a se o ou comes wi h known p obabili y [16], which can ep esen impac s on he esul s expec ed [17]. Risk
ac o s we e conside ed as he di e en a iables ha in luence o gene a e, di ec ly o indi ec ly, exposu e [18,19],
o in o he wo ds, he a iables ha impac he isk o he p ojec po olio.
The emainde o he pape is o ganized as ollows. Fi s ly, he PPRA backg ound is p esen ed; whe eupon, based
on ne wo k heo y and he concep o sys emic and non-sys emic isk de i ed om Mode n Po olio Theo y, he
me hod p oposed is add essed and desc ibed. This is ollowed by an illus a i e example o he me hod applica ion.
Finally, conclusions and sugges ions o u u e esea ch a e summa ized.
2. Risk assessmen om a p ojec po olio pe spec i e
Conside ing he inancial impac o he p ojec po olio on he o ganiza ion, some app oaches o PPRA ha e been
p oposed. In his ega d, Cos a e al. [20] sugges ha no all he isk ac o s ha e he same weigh in he PPR
quan i ica ion o IT p ojec po olios. Fo his eason, hey used expe judgmen h ough pai wise compa ison o
es ima e he in luence o each isk ac o on he po olio and hey used c edi isk heo y and Mon eca lo simula ion
o es ima e he p obabili y dis ibu ion o po olio ea nings and losses. Also o IT p ojec po olios, Pe e s and
Ve hoe [16] p opose a me hod o e alua e he isk e ec s in he p ojec po olio execu ion phase, and de ine a
me hodology o e alua e his impac on he ne p esen alue o each p ojec and he whole p ojec po olio.
In addi ion o he isk measu e based on mone a y uni s, o he p oposals ha e in eg a ed some a ibu es such as
in e dependencies be ween p ojec s and in e dependencies be ween isks. Fo example, o e alua e he impac o he
in e ac ion be ween p ojec s on PPR, Guan e al. [11] desc ibe an app oach based on se heo y and Bayesian ne wo ks.
Cooley e al. [21] used he isk dependencies quan i ica ion app oach o allow a sys emic analysis o he impac ha
he isk ac o s could gene a e on he po olio; howe e , he high amoun o his o ical da a necessa y o ob ain eliable
in o ma ion ep esen he main weakness o his p oposal [21].
Camilo Mican e al. / P ocedia Compu e Science 196 (2022) 948–955 949
A ailable online a www.sciencedi ec .com
ScienceDi ec
P ocedia Compu e Science 00 (2021) 000–000
www.else ie .com/loca e/p ocedia
1877-0509 © 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS - In e na ional Con e ence on ENTERp ise In o ma ion Sys ems /
P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h and Social Ca e In o ma ion Sys ems
and Technologies 2021
CENTERIS - In e na ional Con e ence on ENTERp ise In o ma ion Sys ems / P ojMAN -
In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h
and Social Ca e In o ma ion Sys ems and Technologies 2021
A me hod o p ojec po olio isk assessmen conside ing isk
in e dependencies – a ne wo k pe spec i e
Camilo Micana*, Gab iela Fe nandesb, Madalena A aújoc
aUni e si y o Valle, Logis ic and p oduc ion esea ch g oup, Cll 13 # 100-00, Cali 760034, Colombia
bUni e si y o Coimb a, CEMMPRE, Depa men o Mechanical Enginee ing, Polo II, Coimb a, 3030-788, Po ugal
cUni e si y o Minho, Cen e Algo i mi, A enida da Uni e sidade, Guima ães 4800-058, Po ugal
Abs ac
P ojec po olios ep esen he b idge be ween p ojec s and s a egy. Howe e , he inal esul s may no be as expec ed because
ma e ializa ion o isk ac o s. Hence, li e a u e has acknowledged p ojec po olio isk assessmen as an elemen o he p ojec
po olio isk managemen , being he elemen ha p o ides in o ma ion on he impo ance o isk ac o s. Fo ha , some speci ic
cha ac e is ics should be conside ed, such as isk in e dependency in luence and he isk ac o s impac o e po olio highe le els.
Thus, his s udy is ocused on he de elopmen o a me hod o p ojec po olio isk assessmen ha conside s bo h isk ac o
in e dependencies and hei impac on he s a egic objec i es as a ne wo k. In addi ion, he me hod also allows inco po a ing bo h
isk ac o s de i ed om p ojec s and de i ed a p ojec po olio le el. A ep esen a i e example is p o ided o illus a e he
p oposed me hod.
© 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS - In e na ional Con e ence on ENTERp ise
In o ma ion Sys ems / P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h
and Social Ca e In o ma ion Sys ems and Technologies 2021
Keywo ds: P ojec po olio; isk assessmen ; isk ac o s; ne wo k heo y
*Co esponding au ho .
E-mail add ess: camilo.mican@co eouni alle.edu.co
A ailable online a www.sciencedi ec .com
ScienceDi ec
P ocedia Compu e Science 00 (2021) 000–000
www.else ie .com/loca e/p ocedia
1877-0509 © 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS - In e na ional Con e ence on ENTERp ise In o ma ion Sys ems /
P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h and Social Ca e In o ma ion Sys ems
and Technologies 2021
CENTERIS - In e na ional Con e ence on ENTERp ise In o ma ion Sys ems / P ojMAN -
In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h
and Social Ca e In o ma ion Sys ems and Technologies 2021
A me hod o p ojec po olio isk assessmen conside ing isk
in e dependencies – a ne wo k pe spec i e
Camilo Micana*, Gab iela Fe nandesb, Madalena A aújoc
aUni e si y o Valle, Logis ic and p oduc ion esea ch g oup, Cll 13 # 100-00, Cali 760034, Colombia
bUni e si y o Coimb a, CEMMPRE, Depa men o Mechanical Enginee ing, Polo II, Coimb a, 3030-788, Po ugal
cUni e si y o Minho, Cen e Algo i mi, A enida da Uni e sidade, Guima ães 4800-058, Po ugal
Abs ac
P ojec po olios ep esen he b idge be ween p ojec s and s a egy. Howe e , he inal esul s may no be as expec ed because
ma e ializa ion o isk ac o s. Hence, li e a u e has acknowledged p ojec po olio isk assessmen as an elemen o he p ojec
po olio isk managemen , being he elemen ha p o ides in o ma ion on he impo ance o isk ac o s. Fo ha , some speci ic
cha ac e is ics should be conside ed, such as isk in e dependency in luence and he isk ac o s impac o e po olio highe le els.
Thus, his s udy is ocused on he de elopmen o a me hod o p ojec po olio isk assessmen ha conside s bo h isk ac o
in e dependencies and hei impac on he s a egic objec i es as a ne wo k. In addi ion, he me hod also allows inco po a ing bo h
isk ac o s de i ed om p ojec s and de i ed a p ojec po olio le el. A ep esen a i e example is p o ided o illus a e he
p oposed me hod.
© 2021 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY-NC-ND license (h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0)
Pee - e iew unde esponsibili y o he scien i ic commi ee o he CENTERIS - In e na ional Con e ence on ENTERp ise
In o ma ion Sys ems / P ojMAN - In e na ional Con e ence on P ojec MANagemen / HCis - In e na ional Con e ence on Heal h
and Social Ca e In o ma ion Sys ems and Technologies 2021
Keywo ds: P ojec po olio; isk assessmen ; isk ac o s; ne wo k heo y
*Co esponding au ho .
E-mail add ess: camilo.mican@co eouni alle.edu.co
2 Au ho name / P ocedia Compu e Science 00 (2021) 000–000
1. In oduc ion
A p ojec po olio is “a collec ion o p ojec s, p og ams, subsidia y po olios, and ope a ions managed as a g oup
o achie e s a egic objec i es” [1, p. 3]. Consequen ly, P ojec Po olio Managemen (PPM) ep esen s coo dina ed
managemen o a se o p ojec s ca ied ou by a speci ic o ganiza ion, which allows s a egic managemen o he
p ojec s. PPM help o c ea e a decision-making p ocess ha adds alue o he o ganiza ion, guiding he po olio o
achie e s a egic bene i s. In ac , PPM is a cen al mechanism o implemen success ully he s a egy [2–5]. Thus, he
impo ance o PPM lies in i s impac on he achie emen o compe i i e ad an ages o he o ganiza ion, and i is
conside ed a s a egic weapon ha ep esen s he in es men p io i ies o he o ganiza ion o achie e i s s a egic
objec i es [4,6,7].
The e ec o isk managemen a he p ojec le el has been s udied, e idencing posi i e impac s on he success o
each indi idual p ojec [8], howe e , managing isks only a he le el o p ojec s is no enough because a s a egic
iew o he p ojec po olio is no conside ed. In his ega d, he li e a u e in he ield o isk managemen has
p og essi ely e ol ed om p ojec isk analysis o P ojec Po olio Risk (PPR) analysis [9,10]. A p ojec po olio
o which he isks a e analyzed, e alua ed, and dis ibu ed ac oss se e al p ojec s, has a be e p obabili y o success
[11,12].
P ojec Po olio Risk Assessmen (PPRA), like PPR iden i ica ion and PPR esponse, is an elemen o p ojec
po olio Risk Managemen . PPRA is o ien ed o p o iding in o ma ion on he impo ance o isks and isk ends,
among o he ac o s, in suppo o isk esponse decisions [1]. In his conce n, he PPRA mus allow o iden i y, quali y,
and quan i y he e ec s o isk ac o s. I would gene a e g ea e app oxima ions o eali y, gi ing he decision-make s
a sys emic and dynamic p ojec po olio iew. PPRA would allow ocusing e o s and esou ces on he ac o s ha
a e ele an , and ha a e ep esen a i e o he p ojec po olio execu ion [13,14].
Among o he s, he li e a u e highligh ed wo componen s which should be conside ed as pa o isk assessmen
om a p ojec po olio pe spec i e. On one hand, he isk impac assessmen o e highe le els such as po olio le el
o s a egic objec i es le el [1,10,14]. On he o he hand, isk in e dependencies quali ica ion and quan i ica ion
[10,13,15]. The e o e, his esea ch deepens in hese wo componen s; and p oposes a me hod o PPRA conside ing
impac s on s a egic objec i es and isk ac o s in e dependencies. The e o e, in his s udy isk was concep ualized
wi h a se o ou comes wi h known p obabili y [16], which can ep esen impac s on he esul s expec ed [17]. Risk
ac o s we e conside ed as he di e en a iables ha in luence o gene a e, di ec ly o indi ec ly, exposu e [18,19],
o in o he wo ds, he a iables ha impac he isk o he p ojec po olio.
The emainde o he pape is o ganized as ollows. Fi s ly, he PPRA backg ound is p esen ed; whe eupon, based
on ne wo k heo y and he concep o sys emic and non-sys emic isk de i ed om Mode n Po olio Theo y, he
me hod p oposed is add essed and desc ibed. This is ollowed by an illus a i e example o he me hod applica ion.
Finally, conclusions and sugges ions o u u e esea ch a e summa ized.
2. Risk assessmen om a p ojec po olio pe spec i e
Conside ing he inancial impac o he p ojec po olio on he o ganiza ion, some app oaches o PPRA ha e been
p oposed. In his ega d, Cos a e al. [20] sugges ha no all he isk ac o s ha e he same weigh in he PPR
quan i ica ion o IT p ojec po olios. Fo his eason, hey used expe judgmen h ough pai wise compa ison o
es ima e he in luence o each isk ac o on he po olio and hey used c edi isk heo y and Mon eca lo simula ion
o es ima e he p obabili y dis ibu ion o po olio ea nings and losses. Also o IT p ojec po olios, Pe e s and
Ve hoe [16] p opose a me hod o e alua e he isk e ec s in he p ojec po olio execu ion phase, and de ine a
me hodology o e alua e his impac on he ne p esen alue o each p ojec and he whole p ojec po olio.
In addi ion o he isk measu e based on mone a y uni s, o he p oposals ha e in eg a ed some a ibu es such as
in e dependencies be ween p ojec s and in e dependencies be ween isks. Fo example, o e alua e he impac o he
in e ac ion be ween p ojec s on PPR, Guan e al. [11] desc ibe an app oach based on se heo y and Bayesian ne wo ks.
Cooley e al. [21] used he isk dependencies quan i ica ion app oach o allow a sys emic analysis o he impac ha
he isk ac o s could gene a e on he po olio; howe e , he high amoun o his o ical da a necessa y o ob ain eliable
in o ma ion ep esen he main weakness o his p oposal [21].
950 Camilo Mican e al. / P ocedia Compu e Science 196 (2022) 948–955
Au ho name / P ocedia Compu e Science 00 (2019) 000–000 3
Ano he app oach is p oposed by Bolos e al. [22], which, based on s uc u al unds o Eu opean Union Membe
S a es en i onmen , de eloped an indica o o mone a y loss isk. The indica o is s uc u ed wi h s a is ical measu es,
such as mean, s anda d de ia ion and co a iance o he mone a y expec ed alues, and i is he esul o he in eg a ion
o he mone a y loss p obabili y de i a i e o delays in he execu ion o each p ojec . In his case he du a ion o he
p ojec s ep esen s he inco po a ion o ope a ional measu es associa ed o he p ojec s wi hin he p ojec po olio.
F om ano he pe spec i e, also conside ing isk in e dependencies o dependencies be ween p ojec s, bu based
exclusi ely on impac s on p ojec ope a ional measu es mo e han on inancial measu es, o he app oaches ha e been
p oposed. In his ega d, unde a p ojec in e dependencies pe spec i e, Neumeie e al. [23] highligh ha Bayesian
ne wo ks is an app oach ha has been widely used o assess cascading e ec s on o he esea ch ields and hey sugges
ha echnical and esou ces dependencies can be assessed h ough a ansi i e dependencies model based on Bayesian
ne wo ks. Thus, a Bayesian ne wo k app oach is p oposed o c i ical analysis in IT p ojec po olios con ex , using
he ailu e cos impac o each p ojec on he en i e po olio, and iden i ying he c i ical p ojec s o he po olio, gi en
he cascading e ec ha each p ojec can gene a e on he success o ailu e o o he p ojec s. Also, in he IT p ojec
po olios con ex , Wang e al. [15] p opose ha a p ojec po olio can be seen as a biological ne wo k, and apply
complex ne wo k heo y and social ne wo k analysis o quan i y PPR, being he isk e alua ed as he success o ailu e
p obabili y. Mo eo e , ne wo k heo y has been in eg a ed wi h epidemiology app oaches o ep esen and assess he
“domino e ec ” de i ed om p ojec in e dependencies on he PPR; o example, Guggenmos e al. [24] adop an
app oach in eg a ing ne wo k heo y wi h a suscep ible-in ec ed model o analyze he po olio isk in IT po olios,
while Zou e al. [25] in eg a e ne wo k heo y wi h a suscep ible-in ec ed- eco e ed- ailed model o esea ch and
de elopmen p ojec po olios.
Conside ing ha in eal cases he isk ac o s ha e in e dependencies, Namazian and Yakhchali [13] posi ha he
e ec o he occu ence o non-occu ence o one isk on o he isks can be quan i ied h ough Bayesian ne wo ks and
Mon eca lo simula ion. Thus, Namazian and Yakhchali [13] p opose an app oach based on Bayesian ne wo ks o
ep esen and quan i y he isk in e ac ion unde a pe spec i e associa ed wi h schedule delays and cos o e uns in
gas ield de elopmen p ojec s po olios. Also, conside ing p ojec in e dependency bu ocused exclusi ely on
p ojec po olio esou ce isk, Bai e al. [26] posi an app oach h ough which he po olio isk is de i ed om
esou ces sha ed be ween p ojec s and esou ce cons ains. The e o e, isk ac o s de i ed om esou ces p ojec
in e dependencies we e iden i ied, Bayesian ne wo k me hod o assess he isk in e dependencies was implemen ed,
and uzzy se heo y o cap u e he subjec i i y o expe judgmen s was in eg a ed.
O he p oposals ha e sough o in eg a e bo h isk in e dependencies and dependency be ween p ojec s in o he
PPRA. In his ega d, Ghasemi e al. [14] ep esen he p ojec in e dependency as pa o he isk ac o s o he p ojec
po olio, and based on he Bayesian ne wo k app oach, p opose a PPRA app oach ha allows assessing he in luence
o isk in e dependencies on he p ojec po olio expec ed esul . Also, bu wi h a PPR esponse pe spec i e, Ahmadi
e al. [27] p opose an app oach ha allows assessing he po olio isk conside ing bo h isk in e dependencies and
dependency be ween p ojec s. Thus, an op imiza ion model is p oposed which allows he e alua ion o isk as a
unc ion o he o al cos . In he same ein, Wang e al. [28] posi ha he p ojec po olio analysis canno be sepa a ed
om he s a egic goals o which he p ojec po olio was s uc u ed. Thus, hey p opose a model based on sys em
dynamics and p ojec in e dependencies ep esen a ion, which is o ien ed o assess he impac as he di e ence
be ween he expec ed alue and he ealized alue o he o ganiza ion’s p ojec po olio .
Hence, di e en app oaches o PPRA ha e been p oposed, such as c edi isk heo y and Mon eca lo simula ion,
used o es ima e he p obabili y dis ibu ion o ea nings and losses [20]. Also, ma hema ical modeling [22], complex
ne wo k heo y and social ne wo k analysis [15], se heo y [11], Bayesian ne wo ks [11,13,14,23] and sys em
dynamics [28]. Some o hese app oaches ha e conside ed inancial measu es as ep esen a ion o isk impac measu e
a p ojec po olio le el, while in o he p oposals isk impac is ep esen ed based on ope a ional o ac ical p ojec
measu es. Addi ionally, bo h in e dependencies be ween p ojec s and isk in e dependencies ha e been conside ed and
assessed om di e en pe spec i es as pa o PPRA.
3. P oposed me hod
This sec ion p esen s he s uc u e o he p oposed me hod o PPRA. Fig. 1 shows he p oposed me hod
concep ualiza ion. The me hod is based on he concep ualiza ion ha isk ac o s a e de i ed om each p ojec , bu
4 Au ho name / P ocedia Compu e Science 00 (2021) 000–000
also conside ing ha some isk ac o s a e sha ed be ween p ojec s such as he case o sha ed esou ces be ween
p ojec s showed by Bai e al. [26]. In addi ion, and ollowing he pe spec i e adop ed by Ghasemi e al. [14] and Bai
e al. [26], among o he s, i was adop ed he pe spec i e o conside ing he in luence o p ojec in e dependency and
ep esen ing i h ough isk ac o s. Finally, acknowledging ha some isk ac o s eme ge a p ojec po olio le el and
in luence he whole p ojec po olio [9,14], isk ac o s a he p ojec po olio le el we e also conside ed.
Fig. 1. P oposed me hod concep ualiza ion.
P ojec po olios a e s uc u ed o achie e, o con ibu e o he achie emen , o a se o s a egic goals. In his
ega d, he p oposed me hod add esses he PPR impac owa ds he se o s a egic objec i es. Thus, he me hod seeks
o iden i y he isk ac o s impac on he s a egic aspec s o which he p ojec po olio was s uc u ed.
To achie e i s pu pose he me hod is based on ne wo k heo y. The li e a u e highligh ed ha PPRA should
inco po a e he complexi y associa ed wi h isk ac o in e ac ions o ob ain a comp ehensi e isk-based decision-
making p ocess [14,23]. Ne wo k heo y allows such a comp ehensi e ep esen a ion o isk ac o in e ac ions [15],
and, addi ionally, app oaches based on ne wo k heo y, allow he ep esen a ion o he dynamic p opaga ion o isk in
he po olio ne wo k [25]. In he case o he p oposed me hod, he po olio ne wo k co esponds o Fig. 1. Based on
he abo e concep ualiza ion, Fig. 2 shows he p ocess unde which he PPR can be assessed.
Fig. 2. P ocess o p ojec PPRA.
Camilo Mican e al. / P ocedia Compu e Science 196 (2022) 948–955 951
Au ho name / P ocedia Compu e Science 00 (2019) 000–000 3
Ano he app oach is p oposed by Bolos e al. [22], which, based on s uc u al unds o Eu opean Union Membe
S a es en i onmen , de eloped an indica o o mone a y loss isk. The indica o is s uc u ed wi h s a is ical measu es,
such as mean, s anda d de ia ion and co a iance o he mone a y expec ed alues, and i is he esul o he in eg a ion
o he mone a y loss p obabili y de i a i e o delays in he execu ion o each p ojec . In his case he du a ion o he
p ojec s ep esen s he inco po a ion o ope a ional measu es associa ed o he p ojec s wi hin he p ojec po olio.
F om ano he pe spec i e, also conside ing isk in e dependencies o dependencies be ween p ojec s, bu based
exclusi ely on impac s on p ojec ope a ional measu es mo e han on inancial measu es, o he app oaches ha e been
p oposed. In his ega d, unde a p ojec in e dependencies pe spec i e, Neumeie e al. [23] highligh ha Bayesian
ne wo ks is an app oach ha has been widely used o assess cascading e ec s on o he esea ch ields and hey sugges
ha echnical and esou ces dependencies can be assessed h ough a ansi i e dependencies model based on Bayesian
ne wo ks. Thus, a Bayesian ne wo k app oach is p oposed o c i ical analysis in IT p ojec po olios con ex , using
he ailu e cos impac o each p ojec on he en i e po olio, and iden i ying he c i ical p ojec s o he po olio, gi en
he cascading e ec ha each p ojec can gene a e on he success o ailu e o o he p ojec s. Also, in he IT p ojec
po olios con ex , Wang e al. [15] p opose ha a p ojec po olio can be seen as a biological ne wo k, and apply
complex ne wo k heo y and social ne wo k analysis o quan i y PPR, being he isk e alua ed as he success o ailu e
p obabili y. Mo eo e , ne wo k heo y has been in eg a ed wi h epidemiology app oaches o ep esen and assess he
“domino e ec ” de i ed om p ojec in e dependencies on he PPR; o example, Guggenmos e al. [24] adop an
app oach in eg a ing ne wo k heo y wi h a suscep ible-in ec ed model o analyze he po olio isk in IT po olios,
while Zou e al. [25] in eg a e ne wo k heo y wi h a suscep ible-in ec ed- eco e ed- ailed model o esea ch and
de elopmen p ojec po olios.
Conside ing ha in eal cases he isk ac o s ha e in e dependencies, Namazian and Yakhchali [13] posi ha he
e ec o he occu ence o non-occu ence o one isk on o he isks can be quan i ied h ough Bayesian ne wo ks and
Mon eca lo simula ion. Thus, Namazian and Yakhchali [13] p opose an app oach based on Bayesian ne wo ks o
ep esen and quan i y he isk in e ac ion unde a pe spec i e associa ed wi h schedule delays and cos o e uns in
gas ield de elopmen p ojec s po olios. Also, conside ing p ojec in e dependency bu ocused exclusi ely on
p ojec po olio esou ce isk, Bai e al. [26] posi an app oach h ough which he po olio isk is de i ed om
esou ces sha ed be ween p ojec s and esou ce cons ains. The e o e, isk ac o s de i ed om esou ces p ojec
in e dependencies we e iden i ied, Bayesian ne wo k me hod o assess he isk in e dependencies was implemen ed,
and uzzy se heo y o cap u e he subjec i i y o expe judgmen s was in eg a ed.
O he p oposals ha e sough o in eg a e bo h isk in e dependencies and dependency be ween p ojec s in o he
PPRA. In his ega d, Ghasemi e al. [14] ep esen he p ojec in e dependency as pa o he isk ac o s o he p ojec
po olio, and based on he Bayesian ne wo k app oach, p opose a PPRA app oach ha allows assessing he in luence
o isk in e dependencies on he p ojec po olio expec ed esul . Also, bu wi h a PPR esponse pe spec i e, Ahmadi
e al. [27] p opose an app oach ha allows assessing he po olio isk conside ing bo h isk in e dependencies and
dependency be ween p ojec s. Thus, an op imiza ion model is p oposed which allows he e alua ion o isk as a
unc ion o he o al cos . In he same ein, Wang e al. [28] posi ha he p ojec po olio analysis canno be sepa a ed
om he s a egic goals o which he p ojec po olio was s uc u ed. Thus, hey p opose a model based on sys em
dynamics and p ojec in e dependencies ep esen a ion, which is o ien ed o assess he impac as he di e ence
be ween he expec ed alue and he ealized alue o he o ganiza ion’s p ojec po olio .
Hence, di e en app oaches o PPRA ha e been p oposed, such as c edi isk heo y and Mon eca lo simula ion,
used o es ima e he p obabili y dis ibu ion o ea nings and losses [20]. Also, ma hema ical modeling [22], complex
ne wo k heo y and social ne wo k analysis [15], se heo y [11], Bayesian ne wo ks [11,13,14,23] and sys em
dynamics [28]. Some o hese app oaches ha e conside ed inancial measu es as ep esen a ion o isk impac measu e
a p ojec po olio le el, while in o he p oposals isk impac is ep esen ed based on ope a ional o ac ical p ojec
measu es. Addi ionally, bo h in e dependencies be ween p ojec s and isk in e dependencies ha e been conside ed and
assessed om di e en pe spec i es as pa o PPRA.
3. P oposed me hod
This sec ion p esen s he s uc u e o he p oposed me hod o PPRA. Fig. 1 shows he p oposed me hod
concep ualiza ion. The me hod is based on he concep ualiza ion ha isk ac o s a e de i ed om each p ojec , bu
4 Au ho name / P ocedia Compu e Science 00 (2021) 000–000
also conside ing ha some isk ac o s a e sha ed be ween p ojec s such as he case o sha ed esou ces be ween
p ojec s showed by Bai e al. [26]. In addi ion, and ollowing he pe spec i e adop ed by Ghasemi e al. [14] and Bai
e al. [26], among o he s, i was adop ed he pe spec i e o conside ing he in luence o p ojec in e dependency and
ep esen ing i h ough isk ac o s. Finally, acknowledging ha some isk ac o s eme ge a p ojec po olio le el and
in luence he whole p ojec po olio [9,14], isk ac o s a he p ojec po olio le el we e also conside ed.
Fig. 1. P oposed me hod concep ualiza ion.
P ojec po olios a e s uc u ed o achie e, o con ibu e o he achie emen , o a se o s a egic goals. In his
ega d, he p oposed me hod add esses he PPR impac owa ds he se o s a egic objec i es. Thus, he me hod seeks
o iden i y he isk ac o s impac on he s a egic aspec s o which he p ojec po olio was s uc u ed.
To achie e i s pu pose he me hod is based on ne wo k heo y. The li e a u e highligh ed ha PPRA should
inco po a e he complexi y associa ed wi h isk ac o in e ac ions o ob ain a comp ehensi e isk-based decision-
making p ocess [14,23]. Ne wo k heo y allows such a comp ehensi e ep esen a ion o isk ac o in e ac ions [15],
and, addi ionally, app oaches based on ne wo k heo y, allow he ep esen a ion o he dynamic p opaga ion o isk in
he po olio ne wo k [25]. In he case o he p oposed me hod, he po olio ne wo k co esponds o Fig. 1. Based on
he abo e concep ualiza ion, Fig. 2 shows he p ocess unde which he PPR can be assessed.
Fig. 2. P ocess o p ojec PPRA.
952 Camilo Mican e al. / P ocedia Compu e Science 196 (2022) 948–955
Au ho name / P ocedia Compu e Science 00 (2019) 000–000 5
Phase 1 co esponds o inpu s ep esen a ions, speci ically, se e i y and likelihood o each isk ac o , and he
weigh o each p ojec . Phase 2 is associa ed wi h assessing he in luence o isk in e dependency on he pa ame e s
o he isk ac o s; he e o e, isk in e dependency is ep esen ed as a isk ac o s ne wo k. Phase 3 is o ien ed o
es ablish he con ibu ion o each isk ac o o he PPR. The ex en and he way in which isk ac o s impac on
p ojec s and p ojec po olio was adop ed o be ep esen ed as sys ema ic and non-sys ema ic [30]. Non-sys ema ic
isk ac o s e e o isk ac o s ha gene a e impac s only o one o some p ojec s, and hose impac s ha do no
a ec he p ojec po olio pe o mance in a sys emic way. I a isk ac o impac s on he p ojec po olio in a gene al
way, hen, i can be conside ed as sou ce o sys ema ic isk. Finally, in Phase 4, isk ac o s impac s a e ex ended
h oughou he ne wo k owa ds s a egic objec i es.
4. Illus a i e example
The illus a i e example is ep esen ed as a po olio composed o i e p ojec s, h ee s a egic objec i es, and 15
isk ac o s. Table 1 shows he ela ion be ween p ojec s and s a egic objec i es. Fig. 3 illus a es he
in e dependencies be ween isk ac o s associa ed wi h likelihood and se e i y. In Fig. 3 he weigh o he in luence
be ween each pai o isk ac o s is showed by he numbe on each a ow. The illus a i e example only conside s ha
isk in e dependencies gene a e inc eases in likelihood o se e i y – likelihood o se e i y dec eases de i ed om he
in luence o isk ac o in e dependencies a e no conside ed.
Table 1. Rela ion be ween s a egic objec i es and p ojec s.
P ojec 1
P ojec 2
P ojec 3
P ojec 4
P ojec 5
S a egic Objec i e 1
50%
20%
30%
S a egic Objec i e 2
60%
40%
S a egic Objec i e 3
100%
Fig. 3. Risk ac o in e dependencies.
Table 2 p esen s he ela ion be ween isk ac o s and p ojec s and po olio, showing he ini ial likelihood and
se e i y o he isk ac o s, as well as he likelihood and se e i y adjus ed by isk in e dependencies ( alues in o
pa en hesis). Fo example, in he case o isk ac o 1 he likelihood was no modi ied because isk ac o 1 does no
ha e dependence om o he isk ac o . In he case o isk ac o 2, which is a ec ed by isk ac o 1 and isk ac o
3, i s likelihood was modi ied, mo ing om 60% o 86% (See Table 3). The same p ocess was pe o med o he o he
isk ac o s.
6 Au ho name / P ocedia Compu e Science 00 (2021) 000–000
Table 2. Likelihood and se e i y o each isk ac o
Se e i y
Likelihood
P ojec 1
P ojec 2
P ojec 3
P ojec 4
P ojec 5
Po olio le el
Risk ac o 1
30% (30%)
4 (4)
Risk ac o 2
60% (86%)
3 (3)
3 (3)
Risk ac o 3
50% (50%)
2 (2)
2 (2)
Risk ac o 4
50% (50%)
3 (3)
Risk ac o 5
70% (85%)
2 (2)
Risk ac o 6
20% (40%)
3 (3)
3 (3)
Risk ac o 7
20% (28%)
4 (4)
Risk ac o 8
30% (70%)
2 (3)
Risk ac o 9
60% (88%)
3 (4.4)
Risk ac o 10
80% (97%)
3 (3.8)
Risk ac o 11
30% (100%)
4 (4.9)
4 (4.9)
Risk ac o 12
40% (40%)
1 (1)
Risk ac o 13
50% (50%)
2 (2)
Risk ac o 14
70% (85%)
2 (2)
Risk ac o 15
40% (40%)
3 (3)
Table 3. Example o es ima ion o con ibu ions o he likelihood o isk ac o 2
Risk Fac o
Likelihood
Weigh (W) o in luence
on isk ac o 2
Maximum easible
con ibu ion (MC)
Expec ed alue o he
con ibu ion (EC)
1
30%
1
Max[W;5-L(RF2)*5]
Max[1;5-0.6*5] = 1
MC * L(RF1)
(1)*(0.3) = 0.3
3
50%
3
Max[W;5-L(RF2)*5]
Max[3;5-0.6*5] = 2
MC * L(RF3)
(2)*(0.5) = 1.0
In Table 3, L(RF1), L(RF2) and L(RF3) e e espec i ely o likelihood o he isk ac o 1, 2 and 3; o he
es ima ion o he maximum easible con ibu ion (MC) he likelihood o he isk ac o is mul iplied by 5 o ob ain an
equi alen scale ega ding he scale used o he weigh o in luence alues, so ha , he highes possible alue which
can be ob ained o any isk ac o is 5 (equi alen o 100%). Finally, he o al con ibu ion de i ed om isk ac o s
1 and 3 on isk ac o 2 is 1.3, in his case mo ing om he alue o 3 (equi alen o 60%) o alue o 4.3 (equi alen
o 86%). Fo es ima ion o likelihood adjus ed o each isk ac o he summa ion does no exceed he alue o 5
(equi alen o 100%).
Following he s uc u e desc ibed o phase 3 in ig. 2, he isk con ibu ion o each isk ac o is calcula ed. Table
4 shows an example o he es ima ion o isk con ibu ion o p ojec 1 and o p ojec po olio le el. In his case,
acco ding o Table 2, p ojec 1 is a ec ed by isk ac o s 1, 2, and 3, and po olio le el is a ec ed by isk ac o s 13,
14, 15. In bo h cases he example showed in Table 4 is based on he ini ial case, e. g., wi hou isk in e dependency
conside a ions.
Table 4. Example o es ima ion o isk con ibu ion o p ojec 1 and o po olio le el – ini ial case
Risk ac o
(RF)
Risk con ibu ion (RC)
Rik con ibu ion o he p ojec 1
Risk con ibu ion o po olio le el
1
[L(RF1)/5]*I(RF1) = 6
RC(RF1) + RC(RF2) + RC(RF3)
6 + 9 + 5 = 20
2
[L(RF2)/5]*I(RF2) = 9
3
[L(RF3) /5]*I(RF3) = 5
13
[L(RF13)/5]*I(RF13) = 5
RC(RF13) + RC(RF14) + RC(RF15)
5 + 7 + 6 = 18
14
[L(RF14)/5]*I(RF14) = 7
15
[L(RF15) /5]*I(RF15) = 6
Table 5 shows he esul s ob ained acco ding o he isk con ibu ion om each p ojec and om he po olio le el,
o bo h he ini ial scena io and he scena io wi h isk in e dependencies.
Camilo Mican e al. / P ocedia Compu e Science 196 (2022) 948–955 953
Au ho name / P ocedia Compu e Science 00 (2019) 000–000 5
Phase 1 co esponds o inpu s ep esen a ions, speci ically, se e i y and likelihood o each isk ac o , and he
weigh o each p ojec . Phase 2 is associa ed wi h assessing he in luence o isk in e dependency on he pa ame e s
o he isk ac o s; he e o e, isk in e dependency is ep esen ed as a isk ac o s ne wo k. Phase 3 is o ien ed o
es ablish he con ibu ion o each isk ac o o he PPR. The ex en and he way in which isk ac o s impac on
p ojec s and p ojec po olio was adop ed o be ep esen ed as sys ema ic and non-sys ema ic [30]. Non-sys ema ic
isk ac o s e e o isk ac o s ha gene a e impac s only o one o some p ojec s, and hose impac s ha do no
a ec he p ojec po olio pe o mance in a sys emic way. I a isk ac o impac s on he p ojec po olio in a gene al
way, hen, i can be conside ed as sou ce o sys ema ic isk. Finally, in Phase 4, isk ac o s impac s a e ex ended
h oughou he ne wo k owa ds s a egic objec i es.
4. Illus a i e example
The illus a i e example is ep esen ed as a po olio composed o i e p ojec s, h ee s a egic objec i es, and 15
isk ac o s. Table 1 shows he ela ion be ween p ojec s and s a egic objec i es. Fig. 3 illus a es he
in e dependencies be ween isk ac o s associa ed wi h likelihood and se e i y. In Fig. 3 he weigh o he in luence
be ween each pai o isk ac o s is showed by he numbe on each a ow. The illus a i e example only conside s ha
isk in e dependencies gene a e inc eases in likelihood o se e i y – likelihood o se e i y dec eases de i ed om he
in luence o isk ac o in e dependencies a e no conside ed.
Table 1. Rela ion be ween s a egic objec i es and p ojec s.
P ojec 1
P ojec 2
P ojec 3
P ojec 4
P ojec 5
S a egic Objec i e 1
50%
20%
30%
S a egic Objec i e 2
60%
40%
S a egic Objec i e 3
100%
Fig. 3. Risk ac o in e dependencies.
Table 2 p esen s he ela ion be ween isk ac o s and p ojec s and po olio, showing he ini ial likelihood and
se e i y o he isk ac o s, as well as he likelihood and se e i y adjus ed by isk in e dependencies ( alues in o
pa en hesis). Fo example, in he case o isk ac o 1 he likelihood was no modi ied because isk ac o 1 does no
ha e dependence om o he isk ac o . In he case o isk ac o 2, which is a ec ed by isk ac o 1 and isk ac o
3, i s likelihood was modi ied, mo ing om 60% o 86% (See Table 3). The same p ocess was pe o med o he o he
isk ac o s.
6 Au ho name / P ocedia Compu e Science 00 (2021) 000–000
Table 2. Likelihood and se e i y o each isk ac o
Se e i y
Likelihood
P ojec 1
P ojec 2
P ojec 3
P ojec 4
P ojec 5
Po olio le el
Risk ac o 1
30% (30%)
4 (4)
Risk ac o 2
60% (86%)
3 (3)
3 (3)
Risk ac o 3
50% (50%)
2 (2)
2 (2)
Risk ac o 4
50% (50%)
3 (3)
Risk ac o 5
70% (85%)
2 (2)
Risk ac o 6
20% (40%)
3 (3)
3 (3)
Risk ac o 7
20% (28%)
4 (4)
Risk ac o 8
30% (70%)
2 (3)
Risk ac o 9
60% (88%)
3 (4.4)
Risk ac o 10
80% (97%)
3 (3.8)
Risk ac o 11
30% (100%)
4 (4.9)
4 (4.9)
Risk ac o 12
40% (40%)
1 (1)
Risk ac o 13
50% (50%)
2 (2)
Risk ac o 14
70% (85%)
2 (2)
Risk ac o 15
40% (40%)
3 (3)
Table 3. Example o es ima ion o con ibu ions o he likelihood o isk ac o 2
Risk Fac o Likelihood Weigh (W) o in luence
on isk ac o 2
Maximum easible
con ibu ion (MC)
Expec ed alue o he
con ibu ion (EC)
1 30% 1
Max[W;5-L(RF2)*5]
Max[1;5-0.6*5] = 1
MC * L(RF1)
(1)*(0.3) = 0.3
3 50% 3 Max[W;5-L(RF2)*5]
Max[3;5-0.6*5] = 2
MC * L(RF3)
(2)*(0.5) = 1.0
In Table 3, L(RF1), L(RF2) and L(RF3) e e espec i ely o likelihood o he isk ac o 1, 2 and 3; o he
es ima ion o he maximum easible con ibu ion (MC) he likelihood o he isk ac o is mul iplied by 5 o ob ain an
equi alen scale ega ding he scale used o he weigh o in luence alues, so ha , he highes possible alue which
can be ob ained o any isk ac o is 5 (equi alen o 100%). Finally, he o al con ibu ion de i ed om isk ac o s
1 and 3 on isk ac o 2 is 1.3, in his case mo ing om he alue o 3 (equi alen o 60%) o alue o 4.3 (equi alen
o 86%). Fo es ima ion o likelihood adjus ed o each isk ac o he summa ion does no exceed he alue o 5
(equi alen o 100%).
Following he s uc u e desc ibed o phase 3 in ig. 2, he isk con ibu ion o each isk ac o is calcula ed. Table
4 shows an example o he es ima ion o isk con ibu ion o p ojec 1 and o p ojec po olio le el. In his case,
acco ding o Table 2, p ojec 1 is a ec ed by isk ac o s 1, 2, and 3, and po olio le el is a ec ed by isk ac o s 13,
14, 15. In bo h cases he example showed in Table 4 is based on he ini ial case, e. g., wi hou isk in e dependency
conside a ions.
Table 4. Example o es ima ion o isk con ibu ion o p ojec 1 and o po olio le el – ini ial case
Risk ac o
(RF)
Risk con ibu ion (RC)
Rik con ibu ion o he p ojec 1
Risk con ibu ion o po olio le el
1
[L(RF1)/5]*I(RF1) = 6
RC(RF1) + RC(RF2) + RC(RF3)
6 + 9 + 5 = 20
2
[L(RF2)/5]*I(RF2) = 9
3
[L(RF3) /5]*I(RF3) = 5
13
[L(RF13)/5]*I(RF13) = 5
RC(RF13) + RC(RF14) + RC(RF15)
5 + 7 + 6 = 18
14
[L(RF14)/5]*I(RF14) = 7
15
[L(RF15) /5]*I(RF15) = 6
Table 5 shows he esul s ob ained acco ding o he isk con ibu ion om each p ojec and om he po olio le el,
o bo h he ini ial scena io and he scena io wi h isk in e dependencies.
954 Camilo Mican e al. / P ocedia Compu e Science 196 (2022) 948–955
Au ho name / P ocedia Compu e Science 00 (2019) 000–000 7
Table 5. Risk ac o s con ibu ion a p ojec and po olio le el.
Risk Con ibu ion
P ojec 1
P ojec 2
P ojec 3
P ojec 4
P ojec 5
Po olio le el
Ini ial
20.0
21.5
17.0
24.0
11.0
18.0
Conside ing isk in e dependency
23.9
27.6
34.9
55.6
32.3
19.5
Then, ollowing he s uc u e desc ibed o phase 4 in ig. 2, he in luence o isk ac o s on he s a egic le el is
calcula ed. Table 6 shows an example o es ima ion o non-sys ema ic isk associa ed o s a egic objec i e 1;
acco ding o Table 1, s a egic objec i e 1 is ela ed o p ojec s 1, 4 and 5. The example illus a ed in Table 6 is based
on isk con ibu ion conside ing isk in e dependency. Table 7 shows he PPR consolida ion by conside ing he isk
de i ed om he p ojec s as sou ce o non-sys ema ic isk and he isk de i ed om p ojec po olio le el as sou ce
o sys ema ic isk.
Table 6. Example o es ima ion o in luence on s a egic objec i e 1
P ojec 1
P ojec 2
P ojec 3
S a egic objec i e 1
Risk con ibu ion
23.9
55.6
32.3
(23.9*50%)+(55.6*20%)+(32.3*30%)
= 32.7
Weigh
50%
20%
30%
Table 7. Risk ac o s impo ance a s a egic le el
Non-sys ema ic
Sys ema ic
Po olio isk
S a egic Objec i e 1
32.7
19.5 53.5
S a egic Objec i e 2
38.8
S a egic Objec i e 3
34.9
5. Conclusions
This pape p oposes a me hod o isk assessmen om a p ojec po olio pe spec i e. Analyzing he
in e dependency be ween isks allows a be e ep esen a ion o he PPR, by he ecogni ion o he impac no jus due
o a di ec in luence, bu also h ough he in luence on o he isk ac o s. Like adi ional app oaches o isk
assessmen , he p oposed me hod allows iden i ying ha PPR is in luenced by isk ac o s de i ed om p ojec s wi hin
he po olio. Howe e , he p oposed me hod also ecognizes ha PPR is a ec ed by isk ac o s de i ed om he
p ojec po olio le el. Risk ac o s de i ed om he p ojec po olio le el ha e a gene al o global impac on he
p ojec po olio expec ed esul s, he e o e, he p oposed me hod helps decision-make s o iden i y hei in luence and
impo ance. In his ega d, bo h po olio manage s and o ganiza ional manage s can o ien hei e o s and esou ces
on p o iding isk esponse s a egies o hose isk ac o s ha ha e he g ea es di ec impac on each p ojec and he
o e all p ojec po olio, and hose ha ha e he g ea es in luence on o he isk ac o s.
Fu u e esea ch is needed on he in eg a ion o p ojec in e dependencies ha could lead o a mo e in eg al
ep esen a ion o he PPR. To a ain ha , an app oach based on me a-ne wo ks could be explo ed, and echniques such
as ‘Ma ice d'Impac s C oisés Mul iplica ion Appliquée à un Classemen ’ (MICMAC) can be used o de e mine he
d i e and dependence powe o each isk ac o . Mo eo e , scena io analysis o analysis based on simula ion could be
explo ed o es he obus ness o he p oposed me hod. Finally, conside ing ha jus h ea s we e conside ed o his
illus a i e example, inco po a ion o oppo uni ies and he in e dependency be ween hem, as well as he
in e dependency be ween h ea s and oppo uni ies could be explo ed o assess he compensa o y e ec s.
Acknowledgemen s
This esea ch was sponso ed by Col u u o-Colciencias, Colombia and by FEDER unds h ough he p og am
COMPETE – P og ama Ope acional Fac o es de Compe i i idade – and by na ional unds h ough FCT – Fundação
pa a a Ciência e a Tecnologia –, unde he emi o p ojec s UID/EMS/00285/2020 and UIDB/00319/2020.
Re e ences
[1] PMI (2017) The S anda d o Po olio Managemen , P ojec Managemen Ins i u e, Inc.
8 Au ho name / P ocedia Compu e Science 00 (2021) 000–000
[2] Voss, Ma in. (2012) “Impac o cus ome in eg a ion on p ojec po olio managemen and i s success-De eloping a concep ual amewo k.”
In e na ional Jou nal o P ojec Managemen 30 (5): 567–581.
[3] Meskendahl, Sascha (2010) “The in luence o business s a egy on p ojec po olio managemen and i s success - A concep ual amewo k.”
In e na ional Jou nal o P ojec Managemen 28 (8): 807–817.
[4] Clegg, S ewa , Ca he ine P. Killen, Ch is ophe Biesen hal, and Shanka Sanka an (2018) “P ac ices, p ojec s and po olios: Cu en
esea ch ends and new di ec ions.” In e na ional Jou nal o P ojec Managemen 36 (5): 762–772.
[5] Ma insuo, Miia (2013) “P ojec po olio managemen in p ac ice and in con ex .” In e na ional Jou nal o P ojec Managemen 31 (6):
794–803.
[6] Ca alho, Ma ly Mon ei o, Paula Vilas Boas Vi ei os Lopes Lopes, and Daniela San ana Lambe Ma zagão (2013) “Ges ão de po ólio de
p oje os: con ibuições e endências da li e a u a.” Ges . P od., São Ca los 20 (2): 433–454.
[7] Hansen, La s K is ian, and Pe S ej ig (2018) “Towa ds e hinking P ojec po olio managemen .” EURAM 2018.
[8] Telle , Juliane, Alexande Kock, and Hans Geo g Gemünden (2014) “Risk managemen in p ojec po olios is mo e han managing p ojec
isks: A con ingency pe spec i e on isk managemen .” P ojec Managemen Jou nal 45 (4): 67–80.
[9] Ho man, Ma iusz, Sewe yn Spalek, and G zego z G ela (2017) “Shedding New Ligh on P ojec Po olio Risk Managemen .”
Sus ainabili y, 9 (10), 1798.
[10] Micán, Camilo, Gab iela Fe nandes, and Madalena A aújo (2020) “P ojec po olio isk managemen : A s uc u ed li e a u e e iew wi h
u u e di ec ions o esea ch.” In e na ional Jou nal o In o ma ion Sys ems and P ojec Managemen 8 (3).
[11] Guan, Dujuan, Peng Guo, Kei h W. Hipel, and Liping Fang (2017) “Risk educ ion in a p ojec po olio.” Jou nal o Sys ems Science and
Sys ems Enginee ing 26 (1): 3–22.
[12] Telle , Juliane, and Alexande Kock (2013) “An empi ical in es iga ion on how po olio isk managemen in luences p ojec po olio
success.” In e na ional Jou nal o P ojec Managemen 31 (6): 817–829.
[13] Namazian, Ali, and Siamak Haji Yakhchali (2018) “Modi ied Bayesian Ne wo k – Based Risk Analysis o Cons uc ion P ojec s : Case
S udy o Sou h Pa s Gas Field De elopmen P ojec s.” ASCE-ASME Jou nal o Risk and Unce ain y in Enginee ing Sys ems, Pa A: Ci il
Enginee ing 4 (4): 1–11.
[14] Ghasemi, Fo oogh, Mohammad Hossein Mahmoudi Sa i, Vahid eza Youse i, Reza Falsa i, and Jolan a Tamošai ienė (2018) “P ojec
Po olio Risk Iden i ica ion and Analysis, Conside ing P ojec Risk In e ac ions and Using Bayesian Ne wo ks.” Sus ainabili y 10 (5): 1609.
[15] Wang, Qin, Guangping Zeng, and Xuyan Tu (2017) “In o ma ion Technology P ojec Po olio Implemen a ion P ocess Op imiza ion Based
on Complex Ne wo k Theo y and En opy.” En opy 19 (6): 287.
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[22] Bolos, Ma cel Ioan, Diana Claudia Sabau-Popa, Emil Sca la , Ioana-Alexand a B adea, and Camelia Delcea (2016) “A business in elligence
ins umen o de ec ion and mi iga ion o isk ela ed o p ojec s inanced om s uc u al unds.” Economic Compu a ion and Economic
Cybe ne ics S udies and Resea ch 50 (2): 165–178.
[23] Neumeie , Anna, S en Radszuwill, and Ti azheh Za e Ga izy (2018) “Modeling p ojec c i icali y in IT p ojec po olios.” In e na ional
Jou nal o P ojec Managemen 36 (6): 833–844.
[24] Guggenmos, Flo ian, Pe e Ho mann, and Gilbe F idgen (2020) “How ill is you i po olio? Measu ing c i icali y in IT po olios using
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[25] Zou, Xingqi, Qing Yang, and Qin u Wang (2021) “Analysing he isk p opaga ion in he p ojec po olio ne wo k using he SIRF model.”
ICORES 2021 - P oceedings o he 10 h In e na ional Con e ence on Ope a ions Resea ch and En e p ise Sys ems 226–232.
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In e dependency by he Fuzzy Bayesian Ne wo k.” Complexi y, 2020.
[27] Ahmadi-Ja id, Ami , Seyed Hamed Fa eminia, and Hans Geo g Gemünden (2020) “A Me hod o Risk Response Planning in P ojec
Po olio Managemen .” P ojec Managemen Jou nal 51 (1): 77–95.
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s udy o beha iou al esponses.” Jou nal o he Ope a ional Resea ch Socie y 71 (9): 1426–1436.
[29] Micán, Camilo, Gab iela Fe nandes, Madalena A aújo, and En ique A es (2019) “Ope a ional isk ca ego iza ion in p ojec -based
o ganiza ions: A heo e ical pe spec i e om a p ojec po olio isk lens.” P ocedia Manu ac u ing 41: 771–778.
Camilo Mican e al. / P ocedia Compu e Science 196 (2022) 948–955 955
Au ho name / P ocedia Compu e Science 00 (2019) 000–000 7
Table 5. Risk ac o s con ibu ion a p ojec and po olio le el.
Risk Con ibu ion
P ojec 1
P ojec 2
P ojec 3
P ojec 4
P ojec 5
Po olio le el
Ini ial
20.0
21.5
17.0
24.0
11.0
18.0
Conside ing isk in e dependency
23.9
27.6
34.9
55.6
32.3
19.5
Then, ollowing he s uc u e desc ibed o phase 4 in ig. 2, he in luence o isk ac o s on he s a egic le el is
calcula ed. Table 6 shows an example o es ima ion o non-sys ema ic isk associa ed o s a egic objec i e 1;
acco ding o Table 1, s a egic objec i e 1 is ela ed o p ojec s 1, 4 and 5. The example illus a ed in Table 6 is based
on isk con ibu ion conside ing isk in e dependency. Table 7 shows he PPR consolida ion by conside ing he isk
de i ed om he p ojec s as sou ce o non-sys ema ic isk and he isk de i ed om p ojec po olio le el as sou ce
o sys ema ic isk.
Table 6. Example o es ima ion o in luence on s a egic objec i e 1
P ojec 1
P ojec 2
P ojec 3
S a egic objec i e 1
Risk con ibu ion
23.9
55.6
32.3
(23.9*50%)+(55.6*20%)+(32.3*30%)
= 32.7
Weigh
50%
20%
30%
Table 7. Risk ac o s impo ance a s a egic le el
Non-sys ema ic
Sys ema ic
Po olio isk
S a egic Objec i e 1
32.7
19.5
53.5
S a egic Objec i e 2
38.8
S a egic Objec i e 3
34.9
5. Conclusions
This pape p oposes a me hod o isk assessmen om a p ojec po olio pe spec i e. Analyzing he
in e dependency be ween isks allows a be e ep esen a ion o he PPR, by he ecogni ion o he impac no jus due
o a di ec in luence, bu also h ough he in luence on o he isk ac o s. Like adi ional app oaches o isk
assessmen , he p oposed me hod allows iden i ying ha PPR is in luenced by isk ac o s de i ed om p ojec s wi hin
he po olio. Howe e , he p oposed me hod also ecognizes ha PPR is a ec ed by isk ac o s de i ed om he
p ojec po olio le el. Risk ac o s de i ed om he p ojec po olio le el ha e a gene al o global impac on he
p ojec po olio expec ed esul s, he e o e, he p oposed me hod helps decision-make s o iden i y hei in luence and
impo ance. In his ega d, bo h po olio manage s and o ganiza ional manage s can o ien hei e o s and esou ces
on p o iding isk esponse s a egies o hose isk ac o s ha ha e he g ea es di ec impac on each p ojec and he
o e all p ojec po olio, and hose ha ha e he g ea es in luence on o he isk ac o s.
Fu u e esea ch is needed on he in eg a ion o p ojec in e dependencies ha could lead o a mo e in eg al
ep esen a ion o he PPR. To a ain ha , an app oach based on me a-ne wo ks could be explo ed, and echniques such
as ‘Ma ice d'Impac s C oisés Mul iplica ion Appliquée à un Classemen ’ (MICMAC) can be used o de e mine he
d i e and dependence powe o each isk ac o . Mo eo e , scena io analysis o analysis based on simula ion could be
explo ed o es he obus ness o he p oposed me hod. Finally, conside ing ha jus h ea s we e conside ed o his
illus a i e example, inco po a ion o oppo uni ies and he in e dependency be ween hem, as well as he
in e dependency be ween h ea s and oppo uni ies could be explo ed o assess he compensa o y e ec s.
Acknowledgemen s
This esea ch was sponso ed by Col u u o-Colciencias, Colombia and by FEDER unds h ough he p og am
COMPETE – P og ama Ope acional Fac o es de Compe i i idade – and by na ional unds h ough FCT – Fundação
pa a a Ciência e a Tecnologia –, unde he emi o p ojec s UID/EMS/00285/2020 and UIDB/00319/2020.
Re e ences
[1] PMI (2017) The S anda d o Po olio Managemen , P ojec Managemen Ins i u e, Inc.
8 Au ho name / P ocedia Compu e Science 00 (2021) 000–000
[2] Voss, Ma in. (2012) “Impac o cus ome in eg a ion on p ojec po olio managemen and i s success-De eloping a concep ual amewo k.”
In e na ional Jou nal o P ojec Managemen 30 (5): 567–581.
[3] Meskendahl, Sascha (2010) “The in luence o business s a egy on p ojec po olio managemen and i s success - A concep ual amewo k.”
In e na ional Jou nal o P ojec Managemen 28 (8): 807–817.
[4] Clegg, S ewa , Ca he ine P. Killen, Ch is ophe Biesen hal, and Shanka Sanka an (2018) “P ac ices, p ojec s and po olios: Cu en
esea ch ends and new di ec ions.” In e na ional Jou nal o P ojec Managemen 36 (5): 762–772.
[5] Ma insuo, Miia (2013) “P ojec po olio managemen in p ac ice and in con ex .” In e na ional Jou nal o P ojec Managemen 31 (6):
794–803.
[6] Ca alho, Ma ly Mon ei o, Paula Vilas Boas Vi ei os Lopes Lopes, and Daniela San ana Lambe Ma zagão (2013) “Ges ão de po ólio de
p oje os: con ibuições e endências da li e a u a.” Ges . P od., São Ca los 20 (2): 433–454.
[7] Hansen, La s K is ian, and Pe S ej ig (2018) “Towa ds e hinking P ojec po olio managemen .” EURAM 2018.
[8] Telle , Juliane, Alexande Kock, and Hans Geo g Gemünden (2014) “Risk managemen in p ojec po olios is mo e han managing p ojec
isks: A con ingency pe spec i e on isk managemen .” P ojec Managemen Jou nal 45 (4): 67–80.
[9] Ho man, Ma iusz, Sewe yn Spalek, and G zego z G ela (2017) “Shedding New Ligh on P ojec Po olio Risk Managemen .”
Sus ainabili y, 9 (10), 1798.
[10] Micán, Camilo, Gab iela Fe nandes, and Madalena A aújo (2020) “P ojec po olio isk managemen : A s uc u ed li e a u e e iew wi h
u u e di ec ions o esea ch.” In e na ional Jou nal o In o ma ion Sys ems and P ojec Managemen 8 (3).
[11] Guan, Dujuan, Peng Guo, Kei h W. Hipel, and Liping Fang (2017) “Risk educ ion in a p ojec po olio.” Jou nal o Sys ems Science and
Sys ems Enginee ing 26 (1): 3–22.
[12] Telle , Juliane, and Alexande Kock (2013) “An empi ical in es iga ion on how po olio isk managemen in luences p ojec po olio
success.” In e na ional Jou nal o P ojec Managemen 31 (6): 817–829.
[13] Namazian, Ali, and Siamak Haji Yakhchali (2018) “Modi ied Bayesian Ne wo k – Based Risk Analysis o Cons uc ion P ojec s : Case
S udy o Sou h Pa s Gas Field De elopmen P ojec s.” ASCE-ASME Jou nal o Risk and Unce ain y in Enginee ing Sys ems, Pa A: Ci il
Enginee ing 4 (4): 1–11.
[14] Ghasemi, Fo oogh, Mohammad Hossein Mahmoudi Sa i, Vahid eza Youse i, Reza Falsa i, and Jolan a Tamošai ienė (2018) “P ojec
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