Eme ging digi al echnologies and hei in luence
on elimina ion o supply chain ulne abili y
88 2024, olume 27, issue 3, pp. 88–103, DOI: 10.15240/ ul/001/2024-3-006
Exis ing and o hcoming obs acles
in adop ing echnological ad ances
in ulne able supply chains
Rohi Raj1, Vimal Kuma 2, Jyo i Ranjana3, C. Ani inna4
1 Na ional Taiwan Uni e si y o Science and Technology, Depa men o Business Adminis a ion, Taiwan, ORCID:
0000-0002-2851-7058, [email p o ec ed];
2 Chaoyang Uni e si y o Technology, Depa men o In o ma ion Managemen , Taiwan, ORCID: 0000-0001-7179-
3878, [email p o ec ed] (co esponding au ho );
3 Manipal Uni e si y Jaipu , TAPMI School o Business, India, ORCID: 0000-0002-8945-3457,
jyo i. anjana@jaipu .manipal.edu;
4 Manipal Uni e si y Jaipu , TAPMI School o Business, India, ORCID: 0000-0001-6097-2483,
chi kula.ani inna@jaipu .manipal.edu.
Abs ac : The s udy on supply chain ulne abili ies p esen ed in his esea ch in ends o unco e
key indica o s in luencing he adop ion o new echnological ad ancemen s o enhance supply chain
ope a ions. This wo k used he GRA (g ey ela ional analysis) me hod o conduc semi-s uc u ed
in e iews wi h he PQR company’s au omo i e lead design enginee , manu ac u ing p ocess
enginee , and op ical p ocess enginee in o de o enhance he supply chain p ocess and de elop
a comp ehensi e s uc u al ela ionship o ank hem o s eamline he supply chain ulne abili y
(SCV) indica o s. F om he da a analysis and esul s, cos o implemen a ion (CI), skills gap (SG), and
cul u al shi (CS) a e iden i ied as he op h ee key indica o s. Howe e , en i onmen al conce ns
(EC), egula o y compliance (RC), and supply chain complexi y (SCC) ha e been iden i ied as
he bo om h ee key indica o s, espec i ely. The s udy’s conclusion assis s he company’s supply
chain ulne abili y decision-make s (DMs) in iden i ying c i ical signs impeding he up ake o new
echnology. As a ma ke leade , i cons an ly seeks o enhance he alue o i s goods h ough
supply chain managemen (SCM), s ong managemen eams, and expe decision-making ha
will boos business pe o mance. This s udy is inno a i e in ha i uses he GRA app oach o ank
he impo an indica o s o supply chain ulne abili y. Manage s and p ac i ione s will bene i om
his wo k as he o ganiza ion will be able o adap o new de elopmen s in hei supply chain p ocess
and o e supe io se ices.
Keywo ds: Technological ad ancemen s, ulne able supply chains, obs acles in supply chain,
GRA-MCDM app oach.
Jel Classi ica ion: L91, M00, M11, O39.
APA S yle Ci a ion: Raj, R., Kuma , V., Ranjana, J., & Ani inna, C. (2024). Exis ing and
o hcoming obs acles in adop ing echnological ad ances in ulne able supply chains.
E&M Economics and Managemen , 27(3), 88–103. h ps://doi.o g/10.15240/ ul/001/2024-3-006
In oduc ion
Mode n supply chains (SC) appea o be mo e
agile han e e be o e due o changes in
he co po a e, ecological, and economic con-
ex s o se e al easons. Th oughou he pas
ew decades, ulne abili ies ha e g own in bo h
quan i y and se e i y. D ough s, loods, wind-
s o ms, ea hquakes, hu icanes, and sunamis
a e examples o na u al ca as ophes ha oc-
cu mo e equen ly and ha e bigge inancial
Eme ging digi al echnologies and hei in luence
on elimina ion o supply chain ulne abili y
89
2024, olume 27, issue 3, pp. 88–103, DOI: 10.15240/ ul/001/2024-3-006
epe cussions (Luo e al., 2023; Wagne & Ne-
sha , 2010). The SC li e a u e has highligh ed
he impo ance o managing ulne abili ies in-
side he ope a ional s uc u es o p e en eme -
gencies om dis up ing he SC. This will help
business o ganiza ions become mo e esilien
and p oduc i e and gain a compe i i e ad an-
age (Xu e al., 2020). Adop ing echnological
inno a ions has become a p omising inno a ion
in his con ex . I can enhance in o ma ion and
p ocess esilience, dis up ope a ional p o-
cesses wi hin he p oduc and se ice SC, and
acili a e isk managemen wi hin he in ica ely
linked global SC ecosys em (Chowdhu y e al.,
2023; Wamba & Quei oz, 2020).
A majo i y would ag ee ha dec eased
SC e iciencies and dis up ions o SC ac i i ies
a e a ec ing global supply ne wo ks (Sha ma
e al., 2023). SC a e mo e complica ed now
han hey we e in he pas . The li e a u e (Luo
e al., 2023; Wagne & Nesha , 2010) has docu-
men ed nume ous dis up ions ha highligh
he suscep ibili y o con empo a y SC o dis-
up ions, which can ha e nega i e conse-
quences o businesses (Wang e al., 2024).
SC complexi y can be a ibu ed o se e al
ac o s, including inc eased R&D and manu-
ac u ing he ou sou cing p ocess, ela ionships
be ween supplie s wi hin supplie ne wo ks,
eliance on supplie capaci ies, new echnolo-
gies (such as RFID and he in e ne ), egula o y
equi emen s (such as pos -9/11 secu i y egu-
la ions go e ning ood sa e y), sho e p oduc
li ecycles as a esul o apidly shi ing cus ome
p e e ences, and expansion in o in e na ional
ma ke s and p oduc ion (Ganga aju e al., 2023;
Sha ma e al., 2023). The no ion o supply chain
ulne abili y (SCV) is s ill unde s ood p ima ily
on a concep ual and no ma i e le el, despi e
hese e iden causes o inc eased SCV and
i s e ec on business pe o mance (Fan e al.,
2024; Sha ma e al., 2023). SC execu i es s ill
need o imp o e hei echniques o assessing
and con olling SCV, e en hough i has s a ed
ge ing some empi ical backing (Luo e al.,
2023; Wagne & Nesha , 2010).
Al hough i is s ill la gely unknown,
SCV is a apidly expanding ield o s udy
in managemen (B usse e al., 2023). A b oad-
e in e es in isk managemen in a ious o he
o e lapping a eas o public policy managemen
and comme cial conce n is helping SCV as
a subjec o s udy (Zhang e al., 2023). Manag-
e s and public policymake s would be be e
able o comp ehend he isk exposu e o supply
ne wo ks and pinpoin he a eas whe e mi iga-
ion and isk managemen a e equi ed i hey
could assess and measu e he ulne abili y
o hei supplie ne wo ks (Guo e al., 2024).
In addi ion, companies could e alua e he de-
g ee o SCV bo h be o e and a e pu ing isk
managemen measu es in place, e-e alua e
he ulne abili y as he en i onmen shi s, and
moni o SCV o e ime (Hussain e al., 2023).
I is di icul o measu e SCV. Fi s , SCV is
unable o be di ec ly obse ed o assessed;
ins ead, i is de ined by ac o s known as d i -
e s o ulne abili y, such as SC complexi y,
cus ome o supplie eliance, and globaliza ion
o he sou cing ne wo k.
In ligh o his sho coming, he p ima y
goals o his s udy a e o c ea e a quan i a i e
me hod u ilizing he mul i-c i e ia decision-mak-
ing (MCDM) app oach o quan i y ulne abili y
by analyzing i s p ima y indica o s and hei in-
e dependencies and o show how implemen -
ing new echnologies can educe he esul ing
SCV indica o s. The goal o his s udy is o c e-
a e a amewo k o de e mining and assessing
he key indica o s o SCV mi iga ion.
This is how he es o he pape is s uc-
u ed. A e iew o he heo e ical unde pinnings
o SCV and ela ed indica o s opens in Sec-
ion 1. The g aph app oach is explained in Sec-
ion 2. Findings and commen a y a e p esen ed
in Sec ion 3. Sec ion 3 discusses he discussion
and esul s o he empi ical in es iga ion, and
he ami ica ions. The las sec ion o e s limi a-
ions and sugges ions o u he in es iga ion
as he pape comes o a close.
1. Theo e ical backg ound
1.1 Supply chain ulne abili y (SCV)
indica o s
SCV is an exposu e o se ious dis u bance
and a endency o isk sou ces and isk ac o s
o exceed isk mi iga ing s a egies, hus leading
o de imen al SC consequences (Akhil e al.,
2023; Luo e al., 2023; Sha ma e al., 2023).
Acco ding o Guo e al. (2024), a i m’s loss
is a esul o i s SCV o a pa icula SC dis up-
ion and SCV is an ou come o ce ain SC cha -
ac e is ics. I can be said ha ulne abili y has
mos ly o do wi h an icipa abili y (Dickens e al.,
2023). Since hen, he de ini ion o ulne abili y
in he con ex o SC has been clea e (Neg i
e al., 2024; Wagne & Bode, 2009). A dis up-
ion in he SC is he ca alys ha causes he isk
Eme ging digi al echnologies and hei in luence
on elimina ion o supply chain ulne abili y
90 2024, olume 27, issue 3, pp. 88–103, DOI: 10.15240/ ul/001/2024-3-006
o a ise, bu i is no he only ac o ha de e -
mines he ul ima e loss. I ollows ha he SC’s
ulne abili y o he nega i e e ec s o his ci -
cums ance is likewise highly ele an . The idea
o SCV ollows om his. The undamen al idea
is ha SC ea u es in luence he likelihood
and magni ude o SC dis up ions, ac ing as
p edic o s o SCV.
Wi hin he li e a u e on SC isk and ulne -
abili y, he e m “ isk” inhe en ly akes on a neg-
a i e conno a ion. The ea lies and mos o en
e e enced au ho on his opic (S ensson,
2000), con ex ualized ulne abili y and associ-
a ed ideas, including isk, unce ain y, and eli-
abili y, wi hin he b oade idea o con ingency
planning. Vulne abili y was de ined by Xu e al.
(2023) as a condi ion ha is igge ed by ime
and ela ionship cons ain s in a company’s
ac i i ies in a SC as well as exposu e o se i-
ous dis u bance, esul ing om isks h oughou
he SC as well as isks ex insic o he SC.
The scien i ic de elopmen o a holis ic
SC discipline equi es ha b eak h oughs be
made in he c ea ion o measu emen ins u-
men s (Chen & Paul aj, 2004; Ruzo-Sanma ín
e al., 2024). Acco dingly, i is also necessa y
o measu e and quan i y SCV (Adana e al.,
2024; Kleindo e & Saad, 2005). Due o i s
mul idimensional na u e and he lack o es ab-
lished measu es o assessing he a iables ha
de e mine ulne abili y, SCV measu emen is
gene ally seen as challenging (Guo e al., 2024;
Sha ma e al., 2023). In empi ical echniques,
Pelleg ino (2024) and Luo e al. (2023) exam-
ined indica o s o SCV; Sha ma e al. (2023)
explo ed s a egies o minimizing SCV. S udies
using an analy ical me hod p ima ily assessed
and quan i ied suscep ibili y o moni o ing and
con ol. As Tab. 1 illus a es, his esea ch has
educed and iden i ied he main SCV. These
signs a e easily mi iga ed by he adop ion
o echnological imp o emen s, which also less-
ens he SCV and i s nega i e impac s on bo h
he ocal business and he SC as an ensemble.
Indica o s De ini ion Re e ences
Cos
o implemen a ion
(CI)
The ini ial cos o ins alla ion is one
o he bigges obs acles o he adop ion
o new echnologies. I can be cos ly
o in es in cu ing-edge echnology,
such as IoT gadge s, AI-powe ed so wa e,
o au oma ion equipmen , and no all
businesses ha e he unds o do so.
Ja aid e al. (2023),
Khan e al. (2023)
Skills gap
(SG)
Employees equen ly need o pick up new
skills o adop new echnology. The wo k o ce
may lack he necessa y expe ise, which could
make i mo e di icul o success ully in eg a e
and use echnology.
Jackson e al. (2023),
McGuinness e al. (2023)
Resis ance
o change
(RTC)
The adop ion o echnology may esul
in esis ance om managemen and s a .
The e ec i eness o new echnology can be
diminished and he adop ion p ocess slowed
down by esis ance o change.
Jackson e al. (2023),
Ja aid e al. (2023)
Regula o y
compliance
(RC)
A mul i ude o ules and compliance
equi emen s apply o supply ne wo ks.
I can be di icul o adap echnology while
main aining compliance wi h hese ules.
Jackson e al. (2023),
Kulko e al. (2023)
Supply chain
complexi y
(SCC)
The complexi y o SC is ising due
o he in ol emen o nume ous pa ne s and
s akeholde s in global ne wo ks. Such complex
ne wo ks can be di icul o in eg a e echnology
in o, and i akes a lo o coo dina ion.
A ji e al. (2023),
J aisa e al. (2023)
Tab. 1: Lis o majo indica o s in supply chain ulne abili y – Pa 1
Eme ging digi al echnologies and hei in luence
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2024, olume 27, issue 3, pp. 88–103, DOI: 10.15240/ ul/001/2024-3-006
1.2 Re iew o simila wo ks
Due o businesses expanding globally in sea ch
o cos educ ions, ma ke access, and isk
managemen , he complexi y o supply chain
managemen (SC) has expanded. Bu his
has also inc eased a company’s suscep ibil-
i y o dis up ions in supply chains b ough on
by geopoli ical e en s. Resiliency, ulne abili y,
and isk a e h ee connec ed bu di e en con-
cep s ha desc ibe sys ems ha ace h ea s,
acco ding o Tsang e al. (2024). S udies exam-
ining he ole o eme ging ITs in lowe ing SCV
ha e been conduc ed, howe e many o hem
ocus on speci ic echnologies. One a ea
o emphasis has been he applica ion o big
da a analy ics o demand o ecas ing and bull-
whip e ec minimiza ion (Dubey e al., 2019).
As demons a ed by ou sou cing, in e nal busi-
ness p ac ices also ha e a big impac on SCV
(Ka was a e al., 2021). SCV can also be
Indica o s De ini ion Re e ences
En i onmen al
conce ns
(EC)
Businesses a e unde inc easing p essu e
o implemen eco- iendly p ocedu es.
Technology has he po en ial o inc ease
SC sus ainabili y, bu i may also necessi a e
conside able adjus men s o ma e ials and
p ocedu es, which can be expensi e and
di icul o execu e.
J aisa e al. (2023),
Khan e al. (2023)
Supply chain
dis up ions
(SCD)
The COVID-19 ou b eak b ough a en ion
o how suscep ible supply ne wo ks a e
o hiccups. Technology can educe dange s,
bu i canno comple ely emo e hem.
Dis up ions in he SC can ha e an impac
on how echnology-d i en solu ions a e
implemen ed.
A ji e al. (2023),
J aisa e al. (2023),
Paliwal e al. (2023)
In as uc u e and
connec i i y
(IC)
The deploymen o echnology may be
hampe ed by in as uc u e and connec ion
es ic ions in speci ic a eas o indus ies.
The ad an ages o con empo a y
SC echnologies a e impossible o ully ealize
wi hou dependable in e ne connec i i y and
communica ion ne wo ks.
A ji e al. (2023),
Ciulli and Kolk (2023)
Cul u al shi
(CS)
Technology-d i en SC ans o ma ion equen ly
necessi a es an o ganiza ional cul u e
change. Wo ke s mus accep new me hods
o ope a ion and ecognize he bene i s ha
echnology o e s o hei jobs.
Bialas e al. (2023),
McGuinness e al. (2023)
Supply chain
anspa ency
(SCT)
Technology has he po en ial o inc ease
SC openness, bu i can also e eal laws o
une hical beha io . O ganiza ions need o be
eady o deal wi h and esol e hese kinds
o p oblems when hey come up.
Ciulli and Kolk (2023),
J aisa e al. (2023)
Collabo a ion and
communica ion
(CC)
I is essen ial ha all pa ies in ol ed in
he SC – supplie s, pa ne s, and cus ome s
– coope a e and communica e e ec i ely.
When adop ing new echnologies, ha ing open
lines o communica ion acili a es he alignmen
o objec i es and expec a ions.
Kulko e al. (2023),
McGuinness e al. (2023)
Sou ce: own
Tab. 1: Lis o majo indica o s in supply chain ulne abili y – Pa 2
Eme ging digi al echnologies and hei in luence
on elimina ion o supply chain ulne abili y
92 2024, olume 27, issue 3, pp. 88–103, DOI: 10.15240/ ul/001/2024-3-006
made wo se by an en e p ise’s lack o unde -
s anding and esou ce limi a ions (Ku niawan
e al., 2017). Scipioni e al. (2002) assessed
SCV using con en ional analy ic echniques,
including ailu e mode, impac s, and c i ical
analysis. The u iliza ion o ne wo k me hodolo-
gies in he examina ion o ade- ela ed issues
can un eil he di e se a ibu es and posi ions
o a ious nodes, he in luence o de eloping
na ions on bila e al ade ies, and he inhe en
in e connec edness o ade chains (De e al.,
2014). To e alua e supply chain ulne abili y,
Blackhu s e al. (2018) used Pe i ne s in con-
junc ion wi h iangula clus e ing algo i hms, and
Wang e al. (2023) used sophis ica ed ne wo k
analysis echniques o quan i y he supply chain
isk in China. By conduc ing a ho ough li e a u e
analysis and expe in e iews, Guo e al. (2024)
employed a comp ehensi e MCDM DEMATEL-
ISM s a egy o s udy de eloping ITs as well as
combined solu ions educing SCV issues. Yang
e al. (2021) also de eloped a Bayesian ne -
wo k-based model o e alua e he ulne abili y
o he ene gy SC. These p e ious s udies, how-
e e , ha e no ye examined in de ail he cu en
and po en ial ba ie s o he manu ac u ing sec-
o ’s adop ion o new echnology in ulne able
SC ac o s. P io esea ch on he applica ion
o he GRA echnique o p io i izing SCV indica-
o s has no been explo ed.
2. Resea ch me hodology
2.1 Da a collec ion and sampling
All o he s udy’s da a came om PQR P . L d.,
a Taichung, Taiwan-based co po a ion. A ques-
ionnai e was used o collec he da a om
specialis s employed by di e en b anches
o ca came a manu ac u ing companies.
Manage s and eam leade s ake in o accoun
he con iden iali y o he ac o y’s da a commu-
nica ion. The in o ma ion and numbe s came
om he specialis s wo king in he company’s
manu ac u ing and ope a ions depa men s.
This business is hough o be he bigges sup-
plie o ca came as globally.
To build concep s and heo ies based
on case s udies, academics ha e been mo-
i a ed and encou aged o explo e a wide
ange o issues by he seminal s udy o Eisen-
ha d (1989). To examine and p io i ize hem,
a mul i-c i e ia decision-making p ocedu e has
been used. The s udy employs obse a ional
echniques and concen a es on ques ionnai e-
based quan i a i e and quali a i e p ocedu es.
We i s c ea ed se e al opics and c i e ia
o ga he he opinions o a ious a ge espond-
e s om he company’s op managemen . Nex ,
we o mula ed a se ies o inqui ies. Fi een p o-
essionals om a ious depa men s, such as
ope a ions, p oduc ion, and R&D, ac i ely
pa icipa ed in he p ocess. These esponde s
a e leade s in he ield and ha e a ple ho a
o expe ience. An o e iew o he esponden s’
demog aphic da a is shown in Tab. 2.
The opinions o he 15 esponden s a e highly
ega ded by he academic and p o essional g oups
since hey ha e an excep ional backg ound wo k-
ing wi h manu ac u ing, in en o y managemen ,
and logis ics. Addi ionally, o mee he goals o ou
s udy, we sough o ob ain ho ough insigh s
om a a ie y o eliable expe s. This allowed us
o look a li le de ails ha a e ypically ha d o ge
om la ge-scale su eys, such as he ones Kuma
e al. (2023) used o manage he oppo uni ies
and p io i ize he main di icul ies in SC dis ibu-
ion du ing COVID-19. To lessen he obs acles
o big da a use in sus ainable SC, Raj e al. (2023)
u ilize GRA o a gue ha because hese expe s
ha e a ple ho a o knowledge and expe ise ha
subs an ially enhances he s udy, he quali y o e-
sponses a he han he quan i y ma e s. 15 peo-
ple indeed answe ed he su ey and he small
sample size enables mo e eliable and accu a e
indings. The sample should be ep esen a i e
and co ec ly e lec he popula ion o in e es .
Making a decision could bene i om expe help.
The ac ual cha ac e is ics o he esea ch g oup
a e p obably ep esen ed in he ou comes. I is
c i ical o comp ehend he cons ain s and pos-
sible ou comes o he s udy’s small sample size.
Ini ially, o he e alua ion o iden i ied SCV in-
dica o s, a g oup o expe s o he decision-mak-
ing p ocess is assembled. Du ing he decision
p ocess, i he e is hesi ancy in deciding on
he iden i ied SCV indica o s. Then, in ha case,
go back o he li e a u e o e-e alua ing he in-
dica o . Then, a e ga he ing he esponses,
we iden i ied i een DMs om he esponden s.
Ele en c i ical indica o s a e disco e ed om
he semi-s uc u al in e iew and he cu en
li e a u e. Fig. 1 displays he lowcha o he ec-
ommended esea ch e o o he cu en s udy
o p io i izing he SCV indica o s.
2.2 G ey ela ional analysis (GRA) me hod
As pe Cha e jee and Chak abo y (2014),
he g ey sys em heo y de ines g ey as p imi i e
da a ha ha e weak, incomple e, and unce ain

Eme ging digi al echnologies and hei in luence
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2024, olume 27, issue 3, pp. 88–103, DOI: 10.15240/ ul/001/2024-3-006
in o ma ion, and he g ey ela ion is he impe ec
in o ma ion ela ion among hese da a. The com-
pu a ion o g ey ela ional coe icien s (GRC)
o add ess unclea sys ema ic di icul ies wi h
only pa ially a ailable in o ma ion is de ined as
a g ey ela ional gene a ion (GRG) by Hamzaçe-
bi and Pekkaya (2011). Ra he han depending
on expe judgmen , he GRA echnique is widely
implemen ed, compu ed, and u ilized o choose
and ank pe o mance al e na i es. Based
on his sequence, a e e ence sequence (RS)
(ideal a ge sequence) is c ea ed, and he GRC
be ween each compa abili y sequence (CS) and
he RS is hen calcula ed. These coe icien s a e
hen used o calcula e he g ey ela ional (GR)
g ade. An al e na i e is conside ed bes i he CS
ansla ed om i has he highes GR g ade
be ween he RS and i sel . The las al e na i e
is conside ed he wo s . The ollowing is a lis
o he s eps in ol ed in he GRA echnique (Ku-
ma e al., 2023; Lo i, 1995; Raj e al., 2023).
S ep 1: GRG (no maliza ion)
To u n all o he pe o mance numbe s o
each choice in o a compa able sequence,
no malizing (also e e ed o as GRG o da a
P o ile Classi ica ion Coun
Responden s Male 6
Female 9
Age (yea s)
Unde 21 0
22–32 3
33–41 4
42–51 7
52–61 1
O e 62 0
Wo k expe ience (yea s)
1–5 1
6–11 4
12–21 3
Abo e 22 7
Designa ion o esponden s
Came a es ins umen enginee 3
Au omo i e lead design enginee 2
Manu ac u ing p ocess enginee 7
Op ical p ocess enginee 3
Educa ion
Bachelo s 5
Pos g adua e 6
PhD 3
O he s 1
Depa men o esponden s
Ope a ions managemen 2
P oduc ion managemen 5
R&D 4
P oduc ion enginee ing 4
O he s 0
Sou ce: own
Tab. 2: Demog aphic de ails o esponden s
Eme ging digi al echnologies and hei in luence
on elimina ion o supply chain ulne abili y
94 2024, olume 27, issue 3, pp. 88–103, DOI: 10.15240/ ul/001/2024-3-006
p ep ocessing) is equi ed when he uni s o nu-
me ous selec ion c i e ia disag ee. I he e a e
m op ions and n c i e ia in a decision-making
p oblem, he a h al e na i e can be w i en as
Q
a
= (q
a1
, q
a2
, ..., q
ab
, ..., q
an
)
, whe e
q
ab
is he pe o mance alue o c i e ion b
o al e na i e a.
Using Equa ion (1) o Equa ion (2),
he e m Qa may be con e ed in o he el-
e an CS,
P
a
= (p
a1
, p
a2
, ..., p
ab
, …, p
an
)
(Equa-
ion (2)). The decision ma ix can be no malized
using Equa ion (1) i he c i e ion is help ul,
i.e., a g ea e alue is desi able. Equa ion (2)
can be used o no malize non-bene icial c i e ia.
Fig. 1: Flowcha o he p oposed esea ch wo k
Sou ce: own
Eme ging digi al echnologies and hei in luence
on elimina ion o supply chain ulne abili y
95
2024, olume 27, issue 3, pp. 88–103, DOI: 10.15240/ ul/001/2024-3-006
pa,b = [(qab ) – min(qab , a = 1, 2, … , m)]
[max(qab , a = 1, 2, … , m) – min(qab , a = 1, 2, … , m)] (1)
pa,b = [max(qab , a = 1, 2, … , m) – (qab )]
[max(qab , a = 1, 2, … , m) – min(qab , a = 1, 2, … , m)] (2)
S ep 2: De ine he RS
Once he GRG ope a ion is comple ed, he pe -
o mance alues will ange om 0 o 1. The al-
e na i e pe o mance is he bes o c i e ion b
i he alue
p
ab
, which is no malized using
he GRG p ocess, is equal o o close o 1 han
he alue o he o he al e na i e. Consequen ly,
he op imal choice will be made i all o he al-
e na i e pe o mance numbe s a e nea ly o
equal o 1. The e e ence al e na i e is de ined as
P0 = (p01, p02, …, p0j, ..., p0n) = (1, 1, …, 1, …, …, 1)
and seeks o disco e he al e na i e wi h
he mos simila CS o he RS.
S ep 3: Calcula e he GRC (Ψ)
To es ablish how close
p
ab
is o p0b, he GRC is
u ilized. Equa ion (3) can be employed o ob ain
he GRC. The g ea e he alue Ψ, he clos-
e pab and p0ai a e o each o he .
Ψ(p0, a , pa, b ) = ∆min + ζ ∆max
∆a, b + ζ ∆max
( o a = 1, 2, …, m and b = 1 ,2 ,…, n)
(3)
whe e:
Ψ(p0,a, pa,b) is he GRC be ween pa,b, p0,a,
Δa,b = │p0b − pab│
Δmin = min {Δ a,b, a = 1, 2, …, m; b = 1, 2, …, n}
Δmax= max { Δ a,b, a = 1, 2, …, m; b = 1, 2, …,n}
and ζ is he dis inguish coe icien (ζ ∈ [0, 1]),
gene ally aken as 0.5.
The di e en ia ing coe icien ’s unc ion is
o inc ease he GRC’s ange.
S ep 4: Compu e he GR g ade
E alua ing GRC Ψ(x0a, xab), and GR g ade eas-
ily ob ained using Equa ion (4):
Γ(p0 , pa ) = ∑ wb Ψ(pa , pab )
b = 1
n
( o a = 1, 2, …, m)
(4)
whe e: ∑ wa = 1
b = 1
n
And he weigh o he b h c i e ia (wb), is as-
signed by he DMs. The GR g ade indica es
he deg ee o co ela ion, be ween he RS and
he CS. The bes op ion is indica ed i he CS
o an al e na i e has he highes GR g ade wi h
he RS, indica ing ha he CS and he RS a e
he mos simila .
3. Da a analysis and esul s
The esea ch epo ’s s udy ques ions comp ise
i een decision-make s (DMs) om senio man-
agemen who p o ide PQR P . L d. wi h bes
p ac ices. E e y single one o he i een DMs
(DM1 o DM15) has had good expe iences.
Using semi-s uc u ed in e iews wi h he des-
igna ed decision-make s and exis ing li e a u e,
a lis o ele en key SCV indica o s ha he man-
u ac u ing company has used o e icien ly
manage he SC p ocess. Fu he mo e, he p i-
ma y SC me hods ha e been examined using
an MCDM echnique GRA o asce ain hei
p io i y impo ance wi hin he sec o . Based on
he expe iences o DMs (DM1 o DM15), sco es
ha e been de eloped o each o hese signi i-
can SCVs. The decision ma ix sco es o each
o he majo SC challenges a e shown in Tab. 3.
The main p ocedu e begins wi h S ep 1
o he GRA app oach, which ans o ms he sco e
o each key SC obs acle in o a compa abili y-
no malized sequence. The no malized alues
o he choice ma ix a e gi en in Tab. 4. Using
S ep 2 and his no malized sequence as a ba-
sis, RS is calcula ed and shown in Tab. 5. S ep 3
is used o calcula e he GRC be ween each CS
and he RS, which is hen displayed in Tab. 6.
S ep 4 is now used o calcula e he GR g ade
be ween each CS and he RS, which is shown
in Tab. 7. Acco dingly, based on his com-
pu ed GR sco e, he anking o hese impo an
SCV indica o s ha we e iden i ied is displayed
in Tab. 7. The highe he g ade, he be e
he op ion. The inal ank o key SC challenges
is CI > SG > CS > RTC > SCT > IC > CC >
SCD > EC > RC > SCC. Tab. 7 shows cos
o implemen a ion (CI), skills gap (SG), and cul-
u al shi (CS), while en i onmen al conce ns
Eme ging digi al echnologies and hei in luence
on elimina ion o supply chain ulne abili y
96 2024, olume 27, issue 3, pp. 88–103, DOI: 10.15240/ ul/001/2024-3-006
Indica o s DM1 DM2 DM3 DM4 DM5 DM6 DM7 DM8 DM9 DM10 DM11 DM12 DM13 DM14 DM15
CI 545554555455545
SG 433444455544454
RTC 243323433234332
RC 212223211222121
SCC 312221212221221
EC 122111322222122
SCD 231212212223221
IC 432233322323222
CS 344435443433323
SCT 234333233332333
CC 232211132222232
Min 111111111221121
Max 545555555555555
No e: CI – cos o implemen a ion; SG – skills gap; RTC – esis ance o change; RC – egula o y compliance; SCC – sup-
ply chain complexi y; EC – en i onmen al conce ns; SCD – supply chain dis up ions; IC – in as uc u e and connec i i y;
CS – cul u al shi ; SCT – supply chain anspa ency; CC – collabo a ion and communica ion.
Sou ce: own
Indica o s DM1 DM2 DM3 DM4 DM5 DM6 DM7 DM8 DM9 DM10 DM11 DM12 DM13 DM14 DM15
CI 1.00 0.75 1.00 1.00 1.00 0.75 1.00 1.00 1.00 0.75 1.00 1.00 1.00 0.75 1.00
SG 0.75 0.50 0.50 0.75 0.75 0.75 0.75 1.00 1.00 1.00 0.75 0.75 0.75 1.00 0.75
RTC 0.25 0.75 0.50 0.50 0.25 0.50 0.75 0.50 0.50 0.25 0.50 0.75 0.50 0.50 0.25
RC 0.25 0.00 0.25 0.25 0.25 0.50 0.25 0.00 0.00 0.25 0.25 0.25 0.00 0.25 0.00
SCC 0.50 0.00 0.25 0.25 0.25 0.00 0.25 0.00 0.25 0.25 0.25 0.00 0.25 0.25 0.00
EC 0.00 0.25 0.25 0.00 0.00 0.00 0.50 0.25 0.25 0.25 0.25 0.25 0.00 0.25 0.25
SCD 0.25 0.50 0.00 0.25 0.00 0.25 0.25 0.00 0.25 0.25 0.25 0.50 0.25 0.25 0.00
IC 0.75 0.50 0.25 0.25 0.50 0.50 0.50 0.25 0.25 0.50 0.25 0.50 0.25 0.25 0.25
CS 0.50 0.75 0.75 0.75 0.50 1.00 0.75 0.75 0.50 0.75 0.50 0.50 0.50 0.25 0.50
SCT 0.25 0.50 0.75 0.50 0.50 0.50 0.25 0.50 0.50 0.50 0.50 0.25 0.50 0.50 0.50
CC 0.25 0.50 0.25 0.25 0.00 0.00 0.00 0.50 0.25 0.25 0.25 0.25 0.25 0.50 0.25
Min 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.25 0.00 0.00 0.25 0.00
Max 1.00 0.75 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
No e: CI – cos o implemen a ion; SG – skills gap; RTC – esis ance o change; RC – egula o y compliance; SCC – sup-
ply chain complexi y; EC – en i onmen al conce ns; SCD – supply chain dis up ions; IC – in as uc u e and connec i i y;
CS – cul u al shi ; SCT – supply chain anspa ency; CC – collabo a ion and communica ion.
Sou ce: own
Tab. 3: Sco es o he decision ma ix
Tab. 4: No malized decision ma ix
Eme ging digi al echnologies and hei in luence
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o Cleane P oduc ion, 434, 139754. h ps://doi.
o g/10.1016/j.jclep o.2023.139754
Wagne , S. M., & Bode, C. (2009). Domi-
nan isks and isk managemen p ac ices
in supply chains. In G. A. Zsidisin & B. Ri chie
(Eds.), Supply chain isk: A handbook o as-
sessmen , managemen and pe o mance
(pp. 271–290). Sp inge US. h ps://doi.o g/
10.1007/978-0-387-79934-6_17
Wagne , S. M., & Nesha , N. (2010). As-
sessing he ulne abili y o supply chains using
g aph heo y. In e na ional Jou nal o P oduc-
ion Economics, 126(1), 121–129. h ps://doi.
o g/10.1016/j.ijpe.2009.10.007
Wamba, S. F., & Quei oz, M. M. (2020).
Blockchain in he ope a ions and supply chain
managemen : Bene i s, challenges and u u e
esea ch oppo uni ies. In e na ional Jou nal
o In o ma ion Managemen , 52, 102064.
h ps://doi.o g/10.1016/j.ijin omg .2019.102064
Wang, Q., Zhou, H., & Zhao, X. (2024).
The ole o supply chain di e si ica ion
in mi iga ing he nega i e e ec s o supply
chain dis up ions in COVID-19. In e na ional
Jou nal o Ope a ions & P oduc ion Manage-
men , 44(1), 99–132. h ps://doi.o g/10.1108/
ijopm-09-2022-0567
Wang, Y., Ma, F., Tzacho , A., Wang, P.,
Wang, H., Lyu, J., Yue, Q., Du, T., Chen, W.-
Q., & Liang, S. (2023). Quan i ying economic
sec o al i on commodi y use and ela ed ulne -
abili y in China’s supply chains. Resou ces, Con-
se a ion and Recycling, 198, 107150. h ps://
doi.o g/10.1016/j. escon ec.2023.107150
Xu, S., Zhang, X., Feng, L., & Yang, W.
(2020). Dis up ion isks in supply chain manage-
men : A li e a u e e iew based on bibliome ic
analysis. In e na ional Jou nal o P oduc ion
Resea ch, 58(11), 3508–3526. h ps://doi.o g/
10.1080/00207543.2020.1717011
Xu, W., Lu, Y., & P o e bs, D. (2023).
An e alua ion o ac o s in luencing he ulne -
abili y o eme gency logis ics supply chains.
In e na ional Jou nal o Logis ics Resea ch
and Applica ions, 1–34. h ps://doi.o g/10.1080/
13675567.2023.2209030
Yang, H., Zhai, G., & Zhang, Y. (2021).
Ecological ulne abili y assessmen and spa ial
pa e n op imiza ion o esou ce-based ci ies:
A case s udy o Huaibei Ci y, China. Human and
Ecological Risk Assessmen : An In e na ional
Jou nal, 27(3), 606–625. h ps://doi.o g/10.1080/
10807039.2020.1744426
Zhang, H., Jia, F., & You, J.-X. (2023). S ik-
ing a balance be ween supply chain esilience
and supply chain ulne abili y in he c oss-
bo de e-comme ce supply chain. In e na ional
Jou nal o Logis ics Resea ch and Applica ions,
26(3), 320–344. h ps://doi.o g/10.1080/13675567.
2021.1948978