Co esponding au ho : Samuel Omokha e Yusu
Copy igh © 2025 Au ho (s) e ain he copy igh o his a icle. This a icle is published unde he e ms o he C ea i e Commons A ibu ion License 4.0.
Le e aging AI o eal- ime sus ainable supply chain isibili y: Bene i s and
implemen a ion ba ie s
Richmond Nyamekeh 1, Samuel Omokha e Yusu 2, *, Be na d A oakwah 3, Olai an Ebeneze Oluwada e 4,
Na han Yusu 5 and Joshua Eya u 6
1 Wo ces e Poly echnic Ins i u e, Business School, Depa men , Wo ces e , MA, USA.
2 Independen , Wo ces e , MA, USA.
3 Ins i u e o Science and Technology o De elopmen , Wo ces e Poly echnic Ins i u e, Wo ces e , MA, USA.
4 Di ision o Physics, Enginee ing, Ma hema ics, and Compu e Science, Delawa e S a e Uni e si y, USA.
5 Pla eau S a e, Nige ia.
6 Compu e Science, No heas e n Uni e si y, Sea le.
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 26(02), 422-434
Publica ion his o y: Recei ed on 14 Ma ch 2025; e ised on 03 May 2025; accep ed on 05 May 2025
A icle DOI: h ps://doi.o g/10.30574/wja .2025.26.2.1536
Abs ac
This s udy explo es he ole o A i icial In elligence (AI) in enhancing eal- ime sus ainable supply chain isibili y,
add essing i s bene i s and implemen a ion challenges. Wi h supply chains becoming inc easingly complex and
globalized, he need o anspa ency, e iciency, and sus ainabili y has ne e been g ea e . AI-powe ed sys ems o e
ans o ma i e po en ial by enabling eal- ime acking, p edic i e analy ics, and da a-d i en decision-making, which
imp o e ope a ional e iciency and suppo sus ainabili y goals. Howe e , he adop ion o AI in supply chains aces
signi ican ba ie s, including high cos s, da a in eg a ion challenges, esis ance o change, and e hical conce ns. The
esea ch employs a comp ehensi e li e a u e e iew o analyze exis ing s udies on AI applica ions in supply chain
managemen , ocusing on demand o ecas ing, logis ics op imiza ion, was e educ ion, and sus ainabili y. The s udy
iden i ies key bene i s o AI, such as imp o ed decision-making, enhanced anspa ency, cos educ ion, and
en i onmen al impac mi iga ion. I also highligh s c i ical implemen a ion challenges, such as inancial cons ain s,
da a quali y issues, wo k o ce upskilling needs, and cybe secu i y isks. Key indings e eal ha AI can signi ican ly
enhance supply chain isibili y and sus ainabili y bu equi es s a egic planning, in es men in in as uc u e, and
collabo a ion among s akeholde s. The s udy concludes ha while AI holds immense po en ial o ans o ming supply
chains, add essing implemen a ion ba ie s and e hical conside a ions is c ucial o i s success ul adop ion. Fu u e
esea ch should ocus on empi ical s udies, long- e m sus ainabili y impac s, and he in eg a ion o AI wi h eme ging
echnologies like blockchain and IoT. This pape con ibu es o he g owing body o knowledge on AI-d i en supply
chain managemen , o e ing p ac ical insigh s o o ganiza ions aiming o le e age AI o sus ainable and esilien
ope a ions.
Keywo ds: Sus ainable Supply Chain; Real-Time Visibili y; A i icial In elligence (AI); Fo ecas ing
1. In oduc ion
Supply chain isibili y e e s o he abili y o o ganiza ions o ack and moni o he mo emen o goods, ma e ials, and
in o ma ion ac oss he en i e supply chain in eal ime (Jean, 2024). I is a c i ical componen o mode n supply chain
managemen , enabling businesses o make in o med decisions, op imize ope a ions, and espond swi ly o dis up ions.
In an inc easingly globalized and in e connec ed economy, supply chains ha e become mo e complex, in ol ing
mul iple s akeholde s, geog aphies, and p ocesses. This complexi y has heigh ened he need o anspa ency and eal-
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 26(02), 422-434
423
ime insigh s o ensu e e iciency, educe isks, and mee cus ome expec a ions. Wi hou adequa e isibili y,
o ganiza ions ace challenges such as in en o y inaccu acies, delayed shipmen s, and ine iciencies, which can lead o
inc eased cos s and educed compe i i eness (Des o e al., 2023).
The impo ance o supply chain isibili y has been u he unde sco ed by ecen global dis up ions, such as he COVID-
19 pandemic, geopoli ical ensions, and clima e change- ela ed e en s (Bedna ski e al., 2023). These dis up ions ha e
exposed ulne abili ies in supply chains, highligh ing he need o esilience and agili y. Real- ime isibili y allows
o ganiza ions o an icipa e and mi iga e isks, adap o changing condi ions, and main ain con inui y in ope a ions
(Ailyn, 2024). Mo eo e , i suppo s compliance wi h egula o y equi emen s and enhances cus ome sa is ac ion by
p o iding accu a e and imely in o ma ion abou p oduc a ailabili y and deli e y imelines.
1.1. The Role o AI in Enhancing Real-Time Supply Chain Managemen
A i icial In elligence (AI) has eme ged as a ans o ma i e echnology in supply chain managemen , o e ing ad anced
capabili ies o enhance eal- ime isibili y (Joel e al., 2024). AI-powe ed ools, such as machine lea ning algo i hms,
p edic i e analy ics, and na u al language p ocessing, enable o ganiza ions o p ocess as amoun s o da a om di e se
sou ces, including IoT de ices, senso s, and en e p ise sys ems (Nweje and Taiwo, 2025; Adua, e al., 2024). These ools
can iden i y pa e ns, p edic ou comes, and p o ide ac ionable insigh s, allowing businesses o op imize in en o y
le els, s eamline logis ics, and imp o e demand o ecas ing.
One o he key ad an ages o AI is i s abili y o au oma e and augmen decision-making p ocesses (Pe i anis and
Ki siose , 2023). AI can analyze his o ical da a and eal- ime inpu s o p edic po en ial dis up ions, such as supplie
delays o anspo a ion bo lenecks, and ecommend al e na i e s a egies. Addi ionally, AI-d i en pla o ms can
acili a e collabo a ion among supply chain pa ne s by p o iding a uni ied iew o ope a ions and enabling seamless
communica ion (Riad e al., 2024). By le e aging AI, o ganiza ions can achie e g ea e e iciency, educe cos s, and
enhance hei abili y o espond o dynamic ma ke condi ions.
1.2. The Signi icance o Sus ainabili y in Supply Chain Ope a ions
Sus ainabili y has become a cen al conce n in supply chain managemen , d i en by inc easing egula o y p essu es,
consume demand o e hical p ac ices, and he need o add ess en i onmen al challenges (Gu zawska, 2020).
Sus ainable supply chain ope a ions aim o minimize en i onmen al impac , p omo e social esponsibili y, and ensu e
economic iabili y (Osei e al., 2023). Achie ing sus ainabili y equi es o ganiza ions o adop p ac ices such as educing
ca bon emissions, op imizing esou ce u iliza ion, and ensu ing ai labo p ac ices h oughou he supply chain.
Real- ime isibili y plays a c ucial ole in ad ancing sus ainabili y goals. By p o iding insigh s in o he en i onmen al
and social impac o supply chain ac i i ies, o ganiza ions can iden i y a eas o imp o emen and implemen a ge ed
in e en ions. Visibili y in o anspo a ion ou es and ene gy consump ion can help educe ca bon oo p in s, while
moni o ing supplie p ac ices can ensu e compliance wi h e hical s anda ds (Esan e al., 2024). AI can u he enhances
hese e o s by enabling p edic i e and p esc ip i e analy ics, which can op imize sus ainable p ac ices and suppo
long- e m goals.
This pape seeks o explo e he po en ial o AI in enabling eal- ime sus ainable supply chain isibili y, while also
examining he ba ie s o i s implemen a ion. The p ima y esea ch objec i es a e o:
• in es iga e he bene i s o AI-d i en isibili y o sus ainabili y and ope a ional e iciency
• iden i y he challenges and limi a ions associa ed wi h adop ing AI in supply chain managemen , and
• p opose s a egies o o e coming hese ba ie s.
1.2.1. Key esea ch ques ions include
• How can AI enhance eal- ime isibili y in sus ainable supply chains?
• Wha a e he p ima y implemen a ion challenges, and how can hey be add essed?
• Wha ole do s akeholde s play in d i ing he adop ion o AI o sus ainable supply chain isibili y?
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2. Li e a u e Re iew
2.1. AI in Supply Chain Managemen
A i icial In elligence (AI) has e olu ionized supply chain ope a ions by in oducing inno a i e solu ions ha enhance
e iciency, anspa ency, and decision-making (Joel e al., 2024). A g owing body o li e a u e highligh s he
ans o ma i e ole o AI-d i en echnologies, such as he In e ne o Things (IoT), machine lea ning (ML), p edic i e
analy ics, and blockchain, in add essing he complexi ies o mode n supply chains (Bob o e al., 2021). These
echnologies enable eal- ime da a collec ion, analysis, and ac ionable insigh s, empowe ing o ganiza ions o op imize
p ocesses and espond o dis up ions p oac i ely.
IoT plays a pi o al ole in supply chain isibili y by connec ing physical asse s, such as ehicles, wa ehouses, and
p oduc s, h ough senso s and de ices (Sallam e al., 2023). This connec i i y gene a es eal- ime da a on loca ion,
empe a u e, and condi ion, which is c i ical o moni o ing and ensu ing p oduc quali y. Machine lea ning algo i hms
analyze his da a o iden i y pa e ns, p edic demand luc ua ions, and op imize in en o y le els, educing was e and
imp o ing esou ce alloca ion (Ga la and Manda i, 2020). P edic i e analy ics u he enhances decision-making by
o ecas ing po en ial dis up ions, such as supplie delays o ma ke shi s, enabling o ganiza ions o implemen
mi iga ion s a egies. Blockchain echnology complemen s AI by p o iding a secu e and anspa en pla o m o
eco ding ansac ions and acking goods ac oss he supply chain (Saman a e al., 2024). I s decen alized na u e
ensu es da a in eg i y and builds us among s akeholde s, pa icula ly in indus ies equi ing aceabili y, such as
ood and pha maceu icals. Toge he , hese echnologies c ea e a syne gis ic ecosys em ha enhances supply chain
esilience and sus ainabili y.
Recen s udies emphasize he po en ial o AI-d i en inno a ions o add ess sus ainabili y challenges by op imizing
ene gy consump ion, educing ca bon emissions, and p omo ing e hical sou cing (Adua e al., 2024; Va ghese, 2022).
Howe e , po en ial ba ie s o achie ing his include high implemen a ion cos s, da a p i acy conce ns, and he need
o skilled pe sonnel. Despi e hese challenges, he in eg a ion o AI, IoT, ML, p edic i e analy ics, and blockchain is
eshaping supply chain managemen , o e ing unp eceden ed oppo uni ies o inno a ion and e iciency
2.2. Impo ance o Real-Time Supply Chain Visibili y
Real- ime supply chain isibili y e e s o he abili y o moni o and ack he mo emen o goods, ma e ials, and
in o ma ion ac oss he supply chain ins an aneously (Jean e al., 2024). I s key componen s include da a in eg a ion
om mul iple sou ces, ad anced analy ics o p ocessing and in e p e ing da a, and seamless communica ion among
s akeholde s. Real- ime isibili y enables o ganiza ions o gain ac ionable insigh s, imp o e decision-making, and
enhance ope a ional e iciency (Holloway, 2024). In oday’s as -paced and unp edic able business en i onmen , i is a
c i ical ac o o main aining compe i i eness, educing isks, and mee ing cus ome expec a ions.
AI plays a ans o ma i e ole in enhancing eal- ime isibili y ac oss a ious supply chain unc ions, including logis ics,
in en o y acking, and demand o ecas ing (Ve ma, 2024). In logis ics, AI-powe ed sys ems le e age IoT senso s and
GPS da a o moni o he loca ion and condi ion o shipmen s in eal ime. This allows companies o op imize ou ing,
educe anspo a ion cos s, and mi iga e delays caused by un o eseen e en s such as a ic o wea he dis up ions.
Machine lea ning algo i hms u he enhance logis ics by p edic ing po en ial bo lenecks and sugges ing al e na i e
ou es o modes o anspo a ion (Khed and Rani, 2024).
In in en o y acking, AI in eg a es da a om IoT de ices, RFID ags, and wa ehouse managemen sys ems o p o ide
eal- ime upda es on s ock le els and p oduc mo emen s (Ugbebo e al., 2024). This ensu es accu a e in en o y
managemen , educes o e s ocking o s ockou s, and imp o es o de ul illmen a es. P edic i e analy ics, a subse o
AI, enables o ganiza ions o o ecas demand wi h g ea e accu acy by analyzing his o ical sales da a, ma ke ends,
and ex e nal ac o s such as seasonali y o economic condi ions (Nweje and Taiwo, 2025). This helps businesses align
p oduc ion and in en o y le els wi h an icipa ed demand, minimizing was e and maximizing p o i abili y.
By enhancing eal- ime isibili y, AI no only imp o es ope a ional e iciency bu also suppo s sus ainabili y goals by
op imizing esou ce u iliza ion and educing was e. Howe e , achie ing his le el o isibili y equi es obus
echnological in as uc u e, da a in eg a ion, and collabo a ion among supply chain pa ne s. The li e a u e
unde sco es he g owing impo ance o AI-d i en isibili y as a co ne s one o mode n, esilien , and sus ainable supply
chain managemen .
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2.3. Sus ainable Supply Chain Managemen (SSCM)
Sus ainable Supply Chain Managemen (SSCM) is a holis ic app oach ha in eg a es en i onmen al, social, and
economic conside a ions in o supply chain ope a ions o ensu e long- e m iabili y and minimize nega i e impac s on
socie y and he en i onmen (Abbas e al., 2023). SSCM p inciples ocus on educing ca bon oo p in s, op imizing
esou ce use, p omo ing e hical labo p ac ices, and os e ing ci cula economy models. Key s a egies include g een
p ocu emen , ene gy-e icien logis ics, was e educ ion, and collabo a ion wi h supplie s o ensu e sus ainabili y
ac oss he en i e alue chain (Muchenje e al., 2023). T anspa ency and aceabili y a e also cen al o SSCM, as hey
enable s akeholde s o e i y he sus ainabili y c eden ials o p oduc s and p ocesses. By adop ing SSCM p inciples,
o ganiza ions can enhance hei esilience, comply wi h egula o y equi emen s, and mee he g owing consume
demand o e hically p oduced and en i onmen ally iendly p oduc s.
2.3.1. AI’s Con ibu ion o Reducing En i onmen al Impac and Was e Managemen
A i icial In elligence (AI) has become a c i ical enable o sus ainabili y in supply chain managemen by p o iding da a-
d i en insigh s and op imizing esou ce u iliza ion. AI-powe ed sys ems le e age da a om IoT senso s, sa elli e
image y, and o he sou ces o moni o and educe ca bon emissions (Zejja i and Benhayoun, 2024). Fo example, AI
can op imize anspo a ion ou es o minimize uel consump ion o p edic ene gy usage pa e ns o imp o e e iciency
in wa ehouses and manu ac u ing acili ies. Addi ionally, AI suppo s he in eg a ion o enewable ene gy sou ces by
o ecas ing ene gy demand and op imizing hei use.
In was e managemen , AI enhances ecycling and was e educ ion e o s by iden i ying pa e ns in was e gene a ion
and op imizing disposal p ocesses (Olawade e al., 2024). Machine lea ning algo i hms can classi y was e ma e ials o
e icien ecycling, while p edic i e analy ics helps businesses an icipa e was e p oduc ion and implemen p e en i e
measu es. AI also acili a es ci cula economy p ac ices by enabling p oduc li ecycle acking and p omo ing he euse
o ma e ials. (Oladapo e al., 2024) By in eg a ing AI in o SSCM, o ganiza ions can achie e signi ican en i onmen al
bene i s, educe cos s, and align wi h global sus ainabili y goals, making i a c i ical enable o g eene and mo e
esponsible supply chains.
2.4. Re iew o P io S udies on AI-Powe ed Supply Chain Sys ems
The in eg a ion o A i icial In elligence (AI) in o supply chain sys ems has been a ocal poin o esea ch in ecen yea s,
d i en by he need o g ea e e iciency, anspa ency, and sus ainabili y. P io s udies ha e explo ed a ious
applica ions o AI in supply chain managemen , including demand o ecas ing, in en o y op imiza ion, logis ics, and
sus ainabili y. These s udies highligh he ans o ma i e po en ial o AI in add essing he complexi ies and challenges
o mode n supply chains.
2.4.1. Demand Fo ecas ing and In en o y Op imiza ion
One o he mos widely esea ched a eas is he use o AI o demand o ecas ing and in en o y op imiza ion. T adi ional
o ecas ing me hods o en s uggle o accoun o he ola ili y and complexi y o mode n ma ke s. AI-powe ed sys ems,
pa icula ly hose le e aging machine lea ning (ML) algo i hms, ha e demons a ed supe io accu acy in p edic ing
demand by analyzing his o ical da a, ma ke ends, and ex e nal ac o s such as seasonali y and economic condi ions
(Amosu e al., 2024). Fo ins ance, a s udy by Zhodi e al. (2024) ound ha machine lea ning models signi ican ly
ou pe o med adi ional s a is ical me hods in demand o ecas ing. Simila ly, esea ch by Ismaeil and Lalla (2024)
highligh ed he ole o AI in educing o ecas ing e o s and imp o ing in en o y managemen , leading o educed
s ockou s and o e s ock si ua ions.
2.4.2. Logis ics and T anspo a ion
AI has also been ex ensi ely s udied o i s applica ions in logis ics and anspo a ion. Resea ch by Velu u (2023)
demons a ed how AI-powe ed ou e op imiza ion algo i hms could educe anspo a ion cos s and imp o e deli e y
imes. Mo e ecen s udies ha e explo ed he in eg a ion o AI wi h IoT de ices o enable eal- ime acking and
moni o ing o shipmen s. Fo example, Mille e al. (2025) discussed how AI and IoT oge he could enhance isibili y
and e iciency in logis ics ope a ions, enabling companies o espond swi ly o dis up ions and op imize ou ing in eal
ime.
2.4.3. Sus ainabili y and Was e Managemen
Sus ainabili y is ano he c i ical a ea whe e AI has shown signi ican p omise. S udies ha e explo ed how AI can help
educe he en i onmen al impac o supply chains by op imizing esou ce use and minimizing was e. Fo ins ance,
esea ch by Ina olu and Spi idono a (2025) highligh ed he ole o AI in p omo ing sus ainable p ac ices h ough be e
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demand o ecas ing and in en o y managemen , which in u n educes was e and o e p oduc ion. Addi ionally, AI has
been epo ed o be e icien in op imizing ene gy consump ion in wa ehouses and manu ac u ing acili ies, as
demons a ed by a s udy by Ke ama i e al. (2024). AI's abili y o analyze la ge da ase s and iden i y pa e ns has also
been le e aged o imp o e ecycling and was e managemen p ocesses, as discussed by Olawade e al. (2024).
2.4.4. Supplie Rela ionship Managemen
AI has also been applied o enhance supplie ela ionship managemen . Resea ch by Mohammed (2023) explo ed how
AI could imp o e supplie selec ion and e alua ion p ocesses by analyzing da a on supplie pe o mance, eliabili y,
and isk ac o s. AI-powe ed sys ems can also acili a e be e collabo a ion and communica ion among supply chain
pa ne s, as highligh ed by a s udy by Khed and Rani (2024). These sys ems enable eal- ime da a sha ing and decision-
making, os e ing s onge and mo e anspa en ela ionships wi h supplie s.
2.4.5. Risk Managemen and Resilience
Ano he impo an a ea o esea ch is he use o AI o isk managemen and building esilien supply chains. S udies
ha e shown ha AI can p edic po en ial dis up ions, such as supplie delays o na u al disas e s, and sugges mi iga ion
s a egies. Fo example, a s udy by (Riad, e al. 2024) demons a ed how AI could be used o model and simula e supply
chain dis up ions, enabling companies o de elop con ingency plans. Mo e ecen esea ch by Ismaeil and Lalla (2024)
emphasized he ole o AI in enhancing supply chain esilience by p o iding eal- ime isibili y and p edic i e analy ics.
2.4.6. Resea ch Gaps
Despi e he signi ican ad ancemen s in AI-powe ed supply chain sys ems, se e al esea ch gaps emain. Fi s , while
he e is ex ensi e li e a u e on he echnical aspec s o AI applica ions, he e is a lack o s udies ocusing on he
o ganiza ional and human ac o s ha in luence he success ul implemen a ion o AI in supply chains. Fo ins ance,
mo e esea ch is needed on how o o e come esis ance o change, ain employees, and in eg a e AI sys ems wi h
exis ing p ocesses.
Second, he e is a need o mo e empi ical s udies ha p o ide quan i a i e e idence o he bene i s and challenges o
AI in supply chain managemen . Many exis ing s udies ely on heo e ical models o case s udies, which may no be
gene alizable. La ge-scale empi ical s udies could p o ide mo e obus insigh s in o he impac o AI on supply chain
pe o mance.
Thi d, while AI has shown p omise in enhancing sus ainabili y, he e is limi ed esea ch on i s long- e m en i onmen al
and social impac s. Fo example, he ene gy consump ion o AI sys ems hemsel es and hei po en ial o exace ba e e-
was e a e a eas ha equi e u he in es iga ion. Addi ionally, mo e esea ch is needed on how AI can be used o
p omo e social sus ainabili y, such as ensu ing ai labo p ac ices and e hical sou cing.
Fou h, he in eg a ion o AI wi h o he eme ging echnologies, such as blockchain and IoT, is an a ea ha wa an s
u he explo a ion. While some s udies ha e ouched on his, he e is a need o mo e comp ehensi e esea ch on how
hese echnologies can wo k oge he o c ea e mo e anspa en , e icien , and sus ainable supply chains.
Finally, he e is a gap in esea ch on he e hical and egula o y implica ions o AI in supply chain managemen . As AI
sys ems become mo e pe asi e, issues ela ed o da a p i acy, secu i y, and bias need o be add essed. Mo e esea ch
is needed o de elop amewo ks and guidelines o he e hical use o AI in supply chains.
3. Bene i s o AI in Real- ime Supply Chain Visibili y
The in eg a ion o A i icial In elligence (AI) in o supply chain managemen has e olu ionized he way o ganiza ions
achie e eal- ime isibili y (Jean, 2024). AI-powe ed sys ems enable o ganiza ions o gain ac ionable insigh s, op imize
ope a ions, and espond swi ly o dis up ions. This sec ion explo es he key bene i s o AI in enhancing eal- ime supply
chain isibili y, ocusing on imp o ed decision-making and e iciency, enhanced anspa ency and aceabili y, cos
educ ion and esou ce op imiza ion, and en i onmen al and social bene i s.
3.1. Real-Time Insigh s and P edic i e Analy ics
One o he mos signi ican bene i s o AI in eal- ime supply chain isibili y is i s abili y o p o ide eal- ime insigh s
and p edic i e analy ics (Joel e al., 2024). T adi ional supply chain sys ems o en ely on his o ical da a and manual
p ocesses, which can lead o delays and inaccu acies. AI, on he o he hand, le e ages ad anced algo i hms and machine
lea ning (ML) o p ocess as amoun s o da a om mul iple sou ces, such as IoT senso s, GPS, and en e p ise sys ems,
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in eal ime (Mille e al., 2025). This enables o ganiza ions o moni o ope a ions con inuously and iden i y po en ial
issues be o e hey escala e.
Fo example, AI-powe ed p edic i e analy ics can o ecas demand luc ua ions, supplie delays, o anspo a ion
bo lenecks, allowing businesses o ake p oac i e measu es. AI-d i en demand o ecas ing models can signi ican ly
educe e o s compa ed o adi ional me hods, enabling o ganiza ions o align p oduc ion and in en o y le els wi h
ma ke demand (Amosu e al., 2024). Real- ime insigh s also empowe supply chain manage s o make in o med
decisions quickly, imp o ing o e all e iciency and esponsi eness (Khed and Rani, 2024).
3.1.1. Da a-D i en Decision-Making
AI enhances decision-making by ans o ming aw da a in o ac ionable insigh s. By analyzing pa e ns and ends in
eal- ime da a, AI sys ems can ecommend op imal s a egies o in en o y managemen , logis ics, and p ocu emen
(Khed and Rani, 2024). Fo ins ance, AI can iden i y he mos cos -e ec i e anspo a ion ou es o sugges al e na i e
supplie s in case o dis up ions. This da a-d i en app oach minimizes guesswo k and ensu es ha decisions a e based
on accu a e, up- o-da e in o ma ion.
Mo eo e , AI-powe ed decision-making ools can au oma e ou ine asks, such as o de p ocessing and in en o y
eplenishmen , eeing up human esou ces o mo e s a egic ac i i ies (Joel e al., 2024). This no only imp o es
ope a ional e iciency bu also educes he isk o human e o . O ganiza ions ha adop AI-d i en decision-making
ools expe ience a 20-30% imp o emen in supply chain e iciency (Kelly, 2024)
3.2. AI-Powe ed T acking Sys ems
T anspa ency and aceabili y a e c i ical componen s o mode n supply chains, pa icula ly in indus ies such as ood,
pha maceu icals, and ashion, whe e p oduc au hen ici y and sa e y a e pa amoun (Aung and Chang, 2014). AI-
powe ed acking sys ems enable o ganiza ions o moni o he mo emen o goods a e e y s age o he supply chain,
om aw ma e ials o he end consume (Joel e al., 2024). These sys ems use echnologies such as IoT senso s, RFID
ags, and GPS o collec eal- ime da a on loca ion, empe a u e, and condi ion.
Fo example, in he ood indus y, AI-powe ed acking sys ems can moni o he empe a u e o pe ishable goods du ing
anspo a ion, ensu ing ha hey emain wi hin sa e limi s. I a de ia ion occu s, he sys em can ale s akeholde s
immedia ely, p e en ing spoilage and educing was e. Simila ly, in he pha maceu ical indus y, AI can ack he
au hen ici y o d ugs, educing he isk o coun e ei p oduc s en e ing he supply chain.
3.2.1. Role o Blockchain and IoT in Imp o ing Visibili y
AI wo ks syne gis ically wi h o he eme ging echnologies, such as blockchain and IoT, o enhance anspa ency and
aceabili y (Yahaya e al., 2025). Blockchain p o ides a secu e and immu able ledge o eco ding ansac ions,
ensu ing ha da a canno be ampe ed wi h. When combined wi h AI, blockchain enables o ganiza ions o c ea e a
anspa en and audi able eco d o e e y ansac ion and mo emen wi hin he supply chain (Yekeen e al., 2024).
IoT de ices, such as senso s and sma ags, gene a e eal- ime da a ha AI sys ems can analyze o p o ide insigh s in o
supply chain ope a ions. Fo ins ance, IoT senso s can moni o he condi ion o goods in ansi , while AI algo i hms
analyze he da a o p edic po en ial issues, such as delays o damage. This combina ion o AI, blockchain, and IoT
c ea es a obus ecosys em o eal- ime isibili y, enabling o ganiza ions o build us wi h cus ome s and
s akeholde s.
3.3. Minimizing Was e and Op imizing Resou ce Alloca ion
AI plays a c ucial ole in minimizing was e and op imizing esou ce alloca ion in supply chains. By analyzing eal- ime
da a, AI sys ems can iden i y ine iciencies and ecommend s a egies o imp o emen . Fo example, AI can op imize
in en o y le els o educe o e s ocking o s ockou s, minimizing was e and lowe ing s o age cos s (Çaylı and O alhan,
2024). Simila ly, AI-powe ed p edic i e main enance sys ems can moni o he condi ion o machine y and equipmen ,
p edic ing ailu es be o e hey occu and educing down ime (Pa il, 2024). In addi ion, AI can op imize esou ce
alloca ion by analyzing da a on p oduc ion schedules, labo a ailabili y, and ma e ial equi emen s. This ensu es ha
esou ces a e used e icien ly, educing cos s and imp o ing p oduc i i y.
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3.3.1. Au oma ion in P ocu emen and Logis ics
AI-d i en au oma ion is ans o ming p ocu emen and logis ics p ocesses, leading o signi ican cos sa ings (Ismaeil
and Lalla, 2024). In p ocu emen , AI-powe ed sys ems can analyze supplie pe o mance, ma ke ends, and p icing
da a o iden i y he bes supplie s and nego ia e a o able e ms. Au oma ion also s eamlines o de p ocessing and
in oice managemen , educing adminis a i e cos s and imp o ing accu acy.
In logis ics, AI-powe ed ou e op imiza ion algo i hms can educe anspo a ion cos s by iden i ying he mos e icien
ou es and modes o anspo (Velu u, 2023). Fo example, AI can analyze a ic pa e ns, wea he condi ions, and uel
p ices o ecommend op imal ou es in eal ime. Addi ionally, AI-powe ed wa ehouse managemen sys ems can
au oma e asks such as so ing, packing, and in en o y acking, imp o ing e iciency and educing labo cos s
(Shanka an, 2023).
3.4. AI’s Role in Sus ainable P ac ices
AI is a powe ul ool o p omo ing sus ainabili y in supply chain ope a ions. By p o iding eal- ime isibili y and
p edic i e analy ics, AI enables o ganiza ions o adop sus ainable p ac ices such as educing ca bon emissions,
op imizing ene gy use, and minimizing was e (). Fo example, AI can analyze da a on ene gy consump ion in wa ehouses
and manu ac u ing acili ies, iden i ying oppo uni ies o imp o emen and ecommending ene gy-e icien solu ions.
AI also suppo s ci cula economy p ac ices by enabling p oduc li ecycle acking and p omo ing he euse o ma e ials
(). AI-powe ed sys ems can ack he condi ion o e u ned p oduc s and de e mine whe he hey can be e u bished o
ecycled (). This educes was e and ex ends he li ecycle o p oduc s, con ibu ing o a mo e sus ainable supply chain.
3.4.1. Reducing Ca bon Foo p in s in Supply Chain Ne wo ks
One o he mos signi ican en i onmen al bene i s o AI is i s abili y o educe ca bon oo p in s in supply chain
ne wo ks. AI-powe ed sys ems can op imize anspo a ion ou es o minimize uel consump ion and emissions (Mille
e al., 2024). Fo example, AI can analyze da a on ehicle load capaci y, a ic condi ions, and deli e y schedules o
ecommend he mos e icien ou es (Tsoukas e al., 2023). This no only educes anspo a ion cos s bu also lowe s
he en i onmen al impac o logis ics ope a ions.
In addi ion, AI can suppo he adop ion o enewable ene gy sou ces by o ecas ing ene gy demand and op imizing he
use o sola o wind powe (Onwusinkwue e al., 2024). Fo ins ance, AI-powe ed ene gy managemen sys ems can
analyze wea he pa e ns and ene gy consump ion da a o p edic demand and adjus ene gy usage acco dingly. This
educes eliance on ossil uels and p omo es he use o clean ene gy.
AI also con ibu es o social sus ainabili y by p omo ing e hical p ac ices and ai labo condi ions (Mienye e al., 2024).
AI-powe ed sys ems can moni o supplie compliance wi h labo s anda ds and e hical sou cing p ac ices, ensu ing
ha supply chains a e ee om exploi a ion and human igh s iola ions. This enhances he social esponsibili y o
o ganiza ions and builds us wi h consume s.
4. Implemen a ion ba ie s and challenges
While he bene i s o AI in eal- ime supply chain isibili y a e subs an ial, o ganiza ions ace se e al ba ie s and
challenges in implemen ing AI-powe ed sys ems. These challenges ange om inancial cons ain s and da a quali y
issues o esis ance o change and cybe secu i y conce ns. Add essing hese ba ie s is c i ical o success ul AI
adop ion and maximizing i s po en ial in supply chain managemen . This sec ion explo es he key implemen a ion
challenges and hei implica ions.
4.1. Financial Cons ain s in AI Adop ion
One o he mos signi ican ba ie s o AI adop ion in supply chain managemen is he high ini ial in es men cos (Culo
e al. 2024). Implemen ing AI-powe ed sys ems equi es subs an ial inancial esou ces o acqui ing ad anced
echnologies, such as machine lea ning algo i hms, IoT de ices, and cloud compu ing in as uc u e. Addi ionally,
o ganiza ions mus in es in da a s o age, p ocessing capabili ies, and cybe secu i y measu es o suppo AI sys ems
(Aldose i e al., 2023). Fo small and medium-sized en e p ises (SMEs), hese cos s can be p ohibi i e, limi ing hei
abili y o adop AI echnologies.
E en o la ge o ganiza ions, he inancial commi men equi ed o AI implemen a ion can be a de e en (Cannas e
al., 2024). Many companies s uggle o jus i y he up on cos s o AI adop ion, pa icula ly when he e u n on
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in es men (ROI) is no immedia ely appa en . This inancial cons ain is exace ba ed by he need o ongoing
in es men s in sys em main enance, upda es, and wo k o ce aining.
4.1.1. Implica ion: Cos s. ROI Conside a ions
The high cos o AI implemen a ion o en aises conce ns abou ROI (Ikpe, 2024). While AI has he po en ial o deli e
signi ican long- e m bene i s, such as imp o ed e iciency, cos sa ings, and enhanced decision-making, hese bene i s
may ake ime o ma e ialize. O ganiza ions mus ca e ully e alua e he cos -bene i a io o AI adop ion and de elop a
clea business case o jus i y he in es men .
Fo example, AI-powe ed p edic i e analy ics can educe in en o y cos s and imp o e demand o ecas ing accu acy,
bu he ROI may no be immedia e. Simila ly, while AI-d i en au oma ion can s eamline logis ics and p ocu emen
p ocesses, he ini ial cos s o implemen ing hese sys ems can be subs an ial. O ganiza ions mus weigh hese cos s
agains he po en ial long- e m bene i s and conside ac o s such as payback pe iods and scalabili y.
4.2. Da a Quali y and In eg a ion Issues
Da a quali y and in eg a ion a e c i ical challenges in AI implemen a ion (Aldose i e al., 2023). Many o ganiza ions
s uggle wi h da a silos, whe e in o ma ion is s o ed in isola ed sys ems ha do no communica e wi h each o he . This
lack o in e ope abili y hinde s he abili y o AI sys ems o access and analyze comp ehensi e da ase s, limi ing hei
e ec i eness.
Fo example, supply chain da a may be sca e ed ac oss mul iple pla o ms, such as en e p ise esou ce planning (ERP)
sys ems, wa ehouse managemen sys ems (WMS), and anspo a ion managemen sys ems (TMS). In eg a ing hese
dispa a e sys ems o c ea e a uni ied da a ecosys em is a complex and esou ce-in ensi e p ocess. Wi hou high-quali y,
in eg a ed da a, AI sys ems canno p o ide accu a e insigh s o p edic ions, unde mining hei alue (Sa ikaya, 2024).
4.2.1. Challenges in In eg a ing AI wi h Exis ing In as uc u e
In eg a ing AI wi h exis ing supply chain in as uc u e is ano he signi ican challenge (Ismaeil and Lalla, 2024). Many
o ganiza ions ely on legacy sys ems ha we e no designed o suppo AI echnologies. Upg ading o eplacing hese
sys ems o accommoda e AI can be cos ly and ime-consuming. Addi ionally, in eg a ing AI wi h exis ing p ocesses o en
equi es signi ican cus omiza ion, which can u he inc ease implemen a ion cos s and complexi y.
Fo ins ance, implemen ing AI-powe ed p edic i e main enance sys ems may equi e e o i ing exis ing machine y
wi h IoT senso s and connec ing hem o AI pla o ms. This p ocess can be echnically challenging and may dis up
ongoing ope a ions. O ganiza ions mus ca e ully plan and execu e AI in eg a ion o minimize dis up ions and ensu e
compa ibili y wi h exis ing sys ems.
4.3. O ganiza ional Resis ance o AI Adop ion
Resis ance o change is a common ba ie o AI adop ion in supply chain managemen . Employees and manage s may
be hesi an o emb ace AI echnologies due o ea o job displacemen , lack o unde s anding, o skep icism abou hei
e ec i eness (Golgeci e al., 2025). This esis ance can hinde he success ul implemen a ion and adop ion o AI sys ems.
Fo example, supply chain manage s may be eluc an o ely on AI-d i en insigh s o decision-making, p e e ing o
ely on hei expe ience and in ui ion. Simila ly, wa ehouse wo ke s may esis he in oduc ion o AI-powe ed
au oma ion sys ems, ea ing ha hey will be eplaced by machines (Pudel, 2024). O e coming his esis ance equi es
e ec i e change managemen s a egies, including clea communica ion, aining, and in ol emen o s akeholde s in
he implemen a ion p ocess.
4.3.1. Need o Upskilling Wo k o ce
The success ul implemen a ion o AI in supply chain managemen equi es a skilled wo k o ce capable o de eloping,
managing, and u ilizing AI sys ems. Howe e , many o ganiza ions ace a skills gap, as hei employees lack he echnical
expe ise equi ed o wo k wi h AI echnologies (Mo adini e al., 2023). This skills gap can slow down AI adop ion and
limi i s e ec i eness.
Fo ins ance, da a scien is s and AI specialis s a e in high demand, bu he e is a sho age o quali ied p o essionals in
hese ields (Bababshahi e al., 2024). Addi ionally, exis ing employees may equi e aining o unde s and and use AI-
powe ed ools e ec i ely. O ganiza ions mus in es in upskilling hei wo k o ce h ough aining p og ams,
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ce i ica ions, and pa ne ships wi h educa ional ins i u ions. This no only enhances he capabili ies o employees bu
also os e s a cul u e o inno a ion and con inuous lea ning.
4.4. Cybe secu i y and E hical Conce ns
The in eg a ion o AI in o supply chain managemen in oduces new cybe secu i y isks (Ismaeil and Lalla,2023). AI
sys ems ely on as amoun s o da a, making hem a ac i e a ge s o cybe a acks. Da a b eaches and hacking
inciden s can comp omise sensi i e in o ma ion, dis up ope a ions, and damage an o ganiza ion’s epu a ion.
Fo example, AI-powe ed supply chain pla o ms may s o e da a on supplie s, cus ome s, and logis ics pa ne s, which
could be exploi ed by cybe c iminals (Blake, 2025). Addi ionally, IoT de ices used in AI sys ems a e o en ulne able o
hacking, as hey may lack obus secu i y ea u es. O ganiza ions mus implemen s ong cybe secu i y measu es, such
as enc yp ion, mul i- ac o au hen ica ion, and egula secu i y audi s, o p o ec hei AI sys ems and da a.
4.4.1. E hical Implica ions o AI-D i en Decisions
The use o AI in supply chain managemen also aises e hical conce ns (Mohsen, 2023). AI-d i en decisions a e based
on algo i hms ha analyze da a and iden i y pa e ns, bu hese algo i hms may inad e en ly pe pe ua e biases o
make une hical decisions. Fo example, AI sys ems used o supplie selec ion may a o ce ain supplie s based on
biased da a, leading o un ai ou comes.
Addi ionally, he use o AI in wo k o ce managemen , such as scheduling o pe o mance e alua ion, may aise conce ns
abou ai ness and anspa ency (Maj ashi, 2025). O ganiza ions mus ensu e ha hei AI sys ems a e designed and
implemen ed in an e hical manne , wi h clea guidelines o decision-making and accoun abili y. This includes
add essing issues such as algo i hmic bias, da a p i acy, and he po en ial impac o AI on jobs and socie y.
5. Summa y and Recommenda ion
The in eg a ion o AI in o eal- ime supply chain isibili y o e s ans o ma i e bene i s, including imp o ed decision-
making, enhanced anspa ency, cos educ ion, and en i onmen al sus ainabili y. AI-powe ed sys ems enable
o ganiza ions o gain eal- ime insigh s, op imize ope a ions, and espond swi ly o dis up ions. Howe e , signi ican
ba ie s hinde AI adop ion, such as high ini ial in es men cos s, da a quali y and in eg a ion challenges, esis ance o
change, and cybe secu i y and e hical conce ns. Financial cons ain s and unclea ROI o en de e o ganiza ions, while
da a silos and legacy sys ems complica e AI in eg a ion. Wo k o ce esis ance and skills gaps u he slow adop ion, and
cybe secu i y isks and e hical dilemmas pose addi ional challenges. Add essing hese ba ie s is c i ical o unlocking
AI’s ull po en ial in supply chain managemen .
5.1. P ac ical Recommenda ions
To success ully implemen AI in supply chain managemen , o ganiza ions should adop a s a egic and phased app oach.
S a small by launching pilo p ojec s in high-impac a eas such as demand o ecas ing o in en o y op imiza ion. These
ini ia i es can demons a e AI’s alue and ROI, building con idence be o e scaling up. Simul aneously, in es in obus
da a in as uc u e o ensu e da a quali y and in eg a ion. B eak down da a silos and adop in e ope able sys ems,
le e aging cloud-based pla o ms o seamless da a sha ing and accessibili y.
Upskilling he wo k o ce is equally c i ical. P o ide aining p og ams o equip employees wi h AI- ela ed skills and
os e a cul u e o inno a ion and collabo a ion. This educes esis ance o change and ensu es smoo he adop ion.
P io i ize cybe secu i y by implemen ing measu es like enc yp ion and mul i- ac o au hen ica ion o sa egua d AI
sys ems and sensi i e da a. Addi ionally, adop e hical AI p ac ices by de eloping guidelines o add ess algo i hmic bias,
ensu e anspa ency, and p omo e ai ness in AI-d i en decisions.
Finally, collabo a ion wi h s akeholde s is essen ial. Engage supplie s, cus ome s, and pa ne s in he AI implemen a ion
p ocess o c ea e a uni ied and anspa en supply chain ecosys em. Go e nmen s and egula o y bodies should
es ablish amewo ks o add ess e hical and secu i y conce ns in AI adop ion. Policies should p omo e da a p i acy,
algo i hmic anspa ency, and ai labo p ac ices. Incen i es, such as ax b eaks o g an s, can encou age SMEs o adop
AI echnologies. Addi ionally, in e na ional s anda ds o AI in e ope abili y and cybe secu i y should be de eloped o
acili a e global supply chain in eg a ion. By ollowing hese p ac ical ecommenda ions, o ganiza ions can o e come
implemen a ion ba ie s and ully le e age AI o enhance e iciency, anspa ency, and sus ainabili y in hei supply
chains.
