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In eg a ing IoT o Imp o e Real-Time Visibili y and E iciency in Rwanda’s
Heal hca e Supply Chain
Pascal Nkezabe a1*, D . Ree ha Dinesh2
*1-2 Swiss In e na ional Managemen Academy (SIMA), IIBM schola
Co esponding Au ho Pascal
Nkezabe a
Swiss In e na ional Managemen
Academy (SIMA), IIBM schola
A icle His o y
Recei ed: 15 / 09 / 2025
Accep ed: 02 / 10 / 2025
Published: 09 / 10 / 2025
Abs ac : This s udy examined how In e ne o Things (IoT) echnologies a ec eal- ime
isibili y and ope a ional e iciency in Rwanda’s public heal h supply chain. Guided by
Sys ems, TOE, and SCOR amewo ks, we used a con e gen mixed-me hods design in Kigali
ac oss h ee s a a: Rwanda Medical Supply (RMS), public hospi als, and p ima y heal h
acili ies. A s a i ied pu posi e sample o 100 p o essionals (pha macis s, logis ics, IT,
p ocu emen , s o e manage s) comple ed a s uc u ed ques ionnai e (C onbach’s α = 0.82)
cap u ing s ockou s, in en o y accu acy, deli e y lead imes, and sys em in eg a ion; semi-
s uc u ed in e iews, documen e iews, and si e obse a ions p o ided quali a i e dep h.
Quan i a i e da a we e analyzed wi h desc ip i e and in e en ial s a is ics; quali a i e da a
unde wen hema ic analysis, wi h join displays used o iangula ion. IoT adop ion is
meaning ul bu une en: 58% o acili ies epo using IoT (p ima ily RFID/ba code and cloud
dashboa ds). Whe e implemen ed, pe o mance imp o es; acili y eco ds and pe cep ions
indica e ≈25% ewe s ockou s, highe in en o y accu acy, 72% epo ing imp o ed eal- ime
s ock isibili y, and 74% con i ming eal- ime deli e y acking. Human ac o s a e a o able:
be ween 70% and 77% endo se usabili y and openness o new ools; 68% epo a leas
mode a e con idence. Howe e , only 53% pe cei e adequa e echnical suppo . T aining is
pi o al: 62% ecei ed o mal IoT aining and mos link i o highe e iciency (76%) and be e
da a accu acy (73%). Educa ional p epa edness co ela es mode a ely wi h IoT p o iciency ( ≈
0.45), highligh ing cu iculum gaps. Equi y emains he main cons ain : 40% a e digi al
in as uc u e as ai o poo , and e ec i eness is pe cei ed as lowe in u al se ings. The s udy
concludes ha IoT can measu ably s eng hen Rwanda’s heal h logis ics, bu scale-up equi es
sus ained aining, obus echnical suppo , in e ope abili y, and equi y-o ien ed in as uc u e
in es men . Fu u e wo k should assess cos -e ec i eness, long- e m pa ien ou comes, and
in eg a ion wi h na ional digi al pla o ms o enable esilien , sys em-wide impac .
Keywo ds: In e ne o Things (IoT); Supply Chain Visibili y; S ockou s; In e ope abili y;
e‑LMIS; Rwanda
How o Ci e in APA o ma Nkezabe a, P. & Dinesh, R. (2025). In eg a ing IoT o Imp o e Real-Time Visibili y and E iciency in
Rwanda’s Heal hca e Supply Chain. IRASS Jou nal o Economics and Business Managemen . 2(10)74-80.
In oduc ion
The digi al ans o ma ion o heal hca e has accele a ed
o e he pas decade, wi h he In e ne o Things (IoT) eme ging as
a c i ical inno a ion o supply chain managemen . IoT e e s o
in e connec ed de ices such as senso s, RFID ags, and cloud-
based sys ems ha collec and sha e eal- ime da a o imp o e
isibili y and e iciency (A zo i, Ie a, & Mo abi o, 2010). In
heal hca e, IoT suppo s p edic i e analy ics, eal- ime moni o ing,
and imp o ed coo dina ion be ween supplie s, dis ibu o s, and
se ice p o ide s. Fo example, IoT-enabled cold chain moni o ing
was essen ial du ing he COVID-19 pandemic o ensu e accine
in eg i y and educe was age (WHO, 2021). While high-income
coun ies ha e emb aced such echnologies, low- and middle-
income coun ies (LMICs) ace challenges including in as uc u e
gaps, high cos s, and limi ed echnical expe ise.
In A ica, IoT in eg a ion in heal hca e logis ics emains
limi ed, al hough pilo p ojec s in Kenya, Nige ia, and Sou h A ica
ha e shown p omising esul s in accine acking and in en o y
managemen (Olan ewaju & I e in, 2021). S ill, sys emic ba ie s
such as poo in e ne access, un eliable elec ici y, and a sho age
o skilled pe sonnel slow adop ion (UNCTAD, 2021). Recognizing
hese challenges, he A ican Union’s Digi al T ans o ma ion
S a egy (2020–2030) emphasizes he ole o echnologies like IoT
in s eng hening heal hca e sys ems (A ican Union, 2020).
Simila ly, he Eas A ican Communi y’s Digi al Heal h Roadmap
(2020–2028) highligh s IoT as a ool o add ess ine iciencies in
in en o y moni o ing and las -mile deli e y (EAC Sec e a ia ,
2020).
Rwanda is conside ed a leade in digi al heal h
ans o ma ion in A ica. Following he 1994 genocide, he coun y
in es ed hea ily in ebuilding i s heal hca e sys em h ough
echnology-d i en p og ams such as he Heal h Managemen
In o ma ion Sys em (HMIS), RapidSMS o ma e nal ca e, and he
elec onic Logis ics Managemen In o ma ion Sys em (e-LMIS)
(MOH Rwanda, 2022). Rwanda has also pionee ed d one deli e y
h ough Zipline, d ama ically educing he deli e y ime o i al
medicines and accines. Despi e hese ad ances, challenges pe sis
in u al and unde se ed a eas, whe e delays, s ockou s, and
inconsis en moni o ing o supply chains emain common (Rwanda
Biomedical Cen e, 2023).
Cu en sys ems a e o en agmen ed o limi ed in scope.
Pape -based in en o y me hods a e s ill used in some acili ies,
lea ing p ocesses p one o delays and e o s (Nsanzimana &
IRASS Jou nal o Economics and Business Managemen . Vol-2, Iss-10 (Oc obe -2025), 74-80
Vol-2, Iss-10 (Oc obe -2025)
75
Akumun u, 2024). While sys ems such as e-LMIS and ERP
pla o ms imp o e cen alized moni o ing, hey do no ully
inco po a e IoT capabili ies ha allow dynamic and p edic i e
supply chain managemen (Chemonics In e na ional, 2024). Thus,
Rwanda’s heal hca e supply chain o en emains eac i e ins ead o
p oac i e, unde mining esilience and e iciency.
This s udy examined he ole o IoT in enhancing
Rwanda’s heal hca e supply chain by ocusing on supply chain
isibili y, s ock managemen , and logis ics e iciency. The pu pose
was o e alua e how IoT in luences key pe o mance indica o s
such as s ockou a es, in en o y accu acy, and deli e y imelines,
while also cap u ing he pe spec i es o heal hca e p o essionals on
i s adop ion.
The s udy was guided by he ollowing esea ch ques ions:
1. Wha is he ela ionship be ween IoT use and
imp o emen s in supply chain isibili y wi hin Rwandan
heal hca e ins i u ions?
2. How do heal hca e p o essionals’ a i udes owa d IoT
in luence i s up ake and sus ained use?
3. Does IoT-speci ic aining among heal hca e s a
co ela e wi h imp o ed e iciency?
4. How do manage ial capaci y and educa ional backg ound
shape IoT adop ion?
5. Wha ole do geog aphic dispa i ies play in in luencing
IoT e ec i eness in Rwanda’s heal hca e supply chains?
This esea ch is signi ican in se e al ways. Fi s , i ills a gap in
li e a u e, as mos s udies on IoT in heal hca e logis ics a e based
in high-income con ex s. Second, i p o ides empi ical insigh s
ailo ed o Rwanda, o e ing bo h quan i a i e and quali a i e
e idence o guide policy and implemen a ion. Thi d, i aligns wi h
Rwanda’s Heal h Sec o S a egic Plan IV (2020–2025) and Vision
2050, which emphasize digi al inno a ion as a d i e o sus ainable
heal hca e deli e y (MINICT, 2023). The indings a e expec ed o
in o m policymake s, dono s, and heal hca e manage s abou
scalable IoT applica ions, he eby suppo ing e o s owa d
uni e sal heal h co e age and imp o ed heal h equi y.
Li e a u e Re iew
The In e ne o Things (IoT) has eshaped heal hca e
logis ics by enabling connec ed sensing, eal- ime acking, and
da a-d i en coo dina ion ac oss p ocu emen , s o age, and deli e y
(A zo i, Ie a, & Mo abi o, 2010). In low- esou ce se ings, weak
in en o y con ol, cold-chain b eaks, and limi ed isibili y
con ibu e o s ockou s and was e; IoT o e s co ec i e
anspa ency and esponsi eness (Kabe a & Mukanyangezi, 2024).
Th ee complemen a y amewo ks a e mos ci ed in explaining his
ans o ma ion. Sys ems Theo y highligh s in e dependencies
ac oss logis ics subsys ems and he alue o eedback loops ha
IoT senso s ende isible (Chong e al., 2017). The Technology–
O ganiza ion–En i onmen (TOE) amewo k explains adop ion
de e minan s, including echnological i and in e ope abili y,
o ganiza ional capaci y and leade ship, and en i onmen al enable s
such as policy and in as uc u e (To na zky & Fleische , 2018;
Mpinganji a, 2021). The SCOR model links IoT unc ions o
ope a ional p ocesses—Plan, Sou ce, Make, Deli e , Re u n—
p o iding a s uc u e o pe o mance benchma king a e
digi iza ion (Supply Chain Council, 2012).
Key concep s ecu in he e idence base. Real- ime
acking h ough RFID, GPS, and senso s imp o es accoun abili y
and educes delays (Kache & Seu ing, 2017). P edic i e analy ics
uses his o ical and s eaming da a o an icipa e demand and
eplenish p oac i ely, lowe ing eme gency p ocu emen and
s ockou isk (Chong e al., 2017; Po e & Heppelmann, 2014).
Cold-chain managemen bene i s om con inuous
empe a u e/humidi y moni o ing and ale ing, educing spoilage
(GAVI, 2021). In e ope abili y s anda ds (e.g., HL7 FHIR) a e
essen ial o p e en da a silos and enable end- o-end isibili y
(HL7 In e na ional, 2020).
Empi ical s udies om high-income con ex s epo
subs an ial gains. Sou h Ko ean hospi al deploymen s cu
pha maceu ical s ockou s by abou 30% ia RFID-enabled cabine s
ha synch onized wi h cen al in en o y (Lee & Lee, 2015). No h
Ame ican and Eu opean cases show up o 20% educ ions in
ope a ing cos s, and eme gency logis ics imp o ed wi h connec ed
ambulance pla o ms ha sho ened esponse imes by oughly
15% (Papadopoulos e al., 2017; Wamba & Quei oz, 2019). These
ou comes ely on ma u e in as uc u e, s able unding, and obus
go e nance.
In LMICs, a ge ed IoT pilo s also yield measu able
bene i s. Kenya’s ART and accine logis ics imp o ed deli e y
p ecision and educed d ug loss by abou a qua e (Olan ewaju &
I e in, 2021). Tanzania’s ma e nal-heal h ki s eached emo e
clinics as e wi h GPS-equipped anspo and cold-chain senso s
(GAVI, 2021). In Uganda, con inuous empe a u e moni o ing
inc eased he a ailabili y o iable oxy ocin doses a he poin o
ca e (Muwanguzi & Musoke, 2020). Success ac o s include
in eg a ion wi h na ional in o ma ion sys ems, local capaci y
building, and dono -go e nmen pa ne ships.
Rwanda’s digi al heal h ounda ion—HMIS, e-LMIS/ERP,
RapidSMS, and d one deli e y—posi ions he sys em o IoT
scale-up, ye gaps pe sis . Ru al acili ies s ill ely on pape
p ocesses, cold-chain moni o ing is inconsis en , and digi al skills
and connec i i y a y (Rwanda Biomedical Cen e, 2023;
Nsanzimana & Akumun u, 2024). Na ional digi al s a egies a e
enabling, bu in e ope abili y and las -mile eliabili y emain
une en (MINICT, 2023; HL7 In e na ional, 2020).
Add essed Gaps
Fi s , mos s udies emphasize echnical me ics bu gi e
limi ed a en ion o o ganiza ional beha io , wo k o ce eadiness,
and manage ial use o IoT da a in ou ine decisions (Chong e al.,
2017; To na zky & Fleische , 2018). Second, LMIC e idence is
o en pilo -based and sho - e m, limi ing insigh on sus ainabili y
and cos -e ec i eness (GAVI, 2021; Olan ewaju & I e in, 2021).
Thi d, Rwanda-speci ic, mixed-me hods analyses ha link
quan i a i e KPIs (s ockou s, in en o y accu acy, lead imes) wi h
quali a i e pe spec i es (a i udes, aining, go e nance) a e sca ce
(Rwanda Biomedical Cen e, 2023; Nsanzimana & Akumun u,
2024). This s udy esponds by applying Sys ems–TOE–SCOR
lenses, in eg a ing acili y-le el me ics wi h s akeholde iews,
and o eg ounding in e ope abili y and equi y conside a ions o
gene a e ac ionable, con ex -sensi i e guidance o IoT adop ion in
Rwanda’s heal hca e logis ics.
Me hodology
Resea ch Pa adigm and Design
The s udy ollowed a p agma ic pa adigm, ocusing on
solu ions ha wo k in eal se ings and allowing mul iple me hods
IRASS Jou nal o Economics and Business Managemen . Vol-2, Iss-10 (Oc obe -2025), 74-80
Vol-2, Iss-10 (Oc obe -2025)
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o add ess complex ques ions abou IoT in heal hca e logis ics. A
con e gen mixed-me hods design was used. Quan i a i e and
quali a i e da a we e collec ed a he same ime, analyzed
sepa a ely, and hen in eg a ed o p o ide a comp ehensi e iew o
how IoT a ec s eal- ime isibili y and e iciency.
Se ing, Popula ion, and Sampling
The se ing was Kigali, Rwanda, wi h h ee ins i u ional
s a a: Rwanda Medical Supply (RMS), public hospi als, and
p ima y heal h acili ies. The a ge popula ion included
p o essionals di ec ly in ol ed in supply chain ac i i ies and digi al
sys ems such as eLMIS and SAP ERP. Roles included pha macis s,
da a quali y o ice s, p ocu emen o ice s, s o e manage s, and
RMS dis ibu ion s a . A s a i ied pu posi e sampling app oach
ensu ed co e age o he h ee le els o he sys em. Wi hin each
s a um, pu posi e selec ion a ge ed in o ma ion- ich pa icipan s
wi h hands-on expe ience o IoT o ela ed pla o ms. The sample
size was 100: RMS (n=20), public hospi als (n=20), and p ima y
heal h acili ies (n=60). Inclusion c i e ia equi ed a leas six
mon hs in ole and some in e ac ion wi h digi al logis ics ools.
In e ns, e y new s a , and hose no engaged in logis ics we e
excluded.
Ins umen s and Pilo Tes ing
Quan i a i e ins umen , A s uc u ed ques ionnai e
measu ed key pe o mance indica o s: equency and du a ion o
s ockou s, in en o y accu acy, o de ul illmen a es, deli e y lead
imes, upda e and synch oniza ion equency, use -pe cei ed
eliabili y, and he deg ee o in eg a ion be ween IoT de ices and
exis ing pla o ms. Mos i ems used i e-poin Like scales, wi h a
ew bina y and equency i ems.
Quali a i e ins umen s, A semi-s uc u ed in e iew
guide explo ed pe cep ions o IoT adop ion, aining and suppo ,
in e ope abili y, policy and go e nance, and o ganiza ional
eadiness. Documen e iew checklis s co e ed in en o y logs,
pe o mance epo s, aining eco ds, s a egic plans, and audi s.
Obse a ion checklis s cap u ed on-si e wo k lows, de ice use,
da a en y p ac ices, in as uc u e condi ions, and s a
in e ac ions.
All ins umen s we e expe e iewed and pilo ed wi h 10 logis ics
and pha macy s a . Wo ding and sequencing we e e ined o
cla i y. The ques ionnai e showed C onbach’s alpha = 0.82,
indica ing good in e nal consis ency.
Da a Collec ion P ocedu es
Da a we e collec ed concu en ly ac oss he h ee s a a.
Ques ionnai es we e adminis e ed on pape o digi ally, depending
on access. In e iews las ed 45–60 minu es, we e audio- eco ded
wi h consen , and conduc ed in English o Kinya wanda.
Documen e iews we e ca ied ou on si e whe e possible. Non-
in usi e obse a ions a RMS, hospi als, and heal h acili ies
cap u ed ou ine ope a ions du ing ac i e hou s. Field no es
documen ed p ac ices and con ex .
Da a Analysis
Quan i a i e analysis, Da a we e cleaned and analyzed in
SPSS. Desc ip i e s a is ics ( equencies, means, s anda d
de ia ions) p o iled esponden s and echnology use. Reliabili y
checks we e epea ed o mul i-i em scales. In e en ial es s
examined ela ionships and g oup di e ences: chi-squa e o
ca ego ical associa ions, Pea son co ela ions o linea
ela ionships, independen - es s o wo-g oup compa isons, and
one-way ANOVA ac oss he h ee s a a.
Quali a i e analysis, In e iew ansc ip s, documen s, and
obse a ion no es we e analyzed hema ically using a hyb id
app oach. Deduc i e codes e lec ed he esea ch ques ions
( aining, in e ope abili y, in as uc u e, go e nance), while
induc i e codes cap u ed eme gen ideas (wo ka ounds, s a
con idence). Themes we e e ined h ough i e a i e e iew.
In eg a ion and T iangula ion
Following sepa a e analyses, indings we e me ged and
compa ed o iden i y con e gence, di e gence, and
complemen a i y. A join display aligned quan i a i e indica o s
wi h quali a i e hemes. Fo example, measu ed educ ions in
s ockou equency we e in e p e ed alongside na a i es o
imp o ed anspa ency, as e decisions, and clea e escala ion
pa hways.
E hical Conside a ions
E hical app o al and ins i u ional pe missions we e
ob ained be o e ieldwo k. In o med consen was secu ed om all
pa icipan s. Da a we e anonymized and s o ed secu ely wi h
es ic ed access. Pa icipa ion was olun a y, and in e iews we e
scheduled o a oid dis up ing se ices.
Limi a ions
Findings a e a ec ed by non- andom sampling, po en ial
esponse bias, and in as uc u e cons ain s ha limi ed some
digi al da a collec ion. These isks we e mi iga ed h ough
iangula ion, clea inclusion c i e ia, and mul i-si e co e age
ac oss he h ee s a a.
Resul s
This sec ion p esen s empi ical indings om quan i a i e
and quali a i e da a collec ed ac oss Rwanda Medical Supply
(RMS), public hospi als, and p ima y heal h acili ies in Kigali.
Resul s a e o ganized by he s udy objec i es and show how
In e ne o Things (IoT) echnologies ela e o eal- ime isibili y
and ope a ional e iciency.
Responden Demog aphics
The sample included 100 esponden s ac oss RMS, hospi als, and
heal h acili ies. Below a e he key demog aphic pa e ns.
Figu e 1. Gende dis ibu ion o esponden s
62% male and 38% emale indica es men domina e logis ics and
IT- acing oles. B oade gende inclusion in aining and
IRASS Jou nal o Economics and Business Managemen . Vol-2, Iss-10 (Oc obe -2025), 74-80
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ec ui men could s eng hen digi al heal h adop ion and p oblem-
sol ing di e si y.
Figu e 2. Age g oup dis ibu ion o esponden s
Wi h hal o esponden s aged 26–35 and only 2% abo e 60, he
wo k o ce is young and adap able. This a o s apid up ake o IoT,
especially when aining is p ac ical and hands-on.
Table 1. P o essional oles o esponden s (pe cen )
Pha macis s a e he la ges use g oup, hen logis ics and IT s a .
Design aining, wo k lows, and dashboa d iews wi h
pha macis s’ decision needs in mind, while enabling close
collabo a ion wi h logis ics and IT.
Table 2. Educa ion le els o esponden s (pe cen ).
Mos esponden s hold a bachelo ’s o mas e ’s deg ee, sugges ing
eadiness o ad anced digi al skills. Tailo con en dep h by ole o
ensu e on line s a can apply analy ics in ou ine asks.
Table 3. Facili y ype dis ibu ion (pe cen ).
Six y pe cen a e om heal h acili ies, ensu ing on line eali ies
a e ep esen ed. Insigh s should gene alize well ac oss p ima y ca e
si es in Kigali.
The wo k o ce is ela i ely young and well educa ed. Pha macis s
a e he la ges g oup, ollowed by logis ics and IT s a . Mos
esponden s a e based a heal h acili ies, which a e he p ima y
se ice poin s.
Objec i e One: IoT Use and Supply Chain Visibili y
A majo i y o esponden s epo ed ha hei acili ies use IoT
de ices. Adop ion is highes a RMS and hospi als, wi h lowe
up ake a p ima y heal h acili ies.
Figu e 3. Use o IoT de ices o logis ics
58% epo IoT use, 28% do no , and 14% a e unsu e. Adop ion is
es ablished bu une en; he 'unsu e' g oup signals limi ed exposu e
o weak in e nal communica ion abou ools in use.
Figu e 4. Types o IoT ools used in acili ies.
RFID/ba code domina es, ollowed by dashboa ds and senso s;
GPS acking lags. Si es a e s ong on s ock digi iza ion bu weake
on in- ansi isibili y— a ge GPS whe e mo emen acking is
c i ical. Responden s commonly ci ed RFID o ba code scanne s,
cloud dashboa ds, and sma senso s. GPS acke s we e less
p e alen . Pe cep ions indica e IoT imp o es eal- ime s ock
isibili y, educes los in en o y, and enables acking o
deli e ies.
Objec i e Two: A i udes and Con idence in IoT Use
Con idence Le el
F equency (n)
Pe cen age (%)
No con iden
12
12
Sligh ly con iden
20
20
Mode a ely con iden
38
38
Ve y con iden
30
30
Table 4. Con idence in using IoT ools
68% a e a leas mode a ely con iden , which suppo s sus ained use. The emaining 32% need e eshe aining, quick e e ence aids, and
esponsi e suppo o a oid allback o manual p ocesses.
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Table 5. A i udes owa d IoT in logis ics wo k lows (coun s)
Mos s a say IoT makes wo k easie and a e open o new ools.
Pe cei ed usabili y is solid, bu only abou hal eel echnical
suppo is su icien —suppo is he main bo leneck o scale.
O e all, 68 pe cen o esponden s epo ed a leas mode a e
con idence in using IoT. Se en y pe cen ag eed ha IoT makes
wo k easie , and 77 pe cen we e open o new ools. Usabili y was
iewed posi i ely by 63 pe cen , bu only 53 pe cen el hey had
su icien echnical suppo .
Objec i e Th ee: T aining and Supply Chain E iciency
Figu e 5. Fo mal IoT aining co e age.
T aining eached 62% o s a , lea ing sizable gaps a lowe - ie
acili ies. Closing his gap will imp o e consis ency in eal- ime
da a cap u e and sys em use.
Figu e 6. Numbe o IoT aining sessions in he las 12 mon hs
(among ained s a ).
Nea ly hal o ained s a ecei ed wo o mo e sessions, which
suppo s skill e en ion. Single-session exposu e is common and
usually insu icien o con iden day- o-day use.
S a emen
Ag ee+S ongly Ag ee (%)
T aining inc eased e iciency
76
T aining imp o ed da a accu acy
73
Can p edic /p e en s ockou s
64
Table 6. Pe cei ed impac o aining on pe o mance
Mos esponden s epo be e e iciency, da a accu acy, and s ockou p e en ion a e aining. Make e eshe s ou ine and pai hem wi h on-
he-job coaching o cemen gains.
Objec i e Fou : Educa ion and Manage ial Capaci y
IoT
Tool/Ac i i y
1 (No
p o icien )
2
3
4
5 (Highly
p o icien )
Da a
dashboa d
in e p e a ion
5
8
20
40
27
Gene a ing
in en o y
epo s
7
10
22
35
26
P edic i e
analy ics o
demand
planning
10
15
25
30
20
Coo dina ion
wi h pa ne s
8
12
24
34
22
Response
IoT Makes Wo k
Easie
Open o New Tools
Use -F iendly
Tools
Su icien
Technical Suppo
S ongly Disag ee
(1)
4
3
5
7
Disag ee (2)
8
5
10
14
Neu al (3)
18
15
22
26
Ag ee (4)
46
42
38
35
S ongly Ag ee (5)
24
35
25
18
Response
IoT Makes Wo k
Easie
Open o New Tools
Use -F iendly Tools
Su icien Technical
Suppo
S ongly Disag ee
(1)
4
3
5
7
Disag ee (2)
8
5
10
14
Neu al (3)
18
15
22
26
Ag ee (4)
46
42
38
35
S ongly Ag ee (5)
24
35
25
18
IRASS Jou nal o Economics and Business Managemen . Vol-2, Iss-10 (Oc obe -2025), 74-80
Vol-2, Iss-10 (Oc obe -2025)
79
Table 7. Sel - a ed p o iciency wi h IoT ac i i ies (coun s).
P o iciency is highes o dashboa ds and epo ing, and lowe o
p edic i e analy ics. Add sho applied o ecas ing labs o mo e
eams om epo ing o p oac i e planning.
Responden s a ed hemsel es mos p o icien in dashboa d
in e p e a ion and epo ing. P edic i e analy ics skills we e
weake , indica ing he need o ad anced aining. Only 44 pe cen
el o mal educa ion p epa ed hem well o IoT.
Table 8. Educa ional p epa a ion o IoT (sha es).
Only 44% eel p e-se ice educa ion p epa ed hem well.
S eng hen in-se ice digi al cu icula and embed IoT and heal h
in o ma ics in o uni e si y p og ams.
Objec i e Fi e: Geog aphic Dispa i ies and IoT E ec i eness
Figu e 7. Digi al in as uc u e a ings.
Fo y pe cen a e in as uc u e as ai o poo , indica ing eal
cons ain s o eal- ime sys ems. P io i ize connec i i y, s able
powe , and o line- i s wo k lows in weake si es.
Figu e 8. Pe cei ed IoT e ec i eness ac oss u ban and u al
acili ies.
In e p e a ion: Only 36% say IoT wo ks equally well in u ban and
u al se ings. Add ess u al gaps in connec i i y, de ices, and
suppo o close pe o mance dispa i ies.
Figu e 9. Pe cep ions o geog aphic dispa i ies and ou comes.
Use s ecognize bo h bene i s ( as e esponse) and inequi ies ( u al
challenges and connec i i y ba ie s). A a ge ed u al eliabili y
package can unlock sys em-wide gains.
In as uc u e and connec i i y a ied by se ing. Fo y
pe cen a ed in as uc u e as ai o poo . Responden s widely
ecognized u al challenges and connec i i y limi s. Pe cei ed
gains in pa ien esponse imes suppo he alue o IoT, bu
une en in as uc u e educes e ec i eness ou side u ban cen e s.
Discussion
The indings show ha IoT adop ion in Kigali’s public
heal h logis ics is eal bu une en: 58% o acili ies epo using
ools, led by RFID/ba code and cloud dashboa ds, wi h measu able
gains in isibili y and e iciency, including a epo ed 25% d op in
s ockou s and highe in en o y accu acy. These e ec s mi o
e idence om high-income se ings whe e IoT educes s ockou s
and imp o es ope a ions (Lee & Lee, 2015; Papadopoulos e al.,
2017) and align wi h cos and pe o mance imp o emen s epo ed
elsewhe e (Wamba & Quei oz, 2019). Posi i e use a i udes a e
s ong (70–77%), ye only 53% pe cei e su icien echnical
suppo , indica ing ha o ganiza ional enable s s ill lag. T aining
eme ges as a pi o al le e : 62% ecei ed aining and mos
c edi ed i wi h be e e iciency and da a accu acy, consis en wi h
LMIC pilo s ha link capaci y building o imp o ed deli e y
p ecision (Olan ewaju & I e in, 2021; GAVI, 2021).
The mode a e co ela ion be ween educa ional p epa edness
and p o iciency ( ≈ 0.45) highligh s a cu iculum gap ha limi s
ad anced uses such as p edic i e analy ics. Sys ems Theo y
explains how weak links in suppo o connec i i y p opaga e
sys em-wide, while he TOE amewo k cla i ies slowe up ake in
low- eadiness en i onmen s (To na zky & Fleische , 2018).
U ban– u al dispa i ies in in as uc u e and skills echo p io
Rwanda- ocused analyses (Kabe a & Mukanyangezi, 2024).
Scaling IoT impac will he e o e equi e sus ained aining,
s eng hened suppo , and equi y-o ien ed in as uc u e
in es men s.
Conclusion
This s udy assessed how In e ne o Things (IoT)
echnologies a e shaping eal- ime isibili y and e iciency in
Rwanda’s public heal h supply chain. E idence om 100
pa icipan s ac oss Rwanda Medical Supply (RMS), hospi als, and
heal h acili ies shows meaning ul bu une en adop ion: 58% o
acili ies epo using IoT, led by RFID/ba code and cloud
dashboa ds. Whe e IoT is ac i e, pe o mance gains a e clea
esponden s and acili y eco ds indica e ewe s ockou s (≈25%
educ ion) and highe in en o y accu acy, wi h 72% epo ing
imp o ed s ock isibili y and 74% con i ming eal- ime deli e y
acking.
Human ac o s a e la gely a o able: mos s a iew IoT
posi i ely and 68% epo mode a e o high con idence, ye only
53% pe cei e adequa e echnical suppo . T aining is a decisi e
enable ; 62% ecei ed o mal aining and mos link i o be e
e iciency and da a quali y, while a mode a e co ela ion be ween
educa ional p epa edness and p o iciency ( ≈ 0.45) signals
cu iculum gaps o ad anced asks such as p edic i e analy ics.
The mos pe sis en cons ain is inequi y: in as uc u e, powe ,
and suppo limi a ions in pe i-u ban and u al se ings dampen IoT
e ec i eness, mi o ing he 40% a ing hei digi al in as uc u e
Response
Numbe o Responden s
Pe cen age
Yes
44
44
No
26
26
Somewha
30
30
IRASS Jou nal o Economics and Business Managemen . Vol-2, Iss-10 (Oc obe -2025), 74-80
Vol-2, Iss-10 (Oc obe -2025)
80
as ai /poo and he pe cep ion ha ools wo k less well ou side
u ban cen e s.
Fu u e esea ch should mo e beyond sho - e m ope a ional
me ics o examine longi udinal e ec s on pa ien ou comes,
esilience, and equi y. P io i y di ec ions include: (1) igo ous cos -
e ec i eness and e u n-on-in es men analyses o guide scale-up;
(2) implemen a ion science s udies ha es ―wha wo ks, o
whom, and unde wha condi ions,‖ especially in u al and
esou ce-cons ained acili ies; (3) e alua ions o in e ope abili y
and da a go e nance when in eg a ing IoT wi h e-LMIS,
HMIS/DHIS2, and cold-chain sys ems, including o line- i s and
edge solu ions; (4) con olled ials o capaci y-building models
(e.g., men o ship, e eshe mic o-lea ning) linked o measu able
logis ics KPIs; (5) echnical pe o mance audi s o senso s/GPS
(accu acy, up ime, calib a ion) ied o se ice le els; and (6)
equi y-cen e ed inqui ies on gende , digi al li e acy, and language-
app op ia e UX. Pu suing hese lines will con e p omising pilo s
in o sus ainable, sys em-wide imp o emen s ha eliably deli e
he igh commodi ies, in he igh condi ion, o e e y pa ien in
Rwanda.
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