Relational network of innovation ecosystems generated by digital innovation hubs: a conceptual framework for the interaction processes of DIHs from the perspective of collaboration within and between their relationship levels

Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
h ps://doi.o g/10.1007/s10845-024-02322-5
Rela ional ne wo k o inno a ion ecosys ems gene a ed by digi al
inno a ion hubs: a concep ual amewo k o he in e ac ion p ocesses
o DIHs om he pe spec i e o collabo a ion wi hin and be ween hei
ela ionship le els
Julio C. Se ano-Ruiz1·José Fe ei a2·Rica do Ja dim-Goncal es2·Ángel O iz1
Recei ed: 23 Decembe 2022 / Accep ed: 4 Janua y 2024 / Published online: 2 Ma ch 2024
© The Au ho (s) 2024
Abs ac
Collabo a ion plays a key ole in he success a ained o da e by ne wo ks o inno a ion ecosys ems gene a ed a ound en i ies
knownasDigi alInno a ionHubs(DIHs), ecen lyc ea ed ollowingEu opeanCommissionini ia i es oboos hedigi isa ion
o he Eu opean economic ab ic. This a icle p oposes a concep ual amewo k ha b ings oge he , de ines, s uc u es and
ela es he concep s in ol ed in he collabo a i e in e ac ion p ocesses wi hin and be ween hese inno a ion ecosys ems
o allow comp ehensi e concep ualisa ion. The de eloped amewo k also p o ides an app oach ha helps o angibilise
collabo a ion as a managemen p ocess. He e he goal is o ul ima ely mo e owa ds no only quali a i e, bu also quan i a i e
modelling o b idge he esea chgapin hes a e o he a in his espec . Theda a-d i enbusiness-ecosys em-skills- echnology
(D-BEST) model, de ised o con igu e DIHs se ice po olios in a collabo a i e con ex , p o ides he e e ence basis o he
in e o ganisa ional asse ans e me hodology (IOATM). This is he keys one ha s uc u es he amewo k and cons i u es
i s main con ibu ion. Th ough he IOATM, his concep ual amewo k poin s ou collabo a ion quan i ica ion, and se es as
a le e o i s modelling o deal wi h collabo a ion accoun ing by: u ning i in o a mo e con ollable managemen elemen ;
guiding p ac i ione s’ e o s o imp o e collabo a i e p ocesses e iciency wi h an app oach ha pu sues objec i i y and
maximises syne gies.
Keywo ds Inno a ion ecosys em ·Digi al inno a ion hubs ·Collabo a ion ·In e o ganisa ional asse ans e me hodology
Abb e ia ions
CIGIP Cen o de In es igación en Ges ión e Inge-
nie ía de la P oducción
BJulio C. Se ano-Ruiz
[email p o ec ed].es
José Fe ei a
jap @unino a.p
Rica do Ja dim-Goncal es
g@unino a.p
Ángel O iz
[email p o ec ed].es
1Resea ch Cen e on P oduc ion Managemen and
Enginee ing, CIGIP, Uni e si a Poli ècnica de València
(UPV), Alcoy, Spain
2Cen e o Technology and Sys ems, CTS, UNINOVA, Lisbon,
Po ugal
CPES Cybe –physical ene gy sys em
CPS Cybe –physical sys em
CKTI Compe ences, knowledge, echnology and
in as uc u e
CTTE C oss-domain echnology ans e expe imen
D-BEST Da a-d i en business–ecosys em–skills– ech-
nology e e ence model
DIHs Digi al Inno a ion Hub
DIH4CPS Digi al Inno a ion Hubs o Cybe –Physical
Sys ems
EC Eu opean Commission
EDIHs Eu opean Digi al Inno a ion Hubs
Ei2Ne wo k Eu opean Ne wo k o In e ope abili y and
Inno a ion
ETB Ecosys em– echnology–business model
ETBSD Ecosys em– echnology–business-skills-da a
model
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1506 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
EU Eu opean Union
FTTE Focused echnology ans e expe imen
GDPR Gene al Da a P o ec ion Regula ion
iAE6 Applica ion expe imen numbe 6 o he
DIHCPS p ojec
InnDIHs Digi al Inno a ion Hubs o he economic p o-
mo ion o he Valencian Communi y
IOATM In e o ganisa ionalasse ans e me hodology
ITI Ins i u o Tecnológico de In o má ica
I-VLab Eu opean Vi ual Labo a o y o En e p ise
In e ope abili y
i4OPT Indus ial P oduc ion and Logis ics Op imisa-
ion in Indus y 4.0
I4Q Indus ial Da a Se ices o Quali y Con ol in
Sma Manu ac u ing
KTE Knowledge ans e expe imen s
MaaS Ma ke place-as-a-Se ice
MES Manu ac u ing execu ion sys em
MOM Manu ac u ing ope a ions managemen
M2M Machine- o-machine communica ion
OPC UA Open pla o m communica ions uni ed a chi-
ec u e
PAE Pa h inde Applica ion Expe imen
PoC P oo o concep
R&D Resea ch and De elopmen
SME Small- and medium-sized en e p ises
UPV Uni e si a Poli ècnica de València
ZDMP Ze o-De ec Manu ac u ing Pla o m
In oduc ion
Eu opean small- and medium-sized en e p ises (SMEs) gen-
e ally acea ola ile,complexand ie celycompe i i eglobal
scena io. They ha e a signi ican disad an age in ela ion
o la ge companies and global co po a ions, which usually
be e access inancing, esou ces and skills acquisi ion; all
his enables he la e o compe e wi h highe pe o mance
le els and hey, he e o e, ha e mo e sus ainabili y expec a-
ions (Bakh ia i e al., 2020). Indeed he obs acles ha SMEs
usually encoun e in acqui ing, implemen ing and exploi ing
digi al echnologies and skills espond o his pa e n, which
implies slowing down hei jou ney owa ds digi al ma u i y.
SMEs a e i al o he Eu opean Union’s (EU) economy,
and accoun o 99% o all EU companies and wo hi ds
o all p i a e sec o jobs. Howe e , only 20% o Eu opean
SMEs a e highly digi ised, unlike la ge co po a ions whose
pe cen age eaches 50% (Goua dè es, 2021). The Eu opean
Commission (EC) is awa e o his. This is why i has been
p omo ing p og ammes and ini ia i es o yea s o os e
inno a ion and o acili a e SMEs’ digi al ans o ma ion,
especially hosein hemos digi allyimma u esec o s.Inline
wi h his, a no able suppo line is o inance p ojec s o c e-
a e and s eng hen a se ies o egional inno a ion ecosys ems
in e connec ed in a pan-Eu opean ne wo k. The main d i e s
o such ecosys ems a e he so-called Digi al Inno a ion Hubs
(DIHs), egional coope a ion o ganisa ions made up o many
di e sepa ne s whose mission is wo old: egionally, o help
en ep eneu s in he egion o o e come inno a ion obs a-
cles and digi al ans o ma ion di icul ies by p o iding hem
wi h easy access o knowledge and digi al solu ions (Miö ne
e al., 2019), essen ial ools o success in oday’s global ma -
ke ; a he Eu opean le el, o p omo e he implemen a ion
o an in e disciplina y and collabo a i e ne wo k o inno a-
ion p o ide s, which will help o keep Eu ope in a posi ion
o echnological leade ship in oday’s complex geopoli ical
scena io.
DIHs we e ini ially concei ed by he EC in 2016 as pa
o he i s indus y- ela ed ini ia i e o he Digi al Single
Ma ke package and as one o he mos impo an pilla s
o he Digi ise Eu opean Indus y e o (Rissola & Sö ik,
2018). Since 2021, his e o is being complemen ed by he
igu e o Eu opean DIHs (EDIHs) wi hin he amewo k o
he Digi al Eu ope P og amme (HaDEA, 2022). This p o-
g ammeini iallyplans oinc ease hecapaci ieso hecen es
selec ed by each membe coun y o co e ac i i ies wi h a
clea Eu opean added alue based on he collabo a i e ne -
wo king o hubs and p omo ing knowledge ans e . EDIHs
will also suppo companies and public sec o o ganisa ions
in using digi al echnology o imp o e he sus ainabili y o
hei p ocesses and p oduc s, pa icula ly ega ding ene gy
use and educing ca bon emissions (HaDEA, 2022). DIHs
and EDIHs gene ally p o ide access o know-how and expe -
imen a ion, and o he possibili y o “ y be o e you in es ”
by helping companies o imp o e business/p oduc ion p o-
cesses, p oduc s o se ices ha employ digi al echnologies.
They also p o ide inno a ion se ices, such as ad ice on
inancing, aining and skills de elopmen , which a e neces-
sa y o he success o digi al ans o ma ion (EC, 2022).
This se o inno a ion-d i ing en i ies is gene a ing a ela-
ional ne wo k wi hin he geog aphical amewo k o EU
e i o y in which, om a hie a chical pe spec i e, ou ela-
ionship le els can be iden i ied (Fig. 1): (i) DIH le el: ha
exis ing wi hin each DIH be ween he o ganisa ions mak-
ing i up. A DIH is a one-s op-shop ype s uc u e ha helps
o ganisa ions o be mo e compe i i eby imp o ingp ocesses
and inno a ing p oduc s and se ices ia digi al echnolo-
gies. I may include esea ch cen es, echnology cen es,
uni e si ies, p i a e echnology se ice p o ide s, associ-
a ions, Chambe s o Comme ce, incuba o o accele a o
o ganisa ions, egional de elopmen agencies, and e en go -
e nmen al o ganisa ions (EC, 2022) wi hin a egional scope.
I is a space whe e i o e s suppo se ices o o ganisa ions,
usually h ough a pa ne pla o m. The suppo se ices
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Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1507
Fig. 1 Rela ionship le els wi hin he pan-Eu opean space o DIHs ac ion
ha a DIH can o e include awa eness aising o digi isa-
ion echnologies,inno a ionexplo a ion,de eloping isions
and s a egies o companies, aining, access o unds and
in es men s, collabo a i e esea ch, ad ocacy and ne wo k-
ing e en s, among o he s (Geo gescu e al., 2021). DIHs ha e
ou main unc ions ha cha ac e ise hem, namely ne wo k-
ing, skills and aining, p e-in es men es ing and access
o inance (Asplund e al., 2021); (ii) DIH ecosys em: ha
gene a ed in each indi idual egional inno a ion ecosys em
p omo ed a ound a speci ic DIH be ween i as an inno a ion,
knowledge, and echnology p o ide , plus he o ganisa ions
ecei ing any se ices included in he DIH po olio, ypi-
cally SMEs ha need suppo owa ds digi al ans o ma ion,
albei no exclusi ely; (iii) EDIH le el: ha which in eg a es
a se o ne wo ked DIHs in o a g oup and u ns i in o a
collabo a i e ne wo k wi h p esence beyond a speci ic EU
coun y. Such in eg a ion occu s by ei he belonging o a
join p ojec unded o his pu pose o he exis ence o some
o he ypeo ansac ionalag eemen be weenDIHs; (i )The
highes le el: he pan-Eu opean EDIH ne wo k: he le el
o po en ial in e ac ion in which he di e en exis ing DIH
g oups in Eu ope, supposedly ac ing in compe i ion, can c e-
a e coope a ion channels by gene a ing schemes o so-called
coope i ion (Planko e al., 2019) o simila ; e.g. in e ms o
associa ion o ede a ion o de end hei common in e es s
is-à- is adminis a ions and socie y a la ge, which is s ill
an unde explo ed and unde exploi ed space.
The in e ac ion lows wi hin his ela ional ne wo k
depend on se e al ac o s. They a e mainly de e mined by
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1508 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
he in ol ed ac o s and hei ypology, hei in e es s, s a e-
gies, objec i es, esou ces, capaci ies, ini ia i es, willingness
o wo k in collabo a ion, and he ansac ional o mal o
in o mal ag eemen s, ha exis be ween ac o s. They also
depend on ac o s such as: he needs, in e ms o digi al
knowledge and skills, o he egional business ab ic in which
hey ope a e; he local, egional, na ional and Eu opean leg-
isla ion applicable in each case; o he deg ee and sense o
go e nmen s and public adminis a ions’ in e en ion in he
coope a ion p ocesses a each in e ac ion le el.
The li e a u e does no con ain nume ous con ibu ions
on he s udy o DIHs. The e a e also some amewo ks
and models ha add ess some ypes o collabo a i e in e -
ac ion be ween elemen s o some o he men ioned le els
o in e ac ion, such as collabo a ion i sel , o de i a i es
such as coope a ion, coope i ion, unding, o ins i u ional
suppo , among o he s. Ye , as a as we know, he e is
no gene al concep ual amewo k ha add esses he whole
abo e-desc ibed ela ional ne wo k and suppo s all he
possible basic po en ial in e ac ion lows be ween all he ele-
men s a hei ela ionship le els.
Based on he abo e, he main objec i e o his a icle is
o p esen a concep ual amewo k ha , by building on he
abo e o e iew o he ela ional ne wo k o Eu opean inno-
a ion ecosys ems d i en by EC Digi ise Eu opean Indus y
andDigi al Eu ope P og amme ini ia i es, suppo s heexis -
ingin e ac ionp ocessesho izon allyalong hei ela ionship
le els, and e icallybe ween hem, omdual quali a i eand
quan i a i e pe spec i es. The esea ch ques ions posed a e
he ollowing:
•RQ1 Wha cha ac e is ics de ine he in e ac ions ha ake
place be ween he en i ies making up he DIHs ela ional
ne wo kwhen collabo a i ep ocessesma e ialise be ween
hem?
•RQ2 Wha a e he key dimensions o he in e ac ion
p ocesses be ween he en i ies ha cons i u e he DIH ela-
ional ne wo k; wha a e hei concep ual implica ions in
e ms o a quan i a i e assessmen o collabo a ion?
The es o hea icleiso ganisedas ollows.Sec ."Li e a-
u e e iew" de ines he main concep s, delimi s he esea ch
scope, iden i ies he co ela ions be ween concep s shown
by he li e a u e, explains he sea ch me hod o e iew he
li e a u e, p esen s he s a e o he a om he p e iously
explained me hodology and selec s he mos ele an con-
ibu ions. Sec . "Discussion" i s ly p esen s a concep ual
amewo k by iden i ying he key dimensions o he in e -
ac ion p ocesses ha ake place wi hin a ne wo k om dual
quali a i e and quan i a i e pe spec i es. Secondly, i analy-
ses implica ions in in e ope abili y and sus ainabili y e ms.
Sec ion 4 p esen s a use case o p o ide a p ac ical example
o he applica ion o he amewo k. Sec . "Conclusion"dis-
cusses he p oposed concep ual amewo k and analyses he
manage ial and academic implica ions ha de i e om i .
Finally, Sec . 6 o e s conclusions, including i s con ibu ion
o heo y and p ac ice, along wi h limi a ions and possible
u u e esea ch lines.
Li e a u e e iew
Main concep s
This esea ch ocuses mainly on he in e ela ionships
be ween wo ypes o concep s ha pi o a ound he main
one, he DIH concep : (i) hose used o designa e a ype o
o ganisa ion among hose exis ing in he ela ional ne wo k
o Eu opean inno a ion ecosys ems; (ii) hose used o desig-
na ea ypeo in e ac ion ha ispo en iallypossiblewi hin he
o ganisa ions o he ela ional ne wo k o Eu opean inno a-
ion ecosys ems. To gain a be e unde s anding, his sec ion
mo e p o oundly in oduces he main concep s employed in
bo h ca ego ies.
Table 1in oduces he di e en ypes o o ganisa ions ha
can be ound h oughou he ela ional ne wo k o Eu opean
inno a ion ecosys ems.
All hese concep s a e closely in e ela ed and mu ually
suppo i e. Thus a DIH and i s use s o m a speci ic inno a-
ion ecosys em he ein called he DIH ecosys em. A g oup o
DIHs ha wo k oge he o m a clea example o a collabo a-
i e ne wo k. The sum o se e al DIH ecosys ems ope a ing
as a collabo a i e ne wo k o ms an EDIH. The ne wo k
o med by he se o EDIHs ope a ing in EU e i o y o ms
he pan-Eu opean EDIH ne wo k, an impo an pu pose o
he Digi al Eu ope P og amme. In any case, hese concep s
a e, on he whole, in e connec ed a ound he cen al “DIH”
Concep .
F om i s own de ini ion, i is clea ha bo h he di e -
en collabo a i e ne wo k o inno a ion ecosys em ypes ha
a e gene a ed based on DIHs, and he DIH i sel , make
up complex s uc u es popula ed by en i ies o e y di e se
ypologies. This di e si y gi es ise o a wide ange o pos-
sibili ies o mu ual in e ac ion pu poses. Hubs, ecosys ems
and ne wo ks, public and p i a e en i ies, se ice p o ide s
and use s, egional, na ional o pan-Eu opean ac ion spaces,
and so on, gene a e a mul i ude o ela ionship scena ios wi h
some imes o e lapping, some imes comple ely dispa a e
pu poses and wi h a conside able g adien o possibili ies
be ween hese wo ex emes. In sho , he cosmos o igi-
na ing a ound he DIH concep is a highly he e ogeneous
and complex en i onmen whe e in e ac ion possibili ies go
beyond simple collabo a ion. To da e, his concep has been
he cen al axis o a la ge numbe o he s udies, amewo ks
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Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1509
Table 1 O ganisa ion ypes in he
ela ional ne wo k o Eu opean
inno a ion ecosys ems
O ganisa ion ype De ini ion
Collabo a i e ne wo k An o ganisa ion o a a ie y o en i ies (e.g., o ganisa ions and people) ha
a e la gely au onomous, geog aphically dis ibu ed and he e ogeneous in
e ms o hei ope a ing en i onmen , cul u e, social capi al and goals;
ne e heless, hese en i ies collabo a e o be e achie e common o
compa ible goals (Cama inha-Ma os & A sa manesh, 2005). Nowadays,
collabo a i e ne wo ks a e being applied o a wide a ie y o domains
om academic esea ch o manu ac u ing and o he indus ial
applica ions. These implemen a ions a e suppo ed by a a ie y o
collabo a ion o ms, which ange om “supply chains” o eme ging
dynamic s uc u es in he indus y, science and se ices
(Cama inha-Ma os e al, 2019)
DIH A mul i-pa ne collabo a i e o ganisa ion made up o egional en i ies,
i sel pa o a pan-Eu opean ne wo k o simila o ganisa ions, which, in
possession o in as uc u e, echnology, knowledge, compe ences, unds,
o access o hem, and eady o use hem o se e egional business and
public sec o o ganisa ions, oge he wi h hem o m an inno a ion
ecosys em in which he hub, as a cen al ole, p o ides se ices o suppo
he i ine a y o hese o ganisa ions owa ds a ull and e ec i e
digi alisa ion ha makes hem mo e sus ainable and compe i i e
DIH ecosys em An ecosys em o o ganisa ions ha is gene a ed om a digi al inno a ion
hub as a cen al ac o , whe e i ac s as a sou ce o echnologies,
knowledge o skills, which i dissemina es o he o he membe s ha
make up he ecosys em o pilo , es and expe imen wi h digi al
inno a ions o suppo hei digi al ans o ma ion p ocesses
DIH ne wo k A collabo a i e ne wo k made up o DIHs ha ac in coo dina ion o
con ibu e o he de elopmen o he egions and coun ies o he
Eu opean space om a mo e sus ainable posi ion. This is suppo ed
mainly by a la ge o ganisa ional dimension and by a b oade po olio o
knowledge, skills, echnologies and solu ions han ha o i s indi idual
membe s
DIH use An o ganisa ion ha , h ough a ansac ional ag eemen , uses he se ice
po olio o a DIH o ecei e suppo o i s digi al ans o ma ion p ocess
and hus becomes pa o i s ecosys em
Eu opean DIH (EDIHs) A speci ic ype o DIH ne wo k bo n om Digi al Eu ope P og amme
calls, wi h bo h local and Eu opean unc ions, which has inc eased
capaci ies o encompass ac i i ies wi h a clea Eu opean added alue ha
cen e mainly on ne wo king hubs and p omo ing ans e o expe ise.
EDIHs also ha e he s a ed mission o suppo ing companies and public
sec o o ganisa ions in using digi al echnology o imp o e he
sus ainabili y o hei p ocesses and p oduc s, pa icula ly ega ding
ene gy use and educing ca bon emissions. Beyond ha and wi hin
EDIHs, hei DIHs also ac as a one-s op-shop o help companies o be
mo e compe i i e as ega ds hei business/p oduc ion p ocesses,
p oduc s o se ices using digi al echnologies by p o iding access o
echnical expe ise and expe imen a ion. Thus i ms can “ es be o e
in es ”, and p o ide inno a ion se ices (i.e. inancing ad ice, aining
and skills de elopmen ) needed o success ul digi al ans o ma ion
(Di ec o a e-Gene al o Communica ions Ne wo ks, Con en and
Technology o he EC, 2021)
Eu opean DIH Ne wo k A se o EDIH ne wo ks ope a ing in he EU e i o y
Inno a ion ecosys em An e ol ing se o ac o s, ac i i ies and a i ac s, and ins i u ions and
ela ions, including complemen a y and subs i u e ela ions, ha a e
impo an o he inno a i e pe o mance o an ac o o a popula ion o
ac o s (G ans and & Holge sson, 2020). An inno a ion ecosys em e e s
o a loosely in e connec ed ne wo k o companies and o he en i ies ha
co-e ol e capabili ies a ound a sha ed se o echnologies, knowledge o
skills, and wo k coope a i ely and compe i i ely o de elop new p oduc s
and se ices (Nambisan & Ba on, 2013)
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1510 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
and models in he li e a u e abou DIHs and he a bi a-
ion o he ela ionships be ween he en i ies making hem
up. Some examples a e he ecosys em– echnology–busi-
ness model (ETB; Bu e e al, 2020) and i s wo main
e olu ions, he ecosys em– echnology–business–skills–da a
model (ETBSD; Sassanelli e al., 2020) and he da a-d i en
business–ecosys em–skills– echnology e e ence model (D-
BEST; Sassanelli & Te zi, 2022; Sassanelli e al., 2021b).
D-BEST aims o ac as a e e ence o he DIH ne wo ks
specialised in he cybe –physical ene gy sys ems (CPES)
domain o con igu e hei se ice po olios bo h lexibly and
in e ope ably by in eg a ing hei asse s (se ices, compe-
ences, skills, echnologies) in o digi al pla o ms o achie e
lexibili y and in e ope abili y. Bo h a e c i ical aspec s o
achie e DIH ne wo ks’ sus ainabili y. The D-BEST model
is a miles one in he concep ualisa ion and modelling o col-
labo a ion a DIH ecosys em and EDIH le els by igge ing
he iden i ica ion and ma e ialisa ion o se ice-based c oss-
collabo a i e p ocesses be ween DIH ne wo ks on he one
hand, and be ween DIHs hemsel es and hei use s on he
o he . Howe e , i is necessa y o u he cha ac e ise he col-
labo a ion concep om a holis ic pe spec i e by: (i) i s ly
de ining he concep i sel ; (ii) secondly placing i in con-
as o o he ypes o in e ac ion whose essence, objec i es,
me hod and esul s signi ican ly di e ; (iii) inally, by iden-
i ying he possible in e ac ion ypes o among he en i ies
making up DIH s uc u es, hei ne wo ks and ecosys ems
ha can, o some ex en , albei pa ially, ep esen collabo-
a ion. Only wi h his p io cha ac e isa ion is i possible o
b ing oge he , s uc u e and in e ela e he componen s o
a comp ehensi e concep ual amewo k ha add esses he
collabo a ion phenomenon in DIH ela ional ne wo ks.
Rega ding he de ini ion o ull collabo a ion used he ein
as a e e ence, ha chosen is o Wankmülle and Reine
(2020): “P ocess o s a egically wo king oge he on a
speci ic business ac i i y whe e s uc u es a e aligned, com-
munica ion channels a e s anda dised, isks a e sha ed, and
esou cesa epooledino de omake hema ailable o e e y
pa ne ”. In sho , and acco ding o he pe spec i e p o ided
by his de ini ion, al hough pa ne s e ain hei en i y, hei
le el o commi men o achie e sha ed goals is so impo an
ha hey in ol e hei s uc u es, communica ion channels
and esou ces, as well as aking isks in he long e m.
In con as o he collabo a ion no ion as a p ima ily syn-
e gis ic in e ac ion, in he di e en en i onmen s shaped by
DIHs, he e may be o he o mulas o in e ac ion ha a e
signi ican ly emo ed om his syne gy. They ange om
he simple con aposi ion o buye and selle in e es s in a
comme cial ela ionship o he an agonism shown by wo
compe i o s.Table2se s ou he main po en ial ypeso in e -
ac ion ha i his pa e n.
Wi h ega d o he possibili ies o he en i ies ha make
up one o he abo e-men ioned DIH en i onmen s o in e -
ac acco ding o a o mula ha deno es a ce ain deg ee o
collabo a ion, he e a e se e al al e na i es. Table 3p esen s
he mos ele an possibili ies.
Now ha he le els o he ela ionships in he ne wo k
o Eu opean inno a ion ecosys ems a e known, he ypes o
o ganisa ions ha exis in he ne wo k a e ou lined, and he
ypes o in e ac ion ha can po en ially ake place be ween
he en i ies making up hese o ganisa ions a e p esen ed, he
cons uc ion o he concep ual amewo k he ein p oposed
equi es an addi ional concep , a inal piece, o he com-
ple e mapping o he ela ional ne wo k: se ices esul ing
om collabo a ion. The en i e DIH en i onmen ske ched so
a holds a p ominen ci cums ance: some en i ies, hose pos-
sessing compe ences, knowledge, echnology, in as uc u e
o unds, o ha e access o hem, o e a se ice o o he s,
he companies making up he egional business and pub-
lic sec o o ganisa ions, o achie e hei ull and e ec i e
digi alisa ion. Al hough collabo a ion is also possible o
in e nal o ganisa ional easons as in any o he en i onmen ,
i is when exe cising i s main unc ion, p o iding suppo
se ices in digi al ans o ma ion p ocesses, ha a deepe
unde s anding o collabo a ion amewo ks and hei cha -
ac e is ics becomes aluable o his esea ch. In his sense,
i should be s a ed ha he classi ica ion o se ices in an
DIH en i onmen is a ask al eady aced by academia, wi h
he D-BEST e e ence model by Sassanelli and Te zi (2022)
being he mos e ol ed and upda ed exponen o he s a e
o he a . The D-BEST e e ence model is s uc u ed on
h ee le els: mac oclasses, ypes and classes. All he i e
mac oclasses included in he model (ecosys em, echnology,
business, skills and da a) is di ided in o ypes o se ice, and
hese, in u n in o classes o se ices (Sassanelli & Te zi,
2022) (Table 4).
Wi h he abo e classi ica ion, all he main concep s
in ol ed in his esea ch a e in oduced, and he esea ch
scope is also con igu ed (Fig. 2).
Sea ch que y and selec ed esul s
Asea chin heScopusda abasewaspe o medinacco dance
wi h he de ined esea ch scope o look o he in e sec ion
o he abo e-indica ed seman ic ields. The sea ch was done
wi h he i le, abs ac o keywo ds o a icles, e iews, con-
e ence pape s and con e ence e iews published in English
in compa ible subjec a eas. Fo his pu pose, we used he
sea ch chain TITLE-ABS-KEY [(“digi al inno a ion hub”)
AND(collabo a ion OR“comme cial ela ionship”OR com-
pe i ion OR coope a ion OR coope i ion OR coo dina ion
OR inancing OR unding OR “ins i u ional suppo ” OR
in es men OR “knowledge ans e ” OR pa ne ship OR
sponso ship OR “ echnology ans e ”)] AND (LIMIT-TO
123
Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1511
Table 2 Types o in e ac ion no
aligned wi h he collabo a ion
concep
In e ac ion ype De ini ion
Comme cial ela ionship Any legal ela ionship o a comme cial na u e, whe he con ac ual o no ,
and includes, bu is no limi ed o, a ela ionship a ising om he
ollowing ansac ions: any ade ansac ion o he supply o exchange
o goods o se ices; dis ibu ion ag eemen ; comme cial
ep esen a ion, o agency; ac o ing; leasing; cons uc ion o wo ks;
consul ing; enginee ing; licensing; in es men ; inancing; banking;
insu ance; exploi a ion ag eemen o concession; join en u e and o he
o ms (Comme cial ela ionship de ini ion, 2013)
Compe i ion Ri al y be ween indi iduals, g oups, o ganisa ions o na ions ha a ises
whene e wo pa ies o mo e s i e o some hing ha hey all canno
ob ain (S igle , 1988)
Financing Money is loaned by an indi idual o o ganiza ion o a pa icula pu pose
In es men The ac o pu ing money, e o , ime, e c. in o some hing o make a
p o i o ge an ad an age, o he money, e o , ime, e c. used o do his
(Camb idge Academic Con en Dic iona y, 2020)
Table 3 Types o in e ac ion
aligned wi h he collabo a ion
concep
In e ac ion ype De ini ion
Coope a ion P ocess o wo king on independen business ac i i ies owa ds a common
(ag eed-on) goal in a long- e m iew, whe e communica ion is ela i ely
in o mal, esou ces a e sepa a ed and isks a e sha ed (Wankmülle & Reine ,
2020)
Coope i ion Hyb id beha iou exhibi ed by wo o mo e compe i o s ha in ol es
coope a i e and compe i i e elemen s bu , ins ead o igh ing one ano he in
ie ce compe i ion, consis s o o ganising hemsel es in o, o example,
g oups o associa ions, o mee common objec i es ha would be di icul o
achie e indi idually is-à- is o he o ganisa ions. Some examples a e o he
compe i o s o g oups o compe i o s, adminis a ions, banks, consume
associa ions, among o he s. I in ol es gaining access o addi ional know-how,
skills and esou ces. This beha iou allows isk sha ing and he c ea ion o
secu e con ac s, while p o ec ing one’s own asse s (Bouncken & K aus, 2013)
Coo dina ion P ocess o aligning, o ganising and managing ac o s’ ope a ional business
ac i i ies whe e p i a e in o ma ion, isks and esou ces a e sha ed
(Wankmülle & Reine , 2020)
Funding Money is gi en by an indi idual o o ganisa ion o a pa icula pu pose
Ins i u ional suppo Suppo o e ed by go e nmen au ho i ies and ins i u ions, o hose di ec ly
suppo ed by a go e nmen , ha comes in he o m o policies, plans, laws,
egula ions, inancial o non- inancial aid o p omo e a pa icula indi idual o
o ganisa ion’s in e es s ( o he pu poses o his esea ch, suppo o a
inancial na u e, conside ed sepa a ely, is excluded)
Knowledge ans e The p ocess o ans e ing expe ience, skills, and angible and in ellec ual
p ope y (Uni e si y o Camb idge, 2009) om an indi idual o an
o ganisa ion o ano he one
Ne wo king Ne wo king is a o m o goal-di ec ed beha iou , bo h inside and ou side an
o ganisa ion, which ocuses on c ea ing, cul i a ing and u ilising in e pe sonal
ela ionships (Gibson e al., 2014)
Pa ne ship A pa ne ship is an a angemen in which pa ies, known as pa ne s, ag ee o
coope a e o p omo e hei mu ual in e es s. The membe s o a pa ne ship,
indi iduals o o ganisa ions, join oge he o inc ease he likelihood o each
pa y achie ing i s mission and b oadening i s scope (Pa ne ship, Wikipedia,
2022a,b)
Sponso ship The posi ion o unc ion o a pe son who o g oup ha ouches o suppo ,
ad ises o helps o und ano he pe son o an o ganisa ion o p ojec
(Sponso ship, Wikipedia, 2022a,b)
Technology ans e The p ocess o ans e ing echnology om an indi idual o o ganisa ion ha
owns o holds i o ano he one (Technology ans e , Wikipedia, 2022a,b)
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1512 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
Table 4 The D-BEST model se ice classi ica ion (Sassanelli & Te zi, 2022)
Se ice mac oclass Se ice ype Se ice class
1. Ecosys em 1.1. Communi y building 1.1.1. SME and people engagemen and b oke age
1.1.2. Inno a ion inci a ion, awa ds, and challenges
1.1.3. Technology scou ing
1.2. DIH inno a ion de elopmen 1.2.1. Communica ion and end wa ching
1.2.2. Visioning and s a egy de elopmen
1.3. Ecosys em go e nance 1.3.1. Se ice impac assessmen
1.3.2. Ecosys em managemen
2. Technology 2.1. Ideas managemen and ma e ialisa ion Ideas gene a ion, assessmen , and easibili y s udy
2.2. Con ac esea ch 2.2.1. S a egic and speci ic esea ch and de elopmen (R&D)
2.2.2. Technology concep de elopmen /p oo o concep (PoC)
2.3. P o ision o in as uc u e 2.3.1. Access o in as uc u e and echnological pla o ms
2.4 Technical suppo on scale up 2.4.1. Concep alida ion
2.4.2. P o o yping
2.5. Ve i ica ion and alida ion 2.5.1. P oduc quali ica ion and ce i ica ion
2.5.2. P oduc demons a ion
3. Business Incuba ion accele a ion suppo 3.1.1. Basic acili ies
3.1.2. Specialised acili ies
3.1.3. Business de elopmen
3.1.4. Guidance
3.2. Access o inance 3.2.1. Financial enginee ing
3.2.2. Connec ion o unding sou ce se ices
3.2.3. Me hods and ools
3.3. Business aining and educa ion 3.3.2. Secondmen
3.4. P ojec de elopmen 3.4.1. Iden i ica ion o oppo uni ies
3.4.2. C ea ing conso ia
3.4.3. De elopmen o p oposals
4. Skills 4.1. P ocess and o ganisa ional ma u i y 4.1.1. Ma u i y assessmen
4.1.2. Ma u i y s a egy de elopmen
4.2. Human capabili ies ma u i y 4.2.1. Human skills ma u i y
4.2.2. Skill s a egy de elopmen
4.3. Skills imp o emen 4.3.1. Human up-skilling and e-skilling aining
4.3.2. Educa ional p og ammes
4.3.3. Scou ing and b oke age
5. Da a 5.1. Da a acquisi ion and sensing 5.1.1. Da a acquisi ion
5.1.2. Da a p o ec ion
5.2. Da a p ocessing and analysis 5.2.1. Da a s o age
5.2.2. Da a analy ics
5.3. Decision-making 5.3.1. Cogni i e big da a a chi ec u e
5.3.2. Decision suppo and de elopmen
5.4. Physical-human ac ion and in e ac ion 5.4.1. Collabo a i e in elligence
5.4.2. Use expe ience
5.4.4. Feedback loop
5.5. Da a Sha ing 5.5.1. Gene al da a p o ec ion egula ion (GDPR
5.5.2. Da a spaces
5.5.3. Da a Pla o m
123
Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1513
Fig. 2 Resea ch scope
ep esen a ion om he iple
pe spec i e o DIH collabo a ion
(SUBJAREA, “COMP”) OR LIMIT-TO (SUBJAREA, “DE-
CI”) OR LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO
(SUBJAREA, “BUSI”)) AND (EXCLUDE (DOCTYPE,
“e ”)) AND (LIMIT-TO (LANGUAGE, “English”)). I was
no necessa y o na ow down he sea ched ime pe iod
because all he esea ch publica ions abou DIH a e e y
ecen , as is he concep i sel . The sea ch o he indica ed
e ms inally yielded 17 esul s, all o which a e limi ed o
he pe iod be ween 2018 and 2022. The selec ed esul s a e
iden i ied in Table 5.
Thema ic analysis
Using he con en o he i les and abs ac s o he selec ed
li e a u e as a sou ce, a map o co-occu ing exp essions was
d awnupusing heVOS iewe .1.6.16so wa eapplica ion.
This allows he concep s p esen in he li e a u e e iew wi h
mo e han h ee occu ences o be isualised, as well as hei
dimension and in e ela ionships (Fig. 3).
Fou hema ic g oups o clus e s we e au oma ically iden-
i ied by VOS iewe : (i) ha headed by exp ession DIHs,
which is ed-colou ed in he igu e, g ouping o he s like
inno a ion ecosys em, collabo a ion, in es men , ne wo k-
ing, se ice, se ice po olio, aining o cybe –physical
sys ems (CPS); (ii) he heading o exp ession digi al ans-
o ma ion, colou ed g een in he igu e, which g oups o he s
like coope a ion, inno a ion, digi al echnology, egion, he
EC o oppo uni y; (iii) he heading o exp ession knowl-
edge, colou ed yellow in he igu e, which g oups o he s
like pla o m, knowledge ans e , knowledge managemen ,
SMEs o medium-sized en e p ises; (i ) he heading o
exp ession echnology, which is blue-colou ed in he igu e
and g oups o he s, such as echnology ans e , lexibili y,
p oduc , p oduc ion o obo ics.
As expec ed, as hey cons i u e he hema ic axis o he
esea ch, he bigges numbe o co-occu ences appea s
a ound exp ession DIHs, DIH, digi al inno a ion hubs
and digi al inno a ions hubs. The highes densi y o co-
occu ences can be seen in he clus e s headed by DIHs
and digi al ans o ma ion ( ed and g een); bo h a e ela ed
o: se e al o he concep s unde s udy, such as collabo-
a ion, coope a ion, in es men o ne wo king; se ice and
se ice po olio, which a e in insic concep s o a comme -
cial ela ionship, and ano he o he esea ched concep s;
he o ganisa ion which, depending on he con ex , can be
123
1520 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
eali y u ges us o ad ance in he unde s anding o collabo-
a ion owa ds a so o concep ualisa ion ha does no a oid
quan i a i e cha ac e isa ion.
This sec ion p esen s a concep ual amewo k whose
objec i e is o p o ide a ool ha con ibu es o iden i y,
cha ac e ise, o ganise and quan i y he elemen s, s uc u e,
pa ame e s and a iables ha de e mine, as a whole, he
in e ac ion p ocesses in ol ed in he collabo a ion ha akes
place be ween he en i ies making up he ela ional ne wo k
o Eu opean inno a ion ecosys ems. This amewo k sup-
po s he in e ac ion p ocesses ha exis in collabo a ion
be ween en i ies: ho izon ally along hei ela ionship le -
els, and e ically be ween hem. I is impo an o no e ha
his amewo k is de eloped using a bo om-up app oach.
This in ol es ini ially es ablishing, om he exis ing li e a-
u e, wha he co e elemen s a e ha make up he s uc u e
o he cons uc ha he concep ual amewo k ep esen s,
and allow collabo a ion o be unde s ood as angible and
measu able. This concep ualisa ion app oach is c ucial o
de eloping a solidly based concep ual amewo k. Wi hou
a obus app oach ha p o ides measu abili y, collabo a ion
would emain close o abs ac and in angible and would,
he e o e, di icul o model.
The ex an li e a u e suppo his wi h he D-BEST e e -
ence model (Sassanelli & Te zi, 2022). The ul ima e pu pose
o a DIH, as a p o ide wi hin an inno a ion ecosys em, is o
p o ide a se ice o end use s. In his con ex , collabo a ion
be ween en i ies has always di ec ly o indi ec ly been he
ul ima e pu pose o ans e ing o exchanging some kind o
asse among o ganisa ions o imp o e o p o ide se ices o
end use s. F om his angle, collabo a ion is closely ela ed
o se ice p o ision. Acco ding o his basis, an in e ac ion
o collabo a e implies, as in he p o ision o se ices, he
ans e o exchange o asse s be ween collabo a ing se ice
p o ide o ganisa ions, and be o e and du ing he p o ision
o a se ice. This is whe e he D-BEST model comes in o
play because i no only iden i ies wha asse s a e, bu he
asse s ha i iden i ies happen o be measu able in some way.
Acco ding o he D-BEST model, he asse s equi ed o
se ice p o ision pu poses a e classi ied acco ding o hei
ypology in o compe ences, knowledge, echnology, in as-
uc u e and unds (Fig. 5). All hese asse s a e suscep ible o
measu emen in some way and, as he asse s hey a e, can be
ansla ed somehow in o mone a y e ms: e.g. compe ences
and knowledge can be measu ed by he mone a y alue o he
wo king ime spen in exchange; echnology and in as uc-
u e by he mone a y alue o he amo isa ion ime sha e o
he capi al in es ed in hem, whose uni is he cu ency used
in he alua ion; unds simply o he o al amoun o money
inanced, plus i s associa ed cos s, ega dless o hem being
in e es , ees, s amp du y o gua an ees.
Fig. 5 Elemen a y asse decomposi ion o a D-BEST se ice
As p e iously men ioned, he D-BEST model di ides he
possible se ices ha an o ganisa ion o e s in o i e mac o-
classes, and hese, in u n, in o 20 se ice ypes (Table 4),
which a e iden i ied by wo digi s: he i s one indica es he
mac oclass, while he second deno es he se ice ype is in
he indica ed mac oclass. Thus wi h he help o asse s as an
ins umen , i is possible o map all he asse ypes ha an
o ganisa ion needs o ha e o co e he comple e D-BEST
model se ices ca alogue by iden i ying each se ice ype
and i s co esponding asse s (Fig. 6). This asse map includes
100 ca ego ies.
Based on he p emise o his ca ego isa ion and he map-
ping o he se ices p o ided by he D-BEST model, mo ing
owa ds he de ini ion o a amewo k equi es conside ing
new elemen s, especially in oducing he asse low concep ,
which has no ye been con empla ed by esea ch s udies
o deal wi h in e o ganisa ional collabo a ion. Ma e ialis-
ing collabo a ion in he collabo a i e in e ac ion p ocess
be ween wo DIH o ganisa ions o mo e occu s by c ea ing
a low o angible o in angible asse s om ceding o ganisa-
ions o bene icia y o ganisa ions o alle ia e any de ici s in
he la e and o enable hem o p o ide some speci ic se ice
ypes immedia ely o in he u u e. This eali y is obse able
in p ac ically all o ganisa ional ecosys ems, and is he main
a ionale o a new amewo k o collabo a ion.
To acili a e he unde s anding o he easoning behind
he asse s low concep , he ollowing example is p o ided:
le wo o ganisa ions decide o unde ake a mu ual collabo-
a ion p ocess o, on he one hand, imp o e he ap i ude o
O ganisa ionA o hep o isiono business ainingandedu-
ca ion se ices (se ice ype 3.3) in e ms o in as uc u e,
and skills imp o emen (se ice ype 4.3) in e ms o knowl-
edge; on he o he , O ganisa ion B o acqui e capabili ies in
e ms o p o iding se ices o da a acquisi ion and sensing
(se ice ype 5.1), da a p ocessing and analysis (se ice ype
5.2) and da a sha ing (se ice ype 5.5), and all in echnology
e ms. Seen he o he way a ound, o ma e ialise his collab-
o a i e p ocess, i is necessa y o o ganisa ion A o ac as
123

Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1521
Fig. 6 Asse ype map o he D-BEST se ices ca alogue
Fig. 7 Collabo a ion example
be ween wo o ganisa ions in a
DIH ecosys em
a ans e o o some speci ic enabling asse s o O ganisa ion
B o p o ide se ices ypes 5.1, 5.2 and 5.5. O ganisa ion B
has o ans e hem o A o p o ide se ice ypes 3.3 and
4.3. These indi idual asse lows can be depic ed as shown
in Fig. 7. They a e designa ed as FA−T
O−O, wi h O being he
ans e o o ganisa ion designa ion, O he bene icia y o gan-
isa ion, A he ype o ans e ed asse and T he se ice ype
numbe ed om 1 o 20. This collec i e designa ion indica es
ha indi idual low FA−T
O−Oo asse ype A is ans e ed om
O ganisa ion O o O ganisa ion O o enable he p o ision o
se ice T.
I is possible o add an addi ional laye o cha ac e isa-
ion o he exchange o collabo a i e ela ionships be ween
en i ies because, o each se ice ype, he D-BEST model
p o ides an addi ional le el o classi ica ion called se ice
class (Table 4). By simply coun ing he numbe o se ice
classes in ol ed in each indi idual asse low, e.g. on a pe -
cen age basis, an addi ional cha ac e isa ion o collabo a ion
can be p o ided, which is e e ed o he e as se ice dep h.
This aspec is add essed again la e in his a icle.
This app oach o de ine he o igin, des ina ion, channel
and asse ans e ed in he collabo a ion p ocess is called he
In e o ganisa ionalAsse T ans e Me hodology(IOATM).I
cons i u es heco ne s oneo hep oposedconcep ual ame-
wo k.
I is pe inen he e o commen on o explain his me hod-
ology and he ela ionship le els be ween he en i ies making
up he ne wo k o Eu opean inno a ion ecosys ems. Whe he
i is he ans e o o he bene icia y o a ce ain asse , he
ac ha an o ganisa ion is a a ce ain ela ionship le el does
no p e en i om ans e ing asse s o en i ies a di e -
en le els o , on he con a y, ecei ing hem. The e ical
ans e o asse s, o he ans e be ween di e en le els, is
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1522 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
no es ic ed in ac ual p ac ice, and al hough due o he e y
na u e o le els he mos usual a e ho izon al o in ale el
ans e p ocesses, collabo a ion be ween en i ies a di e en
le els, especially in he i s h ee, is also common; i.e. DIH,
ecosys em and EDIH le els. Acco dingly, i should be no ed
ha IOATM, as a po en ial means o quan i a i ely assess
collabo a ion be ween he en i ies o inno a ion ecosys ems,
does no es ic his ci cums ance in any way and, he e o e,
con e s he amewo k lexibili y in his espec .
This holis ic app oach, based on de ining he o igin, des-
ina ion, channel and ans e ed asse in ol ed in he asse
low o he collabo a ion unde s udy, answe s esea ch
ques ion RQ1, o mula ed abou he cha ac e is ics o he
in e ac ion ha occu s be ween he en i ies composing DIHs’
ela ional ne wo k when collabo a ion akes place be ween
hem. Thanks o his cons uc , collabo a i e p ocesses
be ween o ganisa ions can be ansla ed in asse low e ms.
This ansla ion is pa icula ly signi ican because he map-
ping and iden i ica ion o he indi idual asse lows in ol ed
in collabo a ion enable IOATM as a ool o he quan i a i e
assessmen o collabo a ion p ocesses. On he one hand, he
possibili y o indi idualising each asse ans e acili a es i s
cha ac e isa ion and he es ablishmen o measu able echni-
cal speci ica ions, which a e c ucial o quan i ying he g oss
magni ude Tmb acqui ed by such a ans e ; e.g. in pu ely
mone a y e ms, in e ms o he ime spen o use o he
asse , o by o he quan i ica ion means. On he o he hand,
heiden i ica ion o he indi idualchannel ha conduc s each
indi idual ans e also helps o cha ac e ise i and o es ab-
lish i s speci ica ions. This is undamen al o quan i y he
pe o mance o e iciency e o he channel in he ans e
p ocess; in o he wo ds, o quan i y o ganisa ions’ capaci y
o collabo a e. This abili y o collabo a e, whose alue would
be be ween 0 and 1, depends no only on he pa ame e s ha
de ine and cha ac e ise he ans e channel, especially hose
ela ed o in e ope abili y, bu also on some pa ame e s spe-
ci ic o he in e ening o ganisa ions, all o which could be
he subjec o u he esea ch. This e iciency e modi-
ies downwa dly he g oss magni ude o ans e . Thus hei
join p oduc leads o he ne magni ude o ans e Tmn,an
a i ice ha would make i possible o accu a ely measu e
collabo a ion in hypo he ical quan i a i e modelling. F om
his pe spec i e, bo h he asse s low and ans e channels
can be conside ed he wo key dimensions in collabo a i e
in e ac ion p ocesses, and he elemen s ha mos shape he
quan i a i e assessmen o collabo a ion, dimensions a ound
which he o he elemen s o he p oposed amewo k a e
posi ioned: he se ice ca alogue, he in ol ed ela ionship
le els, he collabo a i e in e ac ion ypes, and o igin and des-
ina ion, all o which essen ially do no shape, bu condi ion
assessmen s. This app oach p o ides an answe o esea ch
ques ion RQ2, o mula ed in he In oduc ion o his a icle.
Rega ding he in e ac ion ypes aligned wi h collabo a-
i e p ocesses, on he con a y i is necessa y o make some
dis inc ions because each ype p esen s i s own peculia i ies
in ela ion o he in ol ed en i ies ypology o he ypes o
ans e ed asse s: (i) coope a ion in ol es ans e ing all o
some o he i s ou asse ypes, i.e. compe ences, knowl-
edge, echnology and in as uc u e (CKTI), and admi s he
exposed me hodology wi hou es ic ions; (ii) coope i ion
p esen s wo aces, coope a i e and compe i i e, and he
IOATM scope is es ic ed o collabo a ion ha ma e ialises
om he coope a i e pe spec i e wi h he ans e o CKTI;
(iii) coo dina ion o ces o ganisa ions o align and o gan-
ise business ac i i ies by sha ing in o ma ion, isks and
esou ces and, he e o e, p oducing he con olled ans e
o CKTI; (i ) in collabo a ion h ough unding, basically a
money ans e occu s in a unidi ec ional way; ha is, when
he ceding o ganisa ion ans e s he asse , he ecei e can,
in u n, ans e o he asse s o he ans e o in esponse,such
as knowledge o echnology, bu no money; ( ) Ins i u ional
suppo in e ac ionsa echa ac e isedby he ans e o o gan-
isa ion being a go e nmen au ho i y o a public ins i u ion,
and low is unidi ec ional. The asse s ans e ed in his col-
labo a ion ype a e no mally skills (indi ec ly acqui ed wi h
he suppo o policies, plans, laws o egula ions) o inanc-
ing; ( i) knowledge ans e is de ined by i s own name; ( ii)
ne wo king basically implies ans e ing knowledge abou
who o collabo a e wi h and in wha subjec s; ( iii) pa -
ne ship; i essen ially ep esen s he same as coope a ion o
coope i ion, bu on a la ge scale and in e ms o he num-
be o in ol ed o ganisa ions, o p oduce he same ype o
asse s ans e as in hese; (ix) sponso ship; a po en ial o -
mula in which he ans e o o ganisa ion is a pe son o g oup
om hep i a esphe e;i isunusualin heinno a ionecosys-
ems con ex ; he e he asse s low is unidi ec ional and i may
in ol eknowledge ans e in he o mo ad iceo some ype
o unding; (x) Technology ans e ; i s e y name cha ac-
e ises i . In any case, albei wi h hei pa icula nuances, all
collabo a i e in e ac ion ypes occupy a place in he me hod-
ology ad oca ed by his amewo k.
Ha ing cla i ied all his, he concep ual amewo k can be
ep esen ed, om a gene al pe spec i e, by b inging oge he
wi hina single ameall hea o emen ioned elemen s.On he
one hand, hose elemen s a e based on he D-BEST model,
which se es as a pla o m o de elop he concep ual ame-
wo k: asse ypes in ol ed in se ices, mac oclasses and
se ice ypes, and he asse ype map o he se ice ca a-
logue. On he o he hand, hose new elemen s ha allow he
collabo a ion p ocess be ween o ganisa ions o be shaped:
ans e channels o asse lows, he ou ela ionship le els
in he ne wo k o Eu opean inno a ion ecosys ems, collab-
o a i e in e ac ion ypes and, ob iously, he ceding and he
ecipien o ganisa ions (Fig. 8). The conjunc ion o all hese
and hei in e ela ionships, wi h a special emphasis on he
123
Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1523
Fig. 8 Collabo a ion F amewo k
in he Ne wo k o Eu opean
inno a ion ecosys ems
co e concep o asse low as he main a ionale, make up he
p oposed concep ual amewo k.
Case s udy: a ood p ocessing applica ion
expe imen o p oduc ion managemen
and p edic i e main enance wi hin he DIH4CPS
amewo k
Eu opean p ojec “Fos e ing DIHs o Embedding In e op-
e abili y in he CPS o Eu opean SMEs” (DIH4CPS) was
an inno a ion ac ion ha ecei ed unding om he Eu o-
pean Union’s Ho izon 2020 p og amme. This c ea ed an
in e disciplina y ne wo k o DIHs) and solu ion p o ide s
specialised in he Indus y 4.0 echnologies applica ion in
SMEs, especially on cybe –physical and embedded sys ems,
in e wea ing knowledge and echnologies om di e en
domains, as well as connec ing egional clus e s wi h his
pan-Eu opean expe pool o DIHs.
When he p ojec inished in Decembe 2022, DIH4CPS’s
ambi ion o become a sus ainable ne wo k ma e ialised ea ly
in 2023, when i was ins an ia ed in he Eu opean Vi ual
Labo a o y o En e p iseIn e ope abili y(I-VLab)unde he
name o Ei2Ne wo k, which is cu en ly ope a ional.
DIH4CPS in eg a ed i s ecosys em wi h 11 ini ial DIHs
om nine coun ies om all egions o Eu ope, and 20
addi ional DIHs ollowing he i s and second open calls,
o p o ide Eu opean indus y wi h unp eceden ed ease o
access o wo ld-class domain expe ise in de eloping CPS
and embedded sys ems. The de elopmen o his expe ise
e ol eda ounda co e expe imen a ionclus e ha consis ed
o 23 applica ion expe imen s co e ing many key indus ial
sec o s and ac i i ies.
This use case app oaches he collabo a i e p ocesses in
Applica ion Expe imen numbe 6 (iAE6) ca ied ou in he
p ojec , which aimed o add ess he di icul ies o hose com-
panies ha , despi e ha ing la ge and aluable p oduc ion
da a gene a ed by powe ul au oma ion sys ems, do no in e-
g a e hem in o he alue chain and end up o en ep esen ing
da a silos ha a e ba ely o no exploi ed a all. The planned
expe imen , implemen ed in p ac ice in o an indus ial pilo
o he ood p ocessing sec o , suppo s he de elopmen
o da a-d i en alue-added se ices, bo h ela ed o he
123
1524 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
manu ac u ing execu ion sys em/manu ac u ing ope a ions
managemen (MES/MOM) unc ional a eas (e.g. p oduc ion
o de con ol o pe o mance analysis) and p edic i e main-
enance. The expe imen acili a ed he de elopmen o a
e ical solu ion ha le e ages he p oduc ion da a gene -
a ed by quali y inspec ion machines o he ag i- ood sec o ,
especially in he p oduc ion o oli es, che y oma oes and
o he ui , which was made possible by he in eg a ion o
alue-added se ices ha collec and maximise he p ocess
da a gene a ed by s a e-o - he-a machine ision so ing and
g ading machines o op imise p oduc ion and main enance
managemen .
Food p ocessing applica ion expe imen iAE6
The applica ion expe imen was alida ed wi h a pilo a
a ood p ocessing company specialised in che y oma oes
(Níja , Alme ía, in Spain), on a che y oma o g ading and
so ing line (Figs. 9,10,11), which was subjec o imp o e-
men (Fig. 9).
The p ocess o so ing and g ading che y oma oes
in ol esse e als eps. Fi s ly,ope a o s anspo palle scon-
aining che y oma oes and eed hem in o he so ing line’s
olle con eyo . F om he e, oma oes mo e along di e en
eeding bel s and en e he Mul iscan MGS so ing and g ad-
ing olle machine. This machine uses compu e ision o
de ec he di e en ea u es o each che y oma o, such as
shape, colou and size, as i olls h ough he machine. In his
way i inspec s he en i e su ace o each ui . The machine
classi ies hem in o di e en quali y ca ego ies de ined by
he use h ough an in ui i e use in e ace, which allows
he h esholds o each p ope y and ca ego y o be se up.
The machine hen acks and guides all he oma oes o
s a egically placed slo s o place hem in o sepa a e exi s.
The objec i e o he applica ion expe imen is o de elop
da a-d i enadded- alueMES/MOMapplica ions oimp o e
manu ac u ing ope a ions (Figs. 10,11).
The main componen s o building blocks making up he
sys em’s a chi ec u e has allowed he expe imen o be de el-
oped, which is o ganised in o clus e s o ie s acco ding o
he di e en le els o a secu e indus ial ne wo k de ined
by he IEC/ISA 62443 se ies o s anda ds as de ailed below
(Fig. 12).
An embedded se e based on he open pla o m com-
munica ions uni ed a chi ec u e (OPC UA) acili a es he
in eg a ion o he p oduc ion da a gene a ed and managed
by he line inspec ion machine in o ex e nal applica ions.
The OPC UA is becoming a s anda d ac o o machine-
o-machine (M2M) communica ions a di e en indus ial
ne wo k communica ion le els. By means o OPC UA Se -
iceDisco e y echnologyand an ad hoc da a model o OPC
UADa aAccessse ices, heapplica ionexpe imen deli e s
a u n-key solu ion o enable “Plug-and-Play” connec i i y.
This OPC UA Se e allows in o ma ion om no only he
quali y inspec ion machine, bu also om o he connec ed
manu ac u ing equipmen , o be exchanged. In his way, he
embedded OPC UA se e allows o he se ices o exchange
ope a ional and main enance da a wi h he quali y inspec ion
machine so ha i is no longe a da a silo, which imp o es
he pe o mance o supply chain p ocesses.
A hyb id edge/cloud se ice pla o m p o ides a un-
ime pla o m and a co e se ice o acili a e access o he
da a gene a ed by he OPC UA o connec ed applica ions so
ha hey can p o ide da a-d i en added alue se ices. The
edge/cloud se ice pla o m p o ides asynch onous da a se -
ices o access eal- ime p oduc ion da a and synch onous
da a se ices o access his o ical da a. This basis enables he
secu e access and exchange o he ope a ional and main-
enance da a be ween he s akeholde s in ol ed h ough a
se o da a se ices designed speci ically o suppo his col-
labo a ion. The edge/cloud se ices also allow da ase s o
be c ea ed o analysis and model aining pu poses. This
edge/cloud pla o m manages he da a s o age o indus ial
da a ime se ies a wo di e en le els: on-p emises (sys ems
ins alled wi hin he pilo company’s bounda ies); in-cloud
(sys ems ins alled in a p i a e cloud). The pla o m keeps he
on-p emises ho da a gene a ed in he nea pas by apply-
ing e en ion policies ha ha e been speci ically de ined o
mee he equi emen s o he added alue se ices ha con-
sume hese da a. The collec ed and s o ed da a include he
indus ial a iables desc ibing he p ocess and quali y o
p oduc s. On he one hand, as men ioned da a se ices s o e
abo e all in o ma ion, e en he in o ma ion collec ed om
line con olle s h ough he embedded OPC UA se e s. This
includesall he p ocess in o ma ion abou he eal- ime s a us
o p oduc ion equipmen and all he p oduc quali y- ela ed
in o ma ion gene a ed by he compu e- ision g ading sys-
em. On he o he hand, da a se ices allow applica ions o
en ich his in o ma ion wi h addi ional con ex in o ma ion,
like he in o ma ion p o ided by ope a o s in na u al lan-
guage o be e desc ibe incidences and machine ailu es.
The implemen ed MES/MOM applica ions a e basically
web applica ions ha p o ide he manu ac u ing execu-
ion sys em and he manu ac u ing ope a ion managemen
unc ionali ies, backed by he edge/cloud pla o m se ices.
These MES/MOM applica ions ocus on some demanded
key unc ionali ies, which mainly e ol e a ound ou mile-
s ones: p oduc ion key pe o mance indica o s (KPIs) moni-
o ing, p oduc ion o de con ol, p oduc ion ba ch aceabil-
i y and p oduc ion p ocess managemen .
Finally, he objec i e o he anomaly de ec ion and p e-
dic i e main enance module is o pu he main enance da a
o good use o p o ide alue da a-d i en se ices in his
a ea. Anomaly de ec ion and p edic i e main enance allow
unexpec ed e en s in machine pe o mance o be epo ed by
123
Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1525
Fig. 9 Scheme o he Mul iscan
MGS che y oma o g ading and
so ing line (sou ce DIH4CPS
p ojec dissemina ion a chi es)
Fig. 10 Mul iscan MGS che y
oma o g ading and so ing line
(sou ce DIH4CPS p ojec
dissemina ion a chi es)
Fig. 11 De ail o Mul iscan MGS
che y oma o g ading and
so ing line (sou ce DIH4CPS
p ojec dissemina ion a chi es)
s udying baseline no mali y ends wi h con ex ual in o ma-
ion, epo ing anomaly de ec ion and he es ima ed ime o
ailu e o machines and p edic ing any likely ailu es. In his
way, he desc ibed module p o ides a solu ion o se e al key
issues: (i) de ec ing and wa ning in eal ime when ope a -
ing pa ame e s de ia e om expec ed machine pe o mance;
(ii) s udying and adjus ing o d i and seasonal a ia ions
in pe o mance es ima o s; (iii) minimising loss o a ailabil-
i y due o unplanned epai and adjus men down ime; (i )
educing ope a ing cos s by planning main enance and s ock-
ing app op ia e spa e pa s on si e in ad ance; ( ) op imally
in eg a ing he planned down ime in o he ope a ing sched-
ule. O he di e en possible models owa ds his endea ou ,
he use o su i al models and classi ie s was chosen o
his p ojec . Su i al models a e app op ia e o ob aining
se e al p obabili y es ima ions o ailu e in di e en u u e
imes by allowing main enance o be adap ed acco ding o
he aken isk. Besides, classi ie models p o ide di e en
p obabili ies o each ailu e ype du ing a gi en ime pe iod.
123

1526 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
Fig. 12 The iAE6 high-le el echnical a chi ec u e
The pilo was p epa ed by p o isioning plan acili ies
wi h labo a o y equipmen , which consis ed o a ood p o-
cessing line simula o , whose main unc ion is o gene a e
alues o he di e en indus ial a iables om he a ail-
able his o ical da a. Wi h his simula o , he ins alla ion and
in eg a ion o he di e en componen s we e alida ed unde
labo a o y condi ions by e alua ing he ins alla ion p oce-
du es and use in e aces o ensu e ha use equi emen s and
accep ancec i e iawe e me .Once hesolu ion was alida ed
unde labo a o y condi ions, he es ed uni was ins alled on
he use ’s p emises and inally commissioned.
Manage ial implica ions o iAE6
F om he knowledge acqui ed du ing he expe imen , se -
e al implica ions o use managemen p ocesses a e wo h
highligh ing: (i) be e moni o ing o p oduc ion KPIs; a
bene i ha comes om he MES/MOM applica ions. This
is because hey calcula e KPIs om he collec ed da a and
display hem on comp ehensi e dashboa ds designed speci i-
cally o di e en use p o iles(ope a o ,p oduc ionmanage
o main enance manage ) so ha e e yone in ol ed in he
p ocess can assimila e in o ma ion; (ii) imp o ed o de con-
ol due o MES/MOM applica ions, which p o ide unc ions
o dispa ch p oduc ion o de s o he shop loo (ope a o s and
manu ac u ing equipmen ), and show he p oduc ion plan
cu en s a us o ele an use s; (iii) enhanced p ocess man-
agemen hanks again o he MES/MOM applica ions by
moni o ing and con olling he manu ac u ing p ocess s a-
us, and by showing ope a o s he cu en s a us o he line
and allowing hem o speci y he cause o s oppage when i is
no de ec ed by a machine; (i ) imp o ed p oduc ion pe o -
mance, de i ed om he bene i s o anomaly de ec ion and
he p edic i e main enance sys em. This e ical solu ion is
expec ed o be well accep ed by he SMEs in ol ed in ood
g ading and so ing, which cu en ly ha e lowe Indus y 4.0
ma u i y le els han la ge companies, which usually access
echnological esou ces mo e easily.
O ganisa ional aspec s o he iAE6 expe imen
Th ee o ganisa ions pa icipa ed in he design and de elop-
men o he iAE6 expe imen : (i) The Uni e si a Poli ècnica
de València (UPV), a membe o he DIH o he eco-
nomic p omo ion o he Valencian Communi y (InnDIH).
I plays he ole o eam leade , sys em a chi ec and DIH
membe ha specialises in p oduc ion managemen ech-
nologies (he e mainly MES/MOM echnologies) h ough he
Cen o de In es igación en Ges ión e Ingenie ía de la P o-
ducción (CIGIP), which belongs o his uni e si y. (ii) The
Ins i u o Tecnológico de In o má ica (ITI), ano he InnDIH
123
Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1527
Fig. 13 iAE6 o e all o ganisa ion
membe .I ac sas aspecialis inmachine lea ningandp edic-
i e main enance echnologies. (iii) The company Mul iscan
Technologies SL. I is he designe , manu ac u e , ins alle
andmain aine o he p oduc (Mul iscanMGSche y oma o
g adingandso inglineo e e encePL18007),andis heend
use o he solu ion. I also ac s as a specialis in machine
ision echnology. These h ee o ganisa ions a e he in e -
ening ac o s ha play an ac i e ole in de eloping he a ge
solu ion o his expe imen . SAT Cos a de Níja , a company
ha p oduces, p ocesses and sells ag icul u al p oduc s, has
o e ed i s acili ies o un he pilo . Howe e , his com-
pany does no play an ac i e ole in expe imen de elopmen ,
which is why i is no conside ed in he use case (Fig. 13).
Implemen ing he collabo a ion amewo k
The iAE6 expe imen p o ides a eal and su icien ly com-
plex case o cons i u e a ep esen a i e example o he
collabo a i e p ocesses ha exis in he inno a ion ecosys-
ems gene a ed a ound DIHs. The applica ion o IOTAM o
his case ini ially equi es de ining he sou ce, des ina ion,
channels and asse s in ol ed in he collabo a ion p ocess.
The example p o ided by he iAE6 expe imen in ol es, as
shown, h ee collabo a ing o ganisa ions, any o which can
ac as bo h he sou ce and des ina ion o asse s du ing he col-
labo a ion p ocess. This ci cums ance p o ides six po en ial
collabo a ion channels (Table 6;Fig.14):
The nex s ep in his bo om-up p ocess is o ca y ou
an analysis o iden i y he di e en se ice ypes exchanged
du ing he collabo a ion p ocess, ega dless o hei o igin
o des ina ion, in o he mos elemen a y se ices o among
he 20 possible ypes o o ganisa ions’ se ices po olio
(Table 7).
Table 7shows he se ice mac oclasses and se ice ypes
named and numbe ed acco ding o he D-BEST po olio, se
ou in Table 4, as well as hei ansla ion in o he IOTAM
code, which numbe s hem wi h a single digi om 1 o
20 (Fig. 6). He ea e in his a icle, he code used will be
IOTAM. To ollow his p ocess, collabo a ion ac i i ies ha e
Table 6 Collabo a ion channels in he use case
Channel Collabo a ion o igin o
asse ans e o
Collabo a ion
des ina ion o asse
ecei e
Channel 1 UPV ITI
Channel 2 ITI UPV
Channel 3 UPV MULTISCAN
Channel 4 MULTISCAN UPV
Channel 5 ITI MULTISCAN
Channel 6 MULTISCAN UPV
Fig. 14 iAE6 de ailed o ganisa ion wi h all he collabo a ion channels
opened among he UPV, ITI and Mul iscan
Table 7 Lis o se ices iden i ied du ing he collabo a i e exchange
p ocess
Se ice
Mac oclass
Se ice
ype
Se ice
ype
(IOTAM
code)
Se ice ype name
Ecosys em 1.3 3 Ecosys em go e nance
Technology 2.1 4 Ideas managemen and
ma e ialisa ion
Technology 2.2 5 Con ac esea ch
Technology 2.3 6 P o ision o in as uc u e
Technology 2.4 7 Technical suppo on scale up
Skills 4.3 15 Skill imp o emen
Da a 5.1 16 Da a acquisi ion and sensing
Da a 5.3 18 Decision-making
Da a 5.5 20 Da a Sha ing
123
1528 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
o be decomposed channel by channel. In he iAE6 expe -
imen , his decomposi ion would ake he ollowing o m
(Table 8):
As p e iously indica ed in he concep ual amewo k p e-
sen a ion, he e is he possibili y o adding an addi ional
cha ac e isa ion laye o he exchange o he collabo a i e
ela ionships among he h ee en i ies because, as o each
se ice ype, he D-BEST model p o ides an addi ional clas-
si ica ion le el called se ice class (Table 4). So i is easible
o measu e he se ice dep h o collabo a i e in e ela ion-
ships by simply coun ing he numbe o se ice classes
in ol ed in each indi idual asse low, e.g. on a pe cen age
basis. By his app oach, le us ake a close look a each ele-
men a y se ice o be e unde s and hei a ionale om a
gene al pe spec i e. Tables 9,10,11,12,13 and 14 show he
se ices p o ided by each collabo a ion, desc ibed pe chan-
nel as displayed in Table 8( he uly in ol ed se ice classes
a e ma ked in bold):
A e iden i ying he channelswi h hei espec i eo igins
and des ina ions, decomposing he collabo a ion p ocess in o
elemen a y se ices, classi ying hese elemen a y se ices
in o ypes and calcula ing he se ice dep h, he nex s age
in he p ocess is o de e mine bo h he asse s in ol ed in
each elemen a y se ice and hei ypology among he i e
possible ones: compe ences, knowledge, echnology, in as-
uc u e and unds (Table 15). This p o ides a i s de ailed
o e iew o he low o he asse s in ol ed in he collabo a-
ion p ocess.
The codes used in columns C, K, T, I and F o Table 15
esul om combining he ini ial le e o he asse name and
he IOTAM code o he in ol ed se ice. Fo example, code
K3 implies knowledge ans e in he Ecosys em Go e nance
Table 8 Se ice decomposi ion o he collabo a ion p ocess
Channel O igin–des ina ion Se ice
Mac oclass
Se ice
ype
Se ice ype name
Channel 1 UPV →ITI Ecosys em 3 Ecosys em go e nance
Technology 4 Ideas managemen and ma e ialisa ion
Da a 16 Da a acquisi ion and sensing (T aining da ase acquisi ion,
p epa a ion and sha ing)
Da a 17 Da a p ocessing and analysis
Channel 2 ITI →UPV Da a 17 Da a p ocessing and analysis
Da a 20 Da a Sha ing (P edic i e main enance model aining,
building, and sha ing)
Channel 3 UPV →MULTISCAN Ecosys em 3 Ecosys em go e nance
Technology 4 Ideas managemen and ma e ialisa ion
Technology 5 Con ac esea ch
Technology 7 Technical suppo on scale up
Skills 15 Skill imp o emen
Da a 16 Da a acquisi ion and sensing
Da a 18 Decision-making
Da a 20 Da a Sha ing
Channel 4 MULTISCAN →UPV Technology 4 Ideas managemen and ma e ialisa ion
Technology 6 P o ision o in as uc u e
Technology 7 Technical suppo on scale up
Skills 15 Skill imp o emen
Da a 16 Da a acquisi ion and sensing
Channel 5 ITI →MULTISCAN Technology 5 Con ac esea ch
Skills 15 Skill imp o emen
Da a 16 Da a acquisi ion and sensing
Da a 18 Decision-making
Da a 20 Da a Sha ing
Channel 6 MULTISCAN →UPV Technology 6 P o ision o in as uc u e
Skills 15 Skill imp o emen
Da a 16 Da a acquisi ion and sensing
123
Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1529
Table 9 Ra ionale o he elemen a y se ices o Channel 1 UPV →ITI
Se ice
Mac oclass
Se ice
ype
Se ice ype name Se ice class Se ice dep h
Ecosys em 3 Ecosys em go e nance: in he ole o
he iAE6 expe imen leade , he UPV
p o ides ITI wi h bo h he se ice
impac assessmen h ough he
co esponding key pe o mance
indica o s and ecosys em managemen ,
which includes engagemen ules and
go e nance s uc u e o ease
ela ionships among o ganisa ions
1.3.1. Se ice impac assessmen 2 Ou o 2 100%
1.3.2. Ecosys em managemen
Technology 4 Ideas managemen and
ma e ialisa ion: he UPV, based on he
pilo knowledge, gene a es he idea o
he applica ion expe imen , e alua es i
and analyses i s easibili y by
subsequen ly in ol ing ITI in he
gene a ed idea
2.1.1. Ideas gene a ion, assessmen ,
and easibili y s udy
1 Ou o 2 50%
2.1.2. Technology eadiness assessmen
Da a 16 Da a acquisi ion and sensing (T aining
da ase acquisi ion, p epa a ion and
sha ing): aining da a acquisi ion o
p e en i e main enance comes mainly
om he MES/MOM managemen
sys em, which ac s as a cen al hub by
collec ing and con ex ualising senso
da a
5.1.1. Da a acquisi ion 1 Ou o 2 50%
5.1.2. Da a p o ec ion
Da a 17 Da a p ocessing and analysis: Da a a e
p epa ed in a i s s age om he side
o UPV acco ding o he equi emen s
o he analy ic models o p edic i e
main enance
5.2.1. Da a s o age 1 Ou o 2 50%
5.2.2. Da a analy ics
Collabo a ion a e age dep h 5 Ou o 8 63%
Table 10 Ra ionale o he elemen a y se ices o Channel 2 ITI →UPV
Se ice
Mac oclass
Se ice
ype
Se ice ype name Se ice class Se ice dep h
Da a 17 Da a p ocessing and analysis: Da a a e
p epa ed om he side o ITI acco ding o
he equi emen s o he on -end sys ems
5.2.1. Da a s o age 1 Ou o 2 50%
5.2.2. Da a analy ics
Da a 20 Da a sha ing: ITI, o being esponsible o
designing he p edic i e main enance
models, de ines bo h he da a space on
which da a models and da a o ma s a e o
be used. The secu i y s anda ds adop ed in
he sys em a chi ec u e enable secu e and
eliable da a exchange. ITI also p o ides
he da a and compu ing in as uc u e o
enable he aining o he model, and
p o ides connec ion se ices o inges he
ain he da ase s deli e ed by he UPV.
The ained models a e hen deployed
using secu e in e aces
5.2.2 Da a analy ics 2 Ou o 3 67%
5.5.1. Gene al Da a P o ec ion
Regula ion (GDPR)
5.5.2. Da a spaces
5.5.3. Da a Pla o m
Collabo a ion a e age dep h 3 Ou o 5 60%
123
1536 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
Fig. 15 Asse low map o he collabo a ion in e ela ionships be ween he UPV, ITI and MULTISCAN
123

Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1537
Table 16 P o essional ca ego ies in ol ed in collabo a ion
UPV and ITI Mul iscan
Ca ego ies MR: Main esea che M: Depa men Manage
ST: Senio echnician
PoD: Pos Doc esea che JT: Junio echnician
P D: P eDoc esea che SW: Skilled wo ke
Table 17 Quan i ica ion o asse lows o he collabo a i e p ocesses in iAE6 o Channel 1 UPV →ITI (pe son ×mon hs)
Channel O igin–des ina ion Se ice ype Se ice ype name K T
Type MR PoD P D Type MR PoD P D
Channel 1 UPV →ITI 3 Ecosys em
go e nance
K3 0.05 0.25 0.90
4 Ideas managemen
and ma
K4 0.05 0.15
16 Da a acquisi ion and
sensing
T16 0.25
17 Da a p ocessing and
analysis
T17 0.20
SUBTOTALS 0.10 0.40 0.90 0.45
Table 18 Quan i ica ion o asse lows o he collabo a i e p ocesses in iAE6 o Channel 2 ITI →UPV (pe son ×mon hs)
Channel O igin–des ina ion Se ice ype Se ice ype name T
Type MR PoD P D
Channel 2 ITI →UPV 17 Da a p ocessing and analysis T17 0.05 0.20
20 Da a Sha ing T20 0.05 0.45
SUBTOTALS 0.10 0.65
cons i u e a su icien esou ce o c ea e a obus model by
i sel because, al hough i allows he de ini ion o bo h he
o igin and des ina ion o he asse s low and he ans e ed
asse i sel , as s a ed abo e, i is essen ial o pay a en ion o
he en i onmen h ough which he ans e occu s because
his medium mus be enable an e icien low o collabo a-
ion o op imally ma e ialise. E iciency he e means ha he
asse low occu s be ween bo h o ganisa ions unde in e op-
e abili ycondi ions and hese condi ionsa e sus ainable om
a comp ehensi e pe spec i e, i.e. o ganisa ions a e in e ope -
able a (i) he da a le el, (ii) he se ice le el, (iii) he p ocess
le el and (i ) he business le el (Fig. 17), and all om he
iple dimension o concep ual, echnological and o ganisa-
ional ba ie s (Ducq e al., 2012).
Indeed he alue o he ans e ed asse s may be al e ed
by he g ea e o lesse abili y o he o ganisa ions in ol ed
o access and p ocess da a om hei mul iple sou ces wi h-
ou losing meaning and subsequen ly in eg a ing hem so ha
any o hem can loca e, explo e and g asp he s uc u es and
con en s o da ase s. Thus an e icien collabo a i e p ocess
equi es he p io assessmen o in e ope abili y a he da a
le el. The same applies when he asse ans e is pe o med
h ough dis ibu ed sys ems, and he abili y o coope a e in
se ices ha implyda aexchanges,despi edi e encesinlan-
guage, in e ace and execu ion pla o ms (Fang e al., 2004),
is also c ucial. Hence oubles wi h he in e ope abili y a
he se ice le el can p o e o be a dis u bing ac o ha
needs e alua ion and con ol, a ask ha mus be done om
a comp ehensi e pe spec i e and in ol es in e ope abili y
suble els, such as he signa u e, p o ocol, seman ic, qual-
i y and con ex suble els (S ang and Linho -Popien, 2003).
In ne wo ked en i onmen s, such as he en i ies making up
inno a ion ecosys ems, i is no less impo an o ensu e ha
he p ocesses o hose o ganisa ions ha in e ac o collab-
o a e a e designed o wo k oge he ( ede a ed ela ionship
app oach) o a e e en concei ed om he ou se as a single
common p ocess (in eg a ed ela ionship app oach) (Ducq
123
1538 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
Table 19 Quan i ica ion o asse lows o he collabo a i e p ocesses in iAE6 o Channel 3 UPV →Mul iscan (pe son ×mon hs)
Channel
O igin–des ina ion
Se ice
ype
Se ice ype name C K T
Type MR PoD P D Type MR PoD P D Type MR PoD P D
Channel 3 UPV →Mul iscan 3 Ecosys em go e nance K3 0.15 0.40
4 Ideas managemen and ma K4 0.20 0.50 1.05
5 Con ac esea ch T5 0.40 1.00 2.05
7 Technical suppo on scale
up
K7 0.35 0.55
15 Skill imp o emen C15 0.40
16 Da a acquisi ion and
sensing
T16 1.25
18 Decision-making T18 0.05 0.70 1.80
20 Da a Sha ing T20 0.55 1.45
SUBTOTALS 0.40 0.35 1.25 1.60 0.45 3.80 5.30
123
Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1539
Table 20 Quan i ica ion o asse lows o he collabo a i e p ocesses in iAE6 o Channel 4 Mul iscan →UPV (pe son ×mon hs)
Channel O ig-
in–des ina ion
Se ice
ype
Se ice ype
name
CK
Type M ST JT SW Type M ST JT SW
Channel 4 Mul iscan →
UPV
4 Ideas man-
agemen
and ma
K4 0.10 0.20
7 Technical
suppo on
scale up
K7
15 Skill
imp o e-
men
C15 0.50 1.20 4.00 0.05
SUBTOTALS 0.50 1.20 4.00 0.10 0.20 0.05
Channel O igin–des ina ion Se ice
ype
Se ice ype
name
TI
Type M ST JT SW Type M ST JT SW
Channel 4 Mul iscan →UPV 6 P o ision o
in as uc u e
I6 0.05 0.10
16 Da a acquisi ion
and sensing
T16 0.10
SUBTOTALS 0.10 0.05 0.10
Table 21 Quan i ica ion o asse lows o he collabo a i e p ocesses in iAE6 o Channel 5 ITI →Mul iscan (pe son ×mon hs)
Channel
O igin–des ina ion
Se ice
ype
Se ice ype name K T
Type MR PoD P D Type MR PoD P D
Channel 5 ITI →Mul iscan 5 Con ac esea ch T5 0.30 1.00 1.75
15 Skill imp o emen C15 0.40
16 Da a acquisi ion and
sensing
T16 1.00
18 Decision-making T18 0.05 0.65 2.05
20 Da a Sha ing T20 0.90 2.20
SUBTOTALS 0.40 0.35 3.55 6.00
e al., 2012). Taking his s ep o add ess he necessa y in e -
ope abili y a he p ocess le el is c ucial o sa egua d he
e iciency o he ans e o such ans e -sensi i e asse s as
knowledge. To succeed in his, a signi ican pa o he e o
should ocus on modelling ne wo k p ocesses and de ining
sys em objec i es (Ducq e al., 2012) om a collabo a i e
wo kingpe spec i e.Rega ding helas in e ope abili y le el
( ha o business), i mus be seen as he las link in he chain
ha joins collabo a ion o e iciency h ough in e ope abil-
i y. Collabo a i e en i ies mus be able o collabo a e bo h
o ganisa ionally and ope a ionally wi h hei ne wo k pa -
ne s, and ega dless o hem being a he same o a di e en
ela ionship le el, o i hey a e o e ec i ely es ablish, con-
duc andde elop hei ela ionshipssuppo edbyin o ma ion
and communica ion echnologies o c ea e alue in o de o
p e en ha di e en legisla ion, co po a e cul u es, gene al,
speci icwo kingp ocedu es,o decision-makingme hodolo-
gies o unde mine he e ec i eness o he ans e o asse s
in collabo a i e p ocesses. A his le el and o ha end, p ac-
icali y mus p e ail. So he ask o being in e ope able will
equi e add essing ou challenges: (i) he in e ope abili y
o in eg a ed alue ne wo ks; (ii) he economic e alua ion
o business in e ope abili y; (iii) he de e mina ion o op i-
mal in e ope abili y le els; (i ) he design o in e nal and
in e o ganisa ional sys ems and p ocess a chi ec u es o
in e ope abili y (Legne & Leb e on, 2007). All his needs
o be aken in o accoun om a manage ial iewpoin .
Nowadays, sus ainabili y is, and igh ly so, a c oss-cu ing
conce n in p ac ically any sec o and ac i i y. Collabo a-
ion be ween o ganisa ions does no escape his g owing
123
1540 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
Table 22 Quan i ica ion o asse lows o he collabo a i e p ocesses in iAE6 o Channel 6 Mul iscan →ITI (pe son ×mon hs)
Channel O ig-
in–des ina ion
Se ice
ype
Se ice ype
name
IC
Type M ST JT SW Type M ST JT SW
Channel 6 Mul iscan →ITI 6 P o ision o
in as uc-
u e
I6 0.05 0.10
15 Skill
imp o e-
men
C15 0.45 1.20 3.80
SUBTOTALS 0.05 0.10 0.45 1.20 3.80
Channel O igin–des ina ion Se ice ype Se ice ype name T
Type M ST JT SW
Channel 6 Mul iscan →ITI 16 Da a acquisi ion and sensing T16 0.10
SUBTOTALS 0.10
end,ce ainlyno be weeno ganisa ionswilling one wo k,
which is he case o he en i ies belonging o he inno a ion
ecosys ems c ea ed in Eu ope a ound DIHs and/o EDIHs.
I is common o he esul s o collabo a ion o all sho
o expec a ions because he abili y o ob ain sa is ac o y
esul s depends on many ac o s ha a e no o en aken in o
accoun by o ganisa ions. Collabo a ion can con e o gan-
isa ions mu ual bene i s by helping o b idge gaps h ough
sha ed e o . Howe e , hese po en ial ad an ages should
no di e ou a en ion om he ac ha collabo a ion does
no always wo k well, and ce ainly no in all con ex s. Col-
labo a i e in e ac ion p ocesses can be misused. I is wo h
emembe ing ha he complexi ies in ol ed in collabo a ion
may be used o p omo e ce ain es ed in e es s. Ac o s wi h
esou ces and skills can use he legi imising powe o collab-
o a i e ini ia i es o p omo e hei own agendas. The e o e,
he i s ques ions o ask a he beginning o e e y collabo a-
i e p ac ice a e: Wha is he pu pose o his collabo a ion?
Whose in e es s does i po en ially se e? To a g ea ex en ,
he sus ainabili y o a collabo a i e ela ionship depends on
he answe o hese ques ions. Conduc in collabo a ion can
be de e mined by moni o ing echniques, such as audi s o
e i ica ions o bes p ac ices, a a mo e quali a i e le el and,
c ucially, by quan i a i e e alua ion me hodologies based
on accoun ing and s a is ics (Fadee a, 2005). This is whe e
he he ein p oposed amewo k akes cen e s age because
i enables hese e alua ion a enues by aking a quan i a i e
app oach.
Conclusion
This a icle p esen s a concep ual amewo k ha i s ly
de ines and s uc u es he ela ional ne wo k o Eu opean
inno a ion ecosys ems d i en by he EC’s Digi ise Eu o-
pean Indus y and Digi al Eu ope P og amme ini ia i es as a
whole. Secondly, i p o ides heo e ical and concep ual sup-
po o he exis ing in e ac ion p ocesses ho izon ally along
hei ela ionship le els and e ically be ween hem om a
dual quali a i e and quan i a i e pe spec i e. The pu pose o
his amewo k is wo old. On he one hand, he ini ial aim
is o b idge he gap in esea ch in o collabo a ion be ween
DIHs by p o iding a b oade concep ualisa ion o he ele-
men s, s uc u e, in e ela ionships and con en making up
he ma e ialised in e ac ions be ween o ganisa ions in he
collabo a i e en i onmen ha exis in a ne wo k gene a ed
om DIHs and/o EDIHs. On he o he hand, he amewo k
mus ep esen an e ec i e le e o mo ing owa ds a model
o collabo a ion in his con ex . This esea ch can con ibu e
signi ican ly o de elop ad anced analysis and e alua ion
ools in collabo a i e p ocesses, which can p o ide a obus
esponse o in e o ganisa ional in e ac ion p oblems. F om
a p e ious and global de ini ion o he pan-Eu opean space
o ac ion o DIHs, on which i s ou le els o ela ionship
a e delinea ed, he a icle begins by de ining he main con-
cep s in ol ed wi hin he scope o his esea ch wo k: (i)
he o ganisa ion ypes exis ing in he ela ional ne wo k o
Eu opean inno a ion ecosys ems; (ii) he ypes o in e ac-
ion no aligned wi h he collabo a ion concep ; (iii) he ypes
o in e ac ion aligned wi h he collabo a ion concep . Like-
wise, he a icle indica es which de ini ion o collabo a ion
is adop ed as he i s e e ence o esea ch, whose au ho s
a e Wankmülle and Reine (2020): “P ocess o s a egically
wo king oge he on a speci ic business ac i i y whe e s uc-
u es a e aligned, communica ion channels a e s anda dised,
isks a e sha ed, and esou ces a e pooled in o de o make
hem a ailable o e e y pa ne ”. Subsequen ly, he clas-
si ica ion o he D-BEST model se ices by Sassanelli and
123
Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1541
Table 23 Summa y able on he quan i ica ions o asse lows (pe son ×mon hs)
Channel O igin–des ina ion S a ca ego ies Asse Sub o al To al
CKTI F
Channel 1 UPV →ITI MR 0.10 0.45 0.55 1.85
PoD 0.40 0.40
P D 0.90 0.90
Channel 2 ITI →UPV MR 0.75
PoD 0.10 0.10
P D 0.65 0.65
Channel 3 UPV →MULTISCAN MR 0.40 0.35 0.45 0.80 13.15
PoD 1.25 3.80 5.45
P D 1.60 5.30 6.90
Channel 4 MULTISCAN →UPV M 0.10 0.10 0.05 0.15 6.30
ST 0.50 0.20 0.10 0.80
JT 1.20 0.05 1.35
SW 4.00 4.00
Channel 5 ITI →MULTISCAN MR 0.40 0.35 0.35 10.30
PoD 3.55 3.95
P D 6.00 6.00
Channel 6 MULTISCAN →ITI M 0.10 0.05 0.15 5.70
ST 0.45 0.10 0.55
JT 1.20 1.20
SW 3.80 3.80
Fig. 16 Fac o s al e ing he
e iciency o collabo a ion
123

1542 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
Fig. 17 Componen s ha make up ans e channel in e ope abili y
Te zi (2022) is in oduced, which in la e sec ions is c ucially
impo an ocons uc his amewo k,and he esea chscope
is delimi ed om he iple pe spec i e o he en i onmen ,
D-BEST se ice, and in e ac ion ype. On his basis, acco d-
ing o he e iew o he scien i ic li e a u e ha ela es DIH
concep s and all he in e ac ion ypes ela ed o he collabo-
a ion p ocess, his esea ch iden i ies, collec s and pe o ms
a hema ic and con en analysis o he 17 selec ed con i-
bu ions o he s a e o he a ha is mos aligned, pa ially
o o ally, and di ec ly o indi ec ly, wi h he pu pose o he
amewo k. The li e a u e e iew eaches h ee main conclu-
sions: (i) mos o he selec ed a icles add ess collabo a ion
be ween DIHs in a angen ial manne ; (ii) he esea ch con-
ibu ions made o da e conside he in e ac ion p ocesses
o collabo a ion o be an in angible elemen o managemen
ha is di icul o pe cei e and, a p io i, is no measu able;
and (iii) as a as we know om his e iew, he e is s ill
no desc ip i e o concep ual amewo k, o a quali a i e o
quan i a i e model, ha deals wi h he concep ualisa ion o
he in e ac ion p ocesses o DIHs om he collabo a ion pe -
spec i e wi hin and be ween i s ou ela ionship le els. The
amewo k buil on a bo om-up app oach is p esen ed below.
The concep ualisa ion pa h guided by his app oach s a s by
explaining he decomposi ion o he D-BEST se ices in o
asse s and ypes. F om his decomposi ion, collabo a ion can
be concep ualised as an asse s low om ceding o ganisa-
ions o bene icia y o ganisa ions o alle ia e any de ici s in
he la e and o, hus, enable hem o p o ide some speci ic
se ice ypes immedia ely o in he u u e. This abs ac ion,
which is called he IOATM, lies a he hea o he concep-
ual amewo k and he a icle o no only es ablishing he
p ecise o igin, des ina ion, channel and ans e ed asse , bu
o also ep esen ing a heo e ical means o quan i a i ely
assess he magni ude o collabo a ion, which is conside ed
hemain con ibu ion o his esea ch. Subsequen ly, oe ec-
i ely show how his amewo k can be implemen ed in a eal
si ua ion, a use case ha add esses collabo a i e p ocesses in
he iAE6 applica ion expe imen o he DIH4CPS p ojec
is p esen ed as an example. To do so, all he s eps o he
ull cha ac e isa ion and quan i ica ion o collabo a ion a e
de ailed.Finally, he con ibu iono he p esen ed amewo k
is discussed on se e al on s: (i) i s sui abili y as a le e o
mo ing owa ds a s ong collabo a i e model; (ii) i s in e op-
e abili y implica ions; (iii) i s sus ainabili y epe cussions.
This app oach p o ides answe s o he o mula ed
esea ch ques ions. The main cha ac e is ic o de ine he
in e ac ions ha ake place be ween he en i ies making up
he ela ional DIHs ne wo k when collabo a ion p ocesses
ma e ialise be ween hem is he possibili y o decomposi-
ion in o he o igin, des ina ion, channel and ans e ed asse
in ol ed in he asse s low o he analysed collabo a ion,
which p o ides an answe o esea ch ques ion RQ1. F om
hispe spec i e,bo h heasse s lowand he ans e channels
canbeconside ed he wokeydimensionsin he collabo a i e
in e ac ion p ocesses and he elemen s ha mos shape he
quan i a i e assessmen o collabo a ion, dimensions a ound
which he o he elemen s o he p oposed amewo k a e
posi ioned: he se ices ca alogue, he le els o ela ionship
123
Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545 1543
in ol ed and he collabo a i e in e ac ion ypes, which do
no essen ially shape, bu condi ion, he assessmen . This
app oach p o ides an answe o esea ch ques ion RQ2 o -
mula ed in he in oduc ion o his a icle. In he end, he
p esen ed amewo k is discussed on h ee ca dinal on s:
(i) i s ap i ude as a le e o mo e owa ds a collabo a ion
model;(ii)i sconno a ionsinin e ope abili y e ms;(iii)how
i ela es o sus ainabili y.
The implica ions o his amewo k a e subs an ial. In he
academic sphe e, i b idges he esea ch gap de ec ed on he
analysed opic, and no only con ibu es a new pe spec i e on
collabo a i e p ocesses in he inno a ion ecosys ems gene -
a ed by DIHs and/o EDIHs, bu also es ablishes he meaning
andin e ela ionships o he concep s makingup i son ology.
I p o ides a nuance ha allows quan i ica ion, and se es
as a le e o model a esea ch objec in clea p og ession,
such as collabo a ion. As o manage ial implica ions, he
main one o highligh is ha he app oach o e ed by IOATM
cons i u es he i s piece o a u u e model ha will enable
dealings wi h he accoun ing o collabo a ion in i s mul iple
ace s and, he e o e, will u n i in o a mo e angible and
con ollable managemen elemen . Howe e , i is also wo h
no ing ha , in addi ion, his amewo k can al eady cons i-
u e a oadmap ha helps p ac i ione s o guide hei e o s
o imp o e he e iciency o collabo a i e p ocesses wi h an
app oach ha pu sues objec i i y, plus he maximisa ion o
syne gies be ween collabo a i e en i ies.
Despi e i s s eng hs, he amewo k p esen s some limi a-
ions ha should be ou lined he e: (i) he D-BEST e e ence
model is speci ically o ien ed o DIHs and, by ex apola ion,
can wo k in EDIHs o in o ganisa ions ha in eg a e inno-
a ion ecosys ems in gene al, such as se ice p o ide s o
end use s. Beyond his collabo a ion scope, he se ice ca a-
logues and he asse ypology may a y ha , in u n, implies
ha he p esen ed amewo k lacks alidi y; (ii) i does no
o e a plane ame o uni o m applica ion o all ypes o col-
labo a ion, bu shows a ia ions in in e p e a ion depending
on whe he in e ac ions a e coope a ion, coope i ion, coo di-
na ion, e c.; (iii) in mul iple collabo a ion schemes in ol ing
mo e han wo o ganisa ions, when he ans e o o gan-
isa ion simul aneously ans e s asse s o se e al ecei e
o ganisa ions, i is no always easy o p ecisely delimi he
asse s ans e ed o each one, especially when in angible
asse s like compe ences o knowledge a e in ol ed, which
implies u he e o o speci y lows and hei di ec ion
om hei o igin h ough he co esponding mapping. Some-
hing simila happens in collabo a i e p ocesses wi h se e al
ans e o o ganisa ions ha simul aneously in e ac wi h a
ecei e o ganisa ion, which equi es addi ional e o s o
delimi asse ans e s, his ime in he des ina ion.
This esea ch lea es se e al open doo s ha can guide
u u e esea ch on he opic unde s udy: (i) he mos ob ious
one is o ad ance owa ds modelling he collabo a i e in e -
ac ion p ocesses o DIHs and i s alida ion h ough empi ical
me hods,suchascomple ecases udieso su eys;(ii)insuch
modelling,i wouldbe e yuse ul o ace he challengeposed
by he e alua ion o he ac o s ha may al e he e iciency
o collabo a ion, such as ha nessing deg ee o collabo a ion
in he ecei ing en i y, asse ans e e iciency depending on
he deg ee o in e ope abili y ha exis s h ough he ans-
e channel, o e iciency o he ans e d i e exe cised by
he ans e o en i y; (iii) wi h a iew o collabo a ion sus-
ainabili y, i also seems app op ia e o s udy in mo e de ail
how o moni o and e alua e he beha iou s exe cised du -
ing collabo a ion p ocesses o a oid misuse, bu o mi iga e
undesi ed e ec s on i s e iciency.
Acknowledgemen s This esea ch was ca ied ou a he Uni e sidade
No adeLisboa(UNINOVA)and heUni e si a Poli ècnicadeValència
(UPV).The au ho sa e g a e ul o he suppo ecei ed om he Cen e
o Technology and Sys ems (CTS) o UNINOVA and he Resea ch
Cen e on P oduc ion Managemen and Enginee ing (CIGIP) o he
UPV o success ully ca y ou his esea ch. Likewise, he au ho s a e
e y g a e ul o all pa icipa ing en i ies in he pilo used as a case s udy.
Au ho con ibu ions Julio C. Se ano concep ualised he me hod
desc ibed in his amewo k, implemen ed i , applied i o he case
s udy, and w o e he main ex o he manusc ip , ables and igu es.
José Fe ei a p o ided o e all supe ision o he ex , con ibu ed o
he concep ualisa ion and p o ided he case s udy. Rica do Ja dim-
Goncal es and Angel O iz e iewed he a icle, con ibu ed o he
discussion sec ion and alida ed he conclusions.
Funding Open Access unding p o ided hanks o he CRUE-CSIC
ag eemen wi h Sp inge Na u e. The esea ch leading o hese esul s
ecei ed unding om he Eu opean Union H2020 Resea ch and Inno-
a ion P og amme wi h G an Ag eemen s No. 872548 “Fos e ing
DIHs o Embedding In e ope abili y in Cybe –Physical Sys ems o
Eu opean SMEs” (DIH4CPS), No. 825631 “Ze o-De ec Manu ac-
u ing Pla o m” (ZDMP), and No. 958205 “Indus ial Da a Se ices
o Quali y Con ol in Sma Manu ac u ing (i4Q)”, om he Eu o-
pean Union Ho izon Eu ope P og amme wi h G an Ag eemen No.
101057294 “AI D i en Indus ial Equipmen P oduc Li e Cycle Boos -
ing Agili y, Sus ainabili y and Resilience” (AIDEAS), and om he
Regional Depa men o Inno a ion, Uni e si ies, Science and Digi al
Socie y o he Gene ali a Valenciana en i led “Indus ial P oduc ion
and Logis ics Op imisa ion in Indus y 4.0” (i4OPT, Re . PROME-
TEO/2021/065).
Da a a ailabili y The da a on which his a icle is based, including he
de eloped me hod and case s udy da a, a e a ailable upon eques . Ne -
e heless, due o con iden iali y conside a ions, de ailed in o ma ion
ega ding he de elopmen o he pilo used in he case s udy canno be
publicly disclosed.
Decla a ions
Con lic o in e es No po en ial con lic o in e es is epo ed by he
au ho s.
E hical app o al No applicable.
In o med consen No applicable.
123
1544 Jou nal o In elligen Manu ac u ing (2025) 36:1505–1545
Consen o publica ion The au ho s consen ha he wo k en i led
“Rela ional Ne wo k o inno a ion ecosys ems gene a ed by digi al
inno a ion hubs: a concep ual amewo k o he in e ac ion p ocesses
o DIHs om he pe spec i e o collabo a ion wi hin and be ween hei
ela ionship le els” o possible publica ion in he Jou nal o In elligen
Manu ac u ing. The au ho s ce i y ha his manusc ip is o iginal and
has no been published in whole o in pa , no is i being conside ed
o publica ion elsewhe e.
Open Access This a icle is licensed unde a C ea i e Commons
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a ion, dis ibu ion and ep oduc ion in any medium o o ma , as
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