Facili a ing AI-Based Solu ions In eg a ion
in P oduc ion Planning: De elopmen
o an In e ope able Da a Model
Bea iz And es(B), Juan Pablo Fiesco, Raul Pole , and Miguel A. Ma eo-Casali
Resea ch Cen e on P oduc ion Managemen and Enginee ing (CIGIP), Uni e si a Poli ècnica
de València (UPV), Camino de Ve a s/n, 46022 Valencia, Spain
{band es,j iesco, pole ,mma eo}@cigip.up .es
Abs ac . Indus ies s i e o imp o e e iciency, minimise en i onmen al
impac s and ensu e ope a ional con inui y. En e p ises digi isa ion a ou s he
achie emen o hese challenges. AI-based ools play a signi ican ole in imp o -
ing en e p ise digi isa ion by o e ing capabili ies ha enhance e iciency and deci-
sion making ac oss a ious unc ions o an o ganiza ion’s ope a ions, p ocesses,
and sys ems. This a icle del es in o planning p ocesses by p oposing he use o an
in e ope able da a model o in eg a e AI-based solu ions de eloped o suppo he
op imisa ion o p oduc ion plans. The p oposed da a model acili a es in e ope -
abili y be ween en e p ise legacy sys ems and AI-based manu ac u ing solu ions
o suppo seamless communica ion be ween hem, hus, assis eplenishmen and
manu ac u ing decisions.
Keywo ds: A i icial In elligence · Sus ainable Manu ac u ing ·
In e ope abili y · Indus ial ood inspec ion equipmen · Indus y 4.0
1 In oduc ion
In an inc easingly in e connec ed and compe i i e wo ld, companies ace he cons an
challenge o imp o ing e iciency by ocusing on en i onmen al impac s and ensu ing he
con inui y o hei ope a ions. In his con ex , he Eu opean AI-D i en Indus ial Equip-
men P oduc Li e Cycle Boos ing Agili y, Sus ainabili y and Resilience (AIDEAS,
2022) p ojec ocuses on de eloping a se o a i icial in elligence (AI) based solu ions
o imp o e a ious indus ial machine y p oduc ion aspec s. AIDEAS p oposes a se o
AI ools ha co e s he en i e li ecycle o hese machines, including he: (i) design phase;
(ii) p oduc ion phase; (iii) use phase; (i ) epai / euse/ ecycling phase. The p ojec aims
o s eng hen he compe i i eness, sus ainabili y and esilience o Eu opean machine y
manu ac u e s and use s.
Fou indus ial pilo s pa icipa e in he AIDEAS p ojec , in which AI-based solu ions
a e demons a ed. The ou pilo s a e manu ac u e s o indus ial equipmen and belong
o di e en sec o s, and p o ide wide- anging case s udies in which di e en p oblems
o he ou li ecycle phases a e add essed.
© The Au ho (s), unde exclusi e license o Sp inge Na u e Swi ze land AG 2025
R. Ca asco-Gallego e al. (Eds.): CIO 2024, LNDECT 239, pp. 220–225, 2025.
h ps://doi.o g/10.1007/978-3-031-82334-3_38
Facili a ing AI-Based Solu ions In eg a ion in P oduc ion Planning 221
Indus y 4.0 (I4.0) and i s echnologies ha e igge ed a majo ans o ma ion o
p ocesses in he manu ac u ing indus y (Oli ei a-Dias e al., 2023). The digi isa ion o
in o ma ion is an un inished ask o many small- and medium-sized en e p ises (SMEs),
especially in indus ial equipmen companies ha handle la ge olumes o da a ela ed
o bills o ma e ials, supplie s, in en o y and manu ac u ing p ocess s a us (Doyle and
Cosg o e, 2019; Shah e al., 2024). Howe e , limi ed esou ces, he high cos s associa ed
wi h implemen ing I4.0 echnology p ojec s, de iciencies in da a and knowledge man-
agemen and lack o ained pe sonnel can signi ican ly a ec hei abili y o inno a e,
compe e in he ma ke and adap o oday’s apidly changing business en i onmen .
To add ess he da a managemen p oblem, his pape iden i ies in he li e a u e a da a
model o collabo a i e manu ac u ing en i onmen s (CMDa a) p oposed by And es,
Pole and Sanchis (2021) o he exchange o da a among AI-based solu ions de el-
oped o suppo he op imisa ion o p oduc ion plans. The p oposed in e ope able da a
model plays a c i ical ole in b idging he gap be ween legacy sys ems (LS) and mode n
AI-based manu ac u ing solu ions, leading o enhanced in e ope abili y, op imized p o-
duc ion plans, cos e iciency, scalabili y, imp o ed decision-making, and he acili a ion
o AI adop ion. To alida e he pape esul s, a pilo case s udy is p esen ed. Acco dingly,
he pape is o ganised as ollows: Sec . 2 in oduces he AI-based solu ions o suppo
he manu ac u ing plans wi hin he Eu opean AIDEAS p ojec scope; Sec . 3 concep-
ualises he in e ope able da a model o suppo communica ion o p oduc ion planning
AI-based solu ions. The p oposed model in e ope a es no only a he solu ions le el,
p ocu emen , p oduc ion and scheduling planning, bu also a he en e p ise LS le el;
Sec . 4 p o ides he conclusions and u u e esea ch lines.
2 AI Solu ions o Suppo he Manu ac u ing Phase
O he ou p oduc li ecycle phases, namely design, manu ac u ing, use and disposal,
his pape ocuses on he manu ac u ing phase, which encompasses he p ocu emen ,
ab ica ion and deli e y p ocesses. Op imising hese p ocesses equi es analysing and
p ocessing la ge amoun s o dynamic da a, such as supplie s, manu ac u e s and cus-
ome s’ in o ma ion, ma e ial supply, cus ome demand o esou ce capaci y, and less
dynamic da a, such as bills o ma e ials o p oduc ca alogues. These da a a e usually
s o ed in en e p ise LS. To make hese da a in e ope able o any in e o ganisa ional
o in ao ganisa ional managemen sys em, i is necessa y o de ine a no malised da a
model.
To imp o e he esilience o supply chain p ocesses, i is i al ha en e p ise LSs
a e open and secu e o os e collabo a ion be ween pa ne s and o be mo e accu a e in
decision making (And es, Pole and Sanchis, 2021). Ano he c ucial aspec is o ensu e
he quali y and connec i i y o he da a o be used o e icien supply chain p og amming
and con ol plans (Bu gg ä e al., 2018).
This pape ocuses on he de ini ion o he in e ope able da a model o he P o-
cu emen Op imise (AI-PO) and he Fab ica ion Op imise (AI-FO) solu ions o he
AIDEAS p ojec . AI-PO op imises he pu chase o ma e ials by minimising in en o y
cos s and imp o ing company p o i s. This solu ion analyses in o ma ion, such as sup-
plie quo a ions, bill o ma e ials and p oduc ion equi emen s o pu chasing decisions.
222 B. And es e al.
AI-FO suppo s in o med decision making on alloca ing esou ces, educing down ime
and inc easing p oduc i i y by allowing manu ac u e s o quickly adap o changes in
demand and ope a ing condi ions. Bo h solu ions le e age AI algo i hms o inc ease
agili y and p oduc i i y in he manu ac u ing phase. In he AI-FO solu ion, he e a e
wo AI-based algo i hms: one ha sol es he mas e p oduc ion plan (MPP) and he
scheduling plan (SP): (i) AI-FOMPP conside s accoun ac o s, such as commi ed o
o ecas demand, ope a o a ailabili y, in en o y le els and o he cons ain s o op imise
esou ce alloca ion and o mee cus ome demand e icien ly; (ii) AI-FOSP se s ou he
de ailed schedule o execu ing he p oduc ion ac i i ies speci ied in he AI-FOMPP.I
de e mines when each p oduc ion ask should begin and end, wha esou ces (machines,
equipmen , ope a o s) should be alloca ed o each ask and he o de in which hey should
be pe o med. P oduc ion planning is an i e a i e and c i ical ask in he manu ac u ing
sec o ha equi es many da a. P oduc ion le els a e planned o each pe iod in p oduc-
ion managemen so ha all o de s can be deli e ed on ime (Cha les e al., 2022). The
complexi y o p oduc ion planning depends on he policies o he p oduc ion sys em and
he amoun o da a o be conside ed (Ka imi e al., 2003). The e o e, o in eg a e AI-PO
and AI-FO, i is necessa y o analyse he policies o he company’s p oduc ion sys em
and o see how his communica ion be ween solu ions can ake place.
3 Da a Model o P oduc ion Planning AI-Based Solu ions
To communica e da a om en e p ise LSs o he AI-based manu ac u ing solu ions
de eloped in he AIDEAS p ojec , an in e ope able da a model is needed. Based on
he pape o And es, Pole and Sanchis (2021), his sec ion desc ibes he use o he da a
model o he CMDa a p oposed by he au ho s. This pape p oposes he in eg a ion o he
CMDa a o acili a ing AI-based Solu ions In eg a ion in P oduc ion Planning, including
AI-PO, AI-FOMPP and AI-FOSP solu ions. The CMDa a ables con ain a homogenised
nomencla u e o he inpu da a and objec i es equi ed o eed AI-based solu ions. Each
CMDa a able is composed o di e en a ibu es ha cha ac e ise i . Hence he CMDa a
able Pa e e s o a p oduc ’s gene ic e minology, including a i m’s aw ma e ials,
componen s o inal p oduc s. The CMDa a Table Pa has se e al a ibu es, such as
code, desc ip ion, in en o y cos , ini ial in en o y, pu chase cos , selling p ice, cu en
in en o y, dimensions, weigh , loca ion in in en o y, among o he s.
AI-based manu ac u ing solu ions ha e he pa icula i y ha hey communica e wi h
one ano he and wo k in an in eg a ed model en i onmen . The ela ions be ween solu-
ions and how hey communica e h ough a cen alised da abase, he AIDEAS da as o e
(AI-DS), is depic ed in Fig. 1.
Wi hin his amewo k, en e p ise LS es ablish a link wi h he AI-DS da abase
h ough a que ying p ocess. This que ying mechanism se es as he condui h ough
which en e p ise LS access and in e ac wi h he da a s o ed in he AI-DS (see low
1-En e p ise Mapping in Fig. 1). Once he da a a e in he AI-DS and acco ding o he
CMDa a s uc u e, AI-FOMPP accesses he AI-DS o ob ain he equi ed inpu da a (2-
AI_FOMPP_Inpu Da a). Subsequen ly, he MPP is calcula ed and he ou pu da a a e
ob ained (3-AI-FOMPP_Ou pu Da a) o p esen he numbe o p oduc s o be p oduced
du ing each pe iod by conside ing each p oduc ’s s a and end manu ac u ing da e. The
Facili a ing AI-Based Solu ions In eg a ion in P oduc ion Planning 223
AI-FOMPP ou pu is s o ed in he AI-DS acco ding o he da a s uc u e de ined by he
CMDa a. Fu he mo e, he AI-FOMPP ou pu se es as inpu o he AI-FOSP and AI-PO
solu ions. Addi ionally, he ou pu om AI-FOMPP is mapped back h ough a que y by
gene a ing a sp eadshee wi h ields ansla ed in o he nomencla u e p e e ed by he
company o da a in e p e a ion. In he pilo phase, he p oduc ion planne e iews he
sp eadshee da a. I he company wishes o in eg a e hese da a in o i s LS, addi ional
que y gene a ion is equi ed. The AI-PO solu ion explodes he ma e ials needed o man-
u ac u e acco ding o he MPP compu ed by AI_FOMPP (3-AI_FOMPP_Ou pu Da a) and
gene a es he pu chasing plan (4-AI_PO_Ou pu Da a), which is s o ed in he AI-DS. I
he e is any empo al in easibili y, i is communica ed o he AI-FOMPP solu ion. In his
way, he AI-FOMPP solu ion ecalcula es he MPP by conside ing he new ma e ial a i al
da es speci ied in 4-AI_PO_Ou pu Da a, which will cause he p oduc ion s a da es o
be delayed. Mo eo e , he AI-PO ou pu is mapped back h ough he que y by gene a -
ing a sp eadshee wi h ields ansla ed in o he nomencla u e p e e ed by he company
Fig. 1. Da a Flow A chi ec u e
224 B. And es e al.
o da a in e p e a ion. In he pilo phase, he pu chase planne e iews he da a on he
sp eadshee . I he company desi es hese da a o appea in i s LS, u he w i e que ies
mus be gene a ed. The AI-FOSP SP solu ion is ed wi h 3-AI_FOMPP_Ou pu Da a and
4-AI_PO_Ou pu _Da a, which enables he p oduc ion schedule calcula ion ha consid-
e s he s a and inish da es o he MPP and he a ailabili y o he ma e ials calcula ed by
he p ocu emen plan. The AI_FOSP solu ion (5-AI_FOSP_Ou pu ) ou pu is s o ed in he
AI-DS wi h in o ma ion abou he ope a o s ha a e going o ca y ou di e en ope a-
ions in ce ain wo k cen es o ul il he de ined MPP. I is possible ha 4-AI_SP_Ou pu
may s chedule a se ies o ma e ials o ope a o s ha canno be ul illed in he sho e m.
This will p omp a eedback loop in o he p ocess o ecalcula e he MPP by AI-FOMPP.
Consequen ly, i he necessa y ma e ials a e una ailable, he p ocu emen plan will be
ecalcula ed o gene a e new 3-AI_FOMPP_Ou pu _Da a and 4- AI_PO_Ou pu _Da a
solu ions. Addi ionally, he AI-FOSP SP ou pu is mapped again h ough he que y and
gene a es a sp eadshee wi h ields ansla ed in o he nomencla u e p e e ed by he
company o da a in e p e a ion. In he pilo phase, he schedule e iews he da a on he
sp eadshee . I he company wishes hese da a o appea in i s LS, o he w i e que ies
mus be gene a ed.
4 Conclusion and Fu u e Resea ch
The AIDEAS p ojec ep esen s a signi ican imp o emen owa ds enhancing sus ain-
abili y and e iciency in he manu ac u ing o indus ial ood inspec ion equipmen using
in e ope able AI solu ions. Conside ing he challenges o digi isa ion and he in eg a ion
o I4.0 echnologies in o SMEs, his pape concep ualises communica ion among h ee
solu ions de eloped in AIDEAS. This communica ion occu s h ough he AI-DS ool
buil wi h an in e ope able da a model ollowing he CMDa a s anda d, and designed
o eed in eg a ed planning models, including p ocu emen , p oduc ion and scheduling,
and o acili a e da a exchange no only be ween AI-based solu ions, bu also wi h he LS
company. S anda dising da a and in eg a ing en e p ise LS wi h ad anced AI echnolo-
gies enable agile and accu a e decision makingby imp o ing he pa h owa ds a mo e
sus ainable and e icien u u e in he indus ial equipmen indus y. As u u e esea ch
lines, expe imen s wi h eal company da a a e p oposed o be ca ied ou by e alua ing
he quali y and iabili y o solu ions in company ope a ions.
Acknowledgemen s. The esea ch ecei ed unding om he Ho izon Eu ope F amewo k P o-
g amme (HORIZON) 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)”. The Regional
Depa men o Inno a ion, Uni e si ies, Science, and Digi al Socie y o he Gene ali a Valen-
ciana “P og ama In es igo” ( e . INVEST/2022/330), which he Eu opean Union suppo ed -
Nex Gene a ionEU wi h Plan de Recupe ación, T ans o mación y Resiliencia. The Gene ali a
Valenciana wi h p og am “Sub enciones pa a la ealización de es ancias de pe sonal in es igado
doc o en emp esas de la Comuni a Valenciana” ( e . CIAEST/2022/39).
Facili a ing AI-Based Solu ions In eg a ion in P oduc ion Planning 225
Re e ences
AIDEAS. AI d i en indus ial equipmen p oduc li e cycle boos ing agili y, sus ainabili y and
esilience. Eu opean Union’s Ho izon Eu ope e-sea ch and inno a ion p og amme unde g an
ag eemen no. 1010572942022. h ps://doi.o g/10.3030/101057294
And es, B., Pole , R., Sanchis, R.: A da a model o collabo a i e manu ac u ing en i onmen s.
Compu . Ind. 126 (2021).h ps://doi.o g/10.1016/j.compind.2021.103398
Bu gg ä , P., Dannap el, M., Fö s mann, R., Adlon, T., Fölling, C.: Da a quali y-based p ocess
enabling: applica ion o logis ics supply p ocesses in low- olume amp-up con ex . In: 2018
In e na ional Con e ence on In o ma ion Managemen and P ocessing (ICIMP). pp. 36–41
(2018). h ps://doi.o g/10.1109/ICIMP1.2018.8325838
Cha les, M., Dauzè e-Pé ès, S., Kedad-Sidhoum, S., Mazhoud, I.: Mo i a ions and analysis o
he capaci a ed lo -sizing p oblem wi h se up imes and minimum and maximum ending
in en o ies. Eu . J. Ope . Res. 302, 203–220 (2022). h ps://doi.o g/10.1016/j.ejo .2021.12.017
Doyle, F., Cosg o e, J.: S eps owa ds digi iza ion o manu ac u ing in an SME en i onmen .
P ocedia Manu . 38, 540–547 (2019). h ps://doi.o g/10.1016/j.p om g.2020.01.068
Ka imi, B., Ghomi, S., Wilson, J.M.: The capaci a ed lo sizing p oblem: a e iew o models and
algo i hms. Omega-In . J. Manage. Sci. 31, 365–378 (2003). h ps://doi.o g/10.1016/S0305-
0483(03)00059-8
Oli ei a-Dias, D., de, Maquei a-Ma in, J.M., Moyano-Fuen es, J., Ca alho, H.: Implica ions o
using Indus y 4.0 base echnologies o lean and agile supply chains and pe o mance. In . J.
P od. Econ. 262 (2023). h ps://doi.o g/10.1016/j.ijpe.2023.108916
Shah, S., Hussain Madni, S.H., Hashim, S.Z.B.M., Ali, J., Faheem, M.: Fac o s in luencing he
adop ion o indus ial in e ne o hings o he manu ac u ing and p oduc ion small and medium
en e p ises in de eloping coun ies. IET Collabo . In ell. Manu . 6 (2024). h ps://doi.o g/10.
1049/cim2.12093
