A i icial In elligence o Suppo Collabo a ion
in he Indus ial Equipmen Li e Cycle
B. And es(B), M. A. Ma eo-Casali, J. P. Fiesco, and Raúl Pole
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,mma eo,j iesco, pole }@cigip.up .es
Abs ac . This pape explo es he po en ial o a i icial in elligence (AI) o sup-
po collabo a ion in he indus ial equipmen li e cycle. The indus ial equipmen
indus y in ol es complex mul idisciplina y collabo a ion wi h supplie s and cus-
ome s ac oss many machine y li e cycle s ages, including design, manu ac u ing,
use and end-o -li e. This pape concep ualises a se o AI-enabled digi al solu-
ions wi hin he AIDEAS Eu opean p ojec scope. Wi h a case s udy o an indus-
ial equipmen company, we illus a e how AI solu ions can be used o suppo
collabo a ion in he supply chain ac oss machine y li e cycles.
Keywo ds: A i icial In elligence ·Collabo a ion ·Indus ial Equipmen ·Li e
Cycle
1 In oduc ion
Inc easing consump ion and economic globalisa ion ha e o ced companies o imp o e
p oduc ion p ocesses h ough Indus y 4.0 (I4.0) echnologies, such as A i icial In elli-
gence (AI), Big Da a Analy ics (BDA), Block Chain (BC), Cloud Sys ems (CS), Cybe -
Physical Sys ems (CPS), In e ne o Things (IoT), Addi i e Manu ac u ing (AM) and
Digi al Twins (DTs). Thanks o hese echnologies, i has been possible o op imise
and imp o e p oduc ion p ocesses. Mo eo e , I4.0 echnologies a e conside ed by [1]
as enabling inc eased collabo a ion be ween companies. To his end, companies a e
explo ing no el app oaches o collec da a h oughou he p oduc ion chain o op imise
indus ial p ocesses, which leads o a digi isa ion and op imisa ion end in indus y
[2]. In oducing new echnologies o e s he possibili y o sha ing di e en esou ces
ac oss in o ma ion sys ems by acili a ing he in eg a ion o di e en solu ions h ough-
ou a p oduc ’s li e cycle. Thanks o his in as uc u e, he in e ac ion be ween di e en
companies is acili a ed by inc easing collabo a ion along he p oduc ion chain and a
machine’s li e cycle.
The ollowing sec ions in oduce he AIDEAS p ojec [3] on which his pape is
based, and whose main aim is o de elop AI echnologies ha can suppo he en i e li e
cycle o indus ial equipmen . The p ima y goal o his p ojec is o de elop and use hese
echnologies as a s a egic ool o enhance collabo a ion among supply chain pa ne s,
© IFIP In e na ional Fede a ion o In o ma ion P ocessing 2023
Published by Sp inge Na u e Swi ze land AG 2023
L. M. Cama inha-Ma os e al. (Eds.): PRO-VE 2023, IFIP AICT 688, pp. 706–719, 2023.
h ps://doi.o g/10.1007/978-3-031-42622-3_50
AI o Suppo Collabo a ion in he Indus ial Equipmen Li e Cycle 707
achie e sus ainabili y p inciples in he ne wo k, inc ease collabo a i e pa ne s’ agili y,
and p omo e collabo a i e en e p ises’ esilience. The p ojec pa icula ly ocuses on
machine y manu ac u ing companies in he Eu opean Union (EU).
The objec i e o he pape is o concep ualise a se o AI-based ools o deal
wi h he collabo a i e pe spec i e in each p oduc li e cycle (PLC), including design,
manu ac u ing, use and disposal, by conside ing he ci cula economy (CE) pe spec i e.
To ul il he indica ed objec i e, he pape is o ganised as ollows: Sec . 2ca ies
ou a s a e o he a ollowing a ou -scope app oach ha add esses he in e connec-
ions be ween collabo a i e ne wo ks (CNs), PLC, I4.0 and CE. Sec ion 3concep u-
alises he AIDEAS AI-based solu ions, o be implemen ed in o he ou di e en PLC
phases. Sec ion 4p esen s a case s udy in a ood inspec ion indus ial equipmen com-
pany, in which he concep ualised AI ools conside ed in i s business p ocesses. Finally,
discussion appea s in he conclusions sec ion.
2 S a e o he A
In eg a ing AI in o manu ac u ing has become inc easingly i al o educing he com-
plexi y o managing manu ac u ing p ocesses. As esea che s con inue o explo e new
ways o imp o e e iciency in he manu ac u ing phase, I4.0 has eme ged as a esponse
o in eg a e ad anced echnologies like AI, Robo s, IoT, and Cloud Compu ing by
enhancing he esilience and sus ainabili y o p oduc ion sys ems [4]. Sma ac o ies
a e an example o his in eg a ion, which employs con ex -awa e applica ions and sel -
egula ing mechanisms o op imise p oduc ion p ocesses [5]. The signi icance o inno a-
ion and digi isa ion in p oduc s, se ices and p ocesses has highligh ed he need o adop
ad anced AI echnologies in manu ac u ing p ocesses. Among he a ious subclasses o
AI, one ha s ands ou p ominen ly in his con ex is Machine Lea ning (ML), which has
eme ged as one o he mos ex ensi ely employed echniques [6]. These algo i hms a e
p o en essen ial ools o handling high-dimensional p oblems and da a, which a e con-
s an cha ac e is ics o CN. By ocusing on compu e science and enginee ing, AI o e s
se e al bene i s in indus ial sec o s, including g ea e inno a ion, p ocess op imisa ion,
esou ce op imisa ion and imp o ed quali y [7]. I is no ewo hy ha hese bene i s ha e
e olu ionised he PLC by enabling he op imisa ion o p ocesses and esou ces, and
imp o ing quali y in all s ages, om design and manu ac u e o emo al and disposal,
and a all he le els o supply chain s akeholde s.
This s a e o he a sec ion explo es he li e a u e om a ou scope app oach: (i)
PLC; (ii) CE, (iii) supply chain collabo a ion; and (i ) I4.0 echnologies. This sec ion
elucida es how hese ou concep s can os e a syne gis ic e ec , culmina ing in a obus
amewo k o p omo ing sus ainable and e icien p ac ices:
•The PLC e e s o he di e en s ages ha a p oduc goes h ough om c ea ion
o disposal. A p oduc ’s li e cycle comp ises ou main phases: (i) beginning o li e,
which includes he design o a p oduc ; (ii) middle o li e, which in ol es he esou ce,
manu ac u ing and dis ibu ion o a p oduc ; (iii) use o li e, which en ails a p oduc
being used and a e -sales suppo o cus ome s; (i ) end-o -li e, which includes a
p oduc ’s e i emen and disposal. Each phase has unique ac i i ies and goals ha a e
708 B. And es e al.
impo an o unde s and by all he supply chain pa ne s o e ec i ely manage he
p oduc , om he p oduc ’s design o i s disposal [8].
•Eme gen aming a ound was e and esou ce managemen has gained momen um
in esponse o he in insic limi o sus ainabili y ha a ec s he mode n wo ld.
I is known as CE. This aming seeks o p o ide an al e na i e o p e alen lin-
ea ake-make-dispose p ac ices pe pe ua ed by cu en business models o p io i ise
con inuous p oduc ion, consump ion and disposal o s ay compe i i e [9]. CE p o-
mo es he was e and esou ce cycling no ion o add ess he exis ing model’s limi s
and weaknesses, a conce n ha has been oiced o wo decades.
•CNs a e acknowledged as a key acili a o in he e olu ion owa ds CE in supply
chains [10]. CNs also play a c ucial ole in in eg a ing I4.0, including AI. Acco d-
ing o he I4.0 ision, sma manu ac u ing en e p ises a e o ganised in o mul iple
laye s o ne wo ked and collabo a i e subsys ems. Each laye becomes a CN o
sma componen s wi h inc easing le els o in elligence and au onomy. The in e ac-
ions be ween hese laye s lead o an exchange among sma p oduc ion uni s, sma
logis ics, sma p oduc s, sma o ganisa ional and enginee ing uni s, and people.
Collabo a ion be ween hese en i ies is a equi emen o suppo agile and esilien
p ocesses [11,12]. Acco ding o [13], in eg a ing collabo a ion in o business p o-
cesses, p ac ices and s anda ds is necessa y o shi om he unsus ainable linea
economic sys em o a mo e sus ainable ci cula sys em. Thus collabo a ion is c ucial
o ha nessing he po en ial bene i s o CE and I4.0, which a e he wo p ominen
indus ial pa e ns in ecen imes. Thei combined implemen a ion in o an indus ial
se ing can enhance supply chain e iciency and compe i i eness [14].
•The p omising scope o AI echniques has led o hem being implemen ed in di e en
PLC phases. Wang e al. [15] p o ide an example o a amewo k ha classi ies he
di e en AI applica ions and how hey can be ansla ed in o di e en li e cycle
phases. To he bes o ou knowledge, he e is no wo k in he li e a u e ha add esses
he ou -scope app oach in which AI echnology suppo s he di e en PLC phases
by conside ing he collabo a i e pe spec i e o supply chain pa ne s in all he phases,
om design o disposal, including he ci cula i y concep .
In a highly globalised wo ld, wi h he eme gence o new playe s, i is c ucial ha
he EU con inues o explo e dis up i e ways o imp o e i s ecosys em and main ain i s
posi ion as he wo ld’s leading p oduce and expo e o machine ools. The EU’s ech-
nological edge in he indus ial equipmen sec o is h ea ened by China’s apid g ow h.
China has launched s a egic plans such as “Made in China 2025” o mode nise i s indus-
ial capabili ies and impac on he compe i i e landscape. Whils EU has highligh ed he
need, acco ding o he cu en Ho izon Eu ope amewo k p og amme, o digi alising
manu ac u ing en e p ises in he scope o he indus ial equipmen li e, especially in
SMEs.
3 Indus ial Equipmen Li e Cycle Suppo ed by AI
One o he challenges ha CNs ace is o de elop solu ions ha es , analyse and hen
decide abou he designs ha a ec he en i e PLC. A his poin , enginee s in ol ed a
he di e en PLC poin s ace he need o be e unde s and all he s ages ha hei p oduc
AI o Suppo Collabo a ion in he Indus ial Equipmen Li e Cycle 709
goes h ough du ing i s whole li e cycle, and ele an in o ma ion o da a. Consequen ly,
hey ha e o handle a la ge amoun o eliable in o ma ion ha will equi e di e en
echnological solu ions being used. A his poin , by p ope ly in eg a ing a ious AI
ools, syne gies can be achie ed ac oss all ac o y unc ions, which will ul ima ely ha e
a posi i e impac on p oduc i i y, quali y, cos s, sus ainabili y, and much mo e. To ob ain
hese bene i s, i is c ucial o ca e ully selec he igh AI echniques and echnologies
o each PLC s age [16].
This sec ion concep ualises o he main AI ools in each phase. Each AI ool wo ks
as a pla o m o suppo he design, manu ac u ing, use and ecycle phases o he PLC
by enabling he connec ion o da a and in o ma ion among he in ol ed supply chain
pa ne s. Each AI ool will be designed o acili a e in o ma ion exchange and can,
he e o e, imp o e he collabo a ion o all he s akeholde s who pa icipa e in he PLC,
including designe s, manu ac u e s, supplie s, cus ome s and se ice p o ide s.
3.1 AI in he Design Phase
In o de o achie e an op imal design ha mee s ma ke needs and enginee ing equi e-
men s, a con inuous p oblem desc ip ion and solu ion de elopmen p ocess is necessa y.
The knowledge p oduced in each design p ocess s age can be cap u ed using compu a-
ional suppo ools, which can also help wi h decision making. To be mo e e ec i e,
designe s mus ha e access o mo e suppo ools h oughou ea ly concep ual design
phases because hese s ages migh make i di icul o hem o p ocess, a ange and
ep esen design da a [17]. The e o e, in he design phase, h ee AI-based ools a e
concep ualised, and each one is explained below:
•Machine Design Op imise (MDO). An AI-powe ed ool o op imising dynamic
machines and hei componen s. The AI assis an adjus s he model pa ame e s based
on use s’ objec i e unc ions while conside ing manu ac u ing and ope a ional limi-
a ions, bounda y condi ions and a ge c i e ia. I also analyses he impac o design
pa ame e s on machine e olu ion du ing i s li e cycle
•Machine Syn he ic Da a Gene a o (MDG). I syn hesises high-quali y da ase s by
simula ions o ain he op imisa ion modules in MDO. This ool allows designe s o
gene a e ealis ic machine design simula ions and o e alua e pe o mance in di e en
scena ios by making AI solu ions accessible o small-scale and sho - e m p ojec s
•CAx Addon. An in e ope abili y mechanism o in eg a e AI-assis ed op imisa ion
modules (MDO and MDG) wi h cu en CAD/CAM/CAE sys ems. APIs and UIs
combine each op imisa ion module’s unique unc ionali y while also conside ing he
needs o o he common CAx solu ions designe s o employ AI-powe ed ools like
MDO and MDG o enhance machine e iciency and o imp o e indus ial equipmen
designs in he design phase
3.2 AI in he Manu ac u ing Phase
AI has he huge po en ial o con ibu e signi ican ly o se e al aspec s o he manu ac-
u ing phase. One key a ea whe e AI can help is o op imise in en o y managemen ,
o educe p oduc ion and se up imes, and o imp o e s o age and deli e y. The com-
plexi y o op imally pe o ming his ype o ac i i y lies in he as amoun o da a
710 B. And es e al.
ha mus be handled because indus ial equipmen and bill o ma e ials a e huge. AI
can analyse la ge amoun s o da a o p edic u u e demand, adjus in en o y le els and
minimise was e and, hus, educes cos s. AI can also op imise wa ehouses and logis ics
o ensu e ha p oduc s a e e icien ly s o ed and deli e ed on ime. By le e aging AI
echnologies, manu ac u e s can signi ican ly imp o e e iciency, p o i abili y and cus-
ome sa is ac ion, which all lead o be e business pe o mance and g ow h. Al hough
he e a e publica ions on AI applica ions o de ec p edic ion o main enance diagnos ic
pu poses, mo e wo k ha ocuses on AI in indus ial equipmen manu ac u ing needs o
be done. The e o e, his sec ion concep ualises h ee ools o co e he main p ocesses
o he manu ac u ing phase.
•P ocu emen Op imise Toolki (PO). I is an AI solu ion ha helps manu ac u e s o
op imise ma e ials in en o y and pu chasing while conside ing cus ome lead imes.
PO educes in en o y cos s and he isk o s ockou s wi h ad anced algo i hms ha
o ecas demand, ecommend eo de poin s, and sugges cos -e ec i e o a ailable
al e na i es
•Fab ica ion Op imise Toolki (FO). I is an AI solu ion ha p edic s p oduc ion and
se up imes, dependences, and o he ac o s ha in luence p oduc ion scheduling and
esou ce alloca ion. This enables manu ac u e s o espond quickly o changing con-
di ions and make in o med decisions abou esou ce alloca ion, educing down ime
and inc easing p oduc i i y. FO op imises manu ac u ing p ocesses, educes was e,
and imp o es quali y and compe i i eness
•Deli e y Op imise Toolki (DO). I is an ad anced solu ion ha op imises p od-
uc s o age, anspo a ion, logis ics scheduling and planning. DO le e ages AI o
deli e he mos e icien solu ions possible by educing anspo a ion cos s, inc eas-
ing deli e y speed and imp o ing cus ome sa is ac ion. I also iden i ies and esol es
bo lenecks in he supply chain, which imp o es e iciency
3.3 AI in he Use Phase
In his s age, he p oduc is in he hands o he end cus ome and/o some se ice
p o ide s, e.g., main enance. The his o y o he p oduc abou condi ions o use, ailu es
and main enance can be collec ed o c ea e an up- o-da e epo on he p oduc ’s condi-
ion. Indus ial equipmen in ol es de ices designed o asks in indus ial en i onmen s.
They a e obus , du able, e icien and p oduc i e, and employ ad anced echnology o
imp o e hei pe o mance. Thei smoo h ope a ion in he use phase ensu es ha quali y
goods o se ices a e p oduced.
AI solu ions, such as quali y moni o ing and machine ision, de ec de ec s in eal
ime and educe he numbe o de ec i e p oduc s, while ML algo i hms and p edic i e
main enance p e en ailu es and de ec s in p oduc ion. The Ze o De ec s philosophy
and AI solu ions aim o imp o e p oduc quali y and inc ease p oduc ion p o i abili y
[18]. In eg a ing hese solu ions in o a ac o y’s daily ope a ion imp o es i s compe i i e
ma ke posi ion.
•Machine Calib a o Toolki (MC). I uses AI o calib a e indus ial equipmen e i-
cien ly by educing ime and cos s. I can also imp o e accu acy and p ecision o
mee equi ed speci ica ions
AI o Suppo Collabo a ion in he Indus ial Equipmen Li e Cycle 711
•Condi ion E alua o Toolki (CE). I employs ad anced algo i hms o de e mine he
condi ion o machines, o iden i y po en ial p oblems be o e hey become c i ical,
and o op imise main enance schedules
•Anomaly De ec o Toolki (AD). I eso s o ML o de ec componen - o machine-
le el anomalies by enabling manu ac u e s o ake co ec i e ac ion be o e p oblems
become c i ical
•Adap i e Con olle Toolki (AC). I ains machine con olle s o pe o m op imally
and o adap o changing condi ions in eal ime by educing he isk o unplanned
down ime and imp o ing e iciency
•Quali y Assu ance Toolki (QA). I applies AI o moni o he quali y o manu ac u ed
p oduc s by iden i ying po en ial p oblems be o e he p oduc is deli e ed o he
cus ome and educing he isk o e u ns and wa an y claims
3.4 AI in he Repai -Reuse-Recycle Phase
This phase is he las li e cycle phase. I aims o ind al e na i es o machines o e u n
o some p e ious phases o hei li e cycle, and always depends on he s a e o he
machine. In his phase, he po en ial o AI-based ools is used o p omo e sus ainabili y
in he machine sec o by ex ending machines’ use ul li e, educing was e and imp o ing
ma e ial low e iciency.
•P esc ip i e Main enance oolki (PM). I uses AI algo i hms o p edic a machine’s
emaining use ul li e and o iden i y necessa y main enance equi emen s. In his
way, main enance can be pe o med mo e accu a ely and cos ly ailu es ha may
comp omise machine pe o mance can be a oided
•Sma Re o i e oolki (SM). I applies AI o op imise wo king condi ions and
p oduc quali y du ing e o i ing. The e o i ing p ocess in ol es upg ading o
imp o ing olde machines o gi e hem a second li e, and o educe he amoun
o gene a ed was e. By means o his ool, cus ome s can simul aneously ob ain
economic and en i onmen al bene i s
•Li e Cycle oolki (LC). I combines AI and li e cycle me hodologies o iden i y
he bes way o end a machine’s li e cycle. A mul i-objec i e op imisa ion s a egy
balances economic, social and en i onmen al bene i s o ensu e a sus ainable solu ion
•Disassemble oolki (DIS). I u ilises AI models o op imise machine disassembly and
ecycling p ocesses, which helps o educe was e and o imp o e ma e ial ci cula ion
e iciency
3.5 Machine Passpo
Machine passpo (MP) will p o ide an in elligen pla o m esponsible o acqui ing,
managing and exchanging la ge-scale da a om mul iple sou ces ac oss de ices. The
MP is, he e o e, designed o s o e and sha e he manu ac u ing da a collec ed h oughou
he di e en PLC phases, including design, manu ac u e, use and epai - euse- ecycle. I
aims o de elop p o ocols, s anda ds and da a exchange in e aces ha acili a e he in e-
g a ion, sha ing and exchange o in elligen and eliable da a be ween di e en ypes o
compu e -aided sys ems and manu ac u ing phases. This is achie ed by using an in elli-
gen pla o m o acqui e, manage and sha e la ge-scale manu ac u ing da a om mul iple
sou ces, which can be isualised wi h a a ie y o de ices and dashboa d in e aces.
712 B. And es e al.
Uni ied s anda d se ice modelling echniques ensu e da a compa ibili y, in e ope -
abili y, consis ency and quali y. The MP he e o e ensu es aceabili y h oughou he
machines’ li e cycle. Wi h his in o ma ion, i is possible o iden i y he phases when
g ea es dis ess occu s. By iden i ying he poin a which a machine’s pe o mance
declines, a plan can be d awn up o imp o e i s design, manu ac u e o use based on eli-
able accu a e in o ma ion. The MP uses explainable AI algo i hms o guide he o ches-
a ion o la ge-scale da a low and knowledge managemen h oughou he PLC man-
u ac u ing phase. By manipula ing ML knowledge, he MP acili a es decision-making
p ocesses ela ed o he PLC by guiding op imal con igu a ion s a egies o epai , euse
and ecycle indus ial equipmen .
Thanks o he MP, i is now possible o ack he condi ion o machines h oughou
hei li e cycle, which makes i possible o iden i y he phase in which machines unde go
he mos s ess. This in o ma ion can hen be used o imp o e p ac ices in ha s age o
o an icipa e po en ial p oblems in ea lie s ages h ough be e designs by elimina ing
ine iciencies o applying al e na i e ma e ials, which esul s in less wea and ea and
a longe li e cycle o machines.
4 Use Case: Food Inspec ion Indus ial Equipmen Company
The AIDEAS p ojec wo ks wi h ou indus ial scena ios, in which AIDEAS solu ions
a e concep ualised. As each o he indus ial pilo s in ol e equipmen manu ac u e s
om di e en sec o s, AIDEAS solu ions add ess di e se se s o collabo a i e p o-
cesses. The pilo used in his pape specialises in he de elopmen and p oduc ion o
a i icial ision equipmen ha u ilises machine ision and X- ay echnologies o so
esh ui and ege ables, and o inspec ood p oduc s. This sec ion ocuses on concei -
ing he AIDEAS solu ions o enable he ood inspec ion indus ial equipmen company
o imp o e collabo a ion wi h supply chain s akeholde s.
To de ine he use case o he a o emen ioned pilo , we an he me hodology o
de ine use cases o he alida ion o Eu opean esea ch p ojec s Resul s (MUCER)
[19] ha consis s o : (i) modelling AS-IS scena ios in each use case; (ii) ede ining
he business p ocesses o each use case as TO-BE scena ios, which inco po a e he
AIDEAS Eu opean p ojec solu ions and show e olu ion om AS-IS scena ios. As he
objec i e o his pape is o show how AIDEAS solu ions can be o mula ed o suppo
collabo a ion wi h supply chain pa ne s in indus ial equipmen ne wo k, his pape
concep ualises TO-BE scena ios o he MUCER me hodology. Acco dingly, he ood
inspec ion company included ou di e en business p ocesses in which o concei e
AIDEAS solu ions o boos ing supplie and cus ome collabo a ion along h ee o he
li e cycle phases, which a e: manu ac u ing, use, and epai - euse- ecycle. Wi h he
AIDEAS solu ions’ concep ualisa ion, he ad ances expec ed o he ood inspec ion
company a e:
•A1. Op imise machine y manu ac u ing and s o age p ocesses o imp o ed e i-
ciency, educed cos s and inc eased p oduc i i y by conside ing supplie s’ es ic ions
and unp edic able beha iou s
•A2. Inc ease e iciency in managing machine deli e ies o cus ome s
AI o Suppo Collabo a ion in he Indus ial Equipmen Li e Cycle 713
•A3. Reduce machine se up imes o op imal cus ome u ilisa ion, which leads o ze o
de ec s o p oduc inspec ion
•A4. Imp o e sma main enance planning
The ollowing sec ions desc ibe he ou scena ios ha ul il he lis ed ad ancemen s.
Each scena io e lec s he conside a ion o some p e iously concep ualised AIDEAS
solu ions. We highligh how AIDEAS ools suppo collabo a ion wi h supply chain
pa ne s. Finally o each scena io, we lis he bene i s de ined by he s udied use case
hanks o conside ing he concep ualised AIDEAS solu ions.
4.1 AI-Based Assessmen o P ocu emen and P oduc ion Planning
in he Manu ac u ing Phase
The manu ac u ing phase comp ises wo use cases ha in ol e he inco po a ion o AI
echnologies o deal wi h he p ocu emen , manu ac u ing and deli e y p ocesses, which
employ he PO, FO, DO solu ions. In his sec ion we deal wi h he PO and FO solu ions,
while nex Sec . 4.2 concep ualises he DO solu ion.
In he ood inspec ion pilo , PO will help o op imise in en o y and eplenishmen
based on equi emen s om manu ac u ing (o componen s) and a e sales (spa e pa s)
and will enable he ad ancemen A1 o be me . In addi ion, i will need o upda e p o-
cu emen o deal wi h he unce ain y o he componen lead ime. FO will be applied o
au oma ing he alloca ion o esou ces and p oduc ion planning based on machine ype,
un imes, a ailable componen s, bill o ma e ials and ope a o s. I will also ecalcula e
p oduc ion planning when componen s a e sho .
FO and PO will play a c ucial ole in collabo a i ely compu ing he Ma e ials
Requi emen Plan (MRP) while conside ing he unce ain ies ha a ise om supplie s.
One o he key challenges in he MRP is he unp edic abili y o supplie pe o mance,
such as delays, quali y issues o unexpec ed changes in supply chain dynamics. By le e -
aging AI algo i hms, such as ML and p edic i e analy ics, PO will be able o analyse
he da a ela ed o supplie pe o mance, including his o ical da a, eal- ime da a and
ex e nal da a sou ces, and can use his in o ma ion o gene a e an accu a e imely MRP
ha allows eal- ime eedback and inpu om supplie s’ unce ain ies.
The expec ed bene i s ob ained by conside ing PO and FO will be o: (i) inc ease
esou ce e iciency; (ii) educe down ime; (iii) cu he manu ac u ing cos ; (i ) imp o e
in en o y and pu chasing o bo h machine componen s and spa e pa s.
4.2 AI-Based Assessmen o Deli e y Planning in he Manu ac u ing Phase
The DO sys em, conside ed in o he ood inspec ion pilo will be used o op imise he
deli e y o inspec ion machines and o ul il ad ancemen A2. In his use case, he
AIDEAS ood inspec ion pilo collabo a es wi h i s cus ome .
The aim will be o op imise space in deli e y and, he e o e, in en o y and anspo
cos s. Cu en ly, he e a e only wo ypes o s anda d pla o ms ha i wo ypes o
machines wi h di e en p opo ions. Howe e , hese pla o ms should be adjus ed o
di e en machines o op imise he space occupied in bo h he company’s wo king a eas
and anspo media.
714 B. And es e al.
DO will be ed by cus ome equi emen s and machine speci ica ions. This may
in ol e unde s anding cus ome expec a ions ega ding packaging equi emen s, and
any special handling o s o age needs. Based on he collec ed equi emen s, he pa ies
in ol ed in he deli e y p ocess will d aw up a plan o how goods o se ices will be
deli e ed, including he imeline, deli e y ou e, and any necessa y anspo a ion and
logis ics a angemen s.
The o eseen ob ained bene i s will be o: (i) imp o e he design o pla o ms o
anspo single and mul iple machines in a s anda d con aine o op imise space; (ii)
op imise he ca go anspo a ion uni ; (iii) op imise he loading o machines in a s anda d
con aine . In Fig. 1, he con igu a ion p ocess o he deli e y p ocess is ep esen ed
wi h business p ocess modelling no a ion (BPMN). I depic s he collabo a ion poin s
be ween he manu ac u e and he cus ome , and how he DO solu ion is used o suppo
he deli e y p ocess o machines.
Fig. 1. BPMN: manu ac u e -cus ome collabo a ion in inspec ion machine manu ac u ing
4.3 AI-Based Assessmen o he Con igu a ion o Inspec ion Machines in he Use
Phase
The use phase is composed o a scena io ha will in ol e inco po a ing AI echnologies
o deal wi h he p ope con igu a ion o he inspec ion machine once i is ins alled a he
cus ome si e, which employs he QA solu ion.
In he use phase, one o he p ocesses o be pe o med is he con igu a ion o
he machine, which will enable o deal wi h ad ancemen A3. In his scena io, o he
AIDEAS ood inspec ion pilo , he company collabo a es wi h i s cus ome o p o ide
p ope machine use when i is a he cus ome si e.
The cus ome will de ine a se o equi emen s o he machine o be manu ac-
u ed and assembled in he ood inspec ion machine y company. The con igu a ion o
