Co esponding au ho : Rukmini Kuma S eepe ambudu u
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Beyond adi ional PLM: Le e aging da a science o compe i i e ad an age
Rukmini Kuma S eepe ambudu u *
Anna Uni e si y, India.
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 26(02), 1051-1057
Publica ion his o y: Recei ed on 17 Ma ch 2025; e ised on 30 Ap il 2025; accep ed on 02 May 2025
A icle DOI: h ps://doi.o g/10.30574/wja .2025.26.2.1611
Abs ac
This a icle explo es he ans o ma ion o P oduc Li ecycle Managemen (PLM) h ough da a science in eg a ion,
add essing how adi ional documen -cen ic sys ems a e e ol ing in o s a egic compe i i e ad an ages. I examines
he limi a ions o con en ional PLM app oaches and highligh s how eme ging da a-d i en pa adigms a e
e olu ionizing p oduc de elopmen ac oss indus ies. The a icle de ails key applica ions, including p edic i e
enginee ing analy ics, in elligen equi emen s managemen , and da a-d i en design decision suppo , while ou lining
he in as uc u e, capabili ies, and o ganiza ional changes needed o build a s ong da a science ounda ion. Th ough
implemen a ion s a egies and case s udies om he au omo i e, ae ospace, and consume elec onics sec o s, i
p o ides p ac ical guidance o o ganiza ions na iga ing his c i ical ansi ion, ul ima ely demons a ing how da a-
d i en PLM can deli e supe io p oduc s wi h g ea e e iciency.
Keywo ds: Da a-D i en Palm; Digi al T ans o ma ion; P edic i e Analy ics; Manu ac u ing In elligence; P oduc
De elopmen Op imiza ion
1. In oduc ion
In oday's apidly e ol ing indus ial landscape, P oduc Li ecycle Managemen (PLM) s ands a a c i ical in lec ion
poin . T adi ional PLM sys ems ha e long se ed as he backbone o o ganizing p oduc da a, managing wo k lows,
and acili a ing collabo a ion ac oss enginee ing eams. Howe e , as digi al ans o ma ion accele a es ac oss
indus ies, he con en ional PLM app oach is p o ing insu icien o mee he demands o mode n p oduc de elopmen
and ma ke compe i ion.
The PLM ma ke is expe iencing unp eceden ed g ow h, p ojec ed o expand om $25.41 billion in 2022 o $66.49
billion by 2029, ep esen ing a compound annual g ow h a e (CAGR) o 14.8% du ing his o ecas pe iod [1]. This
subs an ial expansion e lec s he g owing ecogni ion o PLM's s a egic impo ance in na iga ing he complexi ies o
mode n p oduc de elopmen . The ma ke g ow h is being accele a ed by he inc easing adop ion o digi al win
echnology and he in eg a ion o IoT pla o ms wi h PLM sys ems, enabling mo e sophis ica ed da a collec ion and
analysis capabili ies ac oss he p oduc li ecycle [1]. Fu he mo e, he cloud-based PLM segmen is showing pa icula
dynamism, wi h cloud deploymen models expec ed o gain signi ican ma ke sha e due o hei scalabili y and educed
implemen a ion cos s.
The con e gence o massi e da a a ailabili y, ad anced analy ics capabili ies, and a i icial in elligence has c ea ed an
unp eceden ed oppo uni y o ans o m PLM om a documen a ion and p ocess managemen sys em in o a s a egic
compe i i e ad an age. Recen esea ch has demons a ed ha an in eg a ed PLM app oach combining digi al
echnologies wi h adi ional enginee ing p ocesses can yield signi ican pe o mance imp o emen s. Speci ically,
o ganiza ions implemen ing in eg a ed digi al PLM amewo ks ha e epo ed e iciency gains o up o 30% in p oduc
de elopmen ac i i ies and educ ions in ime- o-ma ke o up o 45% compa ed o hose using con en ional
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 26(02), 1051-1057
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app oaches [2]. These pe o mance di e en ials highligh he compe i i e disad an age aced by o ganiza ions ha ail
o adap o he e ol ing PLM pa adigm.
The in eg a ion o da a science in o PLM p ocesses add esses se e al c i ical challenges in mode n p oduc
de elopmen . S udies in ol ing manu ac u ing en e p ises ac oss mul iple sec o s ha e iden i ied ha ad anced PLM
sys ems inco po a ing da a analy ics capabili ies can educe enginee ing change o de s by app oxima ely 33% and
dec ease he ime equi ed o design i e a ions by up o 40% [2]. These imp o emen s a e achie ed h ough enhanced
collabo a ion ac oss unc ional bounda ies, wi h in eg a ed digi al PLM en i onmen s acili a ing a 75% inc ease in
c oss- unc ional communica ion e ec i eness compa ed o siloed de elopmen app oaches.
This a icle explo es how o wa d- hinking en e p ises a e in eg a ing da a science me hodologies in o hei PLM
s a egies o d i e inno a ion, educe cos s, accele a e ime- o-ma ke , and ul ima ely deli e supe io p oduc s. The
s akes a e subs an ial— esea ch indica es ha digi ally enhanced PLM app oaches co ela e wi h a 26% imp o emen
in p oduc quali y me ics and a 29% educ ion in o e all de elopmen cos s [2]. Mo eo e , hese ad anced PLM
implemen a ions show pa icula s eng h in acili a ing sus ainabili y ini ia i es, wi h o ganiza ions epo ing an
a e age 18% educ ion in ma e ial was e and a 22% imp o emen in ene gy e iciency ac oss he p oduc li ecycle when
da a-d i en design op imiza ion is employed [2]. As we examine he key challenges acing adi ional PLM
implemen a ions and highligh he ans o ma i e po en ial o da a-d i en app oaches, we will p o ide p ac ical
guidance o o ganiza ions looking o emba k on his jou ney owa d nex -gene a ion PLM excellence.
2. The E olu ion o PLM in he Da a E a
2.1. Limi a ions o T adi ional PLM Sys ems
T adi ional PLM sys ems we e designed p ima ily as documen managemen and wo k low ools, ocusing on
cen alizing p oduc - ela ed in o ma ion and s eamlining enginee ing p ocesses. While hese capabili ies emain
aluable, hey ep esen only a ac ion o wha mode n PLM can achie e. Resea ch indica es ha con en ional PLM
sys ems ea da a as s a ic eco ds a he han dynamic asse s ha can gene a e insigh s, wi h adi ional app oaches
cap u ing only abou 60-70% o he po en ial alue om p oduc li ecycle da a [3]. Despi e cen alizing enginee ing
da a, many PLM implemen a ions s ill s uggle wi h in eg a ion ac oss o he business sys ems, c ea ing in o ma ion
silos ha impede he holis ic iew needed o e ec i e decision-making. This agmen a ion leads o app oxima ely 20-
30% o enginee ing ime being spen sea ching o in o ma ion a he han pe o ming alue-adding ac i i ies [3].
T adi ional PLM suppo s decision-making h ough documen a ion and app o al wo k lows bu lacks p edic i e
capabili ies, wi h mos sys ems p o iding his o ical and cu en -s a e iews bu o e ing minimal o wa d-looking
insigh s essen ial in oday's compe i i e landscape.
2.2. The Da a Science Re olu ion in Manu ac u ing
The manu ac u ing sec o has wi nessed a p o ound ans o ma ion h ough Indus y 4.0 ini ia i es, wi h da a science
playing a cen al ole. A comp ehensi e analysis o big da a analy ics h oughou he p oduc li ecycle e eals ha he
implemen a ion o IoT and Digi al Twins has g own signi ican ly, wi h sma manu ac u ing en i onmen s now
gene a ing be ween 1 and 2 e aby es o da a pe hou in ypical p oduc ion se ings [3]. This explosion o da a has
c ea ed unp eceden ed oppo uni ies o p oduc de elopmen insigh s, hough s udies indica e ha only 20-30% o
his da a is cu en ly being le e aged o analy ics and decision suppo . The in eg a ion o AI/ML capabili ies wi h
manu ac u ing da a s eams has demons a ed signi ican po en ial, wi h ea ly implemen a ions showing a po en ial
educ ion o p oduc de elopmen cycles by 20-50% when e ec i ely deployed [3]. Cloud compu ing has eme ged as a
c i ical enable , wi h scalable compu ing esou ces making i possible o p ocess p e iously unmanageable da a
olumes and ad anced analy ics p o iding deepe insigh s in o p oduc pe o mance, quali y, and cus ome usage
pa e ns h oughou he li ecycle.
2.3. The Eme ging Da a-D i en PLM Pa adigm
The new pa adigm o PLM in eg a es he ounda ional capabili ies o adi ional sys ems wi h cu ing-edge da a science
app oaches o c ea e ans o ma i e business alue. O ganiza ions implemen ing mode n PLM solu ions ha e epo ed
a 15-30% educ ion in ime- o-ma ke and a 20-25% dec ease in p oduc de elopmen cos s [4]. The shi om eac i e
o p edic i e app oaches is pa icula ly impac ul, wi h da a-d i en PLM enabling accu a e o ecas ing o p oduc
pe o mance issues be o e hey mani es in p oduc ion o in he ield. Indus y me ics show ha insigh -d i en a he
han p ocess-d i en PLM implemen a ions can inc ease enginee ing p oduc i i y by up o 20% while educing change
o de s by app oxima ely 25% [4]. Lea ning sys ems ha con inuously imp o e h ough eedback loops and da a
analysis ep esen a undamen al depa u e om s a ic eposi o ies, enabling a i uous cycle o ongoing op imiza ion.
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Pe haps mos signi ican ly, he alue-c ea ion ocus a he han he compliance ocus o mode n PLM is shi ing
o ganiza ional p io i ies, wi h success ul implemen a ions demons a ing ROI imp o emen s o 2-4x compa ed o
adi ional app oaches [4]. This e olu ion ep esen s no me ely a echnological upg ade bu a undamen al eimagining
o how p oduc li ecycle in o ma ion can d i e compe i i e ad an age in he digi al e a.
Table 1 T adi ional PLM s. Da a-D i en PLM: Key Pe o mance Me ics [3,4]
Me ic
T adi ional PLM
Da a-D i en PLM
Value Cap u e om P oduc Li ecycle Da a
60-70%
90-100%
Enginee ing Time Spen Sea ching o In o ma ion
20-30%
5-10%
U iliza ion o A ailable Manu ac u ing Da a
20-30%
70-80%
Time- o-Ma ke (Rela i e E iciency)
Baseline
15-30% Fas e
P oduc De elopmen Cos s (Rela i e E iciency)
Baseline
20-25% Lowe
3. Key Da a Science Applica ions in Mode n PLM
3.1. P edic i e Enginee ing Analy ics
P edic i e enginee ing analy ics combines simula ion, da a analy ics, and domain expe ise o o ecas p oduc
pe o mance be o e physical p o o yping. The manu ac u ing indus y gene a es app oxima ely 2 exaby es o da a
daily, ye only 20% o his da a is sys ema ically analyzed and used o decision-making [5]. Simula ion da a mining
enables o ganiza ions o ex ac pa e ns om housands o simula ions uns o iden i y op imal design pa ame e s,
u ning his unde u ilized da a in o aluable design insigh s. Pa ame e space explo a ion h ough machine lea ning
algo i hms e icien ly na iga es as design spaces wi h millions o po en ial con igu a ions, add essing he challenge
ha adi ional manu ac u ing sys ems ypically ope a e wi h less han 50% o da a being in eg a ed ac oss p oduc ion
s ages [5]. Failu e p edic ion capabili ies ha e e ol ed h ough he applica ion o da a science o his o ical es and ield
da a, helping manu ac u e s mo e beyond he cu en s a e whe e only abou 1% o collec ed da a in luences design
decisions. Pe o mance op imiza ion le e aging ad anced algo i hms enables au oma ic design imp o emen s,
add essing he c i ical need o manu ac u ing o become "sma " by u ilizing he as quan i y o da a ha emains
la gely un apped in cu en sys ems [5].
3.2. In elligen Requi emen s Managemen
Requi emen s managemen has been ans o med h ough na u al language p ocessing and ad anced da a analysis
echniques. The in eg a ion o da a science in o equi emen s p ocesses ep esen s a key componen o he 5C
a chi ec u e (Connec ion, Con e sion, Cybe , Cogni ion, and Con igu a ion) ha o ms he ounda ion o Cybe -Physical
Sys ems in sma manu ac u ing [6]. Au oma ed equi emen s analysis using NLP enables mo e e ec i e ga he ing and
p ocessing o he machine and senso da a ha ypically begins a le el 1 (Connec ion) o he sma manu ac u ing
a chi ec u e. Requi emen s clus e ing h ough machine lea ning c ea es sophis ica ed ela ionship ne wo ks be ween
indi idual equi emen s, suppo ing he Con e sion le el whe e aw da a is ans o med in o meaning ul in o ma ion
[6]. Voice-o -cus ome in eg a ion h ough da a analy ics aligns wi h he Cybe le el o he a chi ec u e, whe e
in o ma ion is pushed o he cen al se e o u he analy ics and compa ison wi h his o ical da a. Requi emen s
alida ion h ough his o ical da a analysis suppo s he Cogni ion le el, whe e knowledge is gene a ed o suppo
co ec decision-making ega ding p oduc ea u es and design speci ica ions [6].
3.3. Da a-D i en Design Decision Suppo
Design decisions bene i om ad anced analy ics ha conside mul idimensional ac o s simul aneously. Design space
isualiza ion echnologies c ea e in e ac i e ep esen a ions o complex ade-o s, suppo ing he manu ac u ing
indus y's need o p ocess he eno mous amoun s o da a gene a ed du ing p oduc de elopmen —es ima ed a up o
1000 e aby es o a single complex p oduc [5]. Cos -pe o mance op imiza ion h ough da a science enables balanced
conside a ion o mul iple objec i es, add essing he challenge whe e less han 5% o collec ed manu ac u ing da a is
cu en ly used o eal- ime eedback and op imiza ion [5]. Design euse ecommenda ion sys ems le e age simila i y
analysis o sugges ele an exis ing designs, helping o ganiza ions ex ac mo e alue om hei his o ical da a. Supply
chain impac analysis enables p edic ion o how design decisions a ec manu ac u ing ope a ions, suppo ing
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in eg a ion o he da a lows ha cu en ly emain sepa a ed be ween design, manu ac u ing, and supply chain sys ems
[5].
3.4. In elligen Manu ac u ing In eg a ion
The gap be ween design and manu ac u ing na ows h ough da a-d i en app oaches ha implemen he Con igu a ion
le el o he 5C a chi ec u e, whe e eedback is p o ided om he cybe space o physical space o make machines sel -
con igu able and sel -adap i e [6]. Design o Manu ac u abili y analy ics au oma ically analyzes designs based on
his o ical p oduc ion da a, suppo ing he esilien in o ma ion a chi ec u e needed o Indus y 4.0 implemen a ion.
P ocess planning op imiza ion h ough machine lea ning de elops manu ac u ing p ocesses ha balance quali y,
e iciency, and cos conside a ions, add essing key equi emen s o he cybe -physical in as uc u e whe e machines
can use he gene a ed knowledge o sel -op imize pe o mance [6]. Quali y p edic ion capabili ies le e age design
pa ame e s and manu ac u ing da a o o ecas p oduc ion ou comes, suppo ing he in eg a ion o senso and
con olle ne wo ks wi h decision suppo sys ems. Supply chain digi al wins c ea e i ual ep esen a ions o
manu ac u ing ne wo ks, enabling simula ion o supply chain pe o mance o new p oduc in oduc ions and
suppo ing he indus y-wide shi om isola ed op imiza ion o collabo a i e, ne wo k-wide op imiza ion h ough
in eg a ed da a u iliza ion [5].
Table 2 Manu ac u ing Da a U iliza ion Me ics in PLM Applica ions [5,6]
Me ic
Pe cen age
Manu ac u ing Da a Sys ema ically Analyzed
20%
Da a In eg a ed Ac oss P oduc ion S ages
< 50%
Collec ed Da a In luencing Design Decisions
1%
Manu ac u ing Da a Used o Real- ime Feedback
< 5%
4. Building a Da a Science Founda ion o PLM
4.1. Da a In as uc u e Requi emen s
E ec i e da a-d i en PLM equi es a obus in as uc u e ha can handle he immense olume and complexi y o
p oduc li ecycle da a. A uni ied da a pla o m c ea es a common ounda ion spanning p oduc de elopmen ,
manu ac u ing, supply chain, and cus ome eedback, add essing he challenge ha many manu ac u ing o ganiza ions
s ill ope a e wi h sepa a e ope a ional echnology (OT) and in o ma ion echnology (IT) sys ems [7]. The sma ac o y
concep emphasizes his in eg a ion, wi h connec ed asse s using embedded in elligence and au oma ion o
communica e wi h each o he and sel - egula e p ocesses wi h minimal human in e en ion. Da a lakes and wa ehouses
p o ide he necessa y s o age a chi ec u e o suppo bo h s uc u ed and uns uc u ed da a ypes, enabling
o ganiza ions o collec and p ocess he massi e amoun s o in o ma ion gene a ed by connec ed machines and sys ems
[7]. Real- ime da a p ocessing capabili ies deploy s eam p ocessing o senso da a and con inuous analy ics, allowing
manu ac u e s o mo e om desc ip i e analy ics (wha happened) o p edic i e analy ics (wha will happen) and
ul ima ely o p esc ip i e analy ics (wha should be done) [7]. A comp ehensi e da a go e nance amewo k
es ablishes essen ial policies o da a quali y, secu i y, p i acy, and li ecycle managemen , add essing he inding ha
app oxima ely 48% o manu ac u e s iden i ied da a secu i y and in ellec ual p ope y p o ec ion as a signi ican
ba ie o digi al implemen a ion [8].
4.2. Essen ial Da a Science Capabili ies
O ganiza ions pu suing da a-d i en PLM mus de elop o acqui e speci ic echnical capabili ies o maximize alue
ex ac ion om hei da a asse s. S ong da a enginee ing skills o collec ion, ans o ma ion, and p epa a ion a e
undamen al, wi h s udies showing ha 55.56% o manu ac u ing companies ace challenges in da a in eg a ion and
collec ion when implemen ing digi al ans o ma ion [8]. S a is ical analysis capabili ies enable eams o unde s and
da a dis ibu ions, co ela ions, and signi icance, as well as c i ical skills in an en i onmen whe e analy ical ma u i y
anges widely ac oss o ganiza ions. Machine lea ning enginee ing expe ise o de eloping, aining, and deploying ML
models ep esen s an inc easingly aluable capabili y, suppo ing ad anced analy ics ha can o ecas ends, p edic
po en ial dis up ions, and op imize p ocesses [7]. Visualiza ion de elopmen abili ies enable eams o c ea e in ui i e
ep esen a ions o complex da a, add essing he inding ha 50% o manu ac u ing companies ace challenges in
e ec i ely communica ing insigh s h oughou he o ganiza ion [8]. High-pe o mance compu ing esou ces o
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p ocessing simula ion and analysis wo kloads ound ou he echnical equi emen s, suppo ing he compu a ional
demands o ad anced enginee ing simula ions and analy ics ha o m he backbone o da a-d i en PLM
implemen a ions [7].
4.3. O ganiza ional Alignmen and Skill De elopmen
Technical in as uc u e alone is insu icien wi hou o ganiza ional eadiness o emb ace da a-d i en app oaches.
C oss- unc ional da a eams ha combine domain expe ise wi h da a science skills ha e p o en pa icula ly e ec i e,
add essing he eali y ha success ul digi al ans o ma ion equi es b eaking down o ganiza ional silos be ween IT,
ope a ions, and business uni s [7]. Resea ch indica es ha 48.15% o manu ac u ing o ganiza ions iden i y he absence
o necessa y skills and alen as a key challenge o digi al ans o ma ion [8]. T aining and upskilling ini ia i es de elop
da a li e acy ac oss he o ganiza ion, add essing he inding ha 59.26% o manu ac u ing companies s uggle wi h
employee esis ance o change du ing digi al implemen a ion [8]. Cul u al ans o ma ion ha alues da a-d i en
decision-making o e in ui ion alone ep esen s pe haps he mos challenging aspec o building a da a science
ounda ion o PLM. Many o ganiza ions expe ience a ension be ween he desi e o ans o m h ough new echnology
and he en enched p ocesses and beha io s ha ha e de ined hei ope a ions o decades [7]. S uc u ed change
managemen app oaches acili a e he ansi ion o da a-d i en PLM, wi h esea ch showing ha 51.85% o
manu ac u ing companies ace signi ican challenges ela ed o unclea ans o ma ion s a egy and go e nance,
highligh ing he need o well-de ined oadmaps and leade ship alignmen [8]. Success ul o ganiza ions ecognize ha
building a da a science ounda ion o PLM equi es equal a en ion o he echnological componen s and he human
ac o s ha will ul ima ely de e mine adop ion and alue ealiza ion.
Figu e 1 Digi al T ans o ma ion Challenges: Pe cen age o Manu ac u ing Companies Repo ing Implemen a ion
Obs acles [7,8]
5. Implemen a ion S a egies and Bes P ac ices
5.1. Phased Implemen a ion App oach
Success ul da a-d i en PLM ans o ma ion ypically ollows a measu ed, phased app oach ha balances s a egic
ision wi h p ac ical execu ion. Assessmen and oadmapping o m he c i ical i s phase, wi h esea ch indica ing ha
75% o Indus y 4.0 ini ia i es begin wi h a comp ehensi e e alua ion o cu en capabili ies be o e implemen a ion
[9]. This assessmen should e alua e he o ganiza ion's eadiness ac oss digi al, physical, and human domains o
iden i y gaps equi ing a en ion. Following assessmen , pilo p ojec s p o ide he oppo uni y o demons a e alue
and build momen um, wi h esea ch showing ha 83% o o ganiza ions begin wi h a ge ed demons a ions a he
han en e p ise-wide ollou s [9]. The digi al win concep has p o en pa icula ly e ec i e as a pilo ocus, allowing
o ganiza ions o es da a in eg a ion capabili ies while deli e ing angible business alue. Scaling and in eg a ion
ep esen he nex c i ical phase, wi h 29% o manu ac u e s ha ing al eady achie ed ad anced le els o in eg a ion
be ween hei physical asse s and digi al capabili ies, enabling comp ehensi e isibili y and con ol [10]. The inal phase
emphasizes con inuous imp o emen h ough es ablished eedback mechanisms, wi h esea ch indica ing ha only 5%
o o ganiza ions ha e ully ma u e capabili ies o p edic i e main enance and au onomous op imiza ion, highligh ing
he e olu iona y na u e o his jou ney [10].
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5.2. Technology Selec ion Conside a ions
The echnology landscape o da a-d i en PLM is complex and e ol ing, equi ing ca e ul e alua ion ac oss mul iple
dimensions. Build e sus buy decisions ep esen a undamen al conside a ion, wi h o ganiza ions ypically in es ing
be ween 4-10% o annual e enue on digi al ans o ma ion ini ia i es depending on indus y and ambi ion le el [10].
In eg a ion capabili ies mus be igo ously assessed, pa icula ly gi en ha 72% o manu ac u e s epo da a
in eg a ion as a signi ican challenge when implemen ing digi al manu ac u ing solu ions [9]. E alua ing solu ions
based on hei abili y o connec wi h exis ing PLM and en e p ise sys ems is essen ial, wi h app oxima ely 33% o
manu ac u e s ha ing achie ed ad anced connec i i y ha enables au oma ic da a lows be ween sys ems [10].
Scalabili y and pe o mance conside a ions become inc easingly c i ical as manu ac u ing o ganiza ions p og ess om
basic digi iza ion (whe e app oxima ely 33% cu en ly ope a e) o ull digi al ans o ma ion wi h p edic i e
capabili ies (achie ed by only 5% o manu ac u e s) [10]. The o al cos o owne ship calcula ions mus ex end beyond
ini ial acquisi ion cos s, wi h esea ch indica ing ha o ganiza ions expec digi al in es men s o educe ope a ional
cos s by 3.6% annually while inc easing annual e enues by app oxima ely he same amoun [10].
5.3. Case S udies: Success S o ies and Lessons Lea ned
Examining eal-wo ld implemen a ion examples p o ides aluable insigh s in o success ul app oaches and common
pi alls. In he au omo i e indus y, manu ac u e s implemen ing digi al win echnology ha e achie ed signi ican
bene i s, wi h 70% o o ganiza ions ac oss sec o s epo ing imp o ed p oduc s o p ocesses h ough he in eg a ion
o physical and digi al sys ems [9]. The mos success ul implemen a ions es ablish c oss- unc ional eams and clea
go e nance s uc u es, wi h leading o ganiza ions ecognizing ha digi al ans o ma ion equi es in eg a ion ac oss
enginee ing, in o ma ion echnology, and business uni s. In he ae ospace sec o , o ganiza ions ha e e ec i ely applied
ad anced analy ics o op imize manu ac u ing p ocesses, wi h indus y leade s epo ing 10-12% inc eases in
p oduc ion ou pu and 20-30% imp o emen s in h oughpu ime [10]. These ini ia i es ypically ace challenges in
da a quali y and s anda diza ion, wi h only 25% o manu ac u e s epo ing hey ha e he necessa y s anda ds in place
o machine- o-machine communica ion [9]. Consume elec onics manu ac u e s ha e success ully le e aged
cus ome da a o in o m p oduc de elopmen , wi h 83% o indus ial o ganiza ions belie ing da a will be cen al o
hei decision-making p ocesses wi hin i e yea s [9]. These implemen a ions highligh he impo ance o cybe secu i y,
wi h o ganiza ions in es ing app oxima ely 7-9% o hei digi al ans o ma ion budge s on secu i y measu es o
p o ec hei inc easingly connec ed ope a ions and in ellec ual p ope y [10].
Figu e 2 Digi al Ma u i y Me ics Ac oss he Manu ac u ing Sec o [9, 10]
6. Conclusion
The in eg a ion o da a science in o PLM ep esen s a undamen al eimagining o how p oduc s a e concei ed,
designed, manu ac u ed, and suppo ed h oughou hei li ecycle. O ganiza ions ha success ully na iga e his
ans o ma ion gain mul iple compe i i e ad an ages h ough as e inno a ion cycles, highe p oduc quali y, lowe
de elopmen cos s, and g ea e ma ke esponsi eness. As AI-augmen ed design, au onomous PLM sys ems, ex ended
eali y isualiza ion, and ci cula economy analy ics con inue o ad ance, he gap be ween leade s and lagga ds will
likely widen. Success equi es balanced a en ion o echnology, people, and p ocesses – echnical solu ions alone
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 26(02), 1051-1057
1057
canno deli e ans o ma ion wi hou co esponding changes in o ganiza ional s uc u es, skill se s, and mindse s. By
app oaching da a-d i en PLM holis ically and s a egically, manu ac u e s can con e digi al ans o ma ion
challenges in o sus ainable compe i i e ad an ages. In his e a o da a abundance, he mos success ul companies will
be hose ha ans o m p oduc li ecycle da a in o ac ionable in elligence, d i ing be e decisions a e e y s age.
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