The impact of information disclosure and smart technology integration on e-retailing performance: A production delivery policy framework

Tayyab, Muhammad; Tahi , Hi a; Habib, Muhammad Salman
A icle
The impac o in o ma ion disclosu e and sma
echnology in eg a ion on e- e ailing pe o mance: A
p oduc ion deli e y policy amewo k
Ope a ions Resea ch Pe spec i es
P o ided in Coope a ion wi h:
Else ie
Sugges ed Ci a ion: Tayyab, Muhammad; Tahi , Hi a; Habib, Muhammad Salman (2025) : The
impac o in o ma ion disclosu e and sma echnology in eg a ion on e- e ailing pe o mance: A
p oduc ion deli e y policy amewo k, Ope a ions Resea ch Pe spec i es, ISSN 2214-7160, Else ie ,
Ams e dam, Vol. 14, pp. 1-26,
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The impac o in o ma ion disclosu e and sma echnology in eg a ion on
e- e ailing pe o mance: A p oduc ion deli e y policy amewo k
Muhammad Tayyab a,b,c, Hi a Tahi c, Muhammad Salman Habib d,∗
aIn o ma ion Sys ems and Ope a ions Managemen Depa men , King Fahd Business School, King Fahd Uni e si y o Pe oleum and
Mine als, Dhah an 31261, Saudi A abia
bIn e disciplina y Resea ch Cen e o Finance and Digi al Economy, King Fahd Business School, King Fahd Uni e si y o Pe oleum and
Mine als, Dhah an 31261, Saudi A abia
cIn e disciplina y Resea ch Cen e o Sma Mobili y and Logis ics, King Fahd Business School, King Fahd Uni e si y o Pe oleum and
Mine als, Dhah an 31261, Saudi A abia
dIns i u e o Knowledge Se ices, Cen e o C ea i e Con e gence Educa ion, Hanyang Uni e si y, ERICA Campus, Ansan-si, Gyeonggi-do, 15588, Sou h Ko ea
A R T I C L E I N F O
Keywo ds:
E- e ail managemen
In o ma ion disclosu e
G een echnology in eg a ion
Mul i-shipmen policy
Sma p oduc ion sys em
A B S T R A C T
The elec onic e aile s ace dis inc challenges in in o ma ion sha ing compa ed o hei pu ely o line
coun e pa s, pa icula ly in anspa en ly communica ing hei en i onmen al p ac ices o inc easingly eco-
conscious consume s. This complexi y inc eases in e- e ailing due o he absence o di ec in e ac ion and
makes i di icul o consume s o e alua e he sus ainabili y e o s o e ailing channel’s s akeholde s.
In esponse o i , manu ac u e s and e- e aile s a e le e aging social media and blockchain echnology o
pe sonalized ad e ising o b idge his in o ma ion anspa ency gap. This esea ch p esen s a sus ainable mul i-
i em in eg a ed model o manu ac u e – e aile collabo a ion in e- e ailing by inco po a ing mul iple deli e y
policies and in es men s in echnology aimed a in o ma ion disclosu e and en i onmen al oo p in educ ion.
The manu ac u e adop s a sma p oduc ion sys em and euse e u ned goods in he manu ac u ing p ocess
while in es ing in G een Emissions Reduc ion Technology. Meanwhile, he e- e aile enhances p oduc demand
h ough In o ma ion Disclosu e Technology on social media and blockchain by showcasing hei en i onmen al
p o ec ion e o s. By employing a hyb id analy ic-me aheu is ic app oach, we de e mine op imal p oduc ion
and deli e y policies o imp o e g een consume se ice unde a ying budge a y and spa ial cons ain s. The
esul s demons a e a 4.39% inc ease in online consume demand h ough in o ma ion sha ing and a 3.86%
imp o emen in p o i abili y o he collabo a i e e ailing sys em unde single-se up mul i-deli e y policy ha
con i ms obus ness o he p oposed model. Scena io analysis u he p o ides decision-make s wi h ac ionable
insigh s by showcasing 8.44% inc emen in he sys em p o i by con e ing adi ional p oduc ion in o sma
p oduc ion sys em. Mo eo e , he sensi i i y o he p oposed model o balancing he echnology in es men s
among emission con ol and in o ma ion disclosu e e o s sugges s keeping ack o he e iciency pa ame e s
o hese in es men op ions be o e making echnology budge alloca ions.
1. In oduc ion
In he ecen yea s, consume s ha e shown g owing conce n o e he global ene gy c isis and esul an global wa ming. Consequen ly, hey
ha e been compelled o p io i ize en i onmen al conse a ion and he use o low-ca bon commodi ies [1]. The 2030 Agenda ega ding Sus ainable
De elopmen (SD) om he UN makes sus ainable consump ion a s a egic goal, saying ha he indi iduals should con ibu e o he al e ing
unsus ainable consump ion and p oduc ion pa e ns. The e o e, he p oduc ion and e ail chains o daily consumables a e making e o s o
mi iga e g eenhouse gas emissions and communica ing hei e o s o he po en ial cus ome s h ough a ious channels. Howe e , de e mining he
sus ainabili y o a p oduc is challenging o cus ome s due o he subjec i e na u e o en i onmen al sus ainabili y as a belie -based cha ac e is ic.
E- e aile s ace excep ional di icul ies in communica ing hei en i onmen al p ac ices o eco-conscious consume s in a anspa en manne .
This issue o igina es om he lack o di ec physical in e ac ion inhe en in online sales se ings. This opaci y in he in o ma ion disclosu e makes
i di icul o he p oduc consume s o e alua e he sus ainabili y e o s o e- aile s and hei espec i e supply chain pa ne s. Fo ins ance,
∗Co esponding au ho .
E-mail add ess: [email p o ec ed] (M.S. Habib).
h ps://doi.o g/10.1016/j.o p.2025.100328
Recei ed 31 Oc obe 2024; Recei ed in e ised o m 7 Feb ua y 2025; Accep ed 8 Feb ua y 2025
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
A ailable online 27 Feb ua y 2025
2214-7160/© 2025 The Au ho s. Published by Else ie L d. This is an open access a icle unde he CC BY license ( h p://c ea i ecommons.o g/licenses/by/4.0/ ).
M. Tayyab e al.
while a b ick-and-mo a s o e can simply display ce i ica ions like Fai T ade labels di ec ly on he p oduc s, online e aile s can ely on digi al
ep esen a ions only ha can be easily manipula ed o o e looked. Fu he mo e, explaining complica ed en i onmen al ini ia i es including ca bon
o se ing o sus ainable packaging choices equi es de ailed desc ip ions ha may no e ec i ely ansla e o he online en i onmen . Conside
a as ashion indus y whe e he online e aile s like ASOS and Boohoo ha e aced sc u iny o ‘‘g eenwashing’’ o p omo e en i onmen ally
iendly collec ions wi hou su icien e idence o genuine sus ainable p ac ices. Ano he ob ious case is o Amazon, which aces inqui y abou
i s ca bon oo p in and has esponded o i by commencing ini ia i es including ‘‘Clima e Pledge F iendly’’ wi h he objec i e o highligh ing
sus ainable p oduc s [2]. Simila ly, a globally amous e ail chain named Pa agonia has aced communica ion challenges in le e aging i s e-
comme ce pla o m o ansmi i s en i onmen al e o s in o ma ion by showcasing de ailed li ecycle analyses o i s p oduc s. Howe e , he lack
o di ec consume in e ac ion in e- e ailing adds o he di icul y o e i ying hese claims which leads o skep icism abou he g eenwashing. This
lack o anspa ency in in o ma ion disclosu e can di ec ly wea away consume us and nega i ely impac he b and epu a ion. This highligh s
he p ac ical implica ions o he g een in o ma ion-sha ing challenges o he e- ailing indus y.
The con inuous g ow h o in o ma ion echnology such as social media and blockchain has ans o med indi iduals’ me hods o communica ion
and hei decision-making p ocesses abou pu chases, while also eshaping he digi al ma ke ing en i onmen [3]. The eme gence o pe sonalized
ad e isemen s has p omp ed ad e ising pe sonnel o in es iga e i s e icacy, including i s impac on cus ome a i udes, ad engagemen (such as
clicks o pu chases), and he ac o s ha in luence i [4]. The e o e, blockchain and social media ad e ising o e powe ul pla o ms o he e ail
sec o o in o m and educa e hei consume s abou hei ini ia i es o educe ca bon emissions by making in es men s in ad anced low ca bon
echnology. By i ue o his, p oduc ion and e ail indus ies ha e become capable o e ec i ely d awing in a la ge cus ome base and a ac
mo e demand.
P oduc ion-deli e y eliabili y in addi ion o he sus ainabili y is o u mos impo ance in oday’s globalized wo ld. Failing o sa is y cus ome
expec a ions may lead o a pe manen loss o cus ome s and a dec ease in compe i i eness. As a esul , he supply chains mus op imize hei
p oduc ion and in en o y sys ems o succeed in he long un despi e he complica ed global en i onmen . Achie ing success in manu ac u ing
depends on e ec i ely in eg a ing p oduc ion and in en o ies o he whole sys em. The majo objec i e in he supply chains o in eg a ed
sys ems includes alue c ea ion h ough well-coo dina ed p oduc ion quan i ies and deli e y lo size decisions. The goal is o minimize o al
cos o he supply chain while making a wise ade-o among se up cos and in en o y ca ying cos s o he sys em. Howe e , basic in en o y
managemen models such as EOQ and EPQ p esen ed by Ha is and Ta in 1913 and 1918, espec i ely ha e ce ain limi a ions in e ms o
p ac ical applicabili y [5]. The e o e, se e al s udies ha e ex ended hese models by inco po a ing many eal wo ld condi ions. One o he ecen
addi ion is Vendo Managed In en o y (VMI) in he p oduc ion in en o y sys ems [6]. Se e al supply chains including NIKE, P oc e & Gamble, and
BOSCH implemen Vendo Managed In en o y (VMI) o make decisions a ound deli e y lo sizes and numbe o deli e ies by aking ull con ol
o he e aile in en o ies. On he o he hand, some p oduc ion indus ies implemen Jus In Time (JIT) in en o y con ol sys ems o de elop a
long- e m economic iabili y h ough mu ual coope a ion wi h he downs eam supply chain pa ne s whe e he op imal lo size is b oken down in o
smalle mul i-shipmen s. In his way, he coo dina ed supply chains ensu e highe se ice le els while maximizing economic sus ainabili y. Recen
applica ions o JIT ha e es ablished ha Single-Se up-Mul i-Deli e y-Policy (SSMD) has economic supe io i y o e he Single-Se up-Single-Deli e y
(SSSD) policy [7]. Howe e , he e exis s a high likelihood o sho ages in he sys em, especially unde he cases o ush deli e ies and eme gency
deadlines aced in au omo i e indus y such as Toyo a and echnology companies like DELL [8]. The e o e, he conside a ion o possible sho ages
in model o mula ion h ough deploymen o a obus backo de ing policy is c i ical in de e mining op imal p oduc ion deli e y policies.
Gi en he abo e key conside a ions, we p opose a sus ainable mul i-i em in eg a ed p oduc ion deli e y model o a single-manu ac u e single-
e aile e- e ailing managemen unde SSMD policy and echnology in es men s o ad e isemen and g eenhouse gas emissions con ol. The
manu ac u e ope a es a sma p oduc ion sys em and u ilizes p ede e mined p opo ion o e u ned goods in p oducing he new p oduc s. He
in es s in g een emissions educ ion echnology (GERT) in his p oduc ion sys em, and he e aile in es s in social media ad e isemen in he
o m o in o ma ion disclosu e echnology (IDT) o communica e he emissions con ol e o s o he sys em o he cus ome s in o de o enhance
p oduc demand. In o ma ion Disclosu e Technology (IDT) includes echnological and digi al pla o ms u ilized o anspa en ly communica e
p oduc and p ocess in o ma ion ega ding sus ainabili y e o s o he cus ome s. This includes social media campaigns, in e ac i e e-pages and
websi es, and p oduc labeling ini ia i es ha elabo a es he supply chain’s en i onmen al p o ec ion e o s. Fo ins ance, Pa agonia uses i s websi e
and social media o sha e in o ma ion abou i s sus ainable aw-ma e ial sou cing and ai labo p ac ices, whe eas Unile e ’s Sus ainable Li ing
Plan is communica ed h ough a ious digi al channels and p oduc labels. In his esea ch, IDT ep esen s he e aile ’s in es men in social
media ad e ising o in o m consume s abou he manu ac u e ’s g eenhouse gas emissions educ ion e o s. Fu he , G een Emissions Reduc ion
Technology (GERT) e e s o he in es men s in manu ac u ing and con e sion p ocesses, ools and equipmen , and he echnologies designed o
minimize g eenhouse gas emissions [7]. Some o he examples o GERT include ca bon cap u e and s o age sys ems, enewable ene gy in eg a ion
in manu ac u ing plan s such as sola panels o wind u bines and he u iliza ion o ene gy-e icien equipmen . In his s udy, GERT ep esen s
he manu ac u e ’s in es men in echnologies o educe emissions du ing he p oduc ion p ocess which is hen communica ed o he cus ome s
h ough e aile ’s IDT ini ia i es. G een echnology in es men plays a key ole in supply chain dynamics, impac ing cos s, consume beha io ,
and p oduc demand. By implemen ing sus ainable p ac ices, he companies can no only lowe ope a ional cos s h ough e icien use o esou ces
and was e managemen bu also inc ease p o i o he sys em [9]. Consume s a e inc easingly willing o pay mo e o en i onmen ally iendly
p oduc s, encou aging manu ac u e s o adop g een echnologies [10]. This shi in consume beha io , pa icula ly among hose sensi i e o
ca bon emissions, d i es he need o i ms o in es in sus ainable p ac ices. Fo example, Walma ’s in es men in enewable ene gy and g een
supply chains has no only educed cos s bu a ac ed eco-conscious consume s, and Tesla’s ocus on elec ic ehicles and sus ainable manu ac u ing
has enhanced i s ma ke appeal [11]. O e all, g een echnology in es men s help businesses align wi h consume p e e ences, d i ing demand and
os e ing sus ainabili y [12].
To he bes o au ho ’s knowledge, no such su icing model has been p esen ed ye ha conside s his op imal e-ad e isemen and emissions
con ol s a egy while maximizing he sys em p o i unde sho age-p one SSMD p oduc deli e y mode. We de elop wo majo cases in he
p oposed ad e isemen and emissions con ol e o s based sus ainable in eg a ed model. Case I de elops a non-capaci a ed in eg a ed model
be o e ad e isemen and emissions con ol e o s which is sol ed h ough a combina ion o analy ical op imiza ion echnique and a p oposed
heu is ic app oach. Fu he , Case II p esen s a capaci a ed in eg a ed model wi h echnology in es men s o IDT and emissions con ol which is
sol ed h ough a me aheu is ic app oach o iden i y he op imal ad e isemen and emission con ol policy unde he sma p oduc ion a e. The
p oposed model cases a e alida ed h ough nume ical expe imen and analysis o in e signi ican manage ial insigh s.
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
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M. Tayyab e al.
Fig. 1. Con ibu ion o he p oposed s udy.
1.1. Resea ch mo i a ion
Al hough he impo ance o lexibili y, echnology, and ad e ising [13] o enhancing p o i abili y in o line e ailing is s udied, hei in eg a ion
wi hin an SSMD policy adop ion has no been well esea ched in e- e ailing mechanisms. Fu he , as he SSMD policy adop ion has shown
economic bene i s in JIT sys ems [14], i inc ease he likelihood o sho ages in he in eg a ed sys ems. Hence, conside a ion o backo de ing
policy in such manu ac u e – e aile coo dina ed a angemen is c ucial. This s udy add esses he a o emen ioned esea ch gap by p esen ing a
comp ehensi e in en o y model ha inco po a es echnology in es men s o en i onmen al p o ec ion and ad e isemen e o s wi hin a mul i-
i em supply chain comp ising o a single manu ac u e and a single e aile . Fu he , se e al global companies including Amazon aces inqui y abou
i s ca bon oo p in and has esponded o i by commencing ini ia i es including ‘‘Clima e Pledge F iendly’’ wi h he objec i e o highligh ing
sus ainable p oduc s [2] . Simila ly, a globally amous e ail chain named Pa agonia has aced communica ion challenges in le e aging i s e-
comme ce pla o m o ansmi i s en i onmen al e o s in o ma ion by showcasing de ailed li ecycle analyses o i s p oduc s. Howe e , he lack
o di ec consume in e ac ion in e- e ailing adds o he di icul y o e i ying hese claims which leads o skep icism abou he g eenwashing.
This lack o anspa ency in in o ma ion disclosu e can di ec ly wea away consume us and nega i ely impac he b and epu a ion. This
highligh s he p ac ical implica ions o he g een in o ma ion-sha ing challenges o he e- ailing indus y. The e o e, his s udy examines he
mos e ec i e combina ion o sma p oduc ion, echnological in es men s, and dynamic ad e ising me hods o op imize o e all p o i in he
p esence o SSMD and planned backo de ing s a egies unde collabo a i e e- e ailing. We in es iga e he s a egic choices o echnology budge
dis ibu ion among IDT and GERT unde a ious ealiza ions o he budge a y and space cons ain s. Fu he , we iden i y he c i ical pa ame e s
h ough sensi i i y analysis and de ise ecommenda ions o imp o e economic iabili y o he sys em unde he a ia ions in hese pa ame e s. The
ob ained manage ial insigh s p o ide decision-make s wi h he necessa y ecommenda ions o deploy e icien s a egies and a ain en i onmen al
and economic sus ainabili y. The e o e, ou s udy aims o answe he below esea ch ques ions:
1. How can he in eg a ion o sma p oduc ion sys ems, echnological in es men s, and dynamic ad e ising me hods enhance he p o i abili y
o a manu ac u e – e aile collabo a i e e- e ailing sys em?
2. Wha is he op imal alloca ion o he echnology in es men budge be ween IDT o he e aile and GERT o he manu ac u e o maximize
o e all p o i abili y?
3. How does he adjus men o p oduc ion a es wi hin p ede ined anges in esponse o luc ua ing e-p oduc demand con ibu e o he
p o i abili y and lexibili y o he sma p oduc ion sys em?
4. Wha is he economic ole o ad e ising e iciency in enhancing he o e all p o i abili y o a sus ainable in eg a ed p oduc ion-deli e y
s a egy unde e- e ailing?
1.2. Con ibu ion o he esea ch
On he basis o a o emen ioned discussion, his s udy con ibu es o he e- e ailing li e a u e in he ollowing aspec s.
•This s udy highligh s he signi icance o ad e ising e iciency by es ablishing a clea associa ion be ween ad e isemen e iciency and p o -
i abili y o he sus ainable in eg a ed p oduc ion-deli e y s a egy unde e- e ailing. This unde s anding o e s manage ial ecommenda ions
on imp o ing ad e isemen e o s in o de o gene a e encou aging e ec s on o e all p o i abili y.
•This s udy o e s aluable insigh s in o he s a egic dis ibu ion o he echnology in es men budge among In o ma ion Disclosu e
Technology (IDT) o he e- e aile and G een Emissions Reduc ion Technology (GERT) o he manu ac u e . This con ibu ion enables
manage s and p ac i ione s in imp o ing economic iabili y h ough in elligen echnology budge alloca ion unde a ied ci cums ances.
•This s udy demons a es a cons uc i e impac o con e ing adi ional ixed manu ac u ing sys ems in o sma ones by allowing adjus men
o p oduc ion a es wi hin he p ede ined anges in esponse o a ied e-p oduc demand. Th ough an expe imen al s udy, we answe he
ques ion; Wha is he s a egic ole o lexibili y in enhancing o e all p o i abili y o he sys em?
•This s udy con ibu es o he li e a u e by e i ying he signi ican impac o inco po a ing pa allel echnological in es men s in In o ma ion
Disclosu e Technology (IDT) and G een Emissions Reduc ion Technology (GERT) o imp o e sys em p o i abili y. This insigh enables
manage s and p ac i ione s in making s a egic echnology in eg a ion decisions o collabo a ion among he manu ac u e and e- e aile .
Fig. 1p o ides g aphical ep esen a ion o he majo con ibu ion o his esea ch.
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
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M. Tayyab e al.
1.3. O ien a ion o he esea ch
Res o his esea ch is s uc u ed as below. Sec ion 2p o ides a ho ough e iew o he esea ch o iden i y he key miles ones and need o
he s udy in he ield o p oduc ion and e ail managemen . Nex sec ion explains he p oblem and emphasizes he nomencla u e and assump ions
used o o mula e he p oposed ma hema ical model. Bo h cases o he p oposed sus ainable in eg a ed p oduc ion-deli e y model a e de eloped
in he Sec ion 4, and Sec ion 5illus a es hyb id solu ion me hodology o he p oposed sus ainable in eg a ed in en o y models. An expe imen al
s udy is se up in Sec ion 6 o alida e eal wo ld applica ion o he model and he expe imen al ou comes a e analyzed o a ious ins ances o he
model o de ise signi ican manage ial insigh s. E en ually, Sec ion 7p esen s conclusions, limi a ions, and u u e esea ch a enues o he s udy.
2. Li e a u e e iew
This sec ion p esen s an o e iew o he cu en li e a u e in he ield o e- e ailing managemen wi h a ocus on echnology in eg a ion o
g een p oduc s manu ac u ing and deli e y unde a sma p oduc ion managemen sys em.
2.1. E- e ail managemen
E- e ail managemen is a p ocess o managing online e ail ope a ions including sales, in en o y, and cus ome engagemen . Fo ins ance,
Amazon uses AI-d i en ecommenda ions o pe sonalize shopping expe ience and shopi y pla o ms enable small businesses o se up and manage
online s o es. The businesses can es ablish hei online p esence wi h minimal e o and i has signi ican ly lowe ed he ba ie s o en y in o he
e- e ailing indus y. The online ma ke place including pla o m selling is p ima ily open o all ypes o businesses. Howe e , his con enience o
de elop e- e ailing channels has c ea ed a high deg ee o homogeniza ion in his indus y. The business models de eloped a ound e- e ailing can be
easily eplica ed due o hei wide a ailabili y and a o dabili y. This makes i e en mo e complex o di e en ia e one online e aile om ano he
in he same sec o . This cons uc s a highly compe i i e landscape encompassing a high h ea o new en an s, qui e eadily a ailable dis ibu ion
channels, educed economies o scales, and e en ually low cus ome swi ching cos s. Fu he , he online e aile s ace pe sis en p essu e o enhance
hei deli e y p ocess pe o mance. This is due o he ac ha online consume s ha e high expec a ions ega ding p oduc quali y, deli e y speed
and quali y, and he o e all pu chasing expe ience [15]. As a esul , building a s ong s a egic posi ion ha o e s a sus ainable compe i i e
ad an age is impo an o he e- e aile s. Cu en esea ch on e- e ailing has explo ed a ious aspec s o imp o emen and has iden i ied espec i e
key ac o s o success in e- e ailing [11,16–19]. Ani e al. [16] emphasize he impo ance o managing secu i y conce ns as a c i ical success
ac o in e- e ailing. Fu he , Chen e al. [18] ocused on he ope a ional aspec s o e- e ailing and sugges ed ha a success ul e- e ailing model
should in eg a e simple in o ma ion sea ch, nume ous communica ion channels, and sa e ading and ansac ion p ocesses. Ca aldo e al. [17]
has highligh ed he impo ance o a highly in eg a ed e- e ailing applica ion and a obus o ganiza ional ope a ing sys em. Edghiem e al. [11]
poin ou he c ucial ole o business ne wo ks in c ea ing alue. They ha e obse ed ha e- e aile s ope a e wi hin highly in e connec ed business
ecosys ems ha ex end beyond hei own o ganiza ional bounda ies. Recen ly, Zhang e al. [19] emphasize he impac o key ac o s including
esponsi eness, pe sonaliza ion, ease o use, aes he ics, and pe cei ed isk on he success o e- e ailing businesses.
2.2. In o ma ion sha ing in e- e ailing
In o ma ion disclosu e h ough echnology in eg a ion is a p ac ice o sha ing ele an business in o ma ion wi h s akeholde s o ensu e
anspa ency and build us . Apple Inc. sha es i s annual en i onmen al epo o expose cus ome s o i s ca bon oo p in and sus ainabili y
e o s. Simila ly, Tesla sha es i s qua e ly ea nings epo s ha p o ide insigh s in o i s inancial pe o mance and u u e plans. G een in o ma ion
disclosu e e o s signi ican ly impac consume beha io by imp o ing en i onmen al awa eness and p omo ing in o med pu chasing choices.
Acco ding o he heo y o planned beha io (TPB), consume in en ions o buy sus ainable p oduc s a e shaped by hei a i udes, subjec i e
no ms, and he pe cei ed beha io al con ol. He e, he en i onmen al disclosu e ac s as a key ac o in a ionalizing beha io al decisions. Fu he ,
he S akeholde heo y highligh s he media ing ole o sus ainabili y epo ing in os e ing s akeholde pa icipa ion and imp o ing sus ainable
in o ma ion ans e [20]. In addi ion, he legi imacy and esou ce dependency heo ies emphasize ha en i onmen al epo ing enhances co po a e
image o he i m, i s legi imacy, and he consume us on he i m [21]. Now a days, inc easing en i onmen al consciousness is al e ing he
p e e ences o consume s, placing mo e emphasis on sus ainabili y and e hical manu ac u ing and deli e y h ough e- e ail channels. This ansi ion
equi es clea and open communica ion abou he g een p oduc ion and deli e y p ac ices. Cus omized ad e ising on echnology pla o ms such
as social media and Blockchain enables i ms o di ec ly con ey hei en i onmen ally iendly ac i i ies, in o ming and in ol ing cus ome s in
hei e o s o educe ca bon emissions by using g een echnologies [22]. This no only cul i a es cus ome loyal y bu also b oadens consume
demog aphics and s imula es demand o eco- iendly goods. Howe e , he e ec i eness o his communica ion elies on an e icien supply chain
ha gua an ees p oduc a ailabili y and ul ills consume expec a ions. The e o e, en i onmen ally esponsi e manu ac u e s and e aile s including
P&G, IKEA, and Walma ha e expanded hei ope a ions om physical o digi al pla o ms in esponse o he g ow h o e-comme ce and a e
using AI-powe ed ools and IoT-based acking sys ems. The online pla o m p o ides enhanced lexibili y in esponse o consume demands wi h
ega ds o p icing, quali y, selec ion, and con enience. The s udy conduc ed by Li e al. [23] explains ha e aile s ope a ing in online se ings
ha e signi ican ly imp o ed demand ela ed in o ma ion capabili ies due o conside able in es men s in in o ma ion disclosu e echnology. Wei
e al. [24] sugges s ha he e aile s should be mo i a ed o communica e sensi i e demand in o ma ion o he p oduc supplie s o inc ease
he g een supply chain e iciency. The esea ch conduc ed by Shang e al. [25] e i y he sound impac ha in o ma ion sha ing pu s on supply
chain decisions. In pa icula , Wang and Zhuo [26] s a es ha e aile s who possess ad anced ma ke in elligence can e ec i ely nego ia e mo e
ad an ageous supply con ac s and ansac ion e ms wi h hei supplie s by exchanging demand in o ma ion. Cai e al. [27] ha e obse ed ha he
e aile ’s choice o app oach o disclosing in o ma ion unde olun a y in o ma ion disclosu e depends on he o e ed p oduc p ice and he deg ee
o g eenness. Sa ka e al. [12] has ocused on he impac o ups eam asymme ic in o ma ion and he bullwhip e ec on p o i and cus ome
sa is ac ion unde sus ainable deli e y p ac ices like ad anced anspo a ion and au oma ed inspec ion o de ec managemen , bu hey igno ed
downs eam in o ma ion sha ing. They demons a ed ha in o ma ion sha ing among supply chain playe s enhances p o i s and educes losses
om in o ma ion asymme y wi h nume ical examples alida ing hese indings. Recen ly, Liu e al. [28] ha e examined he ole o ad e isemen
on supply and e ail pe o mance o he commodi y p oduc s. Taking in o accoun he posi i e e ec s o social media ma ke ing in dissemina ing
in o ma ion ega ding he en i onmen ally conscious p oduc ion o p oduc s o consume s and he subsequen ise in p oduc demand, we conside
IDT in es men in ad e isemen o mul i-p oduc ’s g een manu ac u ing ini ia i es o he cus ome s.
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
4

M. Tayyab e al.
Table 1
Majo con ibu ion o he esea ch.
Au ho (s) Model ype Ad e ise-
men
in es men
P oduc ype P oduc
demand
P oduc ion
sys em
P oduc ion
deli e y
policy
P oduc
sho ages
Technology
in es men
o emissions
con ol
Solu ion me hodology
Kang e al.
[33]
Non-
in eg a ed
p oduc ion
No Single-i em Cons an Sma SSSD No allowed No Analy ical op imiza ion
Sa ka e al.
[34]
Supply chain
managemen
No Single-i em Cons an T adi ional SSMD No allowed No Analy ical op imiza ion
Taleizadeh
e al. [35]
Vendo
managed
in en o y
No Single-i em Unce ain T adi ional SSSD Pa ially
backo de ed
No Analy ical op imiza ion
−heu is ic
Sana [36] News endo No Single-i em P ice and
emissions
con ol e o s
dependen
T adi ional SSSD No allowed Yes Analy ical op imiza ion
Sadeghi e al.
[8]
In eg a e
endo –buye
No Single-i em Cons an T adi ional SSMD Fully
backo de ed
No Analy ical op imiza ion
−heu is ic
Dey e al.
[37]
In eg a e
endo –buye
Yes Single-i em Ad e isemen
dependen
Sma SSSD Fully
backo de ed
No Analy ical op imiza ion
Tayyab e al.
[38]
Non-
in eg a ed
p oduc ion
No Single-i em Cons an T adi ional SSSD Fully
backo de ed
No Analy ical op imiza ion
Ka e al.
[39]
Non-
in eg a ed
p oduc ion
Yes Mul i-i em P ice and
ad e isemen
dependen
Sma SSSD No allowed Yes Analy ical op imiza ion
Uma e al.
[7]
Supply chain
managemen
No Single-i em Cons an T adi ional SSMD No allowed No Me aheu is ic
Saxena e al.
[10]
In eg a e
endo –buye
No Single-i em Random Sma SSSD No allowed No Analy ical op imiza ion
Da a e al.
[40]
Dynamic
e ailing
model
Yes Single-i em Ad e isemen
dependen
T adi ional NA No allowed No Analy ical op imiza ion
Mo shedin
e al. [41]
Coo dina ed
supply chain
Yes Single-i em Cons an
Demand
T adi ional SSSD No allowed No Me aheu is ic
Seba jane
and Ade unji
[42]
In eg a ed
p oduc ion
model
No Single-i em P ice and
eshness
dependen
T adi ional SSSD No allowed No I e a i e p ocedu e
Sasanuma
e al. [43]
Re aile
cos
educ ion
No Mul i-i em Random
demand
T adi ional SSSD Allowed No Analy ical op imiza ion
Nobil e al.
[44]
Vendo
economic
decision
No Single-i em Cons an
demand
T adi ional SSSD Allowed No Analy ical op imiza ion
This s udy In eg a ed
manu ac u e –
e aile
Yes Mul i-i em P ice and
ad e isemen
dependen
Sma SSMD Fully
backo de ed
Yes Analy ical op imiza ion
−Me aheu is ic
2.3. Mul i-shipmen policy
Mul i-shipmen policy is a p oduc ion and deli e y s a egy ha allows manu ac u e s o p oduce he goods in a single-se up bu deli e i in
mul iple shipmen s o imp o e logis ics e iciency. Za a’s mul i-shipmen app oach o in e na ional o de s o op imize shipping cos s and deli e y
imes is one o he success ul implemen a ion o his s a egy. The in en o y managemen solu ions such as Vendo Managed In en o y (VMI)
and Single-Se up-Mul i-Deli e y (SSMD) policy play a key ole in op imizing p oduc ion and deli e y s a egies by ensu ing imely deli e ies and
educing he possibili y o sho ages. Ben-Daya e al. [29] p esen ed a h ee-laye in eg a ed supply chain model conside ing SSMD policy. Thei
esea ch ou comes e i ied economic bene i s o SSMD policy o e he SSSD policy. Sa ka and Chung [30] p esen ed a ma hema ical model o
minimize o al cos o a lexible manu ac u ing sys em. The esea ch objec i e was o educe sys em cos in he si ua ions whe e demand du ing
he lead ime is conside ed as ollowing a no mal dis ibu ion. The expe imen al esul s e i ied he impo ance o making echnology in es men s
in de eloping a lexible manu ac u ing sys em by unde s anding i s impac on o al cos o he comple e sys em. They implemen ed a classical
op imiza ion me hod along wi h a heu is ic app oach o de i e exac and app oxima e solu ions o he model a iables. The sensi i i y analysis
indica ed ha hei s udy has achie ed lowes cos by op imizing he decision a iables unde he p esence o echnology in es men s and SSMD
policy. A ecen s udy conduc ed by M idha e al. [31] seeks o enhance he e iciency o a mul i-laye sus ainable supply chain model by speci ically
add essing con ol o ca bon emissions and maximizing p o i abili y o he sys em unde SSMD policy. They p esen ed a sma manu ac u ing sys em
ha inco po a es a model consis ing o a single manu ac u e , a single supplie , and mul iple e aile s. Thei model e ec i ely educes impe ec
p oduc ion and ca bon emissions by implemen ing a wo-s age inspec ion policy, lexible p oduc ion a e s a egy, and selling p ices based on
demand. Thei expe imen al esul s show posi i e en i onmen al and economic impac s. Jauha i e al. [32] has de eloped an in eg a ed in en o y
model o a supply chain in ol ing a endo and a buye by accoun ing o s ochas ic demand, impe ec p oduc ion, and a hyb id sys em combining
egula and g een p oduc ion.
By op imizing ac o s including shipmen quan i y, p oduc ion alloca ion, and de ec a e, hei model minimizes supply chain cos s while
balancing en i onmen al impac and p oduc ion e iciency. Howe e , hey did no conside mul i-shipmen policy o minimize anspo a ion cos s
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
5
M. Tayyab e al.
o he sys em. Chen e al. [45], Sa ka e al. [46], and Kang e al. [33] a e among se e al o he esea che s who implemen SSMD policy unde
manu ac u e – e aile in eg a ed supply chain managemen . Gi en he economic bene i s o SSMD p oduc ion deli e y policy wi hin a coo dina ed
e- e ailing, we inco po a e his policy in he p oposed sus ainable in eg a ed model o imp o e p o i abili y o he sys em.
2.4. G een echnology in eg a ion
G een echnology in eg a ion is he p ocess o inco po a ing eco- iendly echnologies in o business p ocesses o educe i s en i onmen al
impac . Fo ins ance, IKEA uses sola panels echnology and ene gy-e icien ligh ing in i s s o es o educe ene gy consump ion, and Pa agonia
has adop ed ecycled ma e ials in he clo hing p oduc ion. Technology in es men s ma k ou s anding con ibu ion o imp o ing global economy
h ough e ec i ely educing ca bon emissions in he global supply chains. As cus ome en i onmen al awa eness g ows, manu ac u e s’ compe i ion
no longe jus dependen on p o i s. I also includes hei en i onmen al pe o mance. Thus, global p oduc ion and e ailing indus ies need ca bon
emissions educ ion s a egies and low-ca bon p oduc p icing. Song e al. [47] p o ided a mul i s age s ochas ic op imiza ion model o examine
logis ics capaci y inc ease unde a ious ca bon emissions legisla ions. They showed ha capaci y in es men cos ola ili y a ec s op imal capaci y
expansion decisions mo e han p oduc ion cos o low and high ca bon ax a es. Ganda [48] examined how inno a ion and echnology in es men s
a ec ed O ganiza ion o Economic Co-ope a ion and De elopmen membe na ions’ ca bon emissions om he yea 2000 o 2014. Thei analysis
e i ied ha enewable ene gy in eg a ion and R&D spending ha e a s a is ically signi ican nega i e co ela ion wi h he g eenhouse gas emissions.
They sugges ed ha inco po a ing en i onmen al esponsibili y in pa en s and aining esea che s in g een skills may help hem mee ze o-emission
ambi ions. Sana [36] in es iga ed a p ice compe i ion be ween en i onmen ally iendly and non-en i onmen ally iendly manu ac u e s, aking
in o accoun p oduc demand in luenced by sales p ice, in es men s in echnology o educing ca bon emissions, and co po a e social esponsibili y
index. Thei esul s sugges ed con inuous in es men s in g eenhouse gas emissions educ ion and consume awa eness may inc ease p oduc demand
in he longe un. Bose e al. [9] and Sa ka e al. [49] de eloped online- o-o line e ailing models o op imize p icing, ad e ising s a egy, cus ome
ca e e o s and in es men decisions o inc ease p o i unde he conside a ion o budge and space cons ain s, espec i ely, bu hey igno ed g een
echnology in es men s. Thei esea ch indings highligh ha o e ing ee home deli e y and managing de ec i e a es e ec i ely can signi ican ly
enhance p o i abili y o he sys em. Recen ly, Kang and Tan [50] ha e u ilized an e olu iona y-based model o in es iga e he echnology in es men
choices made by supplie s and p oduc manu ac u e s in a supply chain managemen unde ca bon cap-and- ade emissions con ol policy. They
s udied he ac ha how e enue and cap-and- ade- ela ed ac o s a ec g een echnology in es men choices. Thei esea ch p o ides insigh s o
policy make s on how o handle he in e play be ween hese in es men s and esul ing e enues. Jauha i e al. [51] has de eloped a ma hema ical
model o a closed-loop supply chain in ol ing a manu ac u e and e aile in di e en coun ies by conside ing s ochas ic condi ions, impe ec
p oduc ion, and ca bon emissions. By inco po a ing a ca bon ax policy and g een echnology in es men s, hei model aims o minimize o al cos s
while add essing key ac o s like exchange a e unce ain y, p oduc ion de ec i es and deli e y decisions. Jauha i e al. [52] ocused on minimizing
emissions in a supply chain in ol ing a endo and a buye by add essing ca bon egula ions h ough g een echnology in es men s, suppo ed by
go e nmen incen i es and ca bon ax policies. Thei p oposed model op imizes ope a ional decisions and g een in es men s o educe cos s and
emissions, demons a ing imp o emen s in bo h economic and en i onmen al pe o mance. Howe e , hey did no conside he impac o g een
in es men s on consume demand h ough g een in o ma ion sha ing. Gi en he echnology in es men bene i s o en i onmen al p o ec ion, ou
esea ch inco po a es echnology in es men s o g een emission educ ion echnology (GERT) as a s a egic choice.
2.5. Sma p oduc ion sys em
Sma P oduc ion Sys em is an ad anced manu ac u ing sys em ha uses au oma ion, IoT, and da a analy ics o adjus p oduc ion a es in
o de o enhance e iciency and p oduc i i y. Fo ins ance, Siemens uses digi al wins o simula e and op imize p oduc ion p ocesses, and Tesla
has ully au oma ed i s Giga ac o ies o elec ic ehicle manu ac u ing. Being able o adjus he p oduc ion a es in a sma p oduc ion sys em
in acco dance wi h he changes in demand and deli e y ime equi emen s is an essen ial ac o ha impac s manu ac u ing cos s. Such sma
p oduc ion sys em can add ess issues associa ed o he andom, unce ain and some imes unp edic able cha ac e is ics o he ma ke demand, and
he esul ing sho ages. In his di ec ion, Khouja and Meh ez [53] p o ided a me hod o de e mining he op imal lo size using Economic P oduc ion
Quan i y (EPQ) by conside ing bo h a iable p oduc ion a es and he dependen uni p oduc ion cos s. The p oduc ion cos was aken in o accoun
as a unc ion o he changes in p oduc ion a e using ha app oach. This app oach enables e en mo e igo ous in es iga ion o he cos componen s
ela ed o he p oduc ion sys ems by aking in bo h lexibili y o p oduc ion a es and i s impac on uni p oduc ion cos . Fu he , se e al o he
esea che s including AlDu gam e al. [54], and Sa ka and Chung [30] ha e implemen ed a simila ype o app oach by conside ing a iabili y
in he p oduc ion a es o de i e op imal in en o y managemen policies o imp o e p o i abili y o he whole p oduc ion deli e y sys em. Dey
e al. [37] ha e conside ed he impac o lexible p oduc ion a e on economic pe o mance o he supply chain by inco po a ing he ad an ages
o educing lead ime ac oss a ious si ua ions. The equa ion p esen ed by hem shows a p ecise ela ionship be ween o al cos and lead ime,
indica ing ha he cos sa ings om educing lead ime d op as lead ime inc eases. They esea ch ou comes sugges making con inuous in es men s
o imp o e he eliabili y o he p oduc ion p ocess. Sadeghi e al. [8] p o ided an in eg a ed in en o y model wi h sho ages o minimize sys em
cos wi hou conside ing lexible p oduc ion a e. Recen ly, Ka e al. [39] p esen ed a dual-channel ad e isemen and p icing model o mul iple
manu ac u e s and e aile s while igno ing p oduc sho ages. They inco po a ed lexible p oduc ion a e in hei model and p o ed ha such
policy p o ides be e economic bene i s in compa ison o he adi ional ixed p oduc ion a e sys ems. To acqui e an in-dep h unde s anding o
how a iable p oduc ion a es impac he pe o mance o in en o y managemen sys ems, i is ad ised ha in e es ed eade s e e o he esea ch
conduc ed by Glock and G osse [55] as hei s udy o e s a comp ehensi e app ecia ion o his impac . Conside ing a o emen ioned discussion and
analysis o he li e a u e, we conside lexible p oduc ion a e and planned backo de s in ou p oposed sus ainable in eg a ed in en o y model o
a manu ac u e and e aile coo dina ed sys em. Fig. 2p esen s he g aphical illus a ion o he p oposed s udy and Table 1p esen s con ibu ion
o his s udy.
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
6
M. Tayyab e al.
Fig. 2. G aphical ep esen a ion o he p oposed collabo a i e e- e ailing model.
3. P oblem de ini ion, no a ion, and assump ions
This esea ch p oposes a sus ainable mul i-i em in eg a ed model o maximize o al p o i o he manu ac u e – e aile coo dina ed decision-
making unde online sales mechanism. Wi hin his collabo a i e sys em, a con ollable p oduc ion a e (𝑃𝑖∈ {𝑃𝑖,𝑚𝑖𝑛 −𝑃𝑖,𝑚𝑎𝑥}) o he manu ac-
u e [38] is conside ed o ake ad an age o a sma p oduc ion sys em in making p oduc ion-deli e y decisions unde p ice and ad e isemen
dependen p oduc demand. The manu ac u e in es s in g een emissions educ ion echnology (GERT) in his p oduc ion sys em and he e aile
in es s in ad e isemen in he o m o in o ma ion disclosu e echnology (IDT) o communica e he emissions con ol e o s o he sys em o he
cus ome s in o de o enhance p oduc demand. P oduc demand is aken as a unc ion o p oduc p ice and ad e isemen e o s o he e aile .
We use he dependen demand unc ion as 𝐷𝑖=𝛥𝑖−𝛾𝑖𝑝𝑖+𝜓𝑖𝜂𝑖, whe e 𝛥𝑖is he ini ial ma ke size o p oduc ype-𝑖,𝛾𝑖is he p ice sensi i i y,
and 𝜓𝑖is IDT sensi i i y. Simila ype o demand unc ion has been u ilized by Uma e al. [7] and Ka e al. [39] in hei manu ac u e – e aile
op imiza ion models. The e- e aile ecei es annual p oduc -𝑖demand o 𝐷𝑖uni s and he manu ac u e deli e s p oduc s o he e aile in mul iple
equal shipmen s using SSMD policy, whe e he o de ed lo size is 𝑄𝑖=𝑛𝑖𝑞𝑖. In his way, in en o y holding cos o he e aile is educed while
comp omising on he p oduc anspo a ion cos . We conside disc e e IDT in es men o 𝜂𝑖=𝛼𝑖𝑊𝑖,𝑇 𝑒𝑐 ℎ, whe e 𝛼𝑖is he p opo ion o disc e e
echnology in esm ne budge 𝑊𝑖,𝑇 𝑒𝑐 ℎalloca ed o p oduc ype-𝑖. The manu ac u e in es s in GERT echnology o educe he ca bon emissions
o he sys em du ing p oduc ion. We conside a simila disc e e GERT in es men unc ion o ype 𝐸𝑖=𝛽𝑖𝑊𝑖,𝑇 𝑒𝑐 ℎ, whe e 𝐸𝑖is he GERT in es men
o p oduc ype-𝑖, and 𝐸𝑖=𝛽𝑖𝑊𝑖,𝑇 𝑒𝑐 ℎ. I is wo h no ing ha 𝛼𝑖+𝛽𝑖= 1. The impac o disc e e GERT in es men on g eenhouse gas emissions
mi iga ion is inco po a ed as 𝐺𝑖=𝜆𝑖(1 −𝑒−𝜌𝑖𝐸𝑖)=𝜉𝑖𝑒−𝜌𝑖𝐸𝑖, whe e 𝐸𝑖is GERT in es men , and 𝜌𝑖is i s e iciency as a scaling pa ame e . Fu he ,
𝜆𝑖is he sa ings in emissions due o GERT. I is o be no ed ha when 𝐸𝑖→∞, hen 𝐺𝑖=𝜆𝑖, and when 𝐸𝑖= 0, hen 𝐺𝑖= 0. Simila ype o
in es men unc ion has been implemen ed by Ka e al. [39] o g eenhouse gas emissions educ ion. A planned backo de s a egy is in oduced
in he sys em o compensa e he sho ages o igina ed in he sma in eg a ed sys em. Fig. 3p esen s in en o y beha io o he sus ainable in eg a ed
sys em a manu ac u e and e aile . We de elop wo majo case in he p oposed model. Case I de elops a non-capaci a ed in eg a ed model be o e
ad e isemen and emissions con ol e o s which is sol ed h ough a combina ion o analy ical op imiza ion echnique and a p oposed heu is ic
app oach. Fu he , Case II p esen s a capaci a ed sus ainable in eg a ed model wi h echnology in es men s o ad e isemen and emissions con ol
which is sol ed h ough a me aheu is ic app oach o iden i y he op imal ad e isemen and emission con ol policy unde he lexible p oduc ion
a e. The p oposed model cases a e alida ed h ough nume ical expe imen and analysis o in e signi ican manage ial insigh s.
3.1. No a ion
Below no a ion a e u ilized o he p oposed model o mula ion and analysis.
Indices
𝑖index o p oduc ype (𝑖= 1,2,3...𝑦)
𝑚 index o he endo /manu ac u e
𝑟 index o he e aile
Decision Va iables
𝑝∗
𝑖Op imal online selling p ice o p oduc ype-𝑖($ pe i em)
𝑄∗
𝑖Op imal ba ch quan i y (i ems)
𝑞∗
𝑖Op imal shipmen quan i y pe shipmen (i ems)
𝐵∗
𝑖Op imal backo de quan i y (i ems)
𝑃∗
𝑖P oduc ion a e o p oduc ype-𝑖) (i ems pe yea )
𝑊∗
𝑖,𝑇 𝑒𝑐 ℎOp imal echnology in es men s ($)
𝛼∗
𝑖Pe cen age o 𝑊∗
𝑖,𝑇 𝑒𝑐 ℎalloca ed o IDT (pe cen age)
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
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M. Tayyab e al.
𝛽∗
𝑖Pe cen age o 𝑊∗
𝑖,𝑇 𝑒𝑐 ℎalloca ed o GERT (pe cen age)
𝑋[ ]𝑡Decision ec o o his pape
Pa ame e s
𝐷𝑖Re aile ’s es ima ed demand o p oduc −𝑖(i ems pe yea )
𝑇𝑖The cycle ime o p oduc ype_𝑖(yea s)
𝑇1𝑖Maximum in en o y consump ion ime (yea s)
𝑇2𝑖Time o which sys em aces sho ages (yea s)
𝑇3𝑖Time o compensa e he p oduc sho age (yea s)
𝑇4𝑖Time o in en o y ul illmen (yea s)
𝑡𝑖Time in e al among successi e deli e ies (yea s)
𝐾𝑖Se up cos pe se up ($ pe se up)
ℎ𝑖In en o y holding cos ($ pe i em pe uni ime)
𝑏𝑖Fixed backo de cos ($)
𝑔𝑖Va iable backo de cos ($ pe i em)
𝛿𝑖Ba ch anspo a ion cos ($ pe i em)
𝐶𝑖𝑣 Cos o pu chasing and p ocessing i gin aw ma e ial ($ pe i em o aw-ma e ial)
𝐶𝑖𝑟 Cos o incen i e and p ocessing e u ned ma e ial ($ pe i em o aw-ma e ial)
𝜃𝑖Pe cen age o 𝐷𝑖 ul illed h ough emanu ac u ing (pe cen age)
𝐼𝑖,𝑚𝑎𝑥 Maximum a ainable in en o y le el o p oduc ype-𝑖(i ems)
𝑛𝑖Numbe o deli e ies pe cycle o p oduc ype_𝑖(numbe )
𝑑𝑖Ini ial ma ke size o p oduc ype-𝑖(i ems pe yea )
𝑢𝑖Scaling pa ame e o he o de p ocessing cos wi h espec o lexible p oduc ion a e (numbe )
𝑣𝑖Shape pa ame e o he o de p ocessing cos wi h espec o lexible p oduc ion a e (numbe )
𝛾𝑖Sale p ice sensi i i y o ma ke demand (numbe )
𝜓𝑖IDT in es men sensi i i y (numbe )
𝜆𝑖Sa ings in 𝐶 𝑂2emissions h ough GERT in es men (uni s pe i em)
𝜉𝑖𝐶 𝑂2emissions pe i em du ing p ocessing (uni s pe i em)
𝜉𝑖,𝐶 𝑂2Emissions cos pe uni o emissions ($ pe uni )
𝑧𝑖S o age space equi ed o one i em ype-𝑖(cubic uni s)
Exp essions
𝑇 𝑃𝑖,𝑤𝑜𝑡 P o i unc ion o p oduc ype-𝑖in Case-I ($ pe uni ime)
𝑇 𝑃𝑤𝑡 P o i unc ion o sus ainable mul i-i em in Case-II ($ pe uni ime)
3.2. Assump ions
We use below se o assump ions o he p oposed sus ainable in eg a ed p oduc ion deli e y model o mula ion.
1. The p oposed s udy conside s a single-manu ac u e single- e aile in eg a ed sys em o p oduc ion and deli e y o mul i-i ems unde
e- e ailing.
2. A dependen p oduc demand ha is sensi i e o he p oduc p ice and IDT ad e isemen in es men s is conside ed in his esea ch as
𝐷𝑖=𝛥𝑖−𝛾𝑖𝑝𝑖+𝜓𝑖𝜂𝑖, whe e 𝛥𝑖is he ini ial ma ke size o p oduc ype-𝑖,𝛾𝑖is he p ice sensi i i y, and 𝜓𝑖is IDT sensi i i y [4].
3. A sma p oduc ion sys em is conside ed whe e he p oduc ion a e o each p oduc ype-𝑖can be a ied wi hin he in e al {𝑃𝑖,𝑚𝑖𝑛 −𝑃𝑖,𝑚𝑎𝑥]
in esponse o he luc ua ions in online p oduc demand o he e aile [38].
4. In o de o de e mine an op imal ade-o among in en o y holding cos and ba ch anspo a ion cos , an SSMD policy is implemen ed in
his esea ch whe e he manu ac u e ships he p oduc in smalle ba ches o he e- e aile ’s wa ehouse [30].
5. Sho ages a e allowed in his s udy and a e ully backo de ed in he p oposed sus ainable in eg a ed sys em, whe e bo h o he ixed and
a iable po ions o backo de cos a e conside ed.
6. P oduc s a e made wi h i gin and e u ned ma e ial, whe e p e-de ined 𝜃𝑖pe cen age o he online demand o p oduc ype-𝑖is ul illed
using e u n p oduc s as a aw ma e ial.
7. Ini ial deli e y o each p oduc ype-𝑖(𝑖= (1,2,3,…, 𝑛)) akes place a ime 𝑡= 0.
8. The successi e p oduc ion cycles a e iden ical in na u e.
4. Ma hema ical model
Analy ical de i a ion o he p oposed sus ainable in eg a ed model is ini ia ed by de e mining he size o indi idual shipmen o he e- e aile ’s
wa ehouse. The manu ac u e p oduces a quan i y 𝑞𝑖=𝑃𝑖𝑡𝑖o e he ime in e al 𝑡𝑖and he e aile ecei es 𝐷𝑖𝑇𝑖uni s o demand om he ma ke
du ing his ime pe iod. I he wa ehouse in en o y o he e aile is enough, his demand will be me and o he wise he sho age will occu . Hence,
gi en he 𝑛𝑡ℎ
𝑖deli e y, he consumed in en o y o e he las 𝑛𝑖− 1deli e ies can be de e mined as (𝑛𝑖− 1)𝐷𝑖𝑡𝑖.
As he ba ch size is 𝑄𝑖=𝑛𝑖𝑞𝑖, he maximum quan i y o he e aile can be ob ained om Fig. 3as
𝐼𝑖,𝑚𝑎𝑥 =𝑛𝑖𝑞𝑖− (𝑛𝑖− 1)𝐷𝑖𝑡𝑖−𝐵𝑖,
=1
𝑃𝑖[𝐷𝑖𝑞𝑖−𝐵𝑖𝑃𝑖−𝐷𝑖𝑄𝑖+𝑃𝑖𝑄𝑖].(1)
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Table 2
Inpu da a o nume ical expe imen s.
Pa ame e Value Pa ame e Value
𝑦3𝑐𝑣𝑖 [50 60 40] $∕i em
𝛥𝑖[1000 1400 800] uni s∕yea 𝑐𝑟𝑖 [40 50 30] $∕i em
𝛾𝑖[5 3 4] uni s 𝑣𝑖[90 90 90]
𝑃𝑖,𝑚𝑖𝑛 [1500 1800 1200] uni s∕yea 𝑍𝑟1950 uni s3
𝑃𝑖,𝑚𝑎𝑥 [2200 2500 1800] uni s∕yea 𝑢𝑖[0.02 0.01 0.02]
𝜓𝑖[0.03 0.015 0.02] uni s 𝑘𝑖[100 150 80] $∕se up
𝜂0.40 pe cen 𝜌𝑖[0.012 0.021 0.009] uni s
𝑔𝑖[45 50 40] $∕i em∕yea 𝛿𝑖[10 8 13] $∕ba ch
ℎ𝑖[20 20 20] $∕i em∕yea 𝑧𝑖[4 4 4] uni s3
𝜃𝑖[0.25 0.30 0.20] pe cen 𝑊𝑟3000 ($)
Table 3
Op imal solu ion o Case I.
Model ype Op imal solu ion Model ype Op imal solu ion
𝑛, 𝑞∉In .
𝑞∗33.5
𝑛, 𝑞∈In .
𝑞∗27
𝐵∗10.49 𝐵∗10.60
𝑝∗134.46 𝑝∗134.60
𝑛∗2.31 𝑛∗3
𝑇 𝑃∗
𝑤𝑜𝑡 20,749.75 𝑇 𝑃∗
𝑤𝑜𝑡 20,742.60
Fig. 4. Beha io o he objec i e unc ion unde a ia ions in decision a iables o Case I.
6.2. Compu a ional ou comes and discussion
The p oposed model Case I wi hou echnology in es men s o IDT and GERT is sol ed h ough hyb id me hodology o p oduc ype-1 p esen ed
in Sec ion 5.
Rema k 1. A adi ional p oduc ion sys em wi h ixed p oduc ion a e o 𝑃𝑜= 2000 uni s pe yea gene a es a p o i o $19,133.8 pe uni ime
wi h op imal alues o he decision a iables as 𝑞∗= 36.68 uni s, 𝑛∗= 1.96 shipmen s, 𝑝∗= $137.225 pe uni , and 𝐵∗= 9.28 uni s. Howe e , o a
sma p oduc ion sys em, a ying he p oduc ion a e wi h app op ia e s ep size wi hin he in e al 𝑃∈ {𝑃𝑚𝑖𝑛 −𝑃𝑚𝑎𝑥}gene a es a supe io solu ion
a 𝑃= 1500 uni s pe yea . The op imal solu ion o his sma p oduc ion sys em in his case is 𝑞∗= 33.50 uni s, 𝑛∗= 2.31 shipmen s, 𝑝∗= $134.46
pe uni , 𝐵∗= 10.49 uni s, and he o al p o i o $ 20,749.75 pe uni ime. One can no ice a 8.44% imp o emen in he sys em p o i in his case
by ans o ming a ixed sys em in o a sma p oduc ion sys em. Hence, we conside sma p oduc ion sys ems wi h 𝑃∗= 1500 o making u he
analysis.
As indica ed ea lie , he analy ical solu ion app oach o he p oposed sma in eg a ed p oduc ion managemen model does no gua an ee
in ege solu ions o he op imal shipmen size (𝑞∗) and numbe o shipmen s (𝑛∗). Hence we apply u he s eps (S ep VI −S ep XI) o he p oposed
heu is ic o de e mine in ege solu ion o hese a iables in o de o sa is y 𝑞∗∈𝐼 𝑛𝑡𝑒𝑔 𝑒𝑟𝑠 and 𝑛∗∈𝐼 𝑛𝑡𝑒𝑔 𝑒𝑟𝑠.Table 3p esen s a compa a i e analysis
o he in ege and non-in ege solu ions. A educ ion o $7.15 pe uni ime in op imal p o i o he sys em is obse ed o compensa e he in ege
solu ion equi emen s in de ising op imal shipmen size and numbe o shipmen s.
Fig. 4illus a es he conca i y o p o i maximiza ion objec i e p esen ed in Case 1 o di e en alues o he decision a iables as p o ed in
Sec ion 5 h ough analy ical op imiza ion echnique.
The p oposed mul i-i em cons ained in eg a ed model in Case II is sol ed h ough a GA-suppo ed me aheu is ic app oach p esen ed in
Sec ion 5.Table 4p esen s op imal solu ion o he p oposed model unde capaci y limi a ion and echnology in es men s o ad e isemen (IDT)
and emissions con ol (GERT), and Fig. 5shows an ins ance o a GA-suppo ed solu ion algo i hm.
One can obse e ha he op imal solu ion o Case II p esen ed in Table 4de elops a wise ade-o among echnology in es men s o
in o ma ion disclosu e echnology and g een emissions educ ion echnology while sa is ying he capaci y limi a ions.
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Table 4
Op imal solu ion o Case II unde capaci y limi a ion and echnology in es men s.
I em ype 𝑞∗
𝑖
(uni s)
𝐵∗
𝑖
(uni s)
𝑃∗
𝑖
(uni s pe yea )
𝑊∗
𝑖,𝑡𝑒𝑐 ℎ
($)
𝛼∗
𝑖
(%)
𝛽∗
𝑖
(%)
𝑝∗
𝑖
($ pe uni )
𝑛∗
𝑖
(numbe )
𝑇 𝑃∗
𝑤𝑡
($ pe uni ime)
1 55.59 63.50 1500 1200.00 0.84 0.16 136.47 5.28
$ 144,757.292 31.04 0.00 1800 156.46 0.00 1.00 280.78 5.13
3 37.07 14.66 1200 156.37 0.00 1.00 126.75 3.06
Fig. 5. Solu ion algo i hm implemen a ion o Case II on MATLAB.
Table 5
Op imal solu ion o Case II unde capaci y cons ain wi hou echnology in es men s.
I em ype 𝑞∗
𝑖(uni s) 𝐵∗
𝑖(uni s) 𝑃∗
𝑖
(uni s pe
yea )
𝑊∗
𝑖,𝑡𝑒𝑐 ℎ($) 𝛼∗
𝑖(%) 𝛽∗
𝑖(%) 𝑝∗
𝑖($ pe uni ) 𝑛∗
𝑖(numbe ) 𝑇 𝑃∗
𝑤𝑡
($ pe uni
ime)
1 20.19 8.33 1500 0 0 0 134.68 3.69
$ 139,375.392 21.59 0 1800 0 0 0 284.55 4.44
3 35.44 0 1200 0 0 0 127.89 1.54
Rema k 2. Technology in es men s IDT and GERT a e conside ed in Case II o he p oposed model unde capaci y cons ain s. Analysis o he
model ou comes unde capaci y cons ain s and no echnology in es men s scena io indica es an in e io solu ion as shown in Table 5. One can
obse e a dec ease o $5,381.90 in case o no in es men s o echnology in eg a ion, as he model wi hou echnology in es men s p oduce a
maximum p o i o $139,375.39 pe uni ime. Whe eas, he model case wi h echnology in es men s gene a es a p o i o $144,757.29 pe uni
o ime. This subs an ia es a cons uc i e accoun o echnology in es men s o imp o emen s in sys em p o i abili y.
Rema k 3. Fo a non-capaci a ed scena io unde Case II wi h echnology in es men s o IDT and GERT, he model gene a es a supe io solu ion
in compa ison o he capaci a ed model wi h and wi hou echnology in es men s. Table 6shows op imal solu ion o his case, whe e he model
a ains maximum p o i o $145,507.33. I is e iden om he model solu ion o his case ha a ade-o among capaci y expansion cos and
echnology in es men e iciency is c ucial o economic ad ancemen o he p oduc ion sys em. The manage s can iden i y a le el o capaci y
expansion o which he ollowing inequali y holds.
New e enue a e capaci y expansion −Capaci y expansion cos −Addi ional echnology in es men
Cu en e enue wi h no capaci y expansion ≥1,(33)
whe e capaci y expansion cos is calcula ed as uni s o inc emen in capaci y imes pe uni inc emen cos .
We u ilize Eq. (33) o iden i y op imal capaci y le el o Case II he p oposed model unde echnology in es men . Fig. 6p esen s maximum
capaci y le el o he sys em as 2805 uni s o which sys em p o i a ains maximum alue o $145,507.33. Any u he inc emen al capaci y unde
he cu en pa ame ic in o ma ion and echnology in es men budge is su plus o he sys em.
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Table 6
Op imal solu ion o Case II unde echnology in es men s and no capaci y limi a ion.
I em ype 𝑞∗
𝑖
(uni s)
𝐵∗
𝑖
(uni s)
𝑃∗
𝑖
(uni s pe
yea )
𝑊∗
𝑖,𝑡𝑒𝑐 ℎ
($)
𝛼∗
𝑖
(%)
𝛽∗
𝑖
(%)
𝑝∗
𝑖
($ pe uni )
𝑛∗
𝑖
(numbe )
𝑇 𝑃∗
𝑤𝑡
($ pe uni
ime)
1 55.59 63.5 1500 1200 0.84 0.16 136.47 5.28
$ 145,507.332 44.71 43.34 1800 1200 0.86 0.14 283.89 8.56
3 37.06 14.66 1200 156.37 0 1 126.75 3.06
Fig. 6. Maximum capaci y le el o Case II unde echnology in es men .
Fig. 7. Compa ison o he p oposed s udy wi h exis ing esea ch in he li e a u e.
6.3. Compa ison wi h exis ing s udies in he li e a u e
In his sec ion, we compa e he nume ical esul s o p oposed model wi h he exis ing s udies in he li e a u e. I IDT and GERT in es men s,
sma p oduc ion sys em, mul i-i em p oduc ion deli e y mechanism, and g een emissions educ ion e o s-dependen demand a e igno ed, hen
ou s udy educes o Sadeghi e al. [8]. Ou p oposed model u he educes o Sa ka e al. [58] i mul i-shipmen policy and manu ac u e – e aile
collabo a i e decision making a e also igno ed.
Sadeghi e al. [8] p oposed a ma hema ical model o op imize o al cos o a single-manu ac u e single- e aile supply chain by conside ing
mul i-shipmen policy. Howe e , hey igno ed echnology in es men s o educe en i onmen al impac o he supply chain and imp o e p oduc
demand. Fu he , hey conside ed adi ional p oduc ion sys em wi h cons an p oduc ion a e o p oduce a single- ype o p oduc wi h cons an
ma ke demand. Thei nume ical esul s p o ided by hei p oposed model gene a ed a minimum cos o $4321.91. Ou model p o ides a minim
cos o $4039.26 wi h he simila da a se and conside ing IDT and GERT in es men s o emissions educ ion o ul ill he en i onmen al awa eness-
based consume demand. This p o ides a 6.54% cos sa ings, which illus a es he supe io i y o he p oposed model. Mo eo e , Sa ka e al. [58]
p esen ed a single-s age p oduc ion model wi h andom de ec i e a e by conside ing ully backo de ed sho ages. Howe e , hey igno ed he
en i onmen al impac and mul i-shipmen policy in hei model. The nume ical expe imen o hei model p esen ed a minimum cos o $3078.10
whe e he andom de ec i e a e ollows a be a dis ibu ion. Ou model p o ides a minimum cos o $2820.16 o he simila pa ame ic in o ma ion
and he conside a ion o g een echnology in eg a ion o emissions educ ion. This demons a es a cos sa ing o 8.37% in compa ison o he
p oduc ion model p o ided by Sa ka e al. [58]. Fig. 7illus a es he supe io i y o he p oposed model in compa ison o he exis ing s udies in
he li e a u e.
6.4. Impac o echnology in es men o ad e isemen and emissions con ol
Case II o he p oposed in eg a ed p oduc ion model conside s echnology in es men o IDT and GERT. Fig. 8shows he impac o alloca ed
echnology in es men budge on o e all p o i abili y o he sys em ollowing diminishing e u n on in es men . We obse e he a ia ion in
dis ibu ion o he echnology in es men budge among IDT and GERT by a ying 𝛼𝑖, and among di e en p oduc ypes by a ying 𝜂𝑖wi hin he
in e al [0,1] o Case II. Fig. 9p esen s an in e play be ween 𝛼,𝛽, and expec ed p o i o he sys em and Fig. 10 illus a es he impac o a ia ion
in 𝜂𝑖on o al p o i o he sys em. One can obse e ha he a ia ion in maximum allowed p opo ion (𝜂𝑖) o echnology budge o he p oduc
ype-𝑖has a di ec impac on p o i abili y o he sys em. As we educe he limi o dis ibu ion o maximum a ailable echnology in es men budge
om 𝜂𝑖= 80% (∀ 𝑖, 𝑖= (1,2,3)), o al p o i o he sys em educes. Howe e , any such limi a ion abo e his alue (𝜂𝑖≥80%) shows no imp o emen
in he p o i abo e $146,369.13 pe uni ime. The e o e, he manage s should pay close a en ion o he in e play be ween alloca ed in es men
budge amoun and i s dis ibu ion s a egy among di e en aspec s o he p oduc ion sys em while ocusing on sys em p o i abili y.
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Fig. 8. Impac o echnology in es men budge on sys em p o i abili y o Case II.
Fig. 9. Impac o echnology in es men budge dis ibu ion (𝛼 , 𝛽) on sys em p o i abili y o Case II.
Fig. 10. Impac o echnology in es men budge dis ibu ion (𝜂) on sys em p o i abili y o Case II.
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Fig. 11. Impac o p ice sensi i i y and echnology in es men e iciency o ad e isemen on sys em p o i o Case II.
Table 7
Compa ison o model ou comes o SSSD and SSMD policy o Case I.
P oduc ion deli e y policy Op imal solu ion P oduc ion deli e y policy Op imal solu ion
SSSD policy
𝑞∗64.64
SSMD policy
𝑞∗27
𝐵∗10.83 𝐵∗10.60
𝑝∗135.00 𝑝∗134.60
𝑛∗1𝑛∗3
𝑇 𝑃∗
𝑤𝑜𝑡 20,648.58 𝑇 𝑃∗
𝑤𝑜𝑡 20,742.60
6.5. Impac o ad e isemen e iciency
The le el o ad e ising expendi u e elies p ima ily on how e ec i e a company’s ad e ising e o s a e in e ms o eaching and in luencing
he a ge audience h ough he chosen ad e ising channels. Ad e ising i ms de ine hei a ge audience and hen sugges spending plans o
ad e ising Sa ka and Dey [4]. The e o e, manage s o p oduc ion sys ems should ca e ully examine he in e ac ion be ween he inc emen al gains
ob ained om ad e ising ac i i ies ac oss a ious channels. Manage s may s a egically choose he mos bene icial channel by ca e ully e alua ing
he inc emen al bene i s associa ed wi h each op ion and selec ing he one wi h he mos inc emen al bene i .
Rema k 4. In he p oposed model, we conside a p e-de e mined ad e isemen channel wi h ad e isemen e o s e iciencies o 𝜓𝑖= 0.03,
𝜓2= 0.015, and 𝜓3= 0.02 o p oduc ype 1,2, and 3, espec i ely. Adop ion o his ad e isemen policy as IDT gene a es a p o i o $144,757.29
pe uni ime, which is highe han he no-ad e isemen case p esen ed in Rema k 2(wi h a p o i o $139,375.39 pe uni ime). Now we examine
he impac o −50% o +50% a ia ion in he ad e isemen e iciencies o all p oduc ypes unde his channel on expec ed p o i o he p oposed
mul i-i em in eg a ed sys em (see Fig. 11). I is obse ed ha 25%, and 50% educ ion in ad e isemen e iciency ends o educe he sys em p o i
by 3.77% and 11.89%, espec i ely. Howe e , 25%, and 50% inc ease in ad e isemen e iciency imp o es he p o i o he sys em by 7.41% and
22.65%, espec i ely. This e i ies he impo ance o pu ing e o s in imp o ing he ad e isemen e iciency cons an s o he sys em.
6.6. Impac o p oduc selling p ice sensi i i y o consume ma ke
Selling p ice sensi i i y pa ame e (𝛾𝑖) plays a c ucial ole in limi ing he maximum selling p ice asked o he p oduc ype-𝑖. The in e ac ion
be ween ini ial ma ke size and he p oduc selling p ice sensi i i y o he consume ma ke (𝛥𝑖∕𝛾𝑖) pu s an uppe bound on maximum asking p ice
o he p oduc . P oduc selling p ice sensi i i ies o each p oduc ype in he p oposed model a e ini ially se a 𝛾1= 5,𝛾2= 3, and 𝛾3= 4; esul ing
in he pe uni ime p o i o $144,757.29. Making a 25%, and 50% simul aneous educ ion in p oduc p ice sensi i i y o all he p oduc ypes
end o imp o e he sys em p o i by 36.66% and 64.07%, espec i ely. Whe eas, 25%, and 50% inc ease in hese educe he p o i by 32.72% and
53.44%, espec i ely (see Fig. 11).
6.7. Compa ison wi h SSSD policy
The p oposed in eg a ed model sugges s SSMD policy o make a ade-o be ween se up cos , in en o y holding cos , and anspo a ion cos .
We implemen SSSD policy o Case I and Case II by s ic ly limi ing 𝑛𝑖= 1and obse e he a ia ion in sys em p o i (see Fig. 12). Tables 7and 8
p esen a compa a i e analysis be ween SSSD and SSMD policy implemen a ion o Case I and Case II, espec i ely. Fo a model wi h SSSD policy
implemen a ion, we can obse e ha he sys em p o i educes by $94.02 o Case I and $1535.83 pe uni ime o Case II. This analysis e i ies
he supe io i y o SSMD policy adop ion o e he p io one as suppo ed by he ecen esea ch including Sa ka e al. [57], Sadeghi e al. [8],
and Uma e al. [7].
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
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M. Tayyab e al.
Table 8
Compa ison o model ou comes o SSSD and SSMD policy o Case II.
P oduc ion
deli e y
policy
I em ype 𝑞∗
𝑖
(uni s)
𝐵∗
𝑖
(uni s)
𝑃∗
𝑖
(uni s pe
yea )
𝑊∗
𝑖,𝑡𝑒𝑐 ℎ
($)
𝛼∗
𝑖
(%)
𝛽∗
𝑖
(%)
𝑝∗
𝑖
($ pe uni )
𝑛∗
𝑖
(numbe )
𝑇 𝑃∗
𝑤𝑡
($ pe uni
ime)
SSSD
policy
1 106.95 25.52 1500 154.92 0.00 1.00 133.39 1.00
$ 143,221.462 108.72 0.00 1800 138.33 0.00 1.00 281.99 1.00
3 85.07 13.47 1200 124.36 0.00 1.00 127.58 1.00
SSMD
policy
1 55.59 63.50 1500 1200.00 0.84 0.16 136.47 5.28
$ 144,757.292 31.04 0.00 1800 156.46 0.00 1.00 280.78 5.13
3 37.07 14.66 1200 156.37 0.00 1.00 126.75 3.06
Fig. 12. Compa ison o model ou comes o SSSD and SSMD policy adop ion.
Fig. 13. Op imal p oduc ion deli e y policy sugges ion o Case I.
6.7.1. Op imal p oduc ion deli e y policy sugges ion
In his sec ion, we analyze he impac o se up cos a ia ion on he op imal p oduc ion deli e y policy sugges ion o Case I. Fig. 13 illus a es
he impac o se up cos on op imal policy. I is obse ed om his analysis ha lowe se up cos s sugges SSSD as an op imal policy o he sys em.
Whe eas, a se up cos o $13.08 is a c i ical poin , abo e which SSMD policy becomes easible. The s ep-wise inc emen al beha io o 𝑛∗−cu e
in Fig. 13 indica es op imal b eakdown o he ba ch size in o speci ic numbe o shipmen s ha maximizes he sys em p o i . This analysis is
comple ely in line wi h he obse a ions o Ben-Daya e al. [29], Sa ka e al. [57], and Sadeghi e al. [8] ha lowe se up cos s end o educe
he a e age ba ch size and associa ed in en o y holding cos o he p oduc ion sys em which e adica es he need o SSMD policy. Howe e , as he
se up cos o he sys em inc ease o he c i ical poin , he ba ch size and ela ed in en o y holding cos inc ease and he model sugges s shi ing
om SSSD p oduc ion deli e y policy o SSMD policy. Simila ype o policy sugges ions can be de ised o o he pa ame e s o he sys em.
6.8. Sensi i i y analysis
In his sec ion, we examine he impac o a ia ion in o he key model pa ame e s on o al p o i o he sys em o Case II. The pa ame e alues
a e a ied om −50% o +50% wi hin equal in e als o each p oduc ype and he co esponding changes in o al p o i pe uni ime o he sys em
a e eco ded. Table 9shows pe cen age change in o al p o i o he sys em o co esponding pe cen age changes in he model pa ame e s.
I is obse ed om he sensi i i y analysis ha any dec ease in se up cos (𝑘𝑖), a iable backo de cos (𝑏𝑖), in en o y holding cos (ℎ𝑖), ca bon
emissions cos (𝜉𝑖), and ba ch shipmen cos (𝛿𝑖) imp o es o al p o i o he sys em. Whe eas, any dec ease in pe cen age o e u ned p oduc s
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
20

M. Tayyab e al.
Table 9
Sensi i i y analysis o key model pa ame e s.
Pa ame e (s) Changes in pa ame e alue Changes in sys em p o i Pa ame e (s) Changes in pa ame e alue Changes in sys em p o i
𝑘𝑖
−50% +0.31%
𝜉𝑖
−50% +0.33%
−25% +0.15% −25% +0.13%
+25% −0.17% +25% −0.35%
+50% −0.35% +50% −0.76%
𝑏𝑖
−50% +0.40%
𝛿𝑖
−50% +0.15%
−25% +0.095% −25% +0.087%
+25% −0.06% +25% −0.18%
+50% −0.11% +50% −0.26%
ℎ𝑖
−50% +1.139%
𝜃𝑖
−50% −1.08%
−25% +0.56% −25% −0.54%
+25% −0.49% +25% +0.54%
+50% −0.91% +50% +1.08%
Fig. 14. Sensi i i y analysis o key model pa ame e s o Case II.
(𝜃𝑖) used in manu ac u ing educes he o al p o i . One can see ha he sys em p o i is highly sensi i e o ℎ𝑖and 𝜃𝑖, whe e 50% dec ease in ℎ𝑖
imp o es he p o i by 1.139% and he simila dec ease in 𝜃𝑖 educes he p o i by 1.08%. On he o he hand, 50% inc ease in ℎ𝑖 educes he p o i
by 0.91% and he simila le el o inc emen in 𝜃𝑖imp o es he p o i by 1.08%. Any educ ion in 𝑘𝑖and 𝜉𝑖induces simila le el o inc emen in he
p o i , whe e 25% dec ease in hese a iables inc eases he p o i by 0.15% and 0.13%, and 50% dec ease in hese a iables inc eases he p o i
by 0.31% and 0.33%, espec i ely. Howe e , any inc ease in alues o hese a iables show di e en e ec s on he p o i , whe e 25% inc ease in
hese a iables educe he p o i by 0.17% and 0.35%, and 50% inc ease in hese a iables educes he p o i by 0.35% and 0.76%, espec i ely.
Fig. 14 p esen s a g aphical ep esen a ion o he changes in p o i o hese a ia ions in key model pa ame e s.
6.9. Manage ial insigh s
In his sec ion, we de ise signi ican manage ial insigh s om he expe imen al ou comes and ex ensi e analysis o he p oposed in eg a ed model
ha can suppo manage s and decision make s in de e mining op imal p oduc ion deli e y policies o p o i maximiza ion unde collabo a i e
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
21
M. Tayyab e al.
e- e ailing o imp o e consume se ice.
Resea ch ou comes o he p oposed model illus a e ha con e ing a igid manu ac u ing sys em in o a sma one, capable o modi ying ou pu
a es wi hin a de ined ange, esul s in a signi ican 8.44% augmen a ion in he sys em’s p o i abili y. Hence, he manage s should con empla e
using sma p oduc ion sys ems, as seen in Case I, whe e modi ying he p oduc ion a e wi hin he ange 𝑃∈ {𝑃𝑚𝑖𝑛 −𝑃𝑚𝑎𝑥}led o a mo e op imum
esul . Upon analyzing Case II, i becomes e iden ha in o ma ion sha ing and echnological in es men s (IDT and GERT) ha e a signi ican and
a o able e ec on he p oduc demand and p o i abili y o he sys em. The model ha inco po a es echnological in es men s gene a es a p o i o
$144,757.29 pe uni o ime, whe eas he p o i wi hou echnology in eg a ion is $139,375.39. I is ad isable o manage s o alloca e esou ces
owa ds inco po a ing echnology in o hei ope a ions, namely o he sake o in o ma ion disclosu e and emissions educ ion. This in es men
will ul ima ely imp o e he o e all p o i abili y o he sys em.
This s udy e i ies he signi icance o ad e ising e ec i eness in impac ing he p o i abili y o he e- e ailing sys em. A luc ua ion in
ad e isemen e iciency anging om −50% o +50% di ec ly co ela es wi h luc ua ions in sys em p o i . Hence Manage s should p oac i ely
ocus on imp o ing he e iciency cons an s o he ad e ising sys em in o de o gene a e a o able e ec s on p o i abili y. I is c ucial o s ike a
balance be ween capaci y and in es men in echnology o in o ma ion disclosu e. Analysis o he Case II in he p oposed esea ch emphasizes he
signi icance o achie ing a sui able equilib ium be ween capaci y g ow h and echnological in es men in o de o os e economic ad ancemen .
The mos e icien app oach, aking in o accoun bo h limi a ions on capaci y and expendi u es in echnology esul s in a maximum p o i o
$145,507.33. Manage s should ho oughly assess his equilib ium, aking in o accoun he expenses associa ed wi h expanding capaci y and he
e ec i eness o in es ing in echnology, in o de o a ain economic ad ancemen in he p oduc ion sys em.
The esea ch emphasizes he need o manage s s a egically dis ibu ing he mone a y budge o echnology in es men be ween In o ma ion
Disclosu e Technology (IDT) and G een Emissions Reduc ion Technology (GERT). The co ela ion be ween he designa ed in es men budge and i s
dis ibu ion ac oss di e en componen s o he p oduc ion sys em has a subs an ial impac on he o e all p o i abili y. Manage s should p io i ize
inc easing p o i abili y by s a egically alloca ing he echnology budge among a ious p oduc ca ego ies and echnologies.
7. Conclusions
A g owing end owa ds online shopping and en i onmen al awa eness is ans o ming consume choices as hey a e now placing a
s ong emphasis on sus ainabili y and en i onmen ally iendly p oduc s hey pu chase. In o de o deal wi h hese en i onmen ally conscious
commi men s, i is essen ial o he e- e ailing indus y o ha e e ec i e communica ion wi h he consume s ega ding he g een p ac ices o hei
p oduc ion and e ailing channels. In o ma ion echnology pla o ms such as blockchain and social media o e e icien pla o ms o indi idualized
ad e ising ha can be used o educa e he cus ome s abou he emissions con ol e o s pu be manu ac u e s o he said p oduc s. Howe e , he
e ec i eness o his policy hea ily elies on a collabo a ion among he supply chain playe s. The in en o y managemen s a egies like Vendo
Managed In en o y (VMI) and single-se up-mul i-deli e y (SSMD) policy play a c ucial pa in imp o ing p oduc ion and deli e y e iciency by
gua an eeing imely and mos economic a ailabili y o he p oduc .
In he a o emen ioned con ex , his esea ch in oduces a comp ehensi e e- e ailing model o a collabo a i e supply chain managemen be ween
a manu ac u e s and a e aile . The model akes in o accoun SSMD p oduc ion deli e y policy and echnological in es men s o ad e ising and
con olling g eenhouse gas emissions. The manu ac u e employs a sma manu ac u ing sys em and inco po a es a p ede ined a io o e u ned
i ems in o he p oduc ion o new p oduc s. He makes echnological in es men s in G een Emissions Reduc ion Technology (GERT) o he p oduc ion
sys em, while he e aile in es s in In o ma ion Disclosu e Technology (IDT) on social media o p omo e he sys em’s e o s in con olling emissions
h ough in o ma ion sha ing and inc ease cus ome demand o he p oduc . Mul iple model ins ances a e de eloped and a hyb id analy ical-
heu is ic me hod is u ilized o de i e op imal p oduc ion deli e y policy, in es men dis ibu ion, and p oduc p ice. The s udy examines he
s a egic alloca ion o echnology budge be ween IDT and GERT, conside ing se e al scena ios o budge and s ocking capaci y limi a ions. The
sensi i i y analysis iden i ies c i ical pa ame e s, and key ecommenda ions a e p o ided o enhance he economic iabili y o he sys em conside ing
he luc ua ions in hese pa ame e s. The acqui ed managemen insigh s o e decision-make s he essen ial ocus o implemen e ec i e in es men
and p oduc ion deli e y policies in o de o achie e en i onmen al and economic sus ainabili y o he coo dina ed sys em.
The esea ch ou comes p o ide signi ican manage ial insigh s o he decision-make s seeking o maximize p o i s h ough join op imiza ion o
sus ainable p oduc ion deli e y policies o a manu ac u e and e- e aile . Model esul s e i y ha he adop ion o a sma manu ac u ing sys em
o e a ixed one yields a subs an ial 8.44% ise in p o i abili y o he sys em. Fu he mo e, signi ican inc eases in p o i abili y a e posi i ely
co ela ed wi h in es men s in G een Emissions Reduc ion Technology (GERT) and In o ma ion Disclosu e Technology (IDT). This unde sco es he
c i ical need o manage s o alloca e esou ces owa ds he smoo h in eg a ion o sma manu ac u ing echnology and in o ma ion sha ing in o
hei p oduc ion and e ailing p ocesses, espec i ely. Fu he , he esul s indica e ha ad e ising e iciency plays a pi o al ole in imp o ing
economic and en i onmen al sus ainabili y o he sys em. This insigh sugges s o ake p oac i e measu es in imp o ing he e iciency cons an s o
he ad e ising channel. Model analysis ecognized ha accomplishing a balanced app oach be ween expanding p oduc ion capaci y and in es ing
in echnology is i al o os e ing economic success in he manu ac u e – e aile collabo a i e scheme. The policy sugges ion analysis indica es
iden i ica ion and moni o ing o c i ical poin s o he model pa ame e s should be ocused o de e mine he sui able p oduc ion deli e y policy.
While his s udy makes signi ican con ibu ions in he li e a u e, a ew limi a ions may a ec i s p ac ical applicabili y in ce ain scena ios. We
assume cons an cos componen s o model o mula ion and e alua ion while igno ing inhe en unce ain y in he cos s. Addi ionally, we ha e
conside ed he pe ec p oduc ion p ocess by neglec ing he possibili y o de ec i e p oduc s p oduc ion due o he p oduc ion p ocess shi ing o
an ou -o -con ol s a e [34] in se e al eal-wo ld si ua ions. Fu he mo e, he p oposed in eg a ed model is limi ed o a single manu ac u e and
a single e- e aile , whe eas se e al collabo a ions may in ol e mul iple supplie s [10] o e aile s [9] o enhanced economic and en i onmen al
sus ainabili y, and obus op imiza ion [59]. The e o e, o enhance he e ec i eness o his s udy, po en ial esea ch di ec ions include de eloping
an inspec ion schema [31], inco po a ing impe ec p oduc p oduc ion p ocess [49], explo ing in eg a ed decisions wi h mul i-manu ac u e s and
mul i- e aile s [3], and add essing unce ain y issues in he model cos pa ame e s [60] by implemen ing deep ein o cemen lea ning [61], and
uzzy op imiza ion [62] app oaches. These ex ensions may con ibu e o a mo e comp ehensi e and adap i e manu ac u e – e aile in eg a ed
model o online and o line sales.
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
22
M. Tayyab e al.
CRediT au ho ship con ibu ion s a emen
Muhammad Tayyab: W i ing – e iew & edi ing, W i ing – o iginal d a , So wa e, Me hodology, In es iga ion, Funding acquisi ion, Fo mal
analysis, Da a cu a ion, Concep ualiza ion. Hi a Tahi : W i ing – e iew & edi ing, Visualiza ion, So wa e, Da a cu a ion. Muhammad Salman
Habib: Visualiza ion, Supe ision, Resou ces, P ojec adminis a ion, Me hodology, In es iga ion, Fo mal analysis.
Decla a ion o compe ing in e es
The au ho s decla e ha hey ha e no known compe ing inancial in e es s o pe sonal ela ionships ha could ha e appea ed o in luence he
wo k epo ed in his pape .
Acknowledgmen
This esea ch wo k is sponso ed by he In e disciplina y Resea ch Cen e o Finance & Digi al Economy (IRC-FDE) a King Fahd Uni e si y o
Pe oleum & Mine als (KFUPM) unde he P ojec Numbe INFE2401 awa ded on Janua y 3, 2024.
Appendix A
𝛺=𝐵𝑖ℎ𝑖−𝐵2
𝑖𝑔𝑖
2𝑛𝑖𝑞𝑖
−𝐵2
𝑖ℎ𝑖
2𝑛𝑖𝑞𝑖
−ℎ𝑖𝑛𝑖𝑞𝑖
2,
𝜏𝑖= −𝜃𝑖(𝑐𝑣𝑖 −𝑐𝑟𝑖)+𝑐𝑣𝑖 +𝑃𝑖𝑢𝑖+𝑤𝑖
𝑃𝑖
+𝜉𝑖,
𝜎=−𝑏𝑖𝐵𝑖
𝑛𝑖𝑞𝑖
−𝑘𝑖
𝑛𝑖𝑞𝑖
−ℎ𝑖𝑞𝑖
2𝑃𝑖
+ℎ𝑖𝑛𝑖𝑞𝑖
2𝑃𝑖
−𝛥𝑖
𝑞𝑖
−𝜏𝑖,
𝛤=𝐵𝑖𝑔𝑖
2𝑛𝑖
+𝐵𝑖ℎ𝑖
2𝑛𝑖
−𝐵2
𝑖𝑔𝑖
2𝑛𝑖𝑞𝑖
−𝐵2
𝑖ℎ𝑖
2𝑛𝑖𝑞𝑖
−𝑔𝑖𝑞𝑖
8𝑛𝑖
−ℎ𝑖𝑞𝑖
8𝑛𝑖
,
=𝛾𝑖𝛤 ,
𝜒=𝑃𝑖−𝛥𝑖
=𝛤 𝛥𝑖,
=𝛾𝑖+𝛾𝑖,and
=−.
Appendix B
Fi s p inciple mino |(𝐻11)|a he op imal alues is
|(𝐻11)|=−2𝑏𝑖𝐵𝑖𝜇𝑖
𝑛𝑖𝑞3
𝑖
−𝐵2
𝑖𝑃𝑖(𝑔𝑖+ℎ𝑖)
𝑛𝑖𝑞3
𝑖(𝑃𝑖−𝜇𝑖)−2𝜇𝑖(𝑘𝑖+𝛿𝑖𝑛𝑖)
𝑛𝑖𝑞3
𝑖
<0.
Second p inciple mino |(𝐻22)|a he op imal alues is
|(𝐻22)|=1
64𝑛4𝑃2𝑞4(𝑃−𝜇)2
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
8𝐵2𝑃2(24𝑏2𝜇2(𝑃−𝜇)2+ 2𝑔(4𝜇(𝑃−𝜇)(−2𝑏𝜇 𝑞+ 3𝑘𝑃 + 2𝛿 𝑛𝑃 )
+ℎ𝑞2(3𝜇 𝑃− 2𝑛2(𝑃−𝜇)2) )+8ℎ𝜇(𝑃−𝜇)(−2𝑏𝜇 𝑞+ 3𝑘𝑃 + 2𝛿 𝑛𝑃 )
+ 3𝑔2𝜇 𝑃 𝑞2+ℎ2𝑞2(3𝜇 𝑃− 4𝑛2(𝑃−𝜇)2) )−64𝐵3𝜇 𝑃3(𝑔+ℎ)(3𝑏(𝜇−𝑃)
+𝑞(𝑔+ℎ)) − 16𝐵 𝜇 𝑃(𝑃−𝜇)(𝑏(𝜇 𝑃(−3 𝑔 𝑞2− 8(𝑃−𝜇)(3𝑘+ 2𝛿 𝑛))
+ℎ𝑞2(4𝑛2(𝑃−𝜇)2− 3𝜇 𝑃) )+8𝜇 𝑃 𝑞(𝑔+ℎ)(𝑘+𝛿 𝑛))+48𝐵4𝑃4(𝑔+ℎ)2
− 2ℎ𝜇 𝑃 𝑞2(𝑔 𝑞2(𝜇 𝑃− 4𝑛2(𝑃−𝜇)2)+ 8𝑘(𝑃−𝜇)(4𝑛2(𝑃−𝜇)2− 3𝜇 𝑃)
+ 16𝛿 𝜇 𝑛𝑃 (𝜇−𝑃))+𝜇2𝑃2(16𝑘(𝑃−𝜇)(3𝑔 𝑞2+ 16𝛿 𝑛(𝑃−𝜇))−𝑔 𝑞2
(𝑔 𝑞2+ 32𝛿 𝑛(𝜇−𝑃))+ 192𝑘2(𝑃−𝜇)2)−ℎ2𝑞4(𝜇 𝑃− 4𝑛2(𝑃−𝜇)2)2
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
<0.
Thi d p inciple mino |(𝐻33)|a he op imal alues is
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
23
M. Tayyab e al.
|(𝐻33)|= −1
64𝑛5𝑃 𝑞5𝛺3
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
(𝑔+ℎ) (16𝑃2(4𝐵 𝛤(2𝑏𝛺 −𝑞(𝑔+ℎ))− 4𝐵2𝑃(𝑔+ℎ)+𝛤(𝑞2(𝑔+ℎ)+ 8𝑘𝛺))(2𝑏𝐵 𝛤 𝛺−𝐵2𝑃(𝑔+ℎ)+ 2(𝑘+𝛿 𝑛)𝛤 𝛺)
−(−8𝑏𝐵 𝑃 𝛤 𝛺+ 4𝐵2𝑃2(𝑔+ℎ)−𝑃 𝛤(8𝑘𝛺 −𝑔 𝑞2)+ℎ𝑞2(4𝑛2𝛺2+𝑃 𝛤))2)+4(𝛤(𝑞(𝑔+ℎ)− 2𝑏𝛺)+ 2𝐵 𝑃(𝑔+ℎ)) (4𝑃
(𝛤(𝑞(𝑔+ℎ)− 2𝑏𝛺)+ 2𝐵 𝑃(𝑔+ℎ))(2𝑏𝐵 𝛤 𝛺−𝐵2𝑃(𝑔+ℎ)+ 2(𝑘+𝛿 𝑛)𝛤 𝛺)−(𝐵 𝑃(𝑔+ℎ)−𝑏𝛤 𝛺) (8𝑏𝐵 𝑃 𝛤 𝛺− 4𝐵2𝑃2
(𝑔+ℎ)+𝑃 𝛤(8𝑘𝛺 −𝑔 𝑞2)−ℎ𝑞2(4𝑛2𝛺2+𝑃 𝛤) ))−4(𝐵 𝑃(𝑔+ℎ)−𝑏𝛤 𝛺) ( (𝛤(𝑞(𝑔+ℎ)− 2𝑏𝛺)+ 2𝐵 𝑃(𝑔+ℎ)) (8𝑏𝐵 𝑃
𝛤 𝛺− 4𝐵2𝑃2(𝑔+ℎ)+𝑃 𝛤(8𝑘𝛺 −𝑔 𝑞2)−ℎ𝑞2(4𝑛2𝛺2+𝑃 𝛤) )−4𝑃(𝐵 𝑃(𝑔+ℎ)−𝑏𝛤 𝛺) (4𝐵 𝛤(2𝑏𝛺 −𝑞(𝑔+ℎ))− 4𝐵2
𝑃(𝑔+ℎ)+𝛤(𝑞2(𝑔+ℎ)+ 8𝑘𝛺) ))
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
<0.
Fou h p inciple mino |(𝐻44)|a he op imal alues is
|(𝐻44)|=𝛾
512𝑛6𝑃2𝑞6𝛺6
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣
−𝛾((𝑔+ℎ)(𝑞− 2𝐵)(2𝐵+𝑞)𝑃2
𝛺2− 8(𝑏𝐵 +𝑘+𝑛𝛿)𝑃− 4ℎ(𝑛− 1)𝑛𝑞2) (2(4𝛤 𝛺 𝑏2+ 4ℎ𝑞(−𝛤)𝑏+𝑔2𝑞2+ℎ2𝑞2+ 8𝑔 𝑘𝑃
+ 8ℎ𝑘𝑃 + 2𝑔 𝑞(2(−𝛤)𝑏+ℎ𝑞) )𝛤 𝛺((𝑔+ℎ)(4𝐵2−𝑞2)𝑃2+ 4(ℎ(𝑛− 1)𝑛𝑞2+ 2𝑃(𝑏𝐵 +𝑘+𝑛𝛿))𝛺2)−(4(2𝐵(𝑔+ℎ)𝑃
+𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺) ) (𝐵(𝑔+ℎ)𝑃−𝑏𝛤 𝛺)−(𝑔+ℎ) (4𝐵2(𝑔+ℎ)𝑃2−(8𝑘𝛺 −𝑔 𝑞2)𝛤 𝑃− 8𝑏𝐵 𝛤 𝛺 𝑃+ℎ𝑞2(4𝑛2𝛺2+𝑃
𝛤))) ((𝑔+ℎ)𝑃2(𝑞− 2𝐵)2
+ 4ℎ𝑛2𝑞2𝛺2+ 8(𝑏𝐵 +𝑘)𝑃 𝛺2)+ 2(2𝑏𝛺2+ 2𝐵(𝑔+ℎ)𝑃−(𝑔+ℎ)𝑃 𝑞) (4𝑃(4(𝑔+ℎ)𝑃 𝐵2
+ 4𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺)𝐵−((𝑔+ℎ)𝑞2+ 8𝑘𝛺)𝛤) (𝐵(𝑔+ℎ)𝑃−𝑏𝛤 𝛺)−(2𝐵(𝑔+ℎ)𝑃+𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺)) (4𝐵2(𝑔+
ℎ)𝑃2−(8𝑘𝛺 −𝑔 𝑞2)𝛤 𝑃− 8𝑏𝐵 𝛤 𝛺 𝑃+ℎ𝑞2(4𝑛2𝛺2+𝑃 𝛤) )))𝛺2−(2𝑃(8𝑛𝑞 𝛺3+(𝑔+ℎ)𝑃(𝑞− 2𝐵)2
𝛾) (4(2𝐵(𝑔+ℎ)𝑃
+𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺) ) (𝐵(𝑔+ℎ)𝑃−𝑏𝛤 𝛺)−(𝑔+ℎ) (4𝐵2(𝑔+ℎ)𝑃2−(8𝑘𝛺 −𝑔 𝑞2)𝛤 𝑃− 8𝑏𝐵 𝛤 𝛺 𝑃+ℎ𝑞2(4𝑛2𝛺2+
𝑃 𝛤)))−𝛾(2(2𝐵(𝑔+ℎ)𝑃+𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺))(2𝑏𝛺2+ 2𝐵(𝑔+ℎ)𝑃−(𝑔+ℎ)𝑃 𝑞)−(𝑔+ℎ) ( (𝑔+ℎ)𝑃2(𝑞− 2𝐵)2
+ 4ℎ𝑛2𝑞2𝛺2+ 8(𝑏𝐵 +𝑘)𝑃 𝛺2)) ((𝑔+ℎ)(4𝐵2−𝑞2)𝑃2+ 4(ℎ(𝑛− 1)𝑛𝑞2+ 2𝑃(𝑏𝐵 +𝑘+𝑛𝛿))𝛺2)+ 2𝛾(2𝑏𝛺2+ 2𝐵
(𝑔+ℎ)𝑃−(𝑔+ℎ)𝑃 𝑞)( (4𝐵2(𝑔+ℎ)𝑃2−(8𝑘𝛺 −𝑔 𝑞2)𝛤 𝑃− 8𝑏𝐵 𝛤 𝛺 𝑃+ℎ𝑞2(4𝑛2𝛺2+𝑃 𝛤)) (2𝑏𝛺2+ 2𝐵(𝑔+ℎ)𝑃−
(𝑔+ℎ)𝑃 𝑞)−2(𝐵(𝑔+ℎ)𝑃−𝑏𝛤 𝛺)((𝑔+ℎ)𝑃2(𝑞− 2𝐵)2
+ 4ℎ𝑛2𝑞2𝛺2+ 8(𝑏𝐵 +𝑘)𝑃 𝛺2) ))(−4𝐵2(𝑔+ℎ)𝑃2+ 8𝑏𝐵 𝛤
𝛺 𝑃+𝛤(8𝑘𝛺 −𝑔 𝑞2)𝑃−ℎ𝑞2(4𝑛2𝛺2+𝑃 𝛤) )+8𝑃(4𝑃(4𝛤 𝛺 𝑏2+ 4ℎ𝑞(−𝛤)𝑏+𝑔2𝑞2+ℎ2𝑞2+ 8𝑔 𝑘𝑃 + 8ℎ𝑘𝑃 + 2𝑔 𝑞(2(−𝛤)
𝑏+ℎ𝑞 ))𝛤 𝛺(8𝑛𝑞 𝛺3+(𝑔+ℎ)𝑃(𝑞− 2𝐵)2
𝛾)−𝛾(2(2𝐵(𝑔+ℎ)𝑃+𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺)) (2𝑏𝛺2+ 2𝐵(𝑔+ℎ)𝑃−(𝑔+ℎ)
𝑃 𝑞)−(𝑔+ℎ)((𝑔+ℎ)𝑃2(𝑞− 2𝐵)2
+ 4ℎ𝑛2𝑞2𝛺2+ 8(𝑏𝐵 +𝑘)𝑃 𝛺2) )( (𝑔+ℎ)𝑃2(𝑞− 2𝐵)2
+ 4ℎ𝑛2𝑞2𝛺2+ 8(𝑏𝐵 +𝑘)
𝑃 𝛺2)+2𝛾(2𝑏𝛺2+ 2𝐵(𝑔+ℎ)𝑃−(𝑔+ℎ)𝑃 𝑞) (2𝑃(4(𝑔+ℎ)𝑃 𝐵2+ 4𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺)𝐵−((𝑔+ℎ)𝑞2+ 8𝑘𝛺)𝛤) (2𝑏𝛺2
+ 2𝐵(𝑔+ℎ)𝑃−(𝑔+ℎ)𝑃 𝑞)−(2𝐵(𝑔+ℎ)𝑃+𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺)) ( (𝑔+ℎ)𝑃2(𝑞− 2𝐵)2
+ 4ℎ𝑛2𝑞2𝛺2+ 8(𝑏𝐵 +𝑘)𝑃
𝛺2))) (2𝑏𝛤 𝛺 𝐵−𝐵2(𝑔+ℎ)𝑃+ 2𝛤(𝑘+𝑛𝛿)𝛺)+ 4(𝐵(𝑔+ℎ)𝑃−𝑏𝛤 𝛺) (−2𝑃(8𝑛𝑞 𝛺3+(𝑔+ℎ)𝑃(𝑞− 2𝐵)2
𝛾) (4𝑃(4(𝑔+
ℎ)𝑃 𝐵2+ 4𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺)𝐵−((𝑔+ℎ)𝑞2+ 8𝑘𝛺)𝛤) (𝐵(𝑔+ℎ)𝑃−𝑏𝛤 𝛺)−(2𝐵(𝑔+ℎ)𝑃+𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺)) (4
𝐵2(𝑔+ℎ)𝑃2−(8𝑘𝛺 −𝑔 𝑞2)𝛤 𝑃− 8𝑏𝐵 𝛤 𝛺 𝑃+ℎ𝑞2(4𝑛2𝛺2+𝑃 𝛤) ))+𝛾( (𝑔+ℎ)(4𝐵2−𝑞2)𝑃2+ 4(ℎ(𝑛− 1)𝑛𝑞2+ 2𝑃
(𝑏𝐵 +𝑘+𝑛𝛿) )𝛺2)(2𝑃(4(𝑔+ℎ)𝑃 𝐵2+ 4𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺)𝐵−((𝑔+ℎ)𝑞2+ 8𝑘𝛺)𝛤) (2𝑏𝛺2+ 2𝐵(𝑔+ℎ)𝑃−(𝑔+ℎ)
𝑃 𝑞)−(2𝐵(𝑔+ℎ)𝑃+𝛤((𝑔+ℎ)𝑞− 2𝑏𝛺))((𝑔+ℎ)𝑃2(𝑞− 2𝐵)2
+ 4ℎ𝑛2𝑞2𝛺2+ 8(𝑏𝐵 +𝑘)𝑃 𝛺2) )−𝛾( (𝑔+ℎ)𝑃2(𝑞−
2𝐵)2+ 4ℎ𝑛2𝑞2𝛺2+ 8(𝑏𝐵 +𝑘)𝑃 𝛺2)( (4𝐵2(𝑔+ℎ)𝑃2−(8𝑘𝛺 −𝑔 𝑞2)𝛤 𝑃− 8𝑏𝐵 𝛤 𝛺 𝑃+ℎ𝑞2(4𝑛2𝛺2+𝑃 𝛤)) (2𝑏𝛺2+ 2𝐵
(𝑔+ℎ)𝑃−(𝑔+ℎ)𝑃 𝑞)−2(𝐵(𝑔+ℎ)𝑃−𝑏𝛤 𝛺)((𝑔+ℎ)𝑃2(𝑞− 2𝐵)2
+ 4ℎ𝑛2𝑞2𝛺2+ 8(𝑏𝐵 +𝑘)𝑃 𝛺2) ))
⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦
<0,
whe e
𝛺=(𝑃+𝑝𝛾 −𝛥),
𝛤=(𝑝𝛾 −𝛥).
Da a a ailabili y
No da a was used o he esea ch desc ibed in he a icle.
Re e ences
[1] Khan Md Al-Amin, Cá denas-Ba ón Leopoldo Edua do, T e iño-Ga za Ge a do, Céspedes-Mo a A mando. S a egizing emissions educ ion in es men o a li es ock p oduc ion
a m amid powe demand pa e n: A pa h o sus ainable g ow h unde he ca bon cap en i onmen al egula ion. Ope Res Pe spec 2024;13:100313.
[2] Feng Xiaohang Flo a, Liu Xiao, Zhang Shunyuan, S ini asan Kannan. Sus ainabili y and compe i ion on amazon. In: Xiao and Zhang, Shunyuan and S ini asan, Kannan,
Sus ainabili y and Compe i ion on Amazon (Sep embe 16, 2024). 2024.
Ope a ions Resea ch Pe spec i es 14 (2025) 100328
24