Chou, Tung-Hsiang; Chen, You-Sheng; Pan, Chung-Wei; Chang, Hung-Hsuan
A icle
A sma scale o e icien in en o y managemen based
on design science esea ch p inciples
Con empo a y Economics
P o ided in Coope a ion wi h:
VIZJA Uni e si y, Wa saw
Sugges ed Ci a ion: Chou, Tung-Hsiang; Chen, You-Sheng; Pan, Chung-Wei; Chang, Hung-Hsuan
(2024) : A sma scale o e icien in en o y managemen based on design science esea ch
p inciples, Con empo a y Economics, ISSN 2300-8814, Uni e si y o Economics and Human Sciences
in Wa saw, Wa saw, Vol. 18, Iss. 2, pp. 153-170,
h ps://doi.o g/10.5709/ce.1897-9254.531
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/312947
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E en in he con ex o Indus y 4.0, con en ional wa ehouse managemen con inues o encoun e challenges.
Amids hese obs acles, inno a i e solu ions a e impe a i e. O de picking, a c i ical p ocess wi h signi ican
implica ions o cus ome se ice, emains labo -in ensi e wi hin wa ehousing ope a ions. Cu en manual
me hods esul in p olonged in en o y cycles and inhe en accu acy complexi ies. To add ess hese issues,
his esea ch has used he Design Science Resea ch (DSR) me hod o de eloping a de ice called he Sma
Scale, which helps op imize wa ehouse in en o y managemen . This de ice, which is ailo ed speci ically o
ligh weigh i ems such as chewing gum, sc ews, and as ene s, uses weigh senso s o calcula e eal- ime
quan i ies dynamically. By educing human e o and enhancing i em supe ision, he Sma Scale imp o es
in en o y p ecision and ime and cos e iciencies.
1. In oduc ion1. In oduc ion
Indus y 4.0 has exis ed o a while, ye adi-
ional issues such as in en o y p oblems pe sis in
wa ehouse managemen . The ise o e-comme ce
has led o inc eased p oduc ion speed and an in-
c ease in he numbe o o de s o some ac o ies,
esul ing in educed lead imes, sho e p oduc
li es, and inc eased in en o y u no e a es, all
o which inc ease he pace o wo k in wa ehouses
(Tompkins & Smi h, 1998). O de picking—which
is he p ocess o e ie ing a p oduc om a wa e-
house in esponse o a cus ome eques —is a pi -
o al p ocedu e consuming a signi ican amoun
o labo and di ec ly a ec ing he se ice quali y
pe cei ed by downs eam cus ome s (Ba holdi &
Hackman, 2019).
Mos labo -in ensi e ope a ions in wa ehouses
wi h manual sys ems in ol e high ope a ional
cos s and a e ime-consuming (Deshpande & Ku-
ma , 2020). To add ess hese p oblems, wa ehouse
manage s can choose om a ious echnologies.
Acco ding o Kos e (2008), wa ehouses ha mus
quickly handle many cus ome o de lines can use
echniques in ol ing a “spli -case o de -picking”
sys em (i.e., a picke - o-pa s sys em).
The e is no one bes echnology o spli -case
o de picking. Pick- o-ligh sys ems, which use
elec onic labels and ela ed so wa e, a e s anda d
in he in en o y indus y d i en by he In e ne o
Things (IoT) (Su e al., 2019). O he echnologies
used in spli -case o de picking include adio- e-
quency sys ems, ba code scanne s, ision picking,
pu - o- oice sys ems—each ha ing a dis inc se o
applica ions (Be ge & Ludwig, 2007; Fang & An,
2020).
Thanks in pa o i s epu a ion o high-quali y
p oduc ion and as deli e y, Taiwan has es ab
lished
A Sma Scale o E icien In en o y Managemen
Based on Design Science Resea ch P inciples
ABSTRACT
C80, L86.
KEY WORDS:
JEL Classi ica ion:
Indus y 4.0, DSR, IoT, in elligen wa ehousing, o de picking.
Depa men o In o ma ion Managemen , Na ional Kaohsiung Uni e si y o Science and Technology, 1 Uni e si y Rd, Yanchao
Dis ic , Kaohsiung Ci y, Taiwan
Co espondence conce ning his a icle should be add essed o:
Tung-Hsiang Chou, Depa men o In o ma ion Managemen , Na ional
Kaohsiung Uni e si y o Science and Technology, Uni e si y Rd, Yanchao
Dis ic , Kaohsiung Ci y, Taiwan.
E-mail: [email p o ec ed]
Tung-Hsiang Chou , You-Sheng Chen , Chung-Wei Pan , Hung-Hsuan Chang
P ima y submission: 22.01.2024 | Final accep ance: 03.02.2024
154
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10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS
Vol. 18 Issue 2 153-1702024
i sel as a signi ican manu ac u e in he global ma -
ke o sc ews and nu s (Huang, 2021). The sc ews
and nu s s o ed in Taiwanese wa ehouses a e di e se
and nume ous, making he coun ing o hese p od-
uc s bo h cos ly and p one o e o . Indeed, h ough-
ou he wo ld, e icien in en o y managemen e-
mains a p oblem o indus ies dealing wi h small,
ligh weigh ma e ials. To ackle hese issues, a com-
mon p ac ice is o di ide he ma e ials in o ba ches
packaged in plas ic bags o boxes. Howe e , coun -
ing e o s s ill occu in he p oduc ion depa men
owing o he di icul y o p ecisely coun ing small
objec s. Also, no all p oduc s a e able o ha e an
a ached adio- equency iden i ica ion (RFID) ag.
In sho , he e is no p ecise and ool-p oo me hod
o coun ing small objec s indi idually in wa ehouse
in en o y.
Mos exis ing esea ch on o de picking ocuses on
educing a el and balancing p oblems by op imiz-
ing ou ing. Fo example, he e ie al-o de eques
in a wa ehouse can be iewed as a a eling sales-
pe son p oblem (TSP) ha can be e icien ly sol ed
wi h compu e algo i hms (Hall, 1993; Roodbe gen
& Kos e , 2001). Ano he popula opic o esea ch is
he bucke -b igade sys em, whe e each wo ke passes
an i em om one s a ion o ano he and om one
s age o p ocessing o ano he (Alligood e al., 1996;
Ba holdi & Eisens ein, 1996). These and o he s ud-
ies ha e o e ed aluable insigh s in o o de -picking
p oblems and solu ions, bu in ica e challenges pe -
sis wi h espec o he managemen o small-i em
in en o ies (Li e al., 2023; Mugoni e al., 2023).
In he p esen s udy, i s cen al aim is o show how
bo h IoT echnology and cloud echnology, when
based on he DSR me hod, can imp o e he o de -
picking p ocess o small ma e ials. This s udy e-
ol es a ound he ollowing esea ch ques ion: how
can companies le e age he DSR me hod o de elop
a echnical se ice ha educes manual-picking e -
o s and ha imp o es eal- ime calcula ions o
small-i em in en o y? To answe his ques ion, his
esea ch has se ou o achie e h ee p ima y e-
sea ch goals:
1. Use he DSR me hod o de elop he Sma
Scale, which is a de ice ha should enable p ecise,
eal- ime calcula ions and acking o small i ems in
an in en o y.
2. Assess he e ec i eness o he Sma Scale e-
ga ding wa ehouse e iciency and ope a ional cos s
while i e a i ely op imizing he design o he Sma
Scale acco ding o expe opinions.
3. In es iga e he economic alue o implemen -
ing he Sma Scale in small- and medium-sized
en e p ises (SMEs) in Taiwan.
This s udy p oposed Sma Scale should encom-
pass eal- ime managemen unc ionali ies and
da a-inqui y capabili ies, acili a ing ins an in o -
ma ion dissemina ion and manipula ion h ough a
web o mobile in e ace. The algo i hm embedded
wi hin he de ice should help o educe bo h hu-
man e o s and labo cos s. In he end, his esea ch
hopes o add ess manage ial and economic issues
in ways ha bene i SMEs and consume s alike
(Chaopaisa n & Woschank, 2021; Özşahin e al.,
2022).
The s uc u e o his pape is as ollows: In Sec-
ion 2, his esea ch e iews he li e a u e on ela ed
opics. In Sec ion 3, his esea ch discusses he e-
sea ch me hod. In Sec ion 4, his esea ch discusses
he equi emen s and he a chi ec u al design o
i s p oposed Sma Scale. In Sec ion 5, his esea ch
epo s he esul s o he ini ial implemen a ion o
Sma Scale. Finally, in Sec ion 6, his esea ch con-
cludes he pape , poin ou limi a ions o i s ind-
ings, and sugges di ec ions o u u e esea ch.
2. Li e a u e Re iew2. Li e a u e Re iew
In his s udy, i explo es he dynamic in e play
be ween adi ional and cu ing-edge me hods
in he con empo a y landscape o in en o y
managemen . This sec ion comp ehensi ely
examines h ee dis inc ye in e connec ed domains
pi o al o he e olu ion o con empo a y in en o y
con ol: in elligen wa ehousing, coun ing-scale
echnology, and DSR.
2.1. In elligen Wa ehousing and O de Picking
The e m ‘wa ehousing’ e e s o he la ge-
scale s o ing o goods ha a e no immedia ely
used o sold (Kadwe & Saha, 2018). The e m
‘in elligen wa ehousing’ e e s o a s o age-
managemen concep ha in eg a es in o ma ion
and communica ion echnology (ICT) h ough he
IoT, cloud compu ing, and mechanical ci cui s in
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ways ha educe s o age cos s, imp o e e iciency,
and imp o e he abili y o managemen o espond
quickly o changes in demand.
O de picking is a c i ical sub-p ocess o
wa ehousing and ypically equi es ha wa ehouse
wo ke s a eling o a speci ic loca ion in a
wa ehouse, ind he desi ed i em, and e ie e i .
E en wi h he in oduc ion o obo s o dec ease
labo and s eamline he sea ch and e ie al
o i ems in aisleways, human wo ke s a e s ill
esponsible o execu ing s aigh o wa d physical
and cogni i e asks h oughou wa ehouses,
including in packing s a ions (Su e al., 2019). As
a esul , he a el ime equi ed o e ie e an
o de is a non- alue-adding expense and was es
labo hou s. Much esea ch has been conduc ed
on educing a el ime by op imizing ou ing
algo i hms ha conside he geome ic layou o a
wa ehouse and ha quickly iden i y op imal TSP-
based solu ions o a el- ime p oblems (Hall,
1993; Roodbe gen & Kos e , 2001).
Xu e al. (2020) a gue ha managemen can
s eamline wa ehouse ope a ions by in eg a ing
picking asks and s o age-loca ion asks in o a single
p ocess wi hou al e ing he gi en picke ’s exis ing
walking pa h. Some schola s ha e sugges ed ha
me hods using di e en ial equa ions can op imize
wo k lows in complex, dynamic sys ems (Alligood
e al., 1996; Ba holdi & Eisens ein, 1996).
2.1.1. The Spli -Case O de -Picking Sys em
Managemen sys ems and o de -picking sys ems
a e c ucial o wa ehouses ha need o connec
wi h he wo ld a o dably. Fo o de picking,
mos wa ehouses ely on human wo ke s who
e ie e one o mo e uni loads and b ing hem
o a designa ed wa ehouse loca ion by oo o by
ehicle. To imp o e he e iciency and accu acy
o o de picking, many wa ehouses use mul iple
me hods, such as spli -case o de picking (i.e., each
picking, piece picking, o b eak case; Ba holdi
& Hackman, 2019), which in ol es selec ing
indi idual uni s ha a e hen packed in o a ca on.
Mul iple echnologies can augmen he e iciency
Table 1
Types o Spli -case O de -picking Sys ems
Type Ope a ion Fea u es S udy ci a ions
pick- o-ligh (PTL) sys ems Uses so wa e, elec onic la-
bels, and illumina ed digi al
ins uc ions in place o a
adi ional pape sys em o
guide employees.
(1) Is sui able o nume ous
o de s in ol ing a small
p oduc coun .
(2) Can p ocess mul iple
o de s simul aneously.
(3) Suppo s he g ouping
o o de s.
(Su e al., 2019; Swenja e
al., 2022)
oice-picking (pu - o-
oice) sys ems
P o ides wa ehouse wo k-
e s ins uc ions h ough
headse s o picking asks,
ollowed by sys em con i -
ma ion.
(1) Pe mi s hands- ee and
use - iendly in e ac ion
be ween wa ehouse wo k-
e s and he sys em.
(2) Dec eases aining ime
and s anda dizes wo k-
lows.
(3) Facili a es lexible pe -
sonnel scheduling.
(de V ies e al., 2016; Lage
e al., 2021)
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Tung-Hsiang Chou, You-Sheng Chen, Chung-Wei Pan, Hung-Hsuan Chang
10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS
Vol. 18 Issue 2 153-1702024
and accu acy o o de picking, bu he sui abili y
o a pa icula echnology a ies acco ding o he
scena io. Resea ch has iden i ied i e ypes o spli -
case o de -picking sys ems: pick- o-ligh (PTL)
sys ems, oice-picking (pu - o- oice) sys ems,
adio- equency (ba code) sys ems, ision-picking
sys ems, and goods- o-pe son (GTP) sys ems. Each
one p esen s dis inc ope a ional me hods and
ea u es sui able o di e se scena ios and needs, as
shown in Table 1.
Th ough IoT echnology, an in elligen o de -
picking se ice can use senso s o ob ain eal- ime
in o ma ion on he quan i y, ype, empe a u e,
humidi y, weigh , and loca ion o goods in a
wa ehouse. The da a a e hen ans e ed o a cloud
sys em, allowing eal- ime and emo e moni o ing
o goods and o de s. Wi h ad ances in IoT, obo s,
and big-da a echnology, wa ehouse managemen
sys ems can ack and p ocess goods and o de s
wi h e e inc easing e iciency. This esea ch
will demons a e, in his pape , ha Sma Scale
s ands ou o i s abili y o enable bo h eal- ime
calcula ions and he p ecise, s eamlined acking
o small in en o ied i ems.
2.2. Coun ing Scale
Sma Scale elies chie ly on a weigh senso wi h
an elec onic coun ing scale. This scale accu a ely
calcula es quan i ies o in en o ied i ems, imp o es
he e iciency o wa ehouse labo , and educes labo
cos s.
2.2.1. In e ne o Things (IoT)
The IoT e e s o in e connec ed digi al de ices
ha handle la ge amoun s o da a in communica ion
ne wo ks such as he in e ne . IoT echnology has
widesp ead applica ions in a ious indus ies and
ields, including sma homes, logis ics, ag icul u e,
and heal hca e. To ap in o he po en ial o he IoT,
cu en esea ch ocuses on add essing key issues such
as da a collec ion, managemen , secu i y, and p i acy
Mo eo e , eme ging echnologies, such as a i icial
in elligence, blockchains, and edge compu ing ha e
g ea ly enhanced he unc ionali y and pe o mance o
IoT sys ems.
2.2.1. Weigh Senso s
Table 1
Types o Spli -case O de -picking Sys ems (Con inued)
Type Ope a ion Fea u es S udy ci a ions
adio- equency (ba code)
sys ems
Enables employees o scan
RFID o ba codes om
mul iple loca ions o ensu e
he co ec selec ion and
packaging o i ems.
(1) Enhances he use -
iendliness o wa ehouse
ope a ions.
(2) Dec eases aining ime.
(3) Simpli ies shipping p o-
cesses.
(Kubáňo á e al., 2022;
Sa ka e al., 2022)
ision-picking sys ems Employs so wa e-based
augmen ed- eali y (AR)
echnology o acili a e
wa ehouse asks dependen
on p ecise in en o y loca-
ions and imes.
(1) Requi es minimal ain-
ing.
(2) Rapidly loca es in en-
o y.
(3) Facili a es simple ack-
ing ope a ions.
(Fang & An, 2020; Gialos &
Zeimpekis, 2020)
goods- o-pe son (GTP)
sys ems
Pe mi s he olle -con eyo
anspo o con aine s o
selec o s o e ie al o e-
qui ed i ems.
(1) Reduces picking e o s.
(2) O e s e gonomic han-
dling.
(Ashgza i & Gue, 2021;
Boze & Alda ondo, 2018)
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The load cell inside a weigh senso uses a s ain
gauge, which measu es he s ess and o ce—and
ul ima ely he weigh —applied o su ounding me al
componen s. Speci ically, he esis ance alue o he
s ain gauge changes in ela ion o he deg ee o bending
and de o ma ion ha he me al componen s unde go.
The s ain gauge hen calcula es weigh acco ding o
he Whea s one b idge p inciple (Nachazel, 2020).
The ollowing diag am is an example o how weigh
senso s wi h s ain gauges wo k: R1, R2, R3, and RX
a e ou esis o s in a Whea s one b idge ci cui . Th ee
esis o s (R1, R2, and R3) ha e ixed esis ance alues.
When he ou h esis o (RX) changes, he ol age
o he ci cui be ween poin s A and B changes. By
measu ing he di e ence in ol age be ween A and
B (VAB), he s ain gauge can de e mine changes in
physical weigh and quan i y in he en i onmen and
can hus achie e he measu emen objec i e.
Fo example, assume ha he cu en low h ough
R1 and R2 is I1 (Equa ion 1), he cu en low h ough
R3 and he senso (RX) is I2 (Equa ion 3), and he
b idge supply ol age is VCC. These ela ionships a e
exp essed in Equa ions (1) h ough (5), as shown below
(Wasson, 2000). Using Ohm’s law, one can calcula e
he ol age a each end o he esis o s. The R1 and
R2 ci cui s di ide he ol age o he ol age common
collec o (i.e., he VCC), and he ol age a bo h ends
o he R2 esis o is VA (Equa ion 2). In he R3 ci cui
and he senso (RX), R3 and RX di ide he ol age o
he VCC, and he esul ing ol age a bo h ends o he
R3 esis o is VB (Equa ion 4). Le us use Ohm’s law o
calcula e VA and VB he e:
(1)
(2)
(3)
(4)
(5)
The ol age is in equilib ium i VA − VB = 0, meaning
ha R1 = R2 = R3 = RX. In his s a e, one can measu e
a physical quan i y by measu ing he change in he
ol age di e ence VAB, as desc ibed abo e in Equa ion
(5). This concep o igina es om b idge-ci cui
esea ch conduc ed by Wasson (2000). Any al e a ion
in he esis ance alues esul s in an imbalance in he
b idge and leads o a de ec able change in he ol age
di e ence. This change is di ec ly p opo ional o he
a ia ion in he measu ed physical quan i y.
The esis ance-b idge p inciple is a eliable and
accu a e me hod o measu ing physical quan i ies
because i exploi s he balanced ol age equilib ium in
a ci cui . The implemen a ion o his p inciple es s on
Figu e 1
The Con igu a ion o he Whea s one B idge Ci cui Used in his S udy o Senso Analysis and Vol age Calcula ion
No e: (Adap ed om Plunke & C oss, 2014; Wasson, 2000)
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Tung-Hsiang Chou, You-Sheng Chen, Chung-Wei Pan, Hung-Hsuan Chang
10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS
Vol. 18 Issue 2 153-1702024
a combina ion o a mic ocon olle uni (MCU), such
as he LinkI 7697 by MediaTek Inc., and an analog- o-
digi al (A/D) con e e . Toge he , hese componen s
enable he con e sion o analog signals in o digi al
da a, acili a ing e icien da a p ocessing, calcula ion,
and in e acing wi h ex e nal sys ems.
When in eg a ed in o an IoT sys em, weigh
senso s ha a e equipped wi h en i onmen senso s
can acili a e he eal- ime analysis o no jus weigh
and quan i y bu also ype o p oduc , empe a u e,
humidi y, and loca ion. The da a, upon being
ans e ed o a cloud sys em, can p omo e eal- ime
and emo e wa ehouse managemen .
In summa y, coun ing scales accu a ely measu e
quan i ies by employing a weigh senso and by
ollowing he Whea s one b idge p inciple. When
in eg a ed in o weigh senso , componen s like MCUs
and A/D con e e s enable e icien da a handling
and in e acing, p o iding an e ec i e solu ion o he
challenges o wa ehouse managemen .
2.3. Design Science Resea ch (DSR)
Design science esea ch (DSR) is he sys ema ic
de elopmen and e alua ion o solu ions o
p oblems ha a ise in eal-wo ld scena ios (He ne
e al., 2004). Because DSR is a science, i p omo es
he sha ing o esea ch indings wi h he b oade
academic and p o essional communi y. This exchange
o da a and he epea abili y o DSR expe imen s
bene i s he gene a ion o igo ous knowledge.
DSR is an app oach o esea ch ha ocuses on he
de elopmen o inno a i e a i ac s, such as new
echnologies, sys ems, o p ocesses. Like any esea ch
me hod, DSR has i s ad an ages and disad an ages.
The DSR has some ad an ages, i emphasizes he
c ea ion o p ac ical solu ions o eal-wo ld p oblems
and encou ages inno a ion by p omo ing he
de elopmen o no el a i ac s. Resea che s ha e
he oppo uni y o design and implemen c ea i e
solu ions ha add ess speci ic challenges in a ious
domains. DSR also is p oblem-cen ic, meaning i
s a s wi h iden i ying and unde s anding a p oblem
be o e p oposing and c ea ing a solu ion. This
ensu es ha he esea ch is ele an and add esses
ac ual needs. DSR o en in ol es an i e a i e p ocess
o designing, building, and e alua ing a i ac s.
This i e a i e cycle allows esea che s o e ine
and imp o e hei solu ions based on eedback
and es ing. Howe e , DSR may p oduce a i ac s
ha a e speci ic o pa icula con ex s, making i
challenging o gene alize indings o o he se ings.
The ocus on sol ing speci ic p oblems may limi he
b oade applicabili y o he de eloped solu ions. The
design p ocess in ol es subjec i e decisions, and he
esea che 's backg ound, expe iences, and biases can
in luence he ou come. This subjec i i y may aise
ques ions abou he alidi y and eliabili y o he
esea ch. Designing, building, and e alua ing a i ac s
can be ime-consuming. The i e a i e na u e o he
p ocess, while aluable, may ex end he du a ion o
he esea ch p ojec . DSR may equi e signi ican
esou ces, including inancial, echnological, and
human esou ces. Access o specialized expe ise
and equipmen may be necessa y o he success ul
implemen a ion o designed a i ac s. I 's impo an
o no e ha he ad an ages and disad an ages o DSR
depend on he speci ic goals o he esea ch and he
con ex in which i is applied. The e o e, his esea ch
need o conside hese ac o s when choosing he
app op ia e esea ch me hodology o his s udy.
In summa y, Design Science Resea ch in
managemen ocuses on c ea ing p ac ical solu ions
o o ganiza ional challenges h ough an i e a i e
and s akeholde -in ol ed design p ocess. While
i o e s ad an ages in e ms o inno a ion and
ailo ed solu ions, challenges ela ed o complexi y
and esou ce equi emen s should be ca e ully
conside ed. Success ul DSR in managemen can lead
o imp o ed manage ial p ac ices and o ganiza ional
ou comes.
3. Me hodology3. Me hodology
D awing on he DSR app oach desc ibed by
He ne e al. (2004), his esea ch has de eloped he
Sma Scale, which acili a es he eal- ime calcula-
ion o small-i em in en o y quan i ies and educes
manual picking e o s. This echnological inno a-
ion di ec ly op imizes wa ehouse e iciency, e-
duces cos s, and imp o es cus ome sa is ac ion. All
o hese ou comes ha e huge po en ial comme cial
alue. In de eloping he Sma Scale, i gene ally ad-
he ed o a widely accep ed p ocess consis ing o six
s ages: p oblem iden i ica ion, objec i e iden i ica-
ion, design and de elopmen , demons a ion, e alu-
www.ce. izja.pl
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Figu e 2
The Applica ion o DSR o he En i onmen and Knowledge Con ex s o he P oposed Sma Scale
No e: Re ised om He ne e al. (2004), p. 80.
a ion, and communica ion (Sa ka e al., 2022). The
only change ha his esea ch made o his p ocess
was o u n he communica ion s age in o a guide-
line o i s demons a ions and e alua ion s ages.
Thus, in his s udy, Sma Scale es ed on i e, no six,
s ages. Also, his esea ch should no e ha DSR a -
chi ec u e add esses wo con ex s: he en i onmen
con ex and he knowledge con ex . In his s udy, he
en i onmen con ex (i.e., ele ance) conce ns he
o de -picking challenges aced by Taiwanese SMEs
wi h espec o in en o ies; and he knowledge con-
ex (i.e., igo ) encompasses sys em analysis applied
o cu en de elopmen s in he IoT, indus y 4.0, in-
elligen wa ehousing, and spli -case o de -picking
sys ems. Figu e 2 illus a es hese wo con ex s in a
DSR amewo k
.
In conduc ing sys em analysis (SA), i used uni-
ied modeling language (UML) no a ion o p esen
sys em s uc u es and p ocesses wi h g ea e cla i y
(Dennis e al., 2020). One o he p ima y ad an ag-
es o UML no a ion is ha i p o ides s anda dized
symbols and diag ams ha e ec i ely con ey he e-
la ionships among di e en elemen s wi hin a sys em.
i used UML no a ion o cons uc use-case diag ams
ha p ecisely de ine sys em unc ions. This esea ch
also used wo ypes o in e ac ion diag ams: i used
sequence diag ams o illus a e he in e play be ween
a ious objec s, and i used ac i i y diag ams o illus-
a e he in e play be ween a ious ac ions.
As i no ed, he DSR p ocess in his s udy consis s
o i e s ages: p oblem iden i ica ion, objec i e iden-
i ica ion, design and de elopmen , demons a ion,
and e alua ion (wi h communica ion se ing no as
a s age bu as a guideline o he las wo s ages). In
he p oblem-iden i ica ion s age, i iden i ied e o s
ela ed o bo h manual o de picking and he eal-
ime calcula ion o small-i em in en o y quan i ies.
In he objec i e-iden i ica ion s age, his esea ch es-
ablished goals ha would guide de elopmen o he
Sma Scale Se ice. In he design-and-de elopmen
s age, i c ea ed he a chi ec u e o he Sma Scale
Se ice. Then, i unde ook he inal wo s ages—
demons a ion and e alua ion— o alida e he e-
sea ch indings. Table 2 shows how his esea ch ap-
plied he i e DSR s ages o he cu en s udy.
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Tung-Hsiang Chou, You-Sheng Chen, Chung-Wei Pan, Hung-Hsuan Chang
10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS
Vol. 18 Issue 2 153-1702024
Table 2
The Six DSR S ages in De elopmen o he Sma Scale Se ice
DSR S ages Desc ip ion DSR-S age Me h-
ods
DSR Guidelines Sou ce
S age 1: P oblem
Iden i ica ion
De ining challenges
in a sys em.
Pe o m a li e a u e
e iew and ollow he
AEIOU amewo k.
Guideline 1:
Concep ualize he
p oblem.
Guideline 2:
Assess he ele ance
o he p oblem.
Academic li e a u e
Case s udies
Company da a
S age 2: Objec i e
Iden i ica ion
De ining ways o
o e coming chal-
lenges in a sys em.
Conside p e ious
p o o ypes and con-
sul s akeholde s.
Guideline 3:
Concep ualize he
objec i e.
Guideline 4:
Re iew he li e a u e.
Company eedback
S age 3: Design
and De elopmen
C ea ing a se ice—
in his s udy, he
Sma Scale— ha
will achie e he de-
ined objec i es.
Me hodically apply
knowledge o he c e-
a ion o he se ice.
Guideline 5:
Follow igo ous R&D
me hods.
Guideline 6:
Focus on sea ch p o-
cesses.
Exis ing heo ies
Ou own p oblem-sol -
ing knowledge
S age 4: Demon-
s a ion
Pe o ming he se -
ice.
P esen he de ice
o s akeholde s and
seek hei assessmen
o he de ice.
Guideline 7:
Communica e e-
sea ch.
Communica e esea ch
S age 5: E alua ion Assessing he se ice
pe o mance.
No e: e ised om He ne e al. (2004) and an de Me we e al. (2020)
4. The Sys em A chi ec u e4. The Sys em A chi ec u e
The ollowing sec ion desc ibes he DSR p ocess
ha his sec ion ollowed when designing he ini ial
amewo k o he Sma Scale Se ice.
4.1. P oblem iden i ica ion: Ini ial Concep ual
Design
In designing Sma Scale, i conduc ed ield isi s
o a logis ics company ha was loca ed in Kaohsi-
ung, Taiwan and ha elied on PTL sys ems. (Ow-
ing o con iden iali y conce ns on he pa o he
company, his esea ch did no ha e access o ideo
eco dings o he ope a ion.) This esea ch iden i-
ied company equi emen s and p e alen p oblems
wi h company ope a ions by using he AEIOU
amewo k. I se ed as a design-concep ualiza ion
ool o in e p e ing and coding da a collec ed du -
ing i s e hnog aphic esea ch o he Taiwanese com-
pany (Xu e al., 2020). Mo e speci ically, he AEIOU
amewo k iden i ies i e ypes o elemen s o be
coded in esea ch: ac i i ies (usually goal-o ien ed
ac ions), en i onmen s ( he con ex s in which he
ac i i ies occu ), in e ac ions (people’s ac i i ies as
hey ela e o o he people and objec s in he en-
i onmen ), objec s ( he non-human hings ha
people use in ac i i ies), and use s ( he people who
ca y ou he abo e ac i i ies). Table 3 p esen s ap-
plica ion o he AEIOU amewo k o he Kaohsi-
ung-based company whose ope a ions his esea ch
obse ed o he p esen s udy.
Au ho A o he p esen s udy isi ed he Kaohsi-
ung company in Ma ch 2019 and obse ed he chal-
lenges aced by employees dealing wi h wa ehouse
i ems. Au ho A no ed ha employees who packed
i ems aced he challenge o coun ing small i ems.
O de picking is a s anda d aspec o PTL sys ems
and i helps employees quickly iden i y he i ems
on o de and e ie e he co ec numbe o i ems
om he elay a ea. The logis ics company i isi ed
equi ed a ela i ely la ge wo k o ce o coun i ems
www.ce. izja.pl
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Sma Scale s ands ou as a majo echnological
asse o wa ehouse managemen . The de ice o e s
dis inc ad an ages o e es ablished echnologies:
i educes sea ch imes and calcula ion e o s while
imp o ing da a ansmission, wo ke well-being, and
o e all e iciency (Żeb owska-Suchodolska & Ka pio,
2022; Zhao e al., 2022). The scalabili y and adap -
abili y o he Sma Scale make i a iable solu ion o
add essing he e ol ing demands o con empo a y
wa ehouse managemen .
The p esen s udy achie ed h ee p ima y objec-
i es:
1. De elopmen wi hin he DSR F amewo k: This
s udy’s i s objec i e was o c ea e he Sma Scale
acco ding o DSR p inciples. Th ough his app oach,
i success ully enginee ed a scale capable o conduc -
ing eal- ime calcula ions and o accu a ely acking
small-i em in en o y.
2. E alua ion and I e a i e Op imiza ion: This
s udy’s second objec i e was o e alua e and op imize
he Sma Scale’s ope a ional and cos e ec i eness.
Th ough igo ous assessmen s and expe eedback,
his esea ch iden i ied a eas needing imp o emen
and op imized he de ice’s design acco dingly.
3. Explo a ion o Comme cial Viabili y: This
s udy’s hi d objec i e was o e alua e he comme -
cial and economic po en ial o implemen ing he
Sma Scale in he con ex o Taiwan-based SMEs. I s
engagemen in indus y exhibi ions, along wi h con-
s uc i e eedback om indus y expe s, showcased
he p ac icali y and applicabili y o he Sma Scale in
au hen ic logis ics en i onmen s.
In summa y, his esea ch success ully de eloped
he Sma Scale wi hin he DSR amewo k, e alua ed
he de ice’s e ec i eness, and i e a i ely op imized
he de ice’s design, which p o ed o ha e comme cial
iabili y a leas in he con ex o Taiwan-based SME
wa ehouses.
The Sma Scale aligns wi h he g owing need o
e icien , accu a e, and echnologically d i en solu-
ions in wa ehouse ope a ions, and is hus a p omis-
ing ool o mee ing dynamic ma ke demands and
enhancing cus ome se ice. Fu u e esea ch in his
a ea would do well o u he explo e bo h he mana-
ge ial aspec s o echnology-d i en wa ehouse ope a-
ions and he po en ial oles played by DSR he ein.
6. 6.
ConclusionsConclusions
In he p esen s udy, i has p esen ed a Sma
Scale de ice ha —on he basis o DSR p inciples,
UML ools, and IoT and Indus y 4.0 echnology—
acili a es spli -case o de picking o small in en-
o ied i ems.
The Sma Scale ecei ed posi i e eedback om
a a ie y o manu ac u e s. Speci ically, he de ice
has he po en ial o imp o e he eal- ime moni-
o ing o in en o ies, s eamline supply-chain p o-
cesses, educe manu ac u ing cos s, and enhance
cus ome se ice (B zeziński & Bi kowska, 2022).
6.1. Limi a ions and Fu u e Resea ch
As wi h any s udy, i s cu en one has i s sha e
o limi a ions. One limi a ion was ocused on
small, ligh weigh wa ehouse i ems. Fu u e e-
sea ch should ex end his line o esea ch o la ge
i ems and hea ie i ems, which p esen unique
measu emen challenges. IoT de ices ha se ice
such i ems equi e powe ul—and cos ly, esou ce-
demanding—ha dwa e. Ano he limi a ion o his
s udy conce ns cos e ec i eness. Because his e-
sea ch did no ex ensi ely explo e his aspec o
he Sma Scale, u he in es iga ion is necessa y
o assess he economic iabili y o implemen ing
a la ge-scale IoT se ice in wa ehouse se ings. A
igo ous e alua ion in his di ec ion would neces-
sa ily in ol e he analysis o no only po en ial sa -
ings bu also ini ial in es men cos s and long- e m
main enance cos s in e ms o bo h echnology and
labo . A pa icula ly in e es ing opic o esea ch
would be he e ec s, posi i e o nega i e, ha his
echnology migh ha e on employee mo ale.
Fo una oidable easons, he indings o his
s udy es on a small se o empi ical da a. Fu u e
esea ch mus accumula e comp ehensi e da a
se s and analyze hem in line wi h eliable em-
pi ical quan i a i e and quali a i e me hods. Only
in his way can i accu a ely g asp he impac o
sma echnology on indus ial se ings, cus ome
se ice, and—mo e b oadly—ma ke economies.
This esea ch ocused cu en s udy on wa ehouse
ope a ions. O cou se, sma echnology has a as
numbe o ac ual and po en ial applica ions. One
a ea ha in e es s us is he in eg a ion o big da a
and augmen ed eali y (AR) echnologies in o un-
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Tung-Hsiang Chou, You-Sheng Chen, Chung-Wei Pan, Hung-Hsuan Chang
10.5709/ce.1897-9254.531DOI: CONTEMPORARY ECONOMICS
Vol. 18 Issue 2 153-1702024
manned ehicles. Resea ch in his and o he a eas
may e olu ionize he e iciency and accu acy o
wa ehouse ope a ions.
Finally, al hough he se ing o his esea ch was
Taiwan, he echnology ha i is p oposing has ob-
ious applica ions ac oss na ional and in e na ional
con ex s. Fu u e esea ch can explo e whe he na-
ional and egional se ings a ec he pe o mance
o such Sma Scale de ices.
6.2. Challenges o DSR in Managemen
The managemen issues can be complex, and de-
signing e ec i e solu ions may equi e a deep un-
de s anding o o ganiza ional dynamics, human
beha io , and cul u al ac o s. Howe e , imple-
men ing new managemen p ac ices o ools may
equi e in eg a ion wi h exis ing o ganiza ional sys-
ems, and his can pose challenges in e ms o com-
pa ibili y and use adop ion (Adeel e . al, 2023 and
Bache , 2013) This esea ch de eloping and imple-
men ing designed sma scale may demand signi i-
can esou ces, bo h in e ms o ime and inancial
in es men . The design decisions may be subjec i e
and in luenced by he esea che 's pe spec i es, po-
en ially leading o biases in he designed solu ions.
This esea ch implemen ing a sma scale in man-
agemen in ol es in eg a ing echnology and in el-
ligen se ice s a egically o enhance wa ehouse
pe o mance ac oss a ious domains. Howe e ,
i 's impo an o conside e hical implica ions, da a
p i acy, and po en ial challenges associa ed wi h
adop ing ad anced echnologies in he manage-
men con ex o DSR.
Acknowledgmen sAcknowledgmen s
We ex end ou g a i ude o he o ganize s o he
ACIEK 2023 con e ence and all o i s pa icipan s.
By ha ing he oppo uni y o sha e ou esea ch
indings a he con e ence, we we e able o ecei e
aluable insigh s and eedback.
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