In e na ional Jou nal o Managemen , Accoun ing and Economics
Vol. 2, No. 7, July, 2015
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669
Modeling Laying Time o Ge be Machines in
Cu ing Depa men : A S udy on S i Lankan
Appa el Indus y
Bhagya i Sanda eka Haba agoda
1
Depa men o Indus ial Managemen , Facul y o Applied Sciences,
Wayamba Uni e si y o S i Lanka, Kuliyapi iya, S i Lanka
Yaddehi Geda a Shi omani Lakmali
Depa men o Indus ial Managemen , Facul y o Applied Sciences,
Wayamba Uni e si y o S i Lanka, Kuliyapi iya, S i Lanka
Abs ac
P o iding he equi ed cu panels o he sawing depa men on ime, is he
majo ask and esponsibili y o he cu ing depa men o an appa el
manu ac u e . The e o e, he cu ing p ocess is one o he main alue adding
p ocesses. Se e al unc ions a e included in he cu ing p ocess; namely ab ic
laying, cu ing and bundling o cu panels. Since o he p ocesses a e depended
on he ab ic laying, i plays a c ucial ole. This s udy a emp s o de elop a
model o de e mine he ab ic laying ime o Ge be machines used in he cu ing
depa men . Fi s he ac o s ha a ec o he lay ime a e iden i ied. The ac ual
imes aken o each o hese ac o s a e collec ed o non-wo en ouse pa e ns
o a pe iod o mon h o aling 89 da a poin s. Desc ip i e s a is ics, co ela ion
analysis and mul iple eg ession analysis a e used o analyze he da a. O e all,
he esul s show ha six ac o s ou o iden i ied se en a e signi ican ly use ul in
p edic ing o al lay ime. Pa icula ly, he esul s o he eg ession analysis
indica e ha a a α=0.01 le el o signi icance, loading ime, damage check ime,
join ime, p epa a ion ime, e e se ime and cu ing ime a e signi ican ly
con ibu ing o o al lay ime. The eg ession model has an o e all accu acy a e
o 79.2 pe cen .
Keywo ds: Cu ing Depa men , Cu ing P ocess, Fab ic Laying, Fab ic
Laying Time
Ci e his a icle: Haba agoda, B. S., & Lakmali, Y. S. (2015). Modeling Laying Time o
Ge be Machines in Cu ing Depa men : A S udy on S i Lankan Appa el Indus y. In e na ional
Jou nal o Managemen , Accoun ing and Economics, 2(7), 669-675.
1
Co esponding au ho ’s email: sanda [email protected]
In e na ional Jou nal o Managemen , Accoun ing and Economics
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670
In oduc ion
The Tex ile and Appa el indus y occupies a p ominen posi ion in S i Lanka’s
indus ial s uc u e. I is he bigges employe in manu ac u ing sec o and i is conside ed
as S i Lanka’s numbe one o eign exchange ea ne . The Tex ile and Appa el indus y is
no me ely a ype o indus y in S i Lanka bu i ep esen s he majo economic, poli ical
and social changes ha ook place in he coun y.
A e he independence he go e nmen ’s held he ein o he coun y made a emp s
o ini ia e indus ial ac i i ies. A e he in oduc ion o open economic policies in 1977
he ou look o he indus y was o ally changed wi hin an expo -o ien ed s a egy. Since
he la e 1970s he indus y g adually acqui ed he ela i e impo ance o adi ional
ag icul u al expo s and became he highes expo ea ne by he mid-1980s (Tennakoon,
1999). The o al indus ial expo s accoun o app oxima ely 77% o he o al expo s
while ex ile and Wea ing Appa el indus y solely accoun s o 67% o indus ial expo s.
Resea ch P oblem
A p esen laying p ocess ime is calcula ed using analy ical es ima ing and mainly
based on managemen decisions. The measu emen is called SMV (S anda d Minu e
Value). This calcula ed laying imes is used by he p oduc ion depa men o plan hei
p oduc ion. This alue mus be accu a e since o he wise lay plan will no ally wi h he
plan o he p oduc ion depa men . Then he cu ing depa men will be unable o p o ide
he equi ed cu panels o he p oduc ion modules in ime. I is impo an o ha e an
accu a e sys em o calcula e cu ing p ocess ime in o de o ha e a smoo h low o he
wo k in he cu ing depa men and also in he p oduc ion depa men . Figu e 1 illus a e
he di e ence be ween ac ual lay ime and calcula ed lay ime (SMV).
Figu e 1- Di e ence be ween Ac ual Lay Time and SMV
Acco ding o he Figu e 1 he e is a conside able de ia ion in ac ual lay ime and he
SMV. In abo e igu e X-axis ep esen he Obse a ion numbe . Many imes, SMV is
lowe han he ac ual lay ime. The eason is ha some signi ican ac o s may be
0
2000
4000
6000
8000
10000
12000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88
SMV To al Lay Time
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neglec ed when measu ing he SMV. The esea ch is conduc ed o educe he gap be ween
SMV and ac ual lay ime and o ind he ac o s which a ec o he o al lay ime.
Resea ch Objec i es
This esea ch a emp s o de elop a model o de e mine he ab ic laying imes o a
cu ing depa men o an appa el manu ac u ing company. The model can be used o
calcula e he ab ic lay ime and i can be u he used o de elop incen i e schemes o
he cu ing depa men .
Li e a u e Re iew
Acco ding o he de ini ion o Sa ka (2013) cu ing p ocess includes h ee majo
unc ions. The unc ions a e, ab ic laying (sp eading), cu ing and bundling o cu panels.
Gan i (2014) de ines ha , cu ing sys em is a p ocess which cu ou he pa e n pieces
om speci ied ab ic o making ga men s.
Sp eading / Laying P ocess
Fab ic sp eading is e y impo an pa o he p oduc ion p ocess because i is he basic
poin o ob aining a high quali y inal p oduc . Acco ding o Gan i (2014), sp eading is
he p ocess o unwinding la ge olls o ab ic on o long, wide ables in p epa a ion o
cu ing each piece o a ga men . The numbe o laye s o ab ic is dic a ed by he numbe
o ga men s desi ed and he ab ic hickness. Sp eading can be done by hand o machine.
Depending upon he ab ic and cu ing echnology, up o 200 laye s o ab ic may be cu
a one ime. Fab ics ha a e mo e di icul o handle a e gene ally cu in hinne s acks
Fab ic Sp eading Objec i es
In his s udy Abu (2014) s a es ha he e a e numbe o speci ic objec i es a ab ic
sp eading p ocess mus achie e. Some o he impo an objec i es a e as ollows.
To place he numbe o plies o ab ic o he leng h o he ma ke plan co ec ly aligned
as o leng h and wid h and wi hou ension.
1. To cu ga men s in bulk and sa ing in ab ic h ough he use o mul i ga men
make plans and he sa ing in cu ing ime pe ga men ha esul om cu ing many plies
a a ime.
2. To make e e y ply plain and la .
Me hodology
Li e a u e Re iew elie ed he ac o s a ec ing he o al lay ime. Acco dingly se en
ac o s we e iden i ied and he esea ch amewo k employed in he s udy is p esen ed in
Figu e 2. As depic ed in he Figu e 2, ab ic leng h, loading ime, join ime, e e se ime,
cu ing ime, damage checking ime and p epa a ion ime a e he independen a iables in
he model. The dependen a iable is laying ime. The da a o ab ic leng h and numbe
o plies ex ac ed om he company eco ds and o collec he da a o he emaining
independen a iables a da a collec ion o m was designed, using obse a ion me hod he
In e na ional Jou nal o Managemen , Accoun ing and Economics
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672
da a we e collec ed o e a pe iod a mon h s a ing om 09/11/2014 o 10/12/2014. All
he imes we e measu ed in seconds.
Figu e 2- Resea ch F amewo k
Da a Collec ion and Analyzing
Based on he a iables ha we e iden i ied in he esea ch model, he analysis was
ca ied ou and he summa y o he esul s is gi en below.
To analyze he da a, desc ip i e s a is ics (mean, median, mode, s anda d de ia ion
e c.) a e compu ed o desc ibe he a iables in e es ed in. Fu he , a co ela ion analysis
was pe o med o in es iga e he associa ion o each independen a iable wi h he
dependen a iable a bi a ia e se ing. Finally a a mul i a ia e le el, ha conside s all
he independen a iables simul aneously, mul iple eg ession analysis was pe o med o
de elop a model capable o p edic ing laying ime.
Table 1: Summa ize Values
Va iable
Mean
Median
Mode
S d De .
Fab ic Leng h
13.5
13.5
13.5
2.6177
P epa a ion Time
404.96
423
600
170.984
Loading Time
789.47
748
700
334.364
Join Time
633.11
600
600
283.484
Damage Check Time
971.89
1054
1200
207.811
Plies
63.46
70
70
12.82
Re e se Time
831.94
908
930
169.448
Cu ing Time
515.53
557
557
103.001
Lay Time
4157.64
4080
3840
956
Table 1 summa izes he alues o he a iables. Acco ding o able, e e se ime and
cu ing ime include mo e han one mode. As well as mean, median and mode alues a e
Fab ic Leng h
Join Time
Loading Time
Re e se Time
Cu ing Time
P epa a ion
Time
Damage Check
Time
Laying Time
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673
equal o ab ic leng h a iable. The maximum leng h can be used o sp eade machine
is 20 inches acco ding o he able.
Acco ding o he co ela ion es ing, i has been iden i ied ha all independen
a iables a e signi icance a 0.01 le el, which means ha he e is a linea ela ionship
be ween independen and o al lay ime a 90% o con idence. The esul s ha we e aken
om mul iple linea eg ession. S epwise me hod is used o ge he inal esul s and esul s
we e shown in Table 2.
Table 2: Mul iple Linea Reg ession
Summa y o he Findings
R2 Value
0.792
ANOVA Table sig. alue
0.000
Va iables
Coe icien s
Sig. Value
(Cons an )
236.251
.000
Loading Time
1.059
.000
Damage Check Time
2.254
.000
Join Time
.884
.000
P epa a ion Time
.827
.000
Re e se Time
.943
.000
Cu ing Time
1.095
.000
A he α=0.01le el o signi icance, he e exis s enough e idence o conclude ha he
Loading Time, Damage Check Time, Join Time, P epa a ion ime, Re e se Time,
Cu ing ime a iables a e no ze o and, hence, hese a iables a e use ul o p edic he
o al lay ime. As well as cons an is use ul o p edic he o al lay ime.
Fu he mo e a he α= 0.01 le el o signi icance, he e exis s enough e idence o
conclude ha a leas one o he p edic o s is use ul o p edic ing o al lay ime; he e o e
he model is use ul o p edic ing he lay ime.
The coe icien o mul iple de e mina ion is 0.792(R2); he e o e, abou 79.2% o he
a ia ion in he o al lay ime is explained by independen a iables. The eg ession
equa ion appea s o be e y use ul o making p edic ions since he alue o R2 is 0.792.
Acco dingly ha he inal model can be w i en as,
To al lay ime = 236.251 + 1.059*(Loading ime) + 2.254*(Damage check ime) +
0.884*(Join ime) + 0.827*(P epa a ion ime) + 0.943*(Re e se ime) + 1.095*(Cu ing
ime)
Resul s and Discussion
Acco ding o he analysis some ac o s a ec o he o al lay ime. The e o e, o al lay
ime can be con olled by manipula ing he a ec ing ac o s. Since all ac o s a e
posi i ely a ec ed o he model, educ ion o hem will dec ease he o al lay ime.
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674
Following pa ag aphs discuss some emedial ac ions ha can be aken o educe he o al
lay ime.
T aining he Ope a o s:
Ope a o s a e one o he aluable esou ces in appa el manu ac u ing. The e o e,
a emp s mus be aken on de eloping ope a o skills whe e i necessa y. A low skilled
ope a o will consume highe esou ces ( ime) and gi e less ou pu . Tha means o al lay
ime can be ei he inc eased o dec eased acco ding o he skill le el o he ope a o . In
cu ing depa men he ab ic ype can’ be changed, bu he way o handling can be
ained. As an example, damage check ime con ibu es signi ican ly o he o al lay ime.
The e o e he ope a o who check he damages, mus ha e high skill le el.
T aining o Supe iso s:
Supe iso mus be ained wi h undamen al managemen skills and communica ion
skill. Thei main job is p o iding ins uc ion, ans e ing in o ma ion. Line supe iso s
mus be equipped wi h good communica ion wi h sp eade machine ope a o s and
helpe s. Since he line supe iso s mus be esponsible o p epa ing equi ed lay shee s
and checking he ab ics sh inkages.
Ins alling Be e Equipmen :
Ins alla ion o be e equipmen in he p oduc ion p ocess educes he numbe o
b eakdowns. Ha ing p ope ly wo king machines no only enables ope a o s in achie ing
SMV bu also inc ease hei mo i a ion and p oduc i i y.
Be e Ope a o Alloca ion:
Each ope a o has di e en se o skill (ope a ions hey gene ally pe o m well) and
di e en e iciencies a wo k. Alloca ing he mos sui able ope a o o he p ocess educes
he ime, which equi ed o pe o m he ask. I low skilled ope a o s a e assigned o high
con en ope a ions, he lay ime can be inc eased. Be o e alloca ing ope a o s o speci ic
ope a ions hei skill le el should be conside ed.
Conclusion
This s udy a emp ed o in es iga e he ac o s a ec ing he ab ic lay ime o Ge be
machines and o de elop a model o be e p edic he lay ime. The esul s indica e ha
among he iden i ied se en ac o s, six a e use ul in p edic ing he o al lay ime.
Howe e , in in e p e ing he esul s o he s udy, a ew limi a ions we e no ed. Fi s ,
all he imes we e measu ed in seconds, hus he possibili y o ounding o he nea es
second hough i was mili seconds. Second, i was iden i ied ha he e a e con ounding
a iables a ec ing o he iden i ied independen a iables.
The abo e highligh ed limi a ions sugges possible a eas o o u u e esea ch. Fo
example, he p oposed model can be u he imp o ed by using de ices capable o
In e na ional Jou nal o Managemen , Accoun ing and Economics
Vol. 2, No. 7, July, 2015
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675
measu ing ime in mili o nano seconds. Fu he , ac o analysis can be pe o med o
inco po a e he impac o con on a iables.
Re e ences
Abu., M.D. (2014), Fab ic Sp eading Requi emen s & objec s, e ie ed om,
h p:// ex ileapex.blogspo .com/2014/03/ ab ic-sp eading-objec s-
equi emen s.h ml#s hash.cqWejRJm.dpu
Gan i , K., (2014), Sp eading and Cu ing Machines, e ie ed om
www. eliableplan .com /Read/11785/o e all-equipmen -e ec i eness on No embe ,
2014
Sa ka , P., (2013), Tex iles and ga men manu ac u ing indus y, e ie ed om
h p://www.onlineclo hings udy.com/2013/05/ga men -manu ac u ing-p ocess- low-
cha .h ml
Tennakoon, P.T., (1999), Appa el Indus y – Fu u e Scena io, Economic Re iew
June/July 1999, Peoples Bank.
