Problemstatement
Productivityisveryimportanteitherforacountryorforacompanypointofview.Atac o u n t r y level,productivitystimulateseconomicgrowthinshort-runaswellasinlong- run.Ata firmorindustrylevel,productivitycouldcontributethebetterwagesandconditionsforthe workforce,higherprofitforcompany,lowerpriceforcustomer,improvementofenvironmentalprotection andit contributeshighertaxrevenueforgovernment(Dean,2011).
Inthelaborintensiveindustries,whentheinvestmentincapitalsuchasmachineryorf a c t o r y ort e c h n o l o g y b e c o m e s t o b e saturated,t h e l a b o r p r o d u c t i v i t y i s t h e vitalfactort o stimulatetheprofitforfirms.Therefore,themorethefirmcanincreasetheworkerproductivity,t h e mor ethatfirmcan earnprofitandmaintain itscompetitivenessin thatindustry.
InVietnam, garmentproductionisoneofthemostimportantindustrieswiththeexportvaluegrowingrapidlyfrom1.9billionUSDinyear2000tobemorethan15billionUSDinyear2012,asinbelowFigure1.Garm entbecameatopindustryofVietnamintermofexporti n year2012,thisistheresultcamefromthe investmentsinphysicalcapitalinfactoryandm a c h i n e r y aswellastheimprovinginlaborprod uctivityduringthisperiod.
Figure 1: Export of Vietnamese industries 2000-2012 (Unit: 1000USD/year)
TheproblemwithVietnamesesewingfirmscurrentlyisthathowcantheymaintainthegrowthan dtheircompetitivenesswithemergingfirmsinLaos,MyanmarorBangladeshwheret h e y canh a v e l o w e r l a b o r cost.W h a t V i e t n a m e s e firmscand o w h e n t h e investmenti n m a c h i n e r y orfactorybecometobesaturated,inthislaborintensiveindustry?
Firm productivity and individual productivity are fundamentally different concepts While firm productivity is influenced by various factors such as business opportunities, technology, and machinery, individual productivity plays a crucial role, especially when technological advancements are stagnant For instance, Protrade Garment Co Ltd operates four well-equipped factories with similar technology but lacks the capacity to invest in better machinery or facilities Consequently, the company's performance hinges on enhancing worker productivity Workers are encouraged to maximize output during their working hours, as increased production directly correlates with higher individual productivity and efficiency Ultimately, focusing on individual productivity is essential for improving overall firm performance.
Range of Standard Allowed Management
Production layout (Lines or Group)
Factory Number of workers per line / group
Factory #1 Shirt 45 workers per line Group front panels: 45 workers7 lines
Group assembly: 105 workers Factory #2 Junior jeans trousers 17-19 Group
Men jeans trousers Capri pants
Factory #3 8 lines 35-40 workers per line
Group front panels: 50 workers Group back panels: 70 workers Group assembly: 80 workers
Capri pants 60-65 ofworkersusingtheproductiontimetoproduceasmanyproductsaspossible.Thecompany,Protrad e,hasasystemtocalculatethestandardtimeforeachprocessandthesystemtomonitort h e realt i m e whi cheachworkerr e a l l y p r o d u c e d i n t h e i r w o r k i n g t i m e Baseo n collectedinformation,wec ancalculatetheworkerproductivityinpercentageofratioofstandardtimeandactualproduction time ofeachworker.
ProtradeGarmentC o L t d hasabout2 0 0 0 w o r k e r s i n f o u r factoriesw i t h t h e samet e c h n o l o g y andphysicalconditions.Thedifferentbetweenthefourfactoriesaretheyproducediffe rentkindofgarment:Factory1producesshirt,Factory2produceslightoutwearorsportwear,Factor y3producesjeanstrousersintraditionallineswithnormalmachine,andFactory4 alsoproducesjean sinnewlayoutlineswithmodernmachinesandlessworkers.Becauseoft h e differencesi n p r o d u c t types,t h e productionlayout,machinesandmanagementi n eachf a c t o r y isdifferent,thefigure
Meantime,thereisalsoabiggapinworkerefficiencyamongfactories,andalsoamongt h e wor kersw i t h differencesa g e , experiencea n d gender.T h e q u e s t i o n i s t h a t whati s t h e
11 significantfactorseffectt o i n d i v i d u a l p r o d u c t i v i t y betweenw o r k i n g c o n d i t i o n s , ag e,experienceor gender?
Thereweremanypreviousstudiest o find thedeterminantseffectt o i n d i v i d u a l productiv ity,inlaborintensiveindustry.Briefly,thestudyofRuth(1987)foundtheageandexperiencehaveeffect toindividualproductivity,orTrond(2005)foundtheeffectofgenderonindividualproductivity orSusan(2008)alsofoundtheenvironmenthadeffecttoi n d i v i d u a l productivity.
Researchobjectives
ComingfromthefactsofVietnamesesewingindustryasmentionedabove,theobjectiveoft hispaperistofindouttheeffectivedeterminantsofindividualproductivityinProtradeGarm entCo.,Ltd.Then,thefindingscanhelpfirmswiththesimilarstructurewitht h i s companytoim provetheirproductivitybyapplyingrecruitmentortrainingstrategiesorthefirmsareawareofthattheys houldimprovetheworkingconditionssuchasmanagementorchangethe typeofproductscanstimulateproduction.
Scopeof study
Thestudyusesthepaneldatafromasewingcompany,namedProtradeGarmentCo.Ltd. This companyhasfourfactoriesproducingdifferentkindofgarmentproductswithabout2 , 0 0 0 worker s.Thiscompanypaystheworkerbaseonthequalityofsemi- productswhicheachworkermade everymonth.Therefore,ithasasystemtocalculatethestand ardtimeof anyprocessinproduction,anditalsohasagoodITsystemwhichcanrecordallthedataco ncernaboutworkerproductivitymonthly.
2014)infourdifferentfactories.Thestudycollectedtheinformationaboutt h e individual work erssuchasage,experience,gender,monthly productivity.Andwealsocomparedamongf o u r fact oriest o f i n d whethert h e differentw o r k i n g c o n d i t i o n s affectt o i n d i v i d u a l producti vityt.Theweaknessofthepaperisthatit isonlyanalyzeingeneralwhichworkingconditions(whichfactory)canstimulateproductivitybut it is notshowtheparticularfactorsin workingconditionscanaffecttoproductivity.
Structureof thethesis
Theoutline ofthisstudyisasfollow.Chapter1isintroducingtheobjectivesofthe r esearch,theresearchquestionandscope.Secondly,Chapter2presentstheliteratureabouti n d i v i d u a l p r o d u c t i v i t y andi t s determinants.Thirdly,C h a p t e r 3 describest h e m o d e l sp ecifications,researchmethodologyandd a t a description.N e x t , t h e descriptiveo f eachvariableandempiricalresultwillbepresentedinChapter4.Finally,Chapt er5willsummaryt h e conclusionsthengives some policyimplicationsandsuggestionsforfutureresearch.
Conceptsandtheories
Individualproductivity
Productivityistherelationshipbetweenthequantityofoutputandthequantityofinputusedtoge neratethat output.Itis theratioofoutput andcombinedinputswhichusedtocreateo u t p u t :
Productivity measures the efficiency of a company's investments relative to the results achieved, often assessed through financial ratios comparing total values and added value against resource costs over a specific period By evaluating productivity, businesses can gauge their overall health and make informed investment decisions However, relying solely on financial metrics may not provide insights into a company's strengths and weaknesses or areas for improvement For effective management, it is crucial for company leaders to analyze productivity across various dimensions, including human resources, machinery, investment choices, and management practices This comprehensive analysis enables managers to identify strengths and weaknesses, guiding strategic improvements.
Insideacompany,outputismeasuredintwoways:physicalquantitiesandfinancialvalu e.Physicalquantitiesmethodisconductedwhenoutputarehomogeneousandpresentthatn u m b e r o f p r o d u c t s w e r e m a d e w i t h acceptedquality.Financialv a l u e methodaccesst o produ ction’svaluesuchthatsales,valueadded.Inputcontainsalltheresourcessuchaslabor,capital,technol ogyandotherfactorsthatareusedtoproduceoutput.
In Singapore, the measurement of productivity varies significantly depending on the company's type Financial firms typically assess productivity based on the profitability of investment decisions, allowing managers to analyze the effectiveness of each choice and enhance future strategies Conversely, production companies focus on measuring individual worker productivity, which helps identify inefficiencies and improve overall output This tailored approach to productivity measurement enables companies to optimize their performance and drive continuous improvement.
Thei n d i v i d u a l p r o d u c t i v i t y i s measuredbyt h e n u m b e r o f p r o d u c t s o r a d d e d valuewhicht h a t workerm a d e i n t h e i r workingt i m e , int h e sameworkingplaceo r i n t h e sam eworkingconditions.Inanotherword,inthesameworkingconditions,themorevaluewhichani n d i
Factorseffectto individual productivity
RuthKanferi n m a n y yearsb u i l d u p m a n y t h e o r i e s s u p p o s e d t h a t eachindivi dualallocatespersonalresources(e.g.,effort,e n e r g y , s k i l l s ) t o a particularj o b byt h e e f f o r t - performancefunction.ItshowedthattherearerelationshipsbetweenEffort,Ageandexperienceor skills withindividualproductivity.
In1987 ,thepapergivenbyRuthKanferprovedthe relationshipbetweenmotivation( o r ef fort)withindividualperformance Figure2showsingeneralforaspecificofwork,them o r e effort willleadtohigherindividualperformance(Ruth, 1987).
The initial theory suggests that a single individual's effort directly correlates with better results; however, this assumption overlooks the complexities of reality While effort is important, factors such as skill development, knowledge acquisition, and work experience significantly influence performance Additionally, individuals possess varying levels of intelligence and skills, contributing to diverse work outcomes Therefore, it is essential to recognize that individual performance is not solely determined by effort but is a result of a combination of effort, skills, intelligence, the nature of the work, and other personal characteristics.
In2004,apaperofRuthKanferandPhillipL.Ackermangavemoredevelopmentfromt h e first theoryin 1987.Inthisresearch,Kanfermodelsaddedthe efforts,ageandexperiencei n t o a linkwiththeindividualperformance ,showingin figure3and4.
Inthejobrequiredcrystallizedintelligence,whichrequiredskillfulnessandknowledgesuchas teachingorsewingtechnician,Figure3 showsthepersonwithmoreexperienceontheworkwillhavemoreadvantageswithnon- experienceones.Thispointissimilartothetheory“learningcurve”,thatthemoreyoupracticethem oreefficiencyyouwillarchiveinthatkindo f repeatingwork.“Crystallizedintelligence”normallys tartedattheagefromtwentytotwentyfive,andgrowthpersonalskillorknowledgeafterschoolorfromp racticinginthejob.Thecrystallizedintelligencedonothaveadeclinewiththeage,itdependson themotivationofeachperson.
Ont h e o t h e r h a n d , i n t h e j o b requiredf l u i d intelligence,suchass t u d y i n g whi cheverythingisnewandneedtohavefastdecisionorfastcatchup,Figure4showsthatthepersonw i t h y oungeragewillhavebetter performancethantheolderone.Intheotherwords,withthesamebasicofeducationandexperience,the youngpersonwillhavemoreefficiencythantheo l d e r one.The“fluidintelligence”startedfroma geofsixthengetspeakatabouttwentyandt h e n decline aftertwentyfive.
(Source:Ruth &Phillip, 2004)Forexample,forapersonwhenhewasyoungthetaskswhichhecandothebestisstudying,easilytocatchupandunderstandthelessonthan theolder onewhoismorethan twentyfive.And,heissuitablewiththetaskswhorequestfastreflectionsuchasplayingsporto r doingf astmathematic,wecall“fluidintelligence”.Inreverse,thepersonwhowasgraduated,hecanusehis knowledgetoapplyonhisjobwhichtheyoungerpersondoesnothave.And,hecanlearnandmorea ndmoreexperiencetomakehisworkmoreefficient,wecall“crystallizedintelligence”.
Kanfer’smodelalsoshows thefunctionofagewith theperformance-utilityandeffort- u t i l i t y f u n c t i o n “Performance-utilityandeffort– u t i l i t y functionsd e s c r i b e t h e individual’sperceptionso f t he formo f t he relationshipbetwe ent h e attractivenesso f differentl e v e l s o f performanceandeffort,respectively(asFigure5and6). Itmeansthatwithdifferentage,them o t i v a t i o n o f personwill bedifferentwith theattractivenessofdifferentlevel ofperformanceo r effort,thisrelationshipislinkwithbehaviorormotivationofemployee.
Forexamplewithasalesmanj o b , t h e c o m p a n y gavedifferentb o n u s f o r t h e s a l e s m a n w h o achievedifferentturnover.With thehighbonus, a20-years-old-employeewill havemoremotivationthana40-years-oldemployee.
Utilityfunction as afunctionofAge Figure6:HypotheticalEffort-
Experience and age can influence performance, but their correlation is not always clear (Donal P., 1976) For instance, in a sewing factory, older workers typically have longer working hours and may achieve higher performance due to their experience However, if younger workers possess natural talent or superior skills, they might outperform older colleagues despite having less experience Additionally, individual skillfulness significantly impacts productivity, although this paper does not explicitly address its importance in relation to performance.
Tobesumup,untiltheRuth’ssecondpaper,wesawtherelationshipbetweeneffort,agea ndexperiencewithindividualproductivity.However,thelevelofskillfulnessorintelligentwhic hisimportanttoindividualproductivitybutwehavenotyetknownclearlyhowimportantitis.And,thepaper alsomentionedthemotivationdifferencesdependsontheageandtheattractivenessofperformanc e.Theattractivenessismorelinkedtothecompanypolicyo r w o r k i n g environmentwhichw e canca llt h a t theya r e externalf a c t o r s whicheffectt o individualperformance.T h e w o r k i n g envir onment,managementorleadershipneedt o beinvolvedtoindividualproductivitytohelpthepara meterstobemoreclearandmeasureableinsteadoftalkingabout“utility”or“attractiveness”.
In2007 ,agroupofauthorsincludingGiladChen,BradlleyKirkman,DonAllenandRu t h Kanfercontinuouslyd e v e l o p e d externalf a c t o r s linkedt o i n d i v i d u a l performance,t h e m anagementfactorshaveaffectt o t e a m andindividualperformance( R u t h Kanferetal,2007).
Therew e r e manyh y p o t h e s e s t o s h o w t h e relationshipbetweenLeadershipclimate,te amempowerment,individualempowerment,teamperformanceandindividualperformance.
The article emphasizes the critical role of leadership climate in enhancing team and individual performance through empowerment Effective leadership fosters an environment where team members feel autonomous and supported, leading to increased productivity When employees experience less stress and improved working conditions, their performance significantly rises Susan and Raymond (2008) also highlight that a comfortable working environment directly influences individual productivity, indicating that the more at ease employees feel, the greater their contributions to their roles Overall, a positive leadership climate and conducive working conditions are essential for maximizing employee performance.
Howabouttechnology,doesi t impactt o t h e productivity?A famousn e o - c l a s s i c a l economicgrowthmodelssaid“yes”(Sollow,1957).IntheFigure8,withthesamele veloftechnologytheproductionfunctionispositiverelatedwiththecapital.Oncethetechnolo gychange,itwillmaketheproductionfunctionshifttoanothercurve(fromt=1tot=2).Sollowpape ralsoprovedthatoutputpermanhourinlongtermincreasedbythetechnicalchange,increaseofca pitaland increaseofproductivity.
However,Sollowcouldnotmeasurethetechnologychange(A)indirectway,buthecalcu latebythecombinationbetweentheproductionfunctiontodayandproductionfunctionsatleastfort yyearsago,thenhedividebyyears.SollowassumedthatthetechnologyAisaconstantinyearb yyear,andtheshiftofproductionfunctiononlyhappenedinlongterm,not inshort- term.Howtechnologyeffecttoproductivityinshortterm,there’sstillaquestionandf o r nowwecan considerthatinshorttermtechnologydoesnotaffecttoindividualproductivity.F o r example,“productivit yparadoxofinformationtechnology”.
Empiricalevidences
Age,experiencesrelatedto productivity
McEvoy&Cascio(1989)examiningabout100studiesillustratethatitisambiguousrelati onshipbetweentheageof theworkerwithproductivity(workoutput,supervisor ratings)
Kanfer’s model (Ruth K., 1987) posits that individuals allocate personal resources such as effort, energy, and skills to specific tasks based on an effort-performance function This function illustrates the relationship between varying levels of effort and the corresponding performance outcomes for each individual Misperceptions regarding effort and performance can arise when individuals have an inaccurate self-concept or lack sufficient information about how their effort influences their performance levels.
Thisfunctionrelatest o eitheractualo r perceivedrelationshipbetweeneffortandperforman ce.Thereisadirecttrendforgrowthindegreeofeffortwithperformance.However,
Figure10:Effort-performancedifferences,dependson theexperience
InFigure10,itshowsthatwiththesamepercentageofeffortsbuttheperformancewillb e differe ntdependsoftheindividualexperience,andtheslope oftrendwill bealsodifferent.Changesi n effort- performancefunctionsw i t h n u m b e r o f w o r k i n g year,t h e i n c r e a s i n g i n relevantexpe riencepromotesrelativelyhighlevelsofperformance,eveninthehighlevelsofeffort.Withtheexperie ncedemployee,hecangetthehighperformancewithlesseffortbecauseh e alreadyhasexperiencedand masterhisjob.
Then,ifwhenheputsmoreandmoreeffortsinh i s j o b b u t t h e performancew i l l n o t increaseshar ply.O n t h e o t h e r h a n d s , w i t h t h e l e s s experiencedemployee,hewillgetverylowperforma nceatthebeginningevenhealreadytriedm o r e effortthanexperiencedones.Later,ifhepushesmor eandmoreeffortsinhisjob,theperformancewouldbeincreasedsharply.Comebackwiththeexp eriencedworker,itwillbem o r e efficientifhespentlesseffortsinhisexperienced operationsthenusehisbalanceeffortsf o r anotheroperations.Itmeansthat,ifthemanagementca ndeveloptheworkerswithmoreandmoremulti skills, then theproductionwill bemoreefficientifhavethefactoryhavegoodorganizations.
Figure11:Effort-performance differences,dependson theages
Figure11showsthatwiththesamepercentageofeffortsbuttheperformancewillbediffere ntd e p e n d s o f t h e i n d i v i d u a l ages,b u t thes l o p e o f trendw i l l b e differentb u t thedif ferenceo f s l o p e i s n o t s o b i g gapc o m p a r e t o figure6 b C h a n g e s i n effort- performancerelationshipwith age,aslevelsofagedeclineprimarily associatedwith increasin gage,thes l o p e ofthefunctiondecreasessothatevenmaximaleffortleadstoahigherlevelofperf ormancebeingpossibleatyoungerages.Theyoungerworkerswillbehavemorep r o d u c t i v i t y thantheolderonesintheassumptionthattheyspendthesameeffortsinthesamej o b
Insummary,thedeterminationofeffort- performancebytaskdemandsisinthesphereo f anindividual’scognitiveages,abilities,knowledge,a ndskills.However,effectontheeffortperformancefunctionbyagesorexperiencemust beconsideredbythenaturaldemandsof theworko r j o b role.Fore x a m p l e , w i t h t h e k i n d ofj o b whichdoesn’tr e q u i r e m o r e s k i l l s o r knowledgethentheperformancegapbetweenexperiencedandnon- experiencedworkersmayn o t s o huge.
Levelofworker,technologyrelatedto productivity
The role oftechnologyintheproductivitywasemphasizedbyNicolasB.,RemyL.,&Tristan-PierreM.
(2004).Thisresearchconcludegrowthrateoflaborproductivitydependoninvestmenti n technolog yandlevelo f h u m a n capital(levelo f worker).F u r t h e r m o r e , t w o variableshavepositivesig nificantimpacttoproductivity.
In the 1990s, researchers Christoffers S., Malhotra D.K., and Anusua D applied econometric models to analyze productivity in the textile industry Their findings highlighted the crucial role of employer practices in enhancing worker productivity, which is essential for firms to achieve higher profitability The study also compared productivity growth between the textile and apparel industries, revealing that the textile industry experienced more significant productivity gains due to its lower labor intensity compared to the more labor-intensive apparel sector.
Thecontributionoftechnologycouldhelptoincreasetheproductivityatthebeginning.Howeve r,therewasaslowdowninproductivity growthfrom1970sduetothediminishing returnst o technology.Therefore,eventhought e c h n o l o g y h a d a p o s i t i v e relationshipw i t h p r o d u c t i v i t y but this relationship is in thediminishinghorizon(Griliches,1998).
Gender,workenvironmentrelatedto productivity
Gendervariablei s a s i g n i f i c a n t determinanto f worker’sproductivity.W o m e n aree xpectedtohaveproductivityadvantageinsomejobswhilemenhaveproductivityadvantagei n othe r.A l m o s t researcheso n p r o d u c t i v i t y i n manufacturingsectioni n Europes t a t e t h a t occu pationswhichrequiremanipulative skill, womenaremoreefficientthanmenandwomenaremoresuitablethanmenforsomekindofjobespeciallyte xtileindustry.Inaddition,marrieds t a t u s ofworkeralsoaffecttoproductivity.Virtualstudiesargued thatwomenwhoaremarriedhavel e s s p r o d u c t i v i t y b u t m e n arecontrary.Insummary,differenceingenderconcerndifferenceinproductivityandwomenar eslightlylessefficientthanmenthatemphasizedbyTrond,P.,Vemund,S., &EvaM.(2005).
(2001)suggestthattechnology,humanskill,attributeofjob,workingconditionsareinfluentialfact orsofproductivityandassumedthatproductivityfunctionisablet o setup:P=f(T+H+W p +Wc).Thisin vestigationisconductedintruckhaulingoftreesectionwhereworkenvironment havemoreinfluenceonproductivityofworker.
(2003)statedtheimportantofgatheringrightdatatos t u d y andfindoutdeterminantsofproductiv ityofcompaniesfromfirmsinthesteelindustry.Besides,theystated thatomission ofrelevantvariablesinmodelwill bebiasedinresult.
(2008)investigatedtheproductivityinnursingjobandconcludedt h a t besideageandtotalyearo f working,t h e w o r k i n g e n v i r o n m e n t ist h e mostimportantfactorsw h i c h effectproductivity.Int h i s paper,t h e w o r k i n g environmentw e r e measuredthroughmainly03proxiessuchasqu alityofcare provided,jobstressscore,havinghadajobinjury.
Thischapterisdesignedtoexposetheresearchm e t h o d o l o g y ofthisthesis Firstly,thec onceptualframeworkwhichthisthesisisbuiltuptakenfromliteraturereviewandpreviousempirica lstudies.Secondly,analyticalmodelswiththeindependentvariablesanddependentvariablesarein troducedwiththeirexpectedsigns.Thelastsectionwillpresentthedatasourcesanddescriptionsofeachv ariable.
Conceptualframe work
The productivity in a macro view can be calculated using the ratio of Output to Input, with various factors influencing both components, such as capital, multi-factors, or labor In labor-intensive industries like sewing production, focusing on worker efficiency is essential for measuring productivity Empirical studies indicate that several factors significantly impact labor productivity, including age, experience, skill level, technology, gender, and working environment However, technology is treated as a constant in this study, as data was collected from a single company with uniform technology across its four factories The conceptual framework illustrating these relationships is depicted in Figure 13.
(1) Fromthetheories,weproductivityistheratioofoutputoverinput,itreflecttheefficiencyo f u s i n g capitalandlaborormulti-factors.
(2) Garmentis a labor i nt en si ty industry(Christoffersen,2001).Therefore,thelabor p r o d u c t i v i t y isthemostimportantinthiskindofindustry,andthatisthereasonwhythet o p i c focusedinlaborproductivityonly.
(3) Fromtheliteraturereview,therearesixdeterminantseffecttolabor productivity:Age,Exp erience,Worker’slevel(humancapital),Gender,EnvironmentandTechnology.
(4) Thenatureofindustryislabor intensityandthediminishingreturnofcapitalonmarginalp r o d u c t i v i t y (thechangingin moremodernmachinerycouldnot improvetheproductivitys i g n i f i c a n t l y i n garmentfirmsatt h e moment).Inadditional,t h e researchs t u d i e s t h e p r o d u c t i v i t y inone company withthesameleveloftech nology.Therefore,technology couldbeconsideredasunchangedin this research.
GeneralAnalyticalModel
Eff=C+2Workingcondition+3Gender+4Age +5Exp+6Workerlevel+7operations
Productivity per worker over a month is influenced by various factors, including efficiency and working conditions, which refer to the environment in which labor is performed Gender plays a significant role, where a value of 1 indicates male workers and 0 indicates female workers Age is calculated based on the difference between the worker's date of birth and the observation date, while experience is measured in years from the worker's start date to the observation date, divided by 365 Additionally, the worker's skill level, assessed through training and past performance, ranges from 1 to 6 Finally, the number of operations completed by each worker in a month is also a critical metric for evaluating productivity.
Baseonempiricalresearches,in thisstudy,wejustanalysissomeinfluentialfactorsofworker’sproductivity suchasworkenvironm ent,gender,age,experience,workerlevelandtechnology.Howeverapplicationofnewtechnologyi snotimplythatmeantechnologyoftotalp l a n t s i n t h i s f i r m i s t h e samedueto technologyisnotinserted.
Working condition Workingconditions t a n d for4Factories
Dummy(Fac1,Fac2, Fac3,Fac4=0) Positive(+) Gender Genderofeachworker Dummy(1 forMale,0
Datasourceanddescription
Datasource
Dataissecondarydatathatisgatheredfrom4factoriesofonesewingcompany.Eachf a c t o r y isdoingforonedifferentkindofgarment:Shirt,JeansPants,CasualwearandOutwear(Coat,sportco at).Wecollectcrosssectionaldatawithabout2000observationsofworkersin4 factories– samecompany(wecancollectpaneldatabycollecttheobservationmonthly,in1 2 m o n t h s ) , thenweanalysisandprocesstogivecorefinaldataset.
Definitionofproductivity
Efficiency ,asweusedinthispapertomeasuretheproductivityofworkers,itsdatacouldbecollectedi n the followingsteps:
Makingthestandardoftimeforeachsewingprocess:companyhasaprogramcalledG S D (GeneralSewingData),whichitcollectedallthestandardtimeforallsmallmovementsi n sewingind ustry.GSDwouldsumofallthemovementsofeachprocesstomakethestandardo f timeforthatprocess – wecallStandard.Thisstandardwasincludedthedeath- time(forexample:whileworkersgoto therestroom, this isdeath-time).
Collecttheactualtimeofeachworker:anothersystemcalledG- prosystem,whichhelpst o collecttheproductivityofeachworker–howmanysemi- productseachworkerproducedperhour,perdayorpermonth.Takingtheaverageofonemonth,co mpanywouldknowhowl o n g theworkeractuallyusedforaparticularprocess WecallActual.
Calculatingefficiency:thisisthecomparisonbetweenStandardandActual.Efficiencys h o w s howmanypercentageswhichworkercanactuallyproducecomparedwiththeStandard.Figure9giv estheillustration how tocalculatetheefficiency.
The company utilizes an IT system to calculate the average efficiency of each worker on a monthly basis, based on their output and the time spent working Workers tag their completed bundles (approximately five pieces) at their desks, enabling the system to track the actual time spent on each operation against the standard time At the end of the month, the system compiles the total real time and standard time, allowing for an accurate assessment of each worker's efficiency Table 3 outlines the definition and calculation method for efficiency.
The "Clk time" refers to the actual productive hours that workers dedicate to manufacturing each month, excluding any non-productive periods such as restroom breaks and lunch hours This policy is well-known among employees and is strictly adhered to, ensuring that only the time spent actively producing is accounted for in the production metrics.
Each bundle, typically consisting of five semi-finished products, requires workers to scan the machine to initiate the process This action allows the system to electronically tag the bundle, recording the start and end times for each production cycle The total time spent by workers on these tasks is referred to as "Clk-time" (click time).
Standard time of each process
The GSD system is designed to analyze the standard time required for each production process By assessing the complexities and workload associated with each task, the system establishes standard times through a detailed examination of the movements involved in that process.
- For each standard sewing minute, the workers are paid as 308VND This price is fixed the same for all factories, all workers.
- Salary = Total standard time * 308VND Or:
Efficiency (%) = Total Standard time / Clk time.
=> Let's say: the more worker produced faster than the standard time, the more efficiency they have and they can receive higher salary
FactoryProduct typeWash / Non-Workers per production line
Thedescriptionof variables
Age(Year):ageofworker(years/ worker).Thisvariableiscalculatedbytheyeartodate2014 minus thedateofbirthofeachworkerthen divide365days.
Experience(year):Theexperienceofworkermeasuredbytotalnumberofworkingd a y divide365.Itiscalculatedbytakethedifferencebetweenthedateworkerstartworkingf o r the companyuntilyeartodate2014.
Workerlevel(From1to6,with6isthehighestskilllevel):Thelevelofworkerme asuredbygradefrom1to6thatperworkerachievescertificationwhenheorshepassedthes k i l l exam( grade).Thisisconvertedtoasetofdummyvariableswith6categories’(L2,L3,L4,L5,L6,L1=0)
No.ofoperations(number):theoperationswhicheachworkerdidin eachmonth.Aslearncurvet h e o r y that t h e m o r e workersp r a c t i c e o n o n e operationt h e m o r e efficientt h a t workerhas.Thisvariableistomeasuretheorganizationbymiddlemanagementineachfacto ry.Itmeans,ifthemanagementisgood,theywillletworkersfocusonlyfewoperationsthenhelpt o incre aseproductivity.Iftheorganizationinproductionisnotgoodenough,theworkershavet o d o m a n y operationsand it willimpactto lowefficiency.
Workingcondition(dummy):conditionofwork environment,dummyvariableswith4 intercepts(fac1,fac2,fac3,fac4=0).Althoughthefourfactories areunderthesamecompany,b u t eachfactoryhavethedifferencesfromproducttypology,toorganiza tioninproductionanddifferentfactorymanager.Eachfactoryalsohasdifferentcustomerswithdifferent requirementalsohavedifferentpressurefortheworkersin production.
1 Shirt Non-wash No line.Organizebygroupof similaroperations
4 Jeans Wash LEAN,flexible dependson the
Customerhavelongtermplanning,an dnorushorpeakseasons shipment Produceinadvance Customerdropordersinadvance
3 High Seasonalsale Customersalesbaseonthe demand,andrequirerushshipment
4 High Seasonalsale Customersalesbaseon the demand,andrequirerushshipment
Dummy(Fac1,Fac2, Fac3,Fac4=0) Positive(+) Dummy(1 forMale,0
(Dummy) Genderofeachworker forFemale) Un-know(?)
Negative(-)E x p (Year) Experienceofworker YearNegative(+)
(Dummy) Levelofworker(1->6) Dummy(L1=0,L2,L3,L4,L5,L6)Un-know(?)
(Dummy) Numberofoperationse achworkerproduced Quantity Negative(-) in 1 month
ModelEstimationandHypothesisTesting
Them o d e l wast a k e n f r o m analyticalframework,whichi s t h e collectiono f manyvari ablesrelatedtoworkerproductivity.Model estimation of this studyis primarybaseon theresearchofRolfB.(2001)abouttheinfluentialfactorsofproductivity,andusingOLSmethodt o r u n t h e regression.
Theregressionfirstlywillbetestforomittedvariable,Heteroscedasticitytestsandthes o l u t i o n , normaldistributiontestandmulti- collinearitytest.Then,theresultwillbecheckedandcorrectedthroughoutthesignificanceofvari able.
Workingconditi on(Fac1,Fac2,F ac3)
-Gender(0 or1) Genderormale;Genderorfemale Un-know(?)
-Age(year) Ageofworker Negative sign(-)
-Exp(year) Theexperienceof worker Positivesign(+)
Operation(numbe r) Thenumberof operations ofeachworkers Negative(-)
Thischapterpresentstheempiricalfindings.Firstly,therewillbedatadescriptivew i t h t h e relationb e t w e e n eachindependentvariablew i t h dependentv a r i a b l e N e x t , t h e res ultofOLSmodelwhichfollowtheanalyticalframework,withthesquareAgeandExp.T o makes urethereisnomulti- collinearproblembetweenAgeandExp, Iruntwomoremodels:w i t h o u t Agea n d w i t h o u t E x p Finally,I w il l analyzeandr u n s o m e m o r e OLSm o d e l s t o illustratethefindings.
Datadescriptive
AgeandEfficiency :analyzethedatawecanseethesmallcurvewhichcanberelatedbetweenAg eandEfficiency.Figure1 1 s h o w s t h a t w i t h t h e agefrom20u n t i l 3 5 , t h e e f f i c i e n c y s l i g h t l y increased.A n d , fromt h e a g e at3 5 u n t i 5 5 , t h e e f f i c i e n c y i s s l i g h t l y decrea sed.Afterage28,theolderworkerhasnotstableefficiencycoherencewiththeage,andtrendseeml ylessefficiencythanyoungerone.Thedatadescriptiveofageandefficiencys e e m l y itisnotmatchedwi thexpectedsignaftertheperiodofageafter28yearsold.Itisn o t matchedasshowninpreviouscha pter,whichitshouldbelinearlytheefficiencyshouldb e decreasingwith theolderworkers.
Number of operations per worker
ExperienceandEfficiency:data ingeneralalsoshowsthattheefficiencypositivecorre lationwith eachother Figure1 6 shows t h a t thee f f i c i e n c y isincreasingwithmore expe riencedworker.Itisthesamewithexpectedsignshown inpreviouschapter
NumberofoperationsperworkerandEfficiency:Logically, themoreoperationswork ersproducedin1month,thelessefficiencyforthatworker.Thefigure17showsthati n t h e datao f t h i s thesis,t h e negativer e l a t i o n betweene f f i c i e n c y andt h e n u m b e r o f operati onsof theworkers.
Gender:This isadummyvariablewhich“1”isstandofmaleand“0”isstandforfemale.
Total3190observations,thereare42%populationismaleand58%arefemale.Theratiobetweentwog endersineachfactoryisdescriptiveasintablebelow.Factory 2andf a c t o r y 4aremostly thesamepopulation between maleandfemale,whilefactory1andf a c t o r y 3arequited ifferenceintermofgenderwith 40%maleand60%female.
Workingconditionandworkerlevel:in differentworkingcondition(factories),then u m b e r ofworkersforeachworker’slevelaredifferent,and it isdescribedintable10.
Besidet h e discretev a r i a b l e s asm e n t i o n e d a b o v e : Gender,worker’sl e v e l andworkingcondition,Isummarythedataofallcontinuousvariablesbasedonthecollect eddatainTable11.
TheefficiencylevelofthecompanyisquitehighwithMeanandMedianover80%.,b u t thereis abiggapbetweentheminimum(lessthan1%)andthemaximum229%.Thestandarddeviation ofefficiencyis30.29%.
Theworkersageisvariancefrom18yearsoldasayoungestto55yearsoldasano l d e s t T heaverageageisabout25yearsoldwithmean26.74andMedian25.50.T h e mostexperiencedworke ri s a l m o s t 2 2 yearsw o r k i n g f o r t h e company,andtheMediano f experiencei s 2 7 5 y ears.Int h i s , t h e workersf r o m level2 t o level3 i s t a k i n g t h e m o s t p o p u l a t i o n s withExp Meanequalto2.41.ThedeviationofAgeandExpare6.27yearsand
4.04years,respectively Inaverage,eachw o r k e r d i d m o r e t h a n 8 o p e r a t i o n s monthly,fl uctuationin thescopefrom Min 1operationuntil Max34operations.
Variables Eff(%) Exp(year) Age(year)
TheOLSresult
Variables Model1 Model2 Model3 lnIM WithoutAge Without Exp
Exp(experience) :Figure18showsthatthemostdistributionofexperienceisfrom0 yearsu ntil3-4years.
Int h e r e s u l t , Experiencehasexpectedsignandh i g h l y significant( p r o b 0 0 0 0 ) However,theimpactofexperienceinthisstudyisnotmuch.Whichtheworkerwhohaso n e yearmoreexperiencedjustonlyhashigherefficiencyat1.97%.Thisresultisnotsosurprisebe causefromthedatasourcetheexperienceinthisstudyiscalculatedfromthedatet h e workerstartworkin gforthecompany.Forexample:workerAstarttoworkforcompanyfrom0 1 / J a n /
2 0 1 2 then herexperienceis 2 years(calculateas1 / J a n / 2 0 1 4 – 1/Jan/
CheckingthelinearrelationbetweenExperienceandEfficiency,inFigure19,wef i n d thatingeneralthemoreexperienceworkershouldhavehigherproductivity,butthes l o p e i s q u i t e flat.
P re di ct ed P ro du ct iv ity 75 80 85 90 95
Ifurthercheckpredictivemarginswith95%betweenexperienceandpredictedproductivity,andt heresultsshowninFigure2 0 Thisfigureshowsthatwiththeworkerexperiencedfrom0yearsto1 2years,theproductivityhasp o s i t i v e signwithexperience.Butwiththeworkerexperiencedfro m13yearsto22years,t h e e f f i c i e n c y isnegativeco-relationwithexperience.
Workingenvironment(fac1,fac2,fac3) :this isthemostinterestingfinding,whichconfirmedthattheworkingenvironmenthassignificantim pacttoworker’sefficiency.Att h e result,onlyFactory2isnotsignificantwithhighprob.butfor othersallaresignificantw i t h l e s s t h a n 1 % p r o b T h e results h o w s : Factory1i s t h e highe stp r o d u c t i v i t y workingplace,thenfollowingFactory2–
Factory 4 and Factory 3 are part of the same company and share top management, yet their productivity levels differ significantly These discrepancies can be attributed to various factors, including factory managers, product types, managerial mindsets, production line organization, quality control, and working pressures, as illustrated in Tables 4, 5, and 6 of the previous chapter An analysis of operations per worker reveals that factories with fewer average operations per worker tend to exhibit better efficiency, despite the average worker levels being similar across all factories.
Factory# Averageno.operation Average workerlevel
Workerlevelhas significantbutnotasexpectedsigninthisresult.Iexpectedthatt h e hi gherskillworkersshouldhavebetterefficiency.However,thestudy’sresultcomeso u t with negativesign.Itmaybecomesfromtheorganization,whichmeansthattheskillworkershavetod omoreoperationsthanlessskillones.Iwillchecktheco-relationbetweenn u m b e r o f operationsperworkerwithefficiencyfirst,thenwillchecktherelationbetweenn u m b e r o f operationsa n d workerl e v e l Later,t o f i n d t h e reasonwhyt h i s variablehaso p p o s i t e si gnwith theexpected.
Numberofoperationsperworker(noof_operation) :Figure21showsthatmostlyt h e wor kersi n t h e c o m p a n y d o f r o m 1 t o 1 0 operationsmonthly.Logically,them o r e operations theworkerhastoproducethelessproductivityinthismonth.Thatiswhytheorganizationin allfactoriestriedtominimizethis,andweseeverylessworkerwhodidm o r e than
Andtheresult,“numberofoperations”hasexpectedsignbutnotsignificantintheresult,i tisillustratedinFigure23toprovethatthemoreoperations1workerhavetodo,t h e lessprodu ctivity.Infurtheranalysis,Figure22shownthecorrelationbetweenworkerlevelswiththenumbero foperationsandfoundthatinthebigrangefrom5to30operations,t h e workerswithmoreskillha vetoworkmoreoperationsthanthelowerones.Itcanbee x p l a i n e d f o r t h e reasonw h y t h e workersw i t h highers k i l l buthavelowerefficiency:because,inaverage,theyhaveto do moreoperationsthanlowerskillones.
P re di ct ed P ro du ct iv ity 82 84 86 88 90 92
Ialsorananotherregressionwithoutdummiesworkingcondition(fac1,fac2,fac3)t o see thepurerelationsbetweenage,experience,genderandnumberofoperations.InTable1 3 , theresultc omesoutwithallstrongsignificantplessthan1%,exceptagewithplesst h a n 5%.Itshowst hat,whentheworkersdomore1operationinamonth,theefficiencyw o u l d b e slower1.72%.
P re di ct ed P ro du ct iv ity 6 5 7 0 7 5 8 0 8 5 9 0 Predictive Margins with 95% CIs
Age :Themostpopularagerangeisfrom18yearsoldto32yearsold,andit’snotimpacts ignificantlyto theworker’sproductivity,asshowingonFigure24.
Intheresult,thisvariableissignificant,withplessthan10%,positivecorrelationw i t h efficiency.Meansthattheolderworkershavethehigherproductivitythantheyougerones:work erwith1yearolderwillhavehigherefficiency 1.12%.Theresulti s oppositew i t h t h e e xpectedsign.However,i f w e checkt h e predictivemarginsbetweenageandp r o d u c t i v i t y asinFigure25:weseeclearerthatfromtheage18to30,theolderworkershavebettereffici ency.Andfortheagefrom31to55,theolderworkerwouldhavelessp r o d u c t i v i t y th antheyoungerone.Andbecausethedistributionsofagearemostlyintherangefrom18to30,t hatiswhytheresultoflinearagehavepositivesignandoppositewithexpectation.
Gender:the resultshowthat themale workersaremoreefficentthan thefemalewo rkers.I’mquitesupprisedwiththisresultbecausemostthepeoplethoughthatfemaleworkerss houldbemoreskillfullinsewing.Buttheresultcomesoutthatthemaleworkersarefasterthanfema le.Moreover,Table14showsthepopulationbetweenmaleandfemalei n thecompanyandther atio45%Maleand55%Femal,notsobiggapbetweengenders.Fromthisresult,it confirmedthatMaleworkersaremoreproductiveandtheratiobetweenm e n andwomenin sewingindustryis mostlyequal.
Insummary,theempiricalresultisgenerallysupportthetheoreticalexpectations. Thereare2exceptionalvariableswhicharenotoppositewithexpectedsign,ageandworkerlevel.For Age,ithasoppositesignwith expectations inlinearresultbut itismatchedif weconsidertrendofdifferenttheperiodofage.Forworkerlevels,theresultisimpactedbytheorganiz ationoffactories,that push moreoperationsforthemore skillworkerswhichmaket h e e f f i c i e n c y ofhigherskillworkerlowerthanlower skillones.
Thisthesisisstudyingthefactorswhichimpacttoworkerproductivity insewingi n d u s t r y byobservingthe571observationsinfourdifferentfactoriesinthesamecompany,m a k i n g paneldatawithtotal2787observationsbycombinedallefficiencyofworkersfromJ a n u a r y 20 14toMay2014,andrunonlyOLSregression.Firstly,Iwillsummarizesomem a i n findingsof thisthesiswiththeirimplications.Secondly,thischapterwillshowsomel i m i t a t i o n o f t h e studyand finallythe suggestionforfurtherresearch.
Mainfindings
Thefirstfindingo f this thesis is thatworkingenvironment, mainly frommiddl emanagementhassignificantimpacttoproductivity.Inthestudy,fourfactoriesinthesamec o m p a n y andthesamewaytomeasureproductivitybutthedifferentmanagementcanmaket h e differen ceinefficiency.Thetypologyofproductsmayimpactto efficiencybut it is nott h e mainreason.Forexample,factory3andfactory4arealsomakingjeanspantwhichcanb e c onsideredassameproducttypologybuttheefficiencyoffactory3islessthanfactory
O n e o f t h e variablest o measuret h e managementisthattomeasuretheaverageoperationswhich eachworkerdidinonemonth.A s aresult,althoughthefactoryhasskillfulworkersbuttheefficie ncyofthesehighlevelworkersis still low dueto theyhaveto do manyoperationsin 1 month.
Experiencedworkersare moreproductivethan lessexperiencedonesbut itis notm u c h (+0.67%),asit isexpectedfrompreviousstudies.
MaleworkersaremoreproductivethanFemaleworkers,andtheratiobetween2genders in sewingindustryis not so bigin this study(45%Male&55%Female).
Theagein sewingindustryis mainlyfrom18yearsold to
Policyimplications
Macropolicyfromgovernmentmaynotenoughtohelpto increaseproductivityforgarmentindustry,b u t a l s o needt h e effortsfromi n s i d e eachenterpris et o createa g o o d workingenvironmentforworkersbyhaveagoodorganization,goodmanagement skillsandcontinuousimprovementmindseti n ordert o boostu p t h e efficiency.T h e e f f i c i e n c y i s negativesignwithworkerlevel,itdoesn’tmeanthatenterpriseshouldrecruitormaintainm o r e lowskillworkersbutenterpriseshouldfocusonmanagementandtrainingprogramst o ha veasuitableandsustainabledevelopment,dependsonitsproducttypologyandstrategyi n l o n g term.
Limitationsandfurtherresearch
First ,thisthesisusingthedatainonly1companywhichmaynotincludealltheelem entscanimpactproductivity suchaslackingthe investmentcapabilityortheordersreceivin g.Forexample,forthecompanywithmoreinvestmentcanachievehigherproductivity.Or,the companywhichhavinggoodcustomerwithgoodcomingorderscanhelptostabilizetheprodu ctionthenhelpincreasetheefficiencyaswell.Asaresult,itism o r e connectedtothemicrovie wthanthemacroviewofgeneralgarmentindustry.Itwillb e theopportunityforfutureresearchwit hmulti-company’s data,wealsohavetosolvetheproblemtomakesurethatallthecompanyhavethesamecalculationofe fficiencyandhowt o measurethe otherelementslikeinvestment,orderscoming…
Second,the methodofcalculateefficiency ofthecompany inthisthesiswhichI d o n ’ t havereferentdatabasetocrosscheckthatthecalculationisthesamefordifferentprodu cttypology.ThelogicofcalculatetheefficiencyistocreateaStandardwhichbaseont h e movement anddifficultiesoftheoperation,thisdataiscomefrom1companynamedG S D However,I d o n ’ t haveenoughreferentresourcesandk n o w l e d g e t o crosscheckwhetherthestandardis correctforallproducttypeornot.Itshouldbeabettermethodofcalculateproductivityinmore macropointofview,suchasfinancialefficiency,forfutureresearch.
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