Measurng Producton Effcency of Readymade Garment Frms Volume 6, Issue 2, Fall2009 R. N. Josh* and S. P. Sngh** *Senor Lecturer, Department of Textle Technology, SGGS Insttute of Engneerng & Technology, Nanded, Inda. E-mal: rnoshtextle@yahoo.co.n **Professor of Economcs, Department of Humantes & Socal Scences, Indan Insttute of Technology Roorkee, Inda. E-mal: snghfhs@tr.ernet.n ABSTRACT In the garment ndustry, performance of a frm s generally measured by usng conventonal ratos such as number of garments per machne and per operator. These ratos cannot reflect the frm s performance completely as the frm does not use only a sngle nput to produce a sngle output. In ths context, Data Envelopment Analyss (DEA) s an approprate technque as t consders multple nputs and outputs to measure the producton effcency of a frm. Ths paper, therefore, apples ths technque to estmate the producton effcency of ready-made garment frms. The study s based on the prmary data collected from eght ready-made garment frms located n Bangalore, Inda. To measure the effcency, we consder the number of sttchng machnes and number of operators as nput-varables and the number of peces of garment produced as an output-varable. The DEA results show that under the CRS technology assumpton, average producton effcency score n the garment frms works out to be 0.75. Ths ndcates that on an average, the frms could ncrease ther output by 25 percent wth the exstng level of nputs. When the aggregate producton effcency s decomposed nto pure producton effcency and scale effcency usng VRS producton functon, t s found that on an average, the frms are 17 percent neffcent n pure producton effcency and 9 percent n scale effcency. Most of the frms are found operatng under decreasng return to scale. Ths ndcates that the producton effcency of the frms could be mproved by adustng the plant-sze at the optmum level. The study also concludes that the DEA s superor to the rato analyss for performance evaluaton of the garment ndustry. Keywords: Readymade Garment frm, Producton effcency, Data Envelopment Analyss, Rato Analyss I. Introducton Measurement of performance of a garment frm n relaton to other frms s often carred out n the garment ndustry through the rato analyss such as number of garments produced per operator or per machne. Although ths technque s smple, Artcle Desgnaton: Refereed 1 the most mportant drawback s that t s napproprate n makng decsons based on one sngle rato when there are many nputs and outputs (Duzakın and Duzakın, 2007). It cannot capture the effects of factors that affect the performance of an organzaton (Smth, 1990). In practce, no frm uses only
a sngle nput to produce a sngle output. In case of garment ndustry, machne, operator, raw materal, energy, and other nputs are requred to produce a garment. In such cases, Data Envelopment Analyss (DEA) s an approprate tool as t consders multple nputs and outputs to measure the productvty and effcency of any decsonmakng unt. Among the studes avalable on garment productvty n Inda, Khanna (1991), Khanna (1993), Bheda et al. (2001) and Bheda (2002) have used partal factor productvty measure to assess the performance of the frms. Rangraan (2005) and Josh et al. (2005) have also used the number of garments per machne and per operator to compare the productvty of Indan garment ndustry wth neghbourng countres. These ratos cannot reflect the overall performance of the garment frm and are unable to compare the effcent frm wth the neffcent one. Such studes are of lttle sgnfcance when the obectve s to dentfy and analyze maxmally effcent frms n comparson to the less effcent ones. Earler studes on the Indan textle ndustry were carred out by the researchers to assess the performance of ndvdual frms and to compare nter-frm performance. Solankar and Sngh (2000) measure the relatve effcency of 40 Indan textlespnnng frms for the perod 1997-1998 usng DEA-BCC model. Bheda (2002) estmates productvty level of the Indan apparel frms, usng partal factor productvty approach. Hashm (2005) analyzes the productvty level and factor prce and ther nfluence on unt cost growth n the Indan cotton yarn and garment ndustres for the selected states usng panel data for the perod 1989-1997. In ths study, he estmates Total Factor Productvty 1 (TFP) by usng translog multlateral ndex. Bhandar and Ray (2007) measure the levels of techncal effcency n the Indan textles ndustry at the frm level usng DEA. The data used for the study of the frms relate to the producton of cotton, woolen, slk, synthetc and other natural fbers. Bhandar and Mat (2007) use translog stochastc fronter producton functon (SFPF) to measure the techncal effcency of Indan textle frms. Josh and Sngh (2008) examne the TFP growth n Indan textle ndustry usng Malmqust Productvty Index (MPI). The revew of lterature on the subect clearly ndcates that there has not been any study conducted so far on the Indan garment ndustry that has used DEA to measure the producton effcency of ndvdual frms. Keepng ths n vew, ths paper measures the producton effcency of eght readymade garment frms n Bangalore. The paper s structured as follows: Secton II deals wth the data and varables, Secton III descrbes the models followed by results of the DEA analyss n Secton IV and Secton V compares the results of DEA wth rato analyss. The fndngs are dscussed n the fnal secton. II. Data Collecton The study s lmted to garment manufacturers that produce homogenous product (.e, bottoms). The DEA requres that set of the frms beng analyzed should be comparable n the sense that each frm utlzes the same type of nputs to produce the same type of outputs (Odeck 2008). As our selected frms are n the same busness and produce the same product, the DEA s the most sutable technque to be appled for assessng the relatve effcency of these frms and settng benchmarkng for the neffcent frms to mprove ther performance. Further, the sample of frms s restrcted only to the domestc manufacturers as they are under smlar market, envronmental and nfrastructural condtons. Snce the study covers only bottom manufacturers, the results may not be drectly applcable to manufacturers of other garment products. The sample sze s small as some frms dd not provde ther nput-output data and other relevant nformaton. Earler studes on the Indan garment ndustry have also suffered due to manufacturers concern about keepng the Artcle Desgnaton: Refereed 2
nformaton confdental (Bheda et al., 2001; Kalhan, 2008). Intally, we approached Apparel Export Promoton Councl for gettng nformaton on garment manufacturers. The data provded by the councl contaned the addresses and contacts of the manufacturng unts. It was dffcult to dentfy the productwse detals of the frms from that nformaton. We sent e-mals wth a questonnare and datasheet to a large number of manufacturers. We dd not receved any postve response from them. We also tred to contact the garment frms through telephone n Delh, Mumba, and Bangalore but faled to get a postve response. Hence, the next choce was to use the secondary databases lke PROWESS and Captalne. These databases contan data on a large number of manufacturng frms, ncludng readymade garments, but these sources have balance sheet-based fnancal data of ndvdual companes and do not have nformaton about the number of workers and number of machnes of garment frms. In Inda, only Annual Survey of Industry (ASI) provdes the data on number of employees at aggregate level.e. three dgt data. It provdes the data at frm level wthout dsclosng the dentty. However, ASI does not have data on physcal output and number of machnes of the selected ndustry. Therefore, n order to estmate the producton effcency, usng physcal data on workers, machnes and output, we attempted to conduct prmary data survey of ndvdual frms n Bangalore and got the nformaton only from eght bottoms manufacturng unts. In DEA analyss, results are nfluenced by the sze of the sample. In ths case study, the number of garment frms s eght whch are consstent wth the rule of thumb provded by Banker et al. (1984) that the DMU should be at least twce the sum of nput and output (Chu et al., 2008). The sample sze n ths study s qute smlar to the studes of Maumdar (1994). Selecton of Varables Selecton of approprate nput and output varables s an mportant stage n DEA analyss. A model wth a large number of varables s one that may fal to have any dscrmnatory power between frms because most frms wll tend to be rated effcent (Maumdar, 1994). Therefore, nput-output varables n DEA analyss should be mnmal. We dentfy the potental nputoutput varables by revewng the earler studes on performance evaluaton. Bheda (2002) estmates the productvty of the Indan garment frms usng the number of shrts produced as an output and the number of sttchng machnes and operators used as nputs. Hashm (2005) analyzes the productvty level of Indan textle and garment ndustres usng gross output as an output and employee, materal, fuel consumed, and captal as nput varables. Sngh and Agarwal (2006) examne the TFP growth and ts components n the sugar ndustry of Uttar Pradesh usng nstalled capacty, employee, raw materal, fuel as nputs and sugar producton as an output. Chen et al. (2007) also use total energy generated as the output factor and total nstalled capacty (MW), total number of employees, and total producton cost as nput factors to measure the productvty changes n the Tawan thermal power plants. Table 1. Descrptve Statstcs of Selected Varables Varables Garments/year Operators Machnes Mean 417500 358 143 Max 1400000 1500 500 Mn 200000 150 75 Std. Dev. 401452 462 144 Artcle Desgnaton: Refereed 3
Table 2. Correlaton Matrx and R 2 results of Selected Varables Correlaton Matrx Varables Garments/year Operators Machnes Garments/year 1 Operators 0.9908* 1 (0.000) Machnes 0.9952* (0.000) 0.9976* (0.000) Regresson Analyss R 2 Adusted R 2 F- Value Sgnfcance (error) 0.991 0.987 281 0.000 Note: Fgures n parentheses are error levels; * sgnfcant at 0.01 error level, n = 8 1 In the above revewed studes of dfferent sectors, the number of employees and nstalled capacty were used as nput varables and gross output as an output varable. In our study, the number of sttchng machnes and the number of operators are selected as nput varables; and total peces of garment produced as an output varable. The producton of the garment ndustry fully depends on the total number of sttchng operators and total number of sttchng machnes. We do not fnd any dfference n the raw materal consumpton across frms, as most of the frms are usng automatc cutters for cuttng the fabrc. Therefore, there s a mnmum wastage of fabrc. We also do not fnd any dfference n energy consumpton as almost all frms have power drven machnes. We fnd that the electrcty consumpton per sttchng machne s almost equal n the surveyed frms. Hence, we do not consder the raw materal consumpton and energy consumpton as nput varables for the study. The descrptve statstcs of nput-output data are shown n Table 1. Correlaton and adusted R 2 analyses have been conducted to know the extent of varaton n garments produced per year. The results are shown n Table 2, whch ndcates that the output s sgnfcantly correlated wth these nputs. About 99 percent of varatons n the output varable are explaned by these explanatory nput varables. III. Models Used Ths paper apples DEA methodology to measure the producton effcency 2 of the garment frms located n Bangalore, Inda. Usng only observed output and nput data of the frms, ths technque evaluates how effcently the nputs are converted nto outputs. Accordng to lterature, there are two broad methodologes for measurng techncal effcency-the econometrcally specfyng stochastc fronter producton functon (SFPF) and lnear programmng based non-parametrc DEA methodology. The DEA methodology that we use n ths paper has some advantages over the SFPF. Frst, DEA does not assume any specfc functonal form for the producton functon. Second, t does not make a pror dstncton between the relatve mportance of outputs and nputs. Thrd, t s relatvely nsenstve to model specfcaton,.e., the effcency measurement s smlar whether nputorentaton or output-orentaton s used. However, DEA also has some lmtatons. Compared wth the stochastc fronter method, the man dsadvantage of the DEA approach s that t does not provde statstcal tests for the estmated producton functon (Zheng et al., 2003). Artcle Desgnaton: Refereed 4
DEA technque was frst formulated by Charnes, Cooper and Rhodes (CCR) n 1978. In ths model, the rato of the weghted outputs to weghted nputs for each frm beng evaluated s maxmzed (Charnes et al., 1978). It s known as CCR model based on constant returns to scale 3 (CRS). Subsequently, Banker, Charnes and Cooper (1984) proposed another model based on varable return to scale 4 (VRS). In ths study, we use both CCR and BCC models. For mathematcal detals of these models, please see Coell et al. (1998). Here, we have dscussed the nput orented 5 and output orented 6 models brefly. The followng notaton s used n the descrpton of varous DEA models dscussed n ths secton. Overvew of notatons: x = nput vector of th frm y = output vector of th frm x = nput vector of frms, where y = output vector of frms, where 1,2,..., 1,2,..., u = vector of output weghts ν = vector of nput weghts = effcency score correspondng to the nput orented models 1/ = effcency score correspondng to the output orented models = vector of constants Assume, there are data on K nputs and M outputs for each of N frms. For the th frm, nputs and outputs are represented by the column vectors x and y respectvely. The KxN nput matrx, X, and the MxN output matrx, Y, represent the N N data for all N frms. Then, the effcency of a garment frm s defned as the rato of weghted sum of outputs to weghted sum of nputs ( u y / v x ). The optmal weghts are obtaned for the th frm by solvng the mathematcal lnear programmng problem: max s. t. u, v ( u y u y u, v / v x ), / v x 0 1, 1,2,..., N Solvng ths LPP allows fndng values for u and ν, such that the effcency of frm s maxmzed, subect to the restrcton that effcency for the rest of the frms s (1) smaller than or equal to 1. One problem wth ths partcular rato formulaton (1) s that t has nfnte solutons. To avod ths, the next restrcton s mposed v 1, whch provdes: x Artcle Desgnaton: Refereed 5
max s. t. u, v ( u y ), v x u y u, v 1, v x 0 1,2,..., N The equaton (2) s known as multpler form of DEA. Usng the dualty n lnear programmng, the envelopment model can be wrtten as, mn, s. t. x y, Y X where s a scalar and s a Nx1 vector of constants. Equaton 2 nvolves the constrants based on number of frms, on the other hand equaton 3 nvolves the fewer constrants based on the total number of nputs and outputs. Therefore, the envelopment model 3 s generally used based on constant return to scale. The value of s the effcency score of the th frm. When the frm acheves =1, then that frm s techncally effcent. The CRS assumpton s only approprate when all the frms operate at an optmal scale (Coell et al. 1998). In the garment ndustry, the restrctons on garment trade under the Mult Fbre Agreement 7 have been removed from 1 st January 2005. Specfcally, the maor markets lke USA, Europe and Canada have removed the restrctons for the mport of garments from ths date and these are the maor markets for the Indan textle and clothng ndustry. mn,, s. t. x y N1 Y X 1 where, N1 s an Nx1 vector of ones. The above-derved models are nput orented models. In ths study, we prefer to apply the output-orented models because the obectve of garment ndustry s normally to ncrease outputs rather than to decrease (2) (3) From 2001, the restrctons on the nvestment n plant and machnery 8 n the Indan garment ndustry have been removed under the Natonal Textle Polcy 2000. Now, the maor producers have started producng garments on a large scale. Most of the garment frms n Inda are mcro and small-scale. In ths scenaro, these frms have to compete wth the domestc as well as global garment producers. Accordngly, they need to adust ther scale-sze of the plant. Hence, to understand whether the neffcency n the frms s due to neffcent utlzaton of resources or napproprate scale-sze, we decompose the aggregate techncal effcency nto pure techncal effcency and scale effcency usng the BCC model. The BCC model can be wrtten by addng the convexty constrant N 1 1 n equaton (3) whch gves the equaton; (4) nputs. Ths ndustry s an employment generatve ndustry wth small nvestment gvng maxmum value addton to the textle sector. The ndustry has upward lnkages for the weavng ndustry. The garment ndustry consumes 30 to 35 percent Artcle Desgnaton: Refereed 6
Effcency score of fabrcs produced by the weavng ndustry. Hence, mnmzaton of nputs wll affect the entre textle chan. In addton, 70 percent of the garments produced are consumed n the domestc markets and 30 percent are max,, s. t. x y X Y used for export. We, therefore, use the CCR and BCC models wth output orentaton. The output orented CCR model s as follows, (5) By addng the convexty constrant N 1 1 n equaton (5), the BCC output orented model s wrtten as, max, s. t. x N1, y X Y 1, where, 1, and 1 s the proportonal ncrease n outputs that could be acheved by the th frm, wth nput quanttes held constant. Here the 1/ s the producton effcency of garment frms whch vares between zero and one. CCR (6) effcency s consdered as overall producton effcency (OPE) and BCC effcency as pure producton effcency 9 (PPE). Scale effcency 10 (SE) s measured as a rato of CCR effcency to BCC effcency. Fgure 1. Overall Producton Effcency, Pure Producton Effcency and Scale Effcency of the Garment Frms 1 0.8 0.6 0.4 0.2 0 IV. Results of DEA Analyss GF1 GF2 GF3 GF4 The overall producton effcency, pure producton effcency and the scale effcency of the ndvdual garment frms are shown n Fgure 1. The overall Garment frm GF5 GF6 GF7 GF8 OPE PPE SE producton effcency scores suggest that a frm s effcent f t scores equal to one under constant return to scale (CRS) technology. It can be observed from the fgure that out of eght frms, only one frm Artcle Desgnaton: Refereed 7
(GF1) turns out techncally effcent (OPE=1). The remanng frms are neffcent (OPE<1). For neffcent frms, the CCR and BCC models dentfy a set of reference effcent frms that can be used as benchmark for them. We have used the reference set 11, peer count 12 and return to scale obtaned from the CCR model as shown n Table 3. We fnd that the average overall producton effcency of the eght apparel frms s 0.75, whch ndcates that on an average, these frms have to ncrease output by 25 percent usng exstng level of nputs. Table 3. Reference Set, Peer Counts and Return to Scale of Garment Frms Garment Reference set Peer Return to scale frm Count GF1 GF1 5 Constant return to scale GF2 GF2 4 Decreasng return to scale GF3 GF8, GF 2, GF 1 0 Decreasng return to scale GF4 GF 8, GF 2, GF 1 0 Decreasng return to scale GF5 GF 8, GF 2, GF 1 0 Decreasng return to scale GF6 GF 2, GF 1 0 Decreasng return to scale GF7 GF 8, GF 1 0 Decreasng return to scale GF8 GF 8 4 Decreasng return to scale The BCC model assumes the varable return to scale (VRS) and the measured effcency s called pure producton effcency (PPE). It ndcates how effcently the nputs are converted nto outputs, rrespectve of the sze of the frm. It s observed from the fgure that out of eght garment frms, three are effcent under VRS technology (PPE=1). Average pure producton effcency s 0.83, mplyng that an ndvdual frm s neffcent n manageral performance by 17 percent. Out of eght frms, GF3 s the most neffcent frm that has scored the lowest score of 0.64. Ths frm can follow the best practces of frms GF1, GF2 and GF8 for mprovng ts effcency. It s also observed from the fgure that the frm GF2 and GF8 obtan low overall producton effcency, but have 100 percent pure producton effcency. Ths clearly ndcates that these two frms are capable of convertng ts nputs nto output wth 100 percent pure producton effcency, but ther overall producton effcency s low due to low scale effcency. Ths demonstrates that f the effect of scale-sze s neutralzed, frms GF2 and GF8 can become effcent. Of the eght frms, GF1 postons best practce frm by comprsng hghest peers count of fve n the whole sample. It acheves the most productve scale sze (OPE = PPE = SE = 1). Thus, t can be a role model for most of the neffcent frms. Best practces of ths frm can be followed as norms or benchmarkng by them to montor ther performances. Table 4. Descrptve Statstcs of Effcency Scores Varables Overall producton effcency Pure Producton effcency Scale effcency Mean 0.75 0.83 0.91 Mn 0.63 0.64 0.70 Max 1.00 1.00 1.00 Std. Dev. 0.12 0.14 0.09 The scale effcency scores of the ndvdual frms are shown n Fgure 1. It s Artcle Desgnaton: Refereed 8 observed that out of the eght frms, only one frm (GF1) s scale-effcent. Ths frm
operates at the most productve scale sze 13 (MPSS). It s observed from Table 4 that the average scale effcency s 0.91, whch suggests that an average frm may have to correct ts scale-sze by 9 percent to be scale-effcent. The GF8 has the lowest scale effcency (SE=0.70) and operates under decreasng return to scale. Ths frm may decrease ts scale-sze n order to become effcent under constant return to scale. It s observed from Table 3 that all neffcent frms are operatng under decreasng return to scale 14. Ths mples that these frms have excess producton capacty that could not be utlzed effcently n the year 2008. To sum up, on an average, the selected frms have defct of 25 percent n overall producton effcency, 17 percent n pure producton effcency and 9 percent n scale effcency. It s suggested that the garment frms should frst gve more emphass on mprovng the effcency n convertng the nputs nto output (PPE) and then on mprovng the scale effcency through adustng the plantsze at the optmum scale. Target Settng for Ineffcent Frms DEA dentfes nput and output targets for an neffcent frm to render t relatvely effcent. Each of the frms can become effcent by achevng these targets, determned by the effcent reference set for that frm. The neffcent frm can become techncally effcent by maxmzng the outputs. The actual and target nputs and output are gven n Table 5. Table 5. Actual and Target Inputs/Outputs of the Garment Frms (CCR Model) Actual Inputs/Outputs Target Inputs/Outputs Frm Garments/year Operator Machnes Garments/year Operators Machne Codes s s F1 300000 150 75 300000 150 75 GF2 400000 230 120 460000 230 115 GF3 200000 160 80 320000 160 80 GF4 300000 225 100 400000 200 100 GF5 250000 180 90 360000 180 90 GF6 240000 170 90 340000 170 85 GF7 250000 250 90 360000 180 90 GF8 1400000 1500 500 2000000 1000 500 Geom. mean 332999 248 113 445682 223 111 It s observed that except GF1 all remanng frms have to maxmze the outputs to operate at the level of the effcent one. For nstance, GF7 may have to reduce the number of employee from 250 to 180 and needs to ncrease the number of garments produced per year from 250000 peces to 360000 peces. On an average, the garments frms have to ncrease the output by 25 percent along wth the reducton of 10 percent and 1 percent n operators and machnes respectvely. V. Rato Analyss vs. DEA Analyss The conventonal effcency measurement n the garment ndustry consders only a sngle nput and a sngle output. In case of the garment frm GF8 and GF3 the garments per operator (GPO) are 933 and 1250 respectvely as shown n Table 6. Here, f we compare the frm GF8 wth GF3, the frm GF 3 s rated to be more effcent as t produces a hgher number of garments per operator per year. Ths analyss does not take nto consderaton the other nputs lke machne. In order to produce a garment, the frm needs machne, Artcle Desgnaton: Refereed 9
operator, raw materal, energy and other nputs. If we consder the other rato,.e., garments per machne (GPM), we fnd that the frm GF8 has a relatvely hgher productvty (2800 GPM) than that of GF3 (2500 GPM). If we compare the overall producton effcency scores of these two frms, we fnd that GF8 has a better performance than GF3. Thus, the results based on a sngle rato may provde msleadng conclusons related to the performances of a frm. In ths context, DEA s an approprate technque, as t consders multple nput-output varables to measure the relatve performance of ndvdual frms. Garment Frm Table 6. DEA effcency scores and Rato Analyss Indcators DEA Effcency score Rank Garments produced/ operator Garments produced/ machne GF1 1 1 2000 4000 GF2 0.87 2 1739 3333 GF3 0.63 8 1250 2500 GF4 0.75 3 1333 3000 GF5 0.69 6 1389 2778 GF6 0.71 4 1412 2667 GF7 0.68 7 1000 2778 GF8 0.70 5 933 2800 VI. Conclusons Ths paper estmates the producton effcency of the eght garment frms located n Bangalore, Inda usng the DEA technque. The emprcal results suggest that seven out of eght frms are techncally neffcent. That s, these frms have not produced the maxmum attanable output usng the avalable nputs and technology. On an average, the frms have to ncrease the actual producton of garments by 25 percent to acheve the target outputs. In addton, techncal neffcency has been found due to both neffcent scale-sze and resource-utlzaton. The frms are 25 percent neffcent n overall producton effcency, 17 percent neffcent n pure producton effcency and 9 percent neffcent n scale effcency. It s suggested that the garment frms should frst gve more emphass on mprovng the effcency n convertng the nputs nto output (PPE) and then on mprovng the scale effcency through adustng the plant-sze at the optmum scale. Most of the frms are found to operate under the decreasng return to scale. Ths shows that the frms have the excess producton capacty that could not be utlzed effcently n the year 2008. The DEA gves the overall producton effcency, pure producton effcency, scale-effcency, benchmarks, and nputs and output targets for the garment frms. On the other sde, the usual performance ndcators such as the number of garments produced per operator or per machne cannot provde the overall performance evaluaton. Therefore, results based on a sngle rato may provde msleadng conclusons related to the performances of a frm. In ths context, DEA s an approprate technque, as t consders multple nput-output varables to measure the relatve performances of ndvdual frms. VIII. Acknowledgments We are thankful to the referees for valuable comments and suggestons. We are also thankful to Mr. Lokesh, a garment consultant, for cooperaton n collectng the data. Notes Artcle Desgnaton: Refereed 10
1. TFP s a rato of weghted sum of outputs to the weghted sum of nputs over a perod. 2. Producton effcency means producng the maxmum quantty of output usng several nputs. We have used producton effcency as a synonymous word for techncal effcency. 3. Constant returns to scale arses when a proportonal ncrease n the value of all nputs results n the same proportonal ncrease n outputs of the frm. 4. Varable return to scale s defned as the output may change n the ncrease or decrease n proporton to the change n nputs. 5. The nput orentaton measures the nput quanttes, whch can be proportonally reduced wthout changng the output quanttes produced. 6. The output orentaton measures the output quanttes, whch can be proportonally expanded wthout alterng the nput quanttes used. 7. Mult Fbre Agreement was the restrctons on mport and export of textle and clothng from 1974 to 1994. The MFA was fnally expred n 1994 and phased out n four phases durng the perod 1995-2004. Wth the elmnaton of all remanng quotas n textles from January 1, 2005, the textle and apparel ndustres have now fully ntegrated nto the WTO. Now, buyers are thus free to source textle and apparel n any amount from any country. Supplers are free to export as much as they are able whch s subected only to a system of natonal tarff. 8. The Indan garment ndustry was protected for small-scale ndustry untl 2000. There were restrctons on the nvestment n plant and machnery on large scale n the ndustry. 9. Pure producton effcency s attrbuted to effcent converson of nputs nto outputs n whch effect of plant-sze s neutralzed. 10. Scale effcency s the extent to whch a frm can take advantage of return to scale by alterng ts sze towards the optmal scale. 11. A reference set s a set of effcent frms, whch acts as a reference pont for neffcent frms. 12. Peer count shows how many tmes an effcent frm has been referred n the reference set of neffcent frms. Best practce frm wll have a hgher peer count and can be consdered as a benchmark for the neffcent frms. 13. Most productve scale sze s that sze at whch a frm obtans 100 percent pure producton effcency and scale effcency. 14. Decreasng returns to scale exsts when output ncreases less than the proportonal ncrease n the nputs. IX. References Banker, R. D., Charnes, A. and Cooper, W. W. (1984). Model for estmatng techncal and scale effcences n Data Envelopment Analyss. Management Scence, 30(9), 1078-1092. Bhandar, A. K. and Mat, P. (2007). Effcency of Indan manufacturng frms: Textle ndustry as a case study. Internatonal Journal of Busness and Economcs, 6(1), 71-78. Bhandar, A. K. and Ray, S. C. (2007). Techncal effcency n the Indan textles ndustry: A nonparametrc analyss of frmlevel data. Retreved 20 November 2008, from Unversty of Connectcut webste: http://www.econ.uconn.edu/workng/2007-49. Bheda, R., Sngla, M. L. and Narag, A. S. (2001). Productvty n Indan apparel manufacturng ndustry. Productvty, 42(3), October-December 2001, 427-430. Bheda, R. (2002). Productvty n Indan apparel ndustry: Paradgms and paragons. Artcle Desgnaton: Refereed 11
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