DEVELOPMENT OF SUPPLY CHAIN TOOLS IN IMPROVING THE EFFICIENCY OF MANUFACTURING UNIT- A CASE OF INDIA CEMENTS LIMITED

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1 DEVELOPMENT OF SUPPLY CHAIN TOOLS IN IMPROVING THE EFFICIENCY OF MANUFACTURING UNIT- A CASE OF INDIA CEMENTS LIMITED S. SHAKEEL AHAMED Assocate professor, Department of Mechancal engneerng Madna Engneerng College, Kadapa, Andhra Pradesh, Inda E-mal: shaeeldp@gmal.com Dr.G. RANGAJANARDHANA Professor & Prncpal, Department of Mechancal Engneerng Unversty College of engneerng JNTUK Vayanagaram Campus Vayanagaram, Andhra Pradesh, Inda E-mal:ranga.anardhana@gmal.com Dr.E. L. NAGESH Prncpal, Nobel college of Engneerng and Technology for women RR dst, Hyderabad, Inda. E-mal:el.nagesh@gmal.com Abstract. Inventores, Transportaton, Facltes are the mportant tools of supply chan management. Inventory s one of the ey determnants of the productvty of cement ndustry. To effectvely manage nventory levels, t s essental to consder the approprate reorder ponts and the optmzed orderng quantty at that reorder pont. In ths research wor the orderng quantty and reorder ponts that mnmzes the cost are found out usng the concepts of Genetc algorthm by usng the demand rate as well as the assocated soluton demand matrx. Routng has become one of the most mportant types of supply chan management tools n managng the global supply chan. Modfyng the organzaton s capablty to generate products and servces n par wth customer demand s the obectve of faclty plannng. The proposed system s also used to fnd the optmzed usage of the faclty and fnds the best routed suppler wth mnmum routng cost. Keywords: orderng quantty, reorder pont, Genetc algorthm, routng cost. 1.Introducton The productvty of any manufacturng organzaton depends on the avalablty of raw materals and other component parts n the proper quantty, qualty, prce range, and tme. Proper control over nventores provdes the management wth flexblty n mang purchases systematcally rather than buyng strctly accordng to the producton schedule and hand to mouth supples. Effcent management ams at ncreasng the level of nventores as long as the resultng economes and benefts exceed the total cost of holdng such nventores. Proper control over nventores mproves the productvty and proftablty of the enterprses. It also helps n achevng hgher return on nvestment by mnmzng loced up worng captal and also mprovng the cash flow and lqudty poston. Inventory control must meet two opposng needs, vz., () mantenance of an nventory of suffcent sze and dversty for effcent operatons and () mantenance of a fnancally favorable nventory. The basc obectve of nventory management s to optmze the sze of nventory n a frm so that the smooth performance of producton and sales functons may be possble at mnm cost. 2.Proposed wor The author n the proposed wor has selected 4 raw materals that are used n INDIA CEMENTS LIMITED adapa A.P and the average demand for a duraton of one year s taen. The supplers who supples ISSN : Vol. 3 No.12 December

2 the raw materals along wth the purchasng cost, transportaton cost, holdng cost,shortage cost and order cost are consdered for generatng the optmzed orderng quantty at proper reorder pont. The proposed system s also used to fnd the optmzed usage of the faclty of the manufacturng unt and also t fnds the best routed suppler wth mnmum routng cost. 3.Optmzed Inventory Control Usng Genetc algorthm. Let MN be the manufacturng system whch uses the raw materals R { R1, R2, R3} producton where the stoc level of the partcular raw materal mantaned by the manufacturng channel must be adequate to reduce the shortage and mnmal to avod the holdng cost. To estmate the optmzed quantty of order and optmzed reorder pont of MN for the perod of M M, M, M }; 1 12, the { 1 2 demand rate of each raw materal for the past M perod s consdered. Let the demand rate for every materal D D1 1,, R ; 1 M D1 s the demand for the th raw materal for the 1 where n R, be th month whch s predcated usng the observed hstorcal data. (.e.) D1 11 s the predcted demand rate for the raw materal R1 for the month M 1 and D1 21 s the predcted demand rate for the raw materal R 2 for the month M 1 and so on. 3.1 Assocated Soluton demand matrx generaton The assocated soluton demand matrx D D 2 N ; 1,, R ; 1 M 2 max whch contans the expected soluton demands for every raw materal for the perod of M s generated usng the predcted demand rate D 1 where N max Max( D1) 0.20 Max( D1). The randomly generated soluton demand rate for every raw materal s less than N max and every row of the assocated soluton demand matrx gves the expected orderng quantty of each raw materal n R respectvely.(.e.) the frst row of D 2 s the expected soluton demand for the raw materal R1 for the perod of M months, the second row s the expected soluton demand for the raw materal R 2 and so on. 3.2 Determnaton of optmal orderng quantty and reorderng pont usng genetc algorthm The proposed system fnds the optmzed orderng quantty and reorderng pont by means of Genetc Algorthm usng D 1, the predcted demand rate for the raw materal, usng the hstorcal data and D 2, the soluton demand matrx whch contans the generated orderng quantty of the raw materal. 3.3 Populaton generaton and chromosome representaton The genetc algorthm whch ncorporates a ftness functon to numercally evaluate the qualty of each chromosome wthn the populaton, searches for the optmzed orderng quantty and reorder pont. Let R be the raw materal used by MN. The populaton conssts of a group of ndvduals called chromosomes each of whch represents a complete soluton to the defned problem. Intally N c No of chromosomes are randomly r generated. Let the populaton created be P ˆ x( ) : 1, 2 R ; 1 r N where r x ( ) denotes the th gene of the r th chromosome. Each gene represents a randomly generated number between 0 and 2 1 whch s subsequently encoded by employng a decmal to bnary encoder where R s the number of raw materals. 3.4 Ftness evaluaton The genetc algorthm searches for the chromosome wth hghest ftness, where the ftness functon s used to assess the qualty of a gven chromosome wthn the populaton. To fnd the optmzed orderng quantty and c R for ISSN : Vol. 3 No.12 December

3 reorder pont the proposed system uses the D 1, the predcted demand matrx and D 2, assocated soluton demand matrx for fndng the ftness of the chromosome. Let X be the th value of the th ene. The ftness of the chromosome s calculated as follows. FC R FG ; where, 1 M, FGh If P Hc FG and FGs If P Sc Hc P Sc 0 If If If D1 D1 D1 FGh FGs X 30 * ( V V ) * P) ( Pur * ) Ord ) and (( 1 X (( 30 * ( V ) * P) ( Pur * ) Ord ) where V D1 the Depends on the devaton value of the wth D1, the ftness of the gene value changes. If the assocated soluton orderng quantty of the th raw materal for the th month s greater than the D1 then the ftness of the correspondng gene s calculated wth the holdng cost. If the s less than D1, the gene value s calculated wth the shortage cost. The ftness functon s carred out for every chromosome and the best two parent chromosomes are selected accordng to ther hgher values n ftness. 3.5 Crossover Crossover s also nown as recombnaton of component materals due to matng. The outcome of crossover heavly depends on the chromosomes selected from the populaton. Crossover s a bnary genetc operator actng on two parents. Dfferent crossover operators have been developed for varous purposes. The sngle pont crossover operator selects a crossover pont wthn a chromosome at random by usng the cross over rate. Subsequently, genes of the two parent chromosomes n between the pont are nterchanged to produce two new off sprngs. The crossover ponts c 1 s determned as follows, where R denotes no of raw materals and R a denotes the cross over rate. C 1 M * R a 3.6 Mutaton One or more gene values n a chromosome from ts ntal state s altered by the genetc operator nown as mutaton, whch may lead to entrely new gene values beng added to the gene pool. The genetc algorthm may be capable of arrvng at a better soluton than the soluton prevously acheved by employng these new gene values. Owng to the fact that mutaton helps to prevent the populaton from stagnatng at any local optma, t s consdered as an mportant part of the genetc search. Mutaton operator occurs n accordance wth a userdefnable mutaton probablty durng the evoluton. Fgure.1 llustrates a smple example, performed over a chromosome of four genes where the mutaton operator smply nverts the value of the chosen gene (0 to 1 and 1 to 0). The second gene s mutated n ths example. ISSN : Vol. 3 No.12 December

4 3.7 Selecton of optmzed reorder pont Fg 1: Mutaton process. The process n 3.1 to 3.6 are repeated T max number of tmes and best chromosomes are selected from the obtaned group of chromosomes. Here, the best chromosomes are the chromosomes whch have maxmum ftness. The obtaned best chromosome s used for decdng the orderng status of the raw materals (.e.) the reorder pont whch decdes when an order should be made. The value 1 n the gene value of the best chromosome represents the postve orderng status of the correspondng raw materal of the correspondng quantty n. For example f x(1), the frst gene of the best chromosome contans the value 100, the 1 represents the postve orderng status of the raw materal and consequent zeros represents the negatve orderng status of the raw materal. The best chromosome represents the reorder pont status of every raw materal for the perod of M months and the represents the correspondng optmzed orderng quantty. 4. Fndng effcent faclty agreeable soluton demand matrx and mnmum routng cost suppler The orderng quantty s mproved accordng to the faclty of the manufacturng unt by fndng the faclty agreeable effcent soluton demand matrx. Ths research also fnds the best routed suppler for orderng the products. Let MN be the manufacturng system whch uses the raw materals R { R1, R2, R3... Rn} for producton and these raw materals are shpped from the supplers S S, S, S... S }. The demand rate of { n each raw materal for the precedng M perod s forecasted to determne the optmzed amount of order and. Let optmzed reorder pont of MN for the perod of M M, M, M }; 1 12 { 1 2 D1 D1 1,, R ; 1 M be the forecasted demand rate for each materal n R, where D1 s the predcted demand for the th raw materal for the th month forecasted usng the observed hstorcal data. 4.1 Fndng the effcent faclty agreeable soluton demand matrx The forecasted demand rate D 1 s used to create the assocated soluton demand matrx N max ; 1,, R ; 1 M consstng of the forecasted soluton demands for each raw materal for the nterval M, where N max Max( D1) 0.20 Max( D1) The arbtrarly created soluton demand rate for each raw materal s smaller than N max and each row of the connected soluton demand matrx yelds the lely orderng amount of each raw materal n R. From the soluton demand matrx D 2 the effcent soluton demand matrx D Re ; f C ; 1,, R ; 1 M where Re s the reducton amount and Cnt s the no of postve orders n the th month. The generated Cnt orderng quantty n the soluton demand matrx s tuned to be effcent by usng the holdng capacty C of the ISSN : Vol. 3 No.12 December

5 manufacturng unt. The Pseudocode-1 represents the process of fndng the capacty agreeable effcent soluton demand matrx. The generated soluton demand matrx and the maxmum holdng capacty of the manufacturng unt s gven as nput to the procedure. The sum of orderng quantty of every postve order and the number of postve orders are calculated. If the sum of orderng quantty for a month n the demand soluton matrx s greater than the capacty of the manufacturng unt then the orderng quantty s adusted by the Re value so that t can satsfy the holdng capacty. Eventually, we obtan, an effcent soluton matrx that can satsfy the capacty of the manufacturng unt. Re value so that t can satsfy the holdng capacty. Eventually, we obtan, an effcent soluton matrx that can satsfy the capacty of the manufacturng unt Pseudo code 1: The process of fndng faclty agreeable effcent soluton demand matrx Input : Soluton Demand matrx D 2, Maxmum Holdng capacty C Output : The Resultant Soluton demand matrx wth faclty Parameters: M Months D Orderng quantty of th raw materal for the th month. 2 ( ) Re Reducng amount Pseudocode: For each M M Set S Set count th noof postveorder for month Set Re S / count For each If postve order and S C Re End If End For 4.3. Fndng the best routed suppler 'S' that are needed for The manufacturng unt MN purchases the raw materals ' R ' from the suppler producton. Each suppler has the dfferent routng cost for shppng the product from the suppler plant to the manufacturng unt. The same raw materal may have the dfferent routng cost among the varous supplers. For example for the raw materal R1 the Suppler-1 may fx the cost C1 where the Suppler-2 may have the cost C2 whch s greater than C1. The optmzed reorder pont and the raw materals to be purchased are generated usng the concepts of Genetc algorthm. The Table -3 llustrates the sample best chromosome whch represents the optmzed reorder pont of the raw materals for the M months. The table 4 represents that the raw materals to be purchased for the month M1 s R1, The 1 n the table llustrates the postve orderng ISSN : Vol. 3 No.12 December

6 status of the raw materal and 0 represents the negatve orderng status of the raw materal. Let PR ; be the set of the raw materals to be purchased for the th month, where , SC { SC ; 1.. S } be the set of raw materals that are suppled by the each suppler where SC { R ; 1..10} s the raw materals suppled by the th suppler and RC { RC ; 1..10} s the routng cost of the raw materals suppled by the th suppler. For example, from the table 4 the raw materals to be purchased for the 1 st month s R1. DA { DA ; 1..10} s the combnaton of the raw materals suppled by the suppler wth ther routng cost are separated and stored accordng to ther length wse Pseudo code II: represents the best routed suppler Input : Best Chromosome BC, The raw materals suppled by the Supplers SC, RC the routng cost of the raw materals suppled by the suppler, DA r the combnaton database. Output : The Suppler lst Parameters: M Months PR Purchasng raw materal PR comb Pseudo code: For each S wth mnmum routng cost for the requred raw materal. Combnaton lst of purchasng raw materal M M Get PR Generate PR comb Set l length(pr ) Randomly select r < l For each r Sel = r length data n PR comb If Sel exst n DA r RCost RC Endf SS = mn (Rcost) r RR = DA r (mn (Rcost)) r=r-1 End for S = S + RR End for The Pseudo code II represents the steps used for fndng the best routng suppler. From the best chromosome, the raw materal lst to be purchased for a month s dentfed and ther each combnaton lst s generated. The n combnaton lst of suppler havng the mnmum routng cost s found out frst and among ISSN : Vol. 3 No.12 December

7 n combnaton the combnaton havng the mnmum routng cost s selected for the frst month. Ths process s repeated for every month and the suppler lst S wth mnmum routng cost for the requred raw materal s generated. 5. Input data of Inda cements lmted. The raw materals used n INDIA CEMENTS and the monthly demand n tones are lsted below. Table 1. Demand matrx (D1) R/M Bauxte Iron ore Late rte Gypsum M M M M M M M M M M M M Table 1.1.Detals of cost S. no Bauxte Iron ore Late rte Gypsum Purchasng cost/ton Transportato n cost Unloadng cost Total cost(t C) Holdng cost(3% of T C) Shortage cost(2% of T C) ISSN : Vol. 3 No.12 December

8 Table 1.2 Supplers detals for the perod 01/04/2010 to 31/03/2011 S.no Raw materal suppler 1 Bauxte Ganesh enterprses Sr lashm transport Venatesh enterprses Ks transport 2 Iron ore Venatesh enterprses K r r transport Sr lashm transport 3 Late rte K r r transport Sdharta transport Ganesh enterprses Ks transport Sva shat transport 4 Gypsum Ganesh enterprses Sva shat transport Ks transport 6.Results. Ths secton detals the results and performance evaluaton of the proposed approach. The proposed approach s mplemented n the MATLAB platform (verson 7.10). The table 3 represents the sample best chromosome havng the optmzed reorder pont for orderng the raw materals. Table 2.Generated assocated soluton demand matrx. R/M M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 Bauxte Iron ore Late rte Gypsum Table 3. sample best chromosome Months R1 R2 R3 R4 M M M M M M M M M M M M ISSN : Vol. 3 No.12 December

9 From the Table 3 raw materals to be purchased are dentfed by ther values and Table 4 represents the purchasng lst of the raw materals to be purchased for the whole perod. The raw materals whch are suppled by the suppler are lsted and ther combnaton wth the routng cost s stored n the database accordng to ther length wse. Table 4. The Purchasng lst of raw materals. Months M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 Raw materals to be purchased R1 R4 R1 R4 R2 R2,R3,R4 R4 R1,R2,R4 R3,R4 R3 R2,R4 R2,R4 Table 4. Raw materal suppler(one combnaton) lst. Suppler Combnaton Routng cost Table 5. Raw materal suppler(two combnaton) lst. Suppler Combnaton Routng cost 1 1, , , , , , , , , , , ISSN : Vol. 3 No.12 December

10 Table 6. Raw materal suppler(three combnaton) lst. Suppler Combnaton Routng cost 1 1,3, ,3, Table 7. Best suppler combnaton lst wth the mnmzed routng cost Dataset 1 Resource => Suppler => Cost Month :1 1 => 1 => 917 Total Cost :917 Month :2 4 => 1 => 967 Month :3 1 => 1 => 917 Total Cost :917 Month :4 4 => 1 => 967 Total Cost :967 Month :5 1 => 1 => 917 Total Cost :917 Month :6 1 => 1 => 917 Total Cost :917 Month :7 1 => 1 => 917 Total Cost :917 Month :8 1 3 => 1 => Total Cost : Month :9 1 2 => 2 => Total Cost : Month :10 2 => 2 => => 1 => 967 Total Cost :2094 Month :11 3 => 1 => 1620 Total Cost :1620 Month : => 2 => => 1 => 967 Total Cost : ISSN : Vol. 3 No.12 December

11 7.Performance evaluaton The performance of the proposed approach s evaluated usng dfferent data set. The performance s evaluated by comparng the total routng cost gven by the recommended supplers by the proposed method wth the routng cost of the non recommend supplers. The fg 2 represents the comparson graph of the routng cost of the recommend supplers wth the routng cost of the non recommended supplers for dataset-1. 8.Concluson. Fg.2 performance comparson graph In ths case study wth the real exstng data of INDIA CEMENTS LIMITED. The orderng quantty at proper reorder pont has been found out and the total cost of the nventores s compared wth the proposed system to the exstng system. It s proved that the proposed system s much effcent than the exstng system. Further the Facltes(capacty) of the exstng system has been mproved based on the orderng quantty. The performance s evaluated by comparng the total routng cost gven by the recommended supplers by the proposed method wth the routng cost of the non recommended supplers. It s also proved that the proposed system s more effcent n selectng the best routed suppler havng mnmum routng cost. 9. References [1] Adel A. Ghobbar, Chrs H. Frend, The materal requrements plannng system for arcraft mantenance and nventory control: a note, Journal of Ar Transport Management, Vol.10, p.p , [2] Alp Muharremoglu, Nan Yang "Inventory Management wth an Exogenous Supply Process" Operatonal research, ssue X, Vol. 58, No. 1, pp , [3] Arumugam Mahaman and Karanam Prahlada Rao,"Development of a spreadsheet vendor managed nventory model for a sngle echelon supply chan: case study, Serban Journal of Management Vol.5 (2), p.p , [4] Aslam, Farruh, A. R. Gardez and Nasr Hayat, Desgn, Development and Analyss of Automated Storage and Retreval System wth Sngle and Dual Command Dspatchng usng MATLAB, World Academy of Scence, Engneerng and Technology [5] Azzul Baten and Anton Abdulbasah Kaml, "Drect soluton of Rccat equaton arsng n nventory producton control n a Stochastc manufacturng system",internatonal Journal of the Physcal Scences Vol. 5(7), pp , July [6] Behnam Fahmna, Lee Luong, Remo Maran, Optmzaton/smulaton modelng of the ntegrated producton dstrbuton Plan: an nnovatve survey, Wseas transacton on busness and economcs, No. 3, Vol. 5, March [7] Chn-Hsung Hsu, Chng-Shh Tsou, and Fong-Jung Yu, Multcrtra tradeoff n nventory control usng memetc partcle swarm optmzaton, Internatonal Journal of Innovatve Computng, Informaton and Control, No. 11(A), Vol. 5, November [8] Chtr Thotappa, Dr. K.Ravndranath, Data mnng Aded Profcent Approach for Optmal Inventory Control n Supply Chan Management, Proceedngs of the World Congress on Engneerng, Vol. 1, [9] Charu Chandra, Jāns Grabs,"Supply chan confguraton usng smulaton based optmzaton", Proceedngs of the 35th conference on wnter smulaton: drvng nnovaton, [10] Haruho Tomnaga, Tatsush Nsh, and Masam Konsh, "Effects of nventory control on bullwhp n supply chan plannng for multple company, Internatonal Journal of Innovatve Computng, Informaton and Control, No. 3, Vol. 4, March [11] Ismal,E. Hashm,J.A.Ghan,R.Zulfl,N.Kamlah,M. N. A. Rahman,"Implementaton of EIS: A Study at Malaysan SMES",European Journal of Scentfc Research, No.2,Vol..30, pp , [12] Jn-Hwa Song and Martn Savelsbergh, "Performance Measurement for Inventory Routng", Insttute for Operatons Research and the Management Scences, Vol. 41, No.1, February [13] Luca Bertazz, Martn Savelsbergh, and M. Graza Speranza "Inventory Routng",transportaton Scence, Vol.36, p.p.44-54,february [14] Lng-Feng Hseh, Chao-Jung Huang and Chen-Ln Huang, "Applyng Partcle Swarm Optmzaton to Schedule Order Pcng Routes n a Dstrbuton Center", Asan Journal of Management and Humanty Scences, Vol. 1, No. 4, p.p , ISSN : Vol. 3 No.12 December

12 [15] M. Sreenvas, T.Srnvas, Effectveness of Dstrbuton Networ, Internatonal Journal of Informaton Systems and Supply Chan Management, Int l Journal of Informaton Systems and Supply Chan Management, Int l Journal of Informaton Systems and Supply Chan Management, Vol.1(1), p.p.80-86, January-March [16] Mara Sarmento and Raesh Nagy,"A Revew of Integrated Analyss of Producton-Dstrbuton Systems", IIE Transacton,Vol. 31, No. 11, P.p , [17] M.Zadeh and S.Molla-Alzadeh-Zavaedeh,"synchronzed producton and dstrbuton schedulng wth Due wndow ", Journal of appled scences, Vol. 8(15), p.p , [18] Patrc J. Rondeau Lews A. lteral, Evaluaton of manufacturng control system: from reorder pont to enterprse resource plannng", producton and nventory management ournals, [19] Peters mleff, Károly Nehez,Tbor Toth, A new nventory control method for supply chan management, In proceedng of the 12th Internatonal Conference on Machne Desgn and Producton, September [20] P.Radharshnan, Dr. V.M.Prasad,Dr. M. R. Gopalan, Inventory Optmzaton n Supply Chan Management usng Genetc Algorthm, IJCSNS Internatonal Journal of Computer Scence and Networ Securty, No.1,Vol.9, January [21] Phlp Dogans, Elen Aggeloganna, and Haralambos Sarmves, A Model Predctve Control and Tme Seres orecastng Framewor for Supply Chan Management, World Academy of Scence, Engneerng and Technology [22] QM. He a, E.M. Jewes b, J. Buzacott c,"optmal and near-optmal nventory control polces for a mae-to-order nventory producton system European Journal of Operatonal Research Vol.141, p.p ,2002. [23] Sanoy Kumar Paul, Abdullahl Azeem,"Selecton of the optmal number of shfts n fuzzy envronment: manufacturng company s faclty applcaton, ournals of ndustral Engneerng and management, vol.3, no.1. p.p ,2010. [24] Sohel Sad-Nezhad a, Shma Memar Nahavanda and Jamshd Nazema,"Perodc and contnuous nventory models n the presence of fuzzy costs, Internatonal Journal of Industral Engneerng Computatons, [25] SombaSt ndhuchao,"a Very Large Scale Neghborhood (Vlsn) Search Algorthm for an Inventory- Routng Problem", ThammasatI nt. J.Sc.Tech.,Vol.ll., October-December [26] Steven P. Landry, Monterey,"Do Modern Japanese Inventory Methods Apply To Hong Kong?", Internatonal Busness & Economcs Research Journal,Vol. 7, No. 4 Aprl [27] S Shaeel ahamed, G. Ranga Janardhana,E.L.Nagesh, GA Based Inventory Control for Manufacturng Unt, publshed n Amercan Journal of Scentfc Research,2011. [28] Thomas Fg, Karl Isler, Crag Hopperstad,Peter Belobaba," Optmzaton of Mxed Fare Structures: Theory and Applcatons, Journal of Revenue and Prcng Management, 7th Aprl [29] V. A. Temeng,P. A. Eshun,P. R. K. Essey, "Applcaton of Inventory Management Prncples to Explosve Products Manufacturng and Supply A Case Study, Internatonal Research Journal of Fnance and Economcs, [30] Zuo-Jun Max Shen and Lan Q,"Incorporatng nventory and routng costs n strategc locaton models", European Journal of Operatonal Research S.Shaeel ahamed receved hs B.E degree n Mechancal engneerng from Bangalore unversty, Bangalore,Karnataa. Inda n 1992 and Mtech degree n Mechancal engneerng wth Industral engneerng as specalzaton from Sr venateswara unversty college of engneerng, SV Unversty Trupat, Andhra Pradesh,Inda and currently pursung hs PhD under the supervson of Dr.G.Rangaanardhana and Dr.E.L.Nagesh at JNTU Hyderabad. Presently he s worng as Assocate professor n Department of Mechancal engneerng,madna engneerng college adapa.a.p.he has publshed Two Techncal papers n nternatonal ournals,one paper n natonal ournal and one n nternatonal conference.. He s a lfe member of ISTE and Insttute of Engneers (olata). Dr. G. Ranga Janardhana s worng as Professor of Mechancal Engneerng and Prncpal for unversty college of engneerng JNTUK Vayanagaram campus,vayanagaram A.P, Inda. He s havng 19 years of teachng experence and 2 years of ndustral experence. He has guded 8 Ph. D. scholars and s gudng 10 research scholars. He s havng good number of publcatons n Internatonal and Natonal Journals and Conferences. He actvely nvolved n many academc and admnstratve actvtes n Jawaharlal Nehru Technologcal Unversty. He also wored as Exchange Professor at Hoseo Unversty, South Korea and pursued Post Doctoral Program n ths unversty. Hs areas of nterests are Intellgent Manufacturng Systems, Metal Castng, Machnng, and Industral Engneerng. ISSN : Vol. 3 No.12 December