This work is aimed at optimizing the operational management bydeciding upon articles composition to be made on differentprocess flow and capacity within an industrial situation. Morespecifically: matching the quantity and arrangement of ordervariation to assign appropriate tasks to applicable productionunits.The problem arises due to various process flows and task timenecessary to produce different articles. Appropriate productcomposition is expected to minimize set up time, and increaseoverall machine utilization and efficiency. Similar issue have astrong importance in textile industries, eminently in filamentwoven textile manufacture in which the process load variesdynamically upon fabrics construction.To present the solution, an MRP model is constructed as apreliminary analysis on process and raw material requirementfor each order. The MRP model\u27s output will be mapped into acapacity map that is constructed based on real life machinescapacities and task times. Subsequently, product mixcombination is derived through application of linearprogramming simulation to minimize capacity waste

Firman, A. F. (Aries)

Liong, A. C. (Anna)

This work is aimed at optimizing the operational management bydeciding upon articles composition to be made on differentprocess flow and capacity within an industrial situation. Morespecifically: matching the quantity and arrangement of ordervariation to assign appropriate tasks to applicable productionunits.The problem arises due to various process flows and task timenecessary to produce different articles. Appropriate productcomposition is expected to minimize set up time, and increaseoverall machine utilization and efficiency. Similar issue have astrong importance in textile industries, eminently in filamentwoven textile manufacture in which the process load variesdynamically upon fabrics construction.To present the solution, an MRP model is constructed as apreliminary analysis on process and raw material requirementfor each order. The MRP modelâ€™s output will be mapped into acapacity map that is constructed based on real life machinescapacities and task times. Subsequently, product mixcombination is derived through application of linearprogramming simulation to minimize capacity waste.Keywords: textile, MRP, linear programming, productionplanning, strategic capacity mappin

Liong, Anna Carolina

Firman, Aries F

SBM ITB Journal System

118  THE INDONESIAN JOURNAL OF  BUSINESS ADMINISTRATION Vol. 1, No. 2, 2012: 118-122    DEFINING THE PRODUCT MIX BASED ON STRATEGIC CAPACITY MAPPING IN WOVEN TEXTILE MANUFACTURE   Anna Carolina Liong and Aries F. Firman School of Business and Management  Institute Teknologi Bandung, Indonesia lionganna@gmail.com   Abstract—This work is aimed at optimizing the operational management by deciding upon articles composition to be made on different process flow and capacity within an industrial situation. More specifically: matching the quantity and arrangement of order variation to assign appropriate tasks to applicable production units. The problem arises due to various process flows and task time necessary to produce different articles.  Appropriate product composition is expected to minimize set up time, and increase overall machine utilization and efficiency. Similar issue have a strong importance in textile industries, eminently in filament woven textile manufacture in which the process load varies dynamically upon fabrics construction. To present the solution, an MRP model is constructed as a preliminary analysis on process and raw material requirement for each order.  The MRP model’s output will be mapped into a capacity map that is constructed based on real life machines capacities and task times.  Subsequently, product mix combination is derived through application of linear programming simulation to minimize capacity waste.   Keywords:  textile, MRP, linear programming, production planning, strategic capacity mapping  I.  INTRODUCTION  In After the signing of CAFTA Agreement in 2008, textile companies in Indonesia have been facing fierce competition to survive. With ever increasing labor and electricity cost and auxiliary price, Textile companies have to survive with very thin margin. Thus make continuous effort to increase efficiency, effectiveness and innovation extremely important. As the ground base of fashion industry, appropriate machine composition is crucial to increase the survival rate of a mill upon shift in fashion trend. Thus, suitable product to machine capacity mapping needs to be applied to accommodate the machine composition to achieve high utilization and production output rate. A strategy in choosing the right product mix to increase machine utilization and diversify risk has to be derived to increase competitiveness.  II. BUSINESS ISSUE EXPLORATION  A.  Conceptual Framework Surviving in oligopoly competition, it is crucial for textile companies to maximize their output to reduce fixed cost per unit fabrics. In order to boost its competitive advantage, the company has to find appropriate product mix that maximizes its machine utilization rate and productivity.  Inappropriate capacity mapping causes waste of resources, stacking up inventory in buffer of each process, and machine down time due to raw material unavailability.  Based on the commonly run fabrics construction in the market, the research tries to propose a generic solution/formulation of the correct product mix for the installed capacity in a weaving mill through strategic capacity management and line balancing approach.  Since similarity to real problem is crucial, this analysis is conducted in a weaving mill that performs in house pre-weaving treatment.  Aside from repackaging of the yarn, sizing, twisting, interlacing and process will be taken into account to add complexity to mimic real life situation. Sizing of warp yarn is required except for twisted and highly interlaced yarn.  Based on the process flow, the product can be categorized into three:  • Fabrics made of Twisted Warp and Twisted Weft Yarn  Both warp and weft have to undergo twisting process  • Fabrics made of Sized Warp and Twisted Weft Yarn  Warp yarn will be repackage in sizing machine prior to application of sizing agent, while weft yarn has to undergo twisting process • Fabrics made of Sized Warp and Non-Twisted Weft Yarn.   The complete process flow is described in the figure below: B. The MeDivergeimpact in task time Thus, the uto the lengtHence, pre existincalculate thof the mach C. AnalysiFor eacgradually mechanics machine aluntil it reagradually dOn ideaorder shoulthe pattern downtime unavailabilcomparisoncompare to  Irregulathere is laproduction Liong and FirFigure 1thod of Data Cnce of severalthe overall peof a weaving se of primary dhy period of dathe project wig daily, weee efficiency, pines.  s of Business Sh order, the increase depavailable. A located for an och its targetecrease until thl situation, thed follow trapeindicates overdue to chanity of raw m between ide real life situatFigure 2. Macrities was obsck of coordiplanning.  man / The Indo. Process Flow  ollection and  hours in the tarformance duebeam is arouata analysis ista collection. ll use secondakly and moroductivity anituation  number of mending on tnormal produrder should ined productione end of the b graph of machzium like shaptime of mechage in scheduaterial. Figural graphs of ion.  hine Running Cerved in seriesnation or sudnesian Journal o  Analysis sk time has lit to the averand 20-30 day not suitable d  ry data based nthly report d utilization raachine run whe number ction curve ancrease gradual rate, and theam. ine run for eae. Irregularity nics or machiling policy e 2 shows tmachine run  hart  3, showing thden change f Business Adm tle ge s. ue on to te ill of d ly en ch of ne or he as at in SuchmachinTheflows rrecognistarvati In strategiorder aproduceto be ceach ordA. DefmapIn orderneeds resourcTheThequantityin a unbased oeach prthe weaaccordinallocatiis illustrFabFilaWThefinis  Procs Pirn Winding Two for One  Vacuum Heat SettCreeling/ WarpingBeamingLeasing Drawing  inistration, Vol. changes are e downtime du problem arisequired to fabzing the capon and stockinIII. Border to detec capacity mand define thd. Prior to the onstructed to er quantity.  ining product mping   to construct tto be generes and work ho MRP Model  MRP model  as input and it of work hon the batch siocess. Due to ving process, tg to the fabon and work hated below: rics Construcment based fabarp Denier –WWarp DensW tables belowh an order basTablees Unit of Resources   Spindles   Spindles    er  Box  1 VHS Box =SpindlesMachine  1 Creeling Machine = n Spindles/ ConMachine  Weaving Bea= M meters Machine  In Rack / Operat1, No.2, 2012: 1observed to be to set up times due to thericate differenacity mapping up buffer in cUSINESS SOLU rmine the bup is constructe ideal quantcapacity map define the reqix through mhe mapping taated to calcuur to complet  takes up fabricalculates the ur. The resouze mapping anthe dynamic whe batch size arics constructour based on tion  rics constructiarp Twist x WTwist; ity x Weft DenOr d1 T1 x d2 T2d x Pick X In show the reed on the fabri 1. Warp PrepResourTotal end = Machines RMachines R n VHS Requies Machines R m Process ReqProcess Reqor  Process Reqmachine all 18-122 e the main caue and waiting t variety of prt articles. Failug might leaertain processTION siness solutied to simulatity that shou, an MRP moduired resourcachine to prodble, an MRP mlate the reqe an order projcs constructionecessary resorces requiremd the work hoork load natund work hour ion. The resofabrics construon is defined aeft Denier - Wsity x Width ; ch source requircs constructionaration ces Needed  Wd x Width equired  = equired  = red = equired  = uired = uired = # of Beamuired = # of weavinocated se of ime.  ocess re in d to . on, a e real ld be el has es for uct odel uired ect.  n and urces ent is ur of re of varies urces ction s :  eft ed to .      g Liong and Firman / The Indonesian Journal of Business Administration, Vol.1, No.2, 2012: 118-122 120 Table 2. Weft Preparation  Process Unit of Resources Resources Needed Pirn Winding Spindles   Spindles Required = ்௢௧௔௟ ௐ௘௙௧ ௅௘௡௚௛௧௉௜௥௡ ஼௔௣௔௖௜௧௬ TFO  Spindles   Spindles Required = ்௢௧௔௟ ௐ௘௙௧ ௅௘௡௚௛௧௉௜௥௡ ஼௔௣௔௖௜௧௬ VHS  Box  1 VHS box = n Spindles VHS Required = ்௢௧௔௟ # ௢௙ ௉௜௥௡௏ுௌ ஼௔௣௔௖௜௧௬ Winding 2  Spindles    Spindles Required = ்௢௧௔௟ ௐ௘௙௧ ௅௘௡௚௛௧஻௢௕௕௜௡ ஼௔௣௔௖௜௧௬  Defining the Task Time and Batch Size of Each Process  The task time of each process in weaving and weaving preparation is defined as follow:  • Weaving  The production task time in weaving machine is calculated as: ܨܾܽݎ݅ܿܽݐ݅݋݊ ܵ݌݁݁݀ ൌ  ܴܲܯܲ݅ܿ݇ ݄݅݊ܿ/݉݅݊ݑݐ݁ݏ  ܤ݁ܽ݉ ܶܽݏ݇ ܶ݅݉݁ ሺ ܣ݃݁ሻൌܤ݁ܽ݉ ܥܽ݌ܽܿ݅ݐݕ ሺ݈݄݁݊݃ݐሻܨܾܽݎ݅ܿܽݐ݅݋݊ ܵ݌݁݁݀  • Drawing In and Leasing The drawing in process tasks speed is defined as n/minutes while n equal to the number of warp yarn in a fabric construction.    • Beaming, Creeling/Warping and Sizing   The speed of beaming, creeling/warping and sizing process is defined in m/minutes unit.  • Vacuum Heat Setting  The VHS process task time is defined in T minutes.  • Twisting  The yarn speed of TFO process is defined as: ܻܽݎ݊ ܵ݌݁݁݀ ൌ  2 ݔ ܴܲܯ ܶݓ݅ݏݐ ܲ݁ݎ ܯ݁ݐ݁ݎ   And thus TFO task time =  ܶܽݏ݇ ܶ݅݉݁ ൌ  ܻܽݎ݊ ܮ݄݁݊݃ݐܻܽݎ݊ ܵ݌݁݁݀  • Winding Machine Winding machine is used to repackage yarn and its task time is defined in m/minutes unit.   Strategic Capacity mapping and Linear Programming   Based on the MRP Model, an aggregate MRP model is constructed to forecast the amount of resources and work hour required to finish a variety of orders quantity and fabrics construction that comes simultaneously.  The capacity used for a total order running is calculated by summating the entire working hour and resources needed by the total order.   Capacity Used The Capacity Used on each 4 core processes is formulated as:  Weaving Capacity Used = 1ܴܲܯ ݔ 60 ݔ 39.37  ෍ ܲ݅ܿ݇௜ ݔ ܳݑܽ݊ݐ݅ݐݕ௜௡௜ୀଵ   Sizing/ Beaming/ Creeling Capacity Used = 1ܵ݌݁݁݀ ෍  ܳݑܽ݊ݐ݅ݐݕ௜௡௜ୀଵ   TFO Capacity Used for Warp   =  ଵ்ிை ௌ௣௜௡ௗ௟௘௦ ቀ∑ ொ௨௔௡௧௜௧௬೔ ௫ ௐௗ೔ ௫ ௐ௜ௗ௧௛ ೔ ௫ ௐ௔௥௣ ்௉ெ೔ଶ ௫ ோ௉ெ೔௡௜ୀଵ  ቁ   TFO Capacity Used for Weft =  ෍ܳݑܽ݊ݐ݅ݐݕ௜ ݔ ܲ݅ܿ݇௜ݔ ሺܹ݅݀ݐ݄௜ ൅ 4ሻݔ ܹ݂݁ݐ ܶܲܯ௜2 ݔ ܴܲܯ௜௡௜ୀଵ  A linear programming approach will then be applied to modify the order quantity to seek the combination of order that minimize the capacity waste.    Minimize (Capacity Waste) = Total Capacity Available – Capacity Used   The Application of The Strategic Capacity Mapping Model  The Application of the strategic capacity mapping model is illustrated below  1. Input Field  a. Fabrics Construction Fabrics and its construction should be inputted into the model since the workload of the machine is dynamic to the construction.  Figure 3. Fabrics Construction  b. Available Machines  and Machine’s Setting  The fields take up the number of available machines in a mill and their setting.  Liong and Firman / The Indonesian Journal of Business Administration, Vol.1, No.2, 2012: 118-122  121  Figure 4. Machine Variables  c. Solver Variable  The solver variable is used in linear programming to calculate the excess capacity in the mill according to the order quantity available.   Figure 5. Solver Variables  2. MRP Cells The MRP Cells will display the necessary resources according to the fabrics construction and quantity.   Figure 6. MRP Cells  3. Result   The result of the application of linear programming to the strategic capacity mapping model for the product mix problem is illustrated in the table below:  Table 3. Product Mix Problem Solution  Adjustable Cells Cell In meter Fabrics  $E$23 69,992.34 Faille I  $E$24 77,344.89 Chiffon Crepe  $E$25 58,927.07 Lining  $E$26 69,711.69 Faille II $E$27 76,982.66 Sateen  $E$28 86,592.77 China de Crepe  $E$29 62,958.56 Faille III$E$30 84,216.70 Wool Peach  $E$31 103,866.06 Double Chiffon  $E$32 71,517.52 Faille IV $E$33 118,724.41 Chiffon - 22  $E$34 83,867.76 Chiffon -27 $E$35 58,199.72 Crepe 200 $E$36 60,801.60 Suiting I  $E$37 68,775.83 Suiting II  $E$38 66,374.53 Suiting III  $E$39 68,029.53 Batik    B. Analysis of Business Solution   The model was constructed to derive the machine-product composition mapping as an improved production strategy.  Run on forecasted fabrics construction, the model has shown that a mixture of the three products will be able to increase production output by 30% as compare to running of all sized warp or all twisted warp on a mill consisting of 80 twisting machine, 1 sizing machine, 1 creeling machine and 172 weaving machines. Different machine combination and fabrics construction will yield different result though the same algorithm applies.  Without taking into account the scheduling policy and the time of order placing, strategic capacity mapping and linear programming can be used to increase efficiency and machine utilization rate through suitable product composition. The application of the strategic capacity mapping model has shown that machine-product composition and quantity affect the total output of the mill to a great extend.  The model should also be taken into account upon deciding the bottleneck process that requires capacity addition. It can also be applied on new investment planning to formulate twisting –sizing to weaving capacity ratio based on forecasted market strategy.  IV. CONCLUSION AND IMPLEMENTATION PLAN  A. Conclusion   Based on the research, it is concluded that strategic capacity mapping can be used to formulate generic product mix solution.  Implementing strategic capacity mapping through the excel model, minimum capacity waste can be achieved by mixing twisted warp and sized warp order. Inputting regular market items on the excel model generate a guideline for the factory to decide upon which order to choose to improve their efficiency.   Based on the scheduling data, bottleneck process can also be detected. Running the strategic capacity map, the necessary capacity addition can be detected in order to achieve higher overall utilization rate.  B. Implementation Plan   The action plan for the product mix solution is define as follow:  Liong and Firman / The Indonesian Journal of Business Administration, Vol.1, No.2, 2012: 118-122 122  Figure 7. Implementation Plan  After the model design and the product mix formulation have been defined, the new product mix strategy is introduced to marketing department to align the order with the product mix. The production performance will then be assessed to analyze the impact of the new strategy to the utilization and efficiency rate.  Afterwards, the data and constraint in regards to the product mix solution should be collected to perform model customization before the final application of the model. Since the mill takes up order around 1 month prior to the production, the product mix formulation can only be assessed in two months after aligning with marketing department.  The constraint and downfall of the product mix solution will be collected as daily report upon the application of the product mix solution. Model customization will be done if necessary before the final application of the strategy.    ACKNOWLEDGMENT  This paper is written based on the author’s final project at MBA ITB supervised by DR. IR. Aries F. Firman, who has been relentlessly motivating the author to accomplish the final project.   REFERENCES Chase, B. J. (2006). Operation Management for Competitive Advantage with Global Cases , Eleventh Edition . New York : Mc Graw-Hill . Eric Sugiarto, M. (2011). Perancangan Sistem Optimasi Rantai Produksi Divisi Twisting dan Model Sistem Teknologi Informasi Pendukungnya. Bandung: MBA-ITB. Giovanni Castelli, S. M. (2000). Reference Books of Textile Technologies - Weaving. Milan: Italian Association of Textile Machinery Producers Moral Body. Kadolph, S. J. (2007 ). Textiles Tenth Edition =. New Jersey : Pearson Prentice Hall . Sabit Adanur, P. (2001). The Handbook of Weaving . Switzerland: Sulzer Textile Limited Zunitch, V.M. 2003   

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Defining The Product Mix Based on Strategic Capacity Mapping In Woven Textile Manufacture

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Defining the Product Mix Based on Strategic Capacity Mapping in Woven Textile Manufacture

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