ASSESSING THE AVAILABILITY AND ALLOCATION OF PRODUCTION CAPACITY IN A FABRICATION FACILITY THROUGH SIMULATION MODELING: A CASE STUDY
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1 Internatonal Journal of Industral Engneerng, 15(2), , ASSESSING THE AVAILABILITY AND ALLOCATION OF PRODUCTION CAPACITY IN A FABRICATION FACILITY THROUGH SIMULATION MODELING: A CASE STUDY J.H. Marvel 1, M.A. Schaub 2, and G.R. Weckman 3 1 Department of Management, Gettysburg College Gettysburg, PA Mark Schaub Consultng, Mt. Pleasant, SC Department of Industral and Manufacturng Systems Engneerng, Oho Unversty, Athens OH Correspondng author s e-mal: {marvelj@gettysburg.edu} For a ter two automoble suppler s fabrcaton faclty, the manufacturng process requred producng two classes of products, those produced on a repettve and those produced on a perodc bass. Statc capacty analyss determned f suffcent gross capacty exsted n the producton system n order to meet customer demand but was unable to determne f the capacty was avalable at the desred tmes to preserve an acceptable servce level. A smulaton model was developed to confrm the sequencng and schedulng of both classes of products. The model also ncorporated the logstcal constrants of customer suppled materals used n the producton process. The smulaton output was able to evaluate the system performance metrcs regardng materal avalablty, transportaton effcences, product backorders, and nterruptons to the producton process. The model provded a plannng tool that assessed the quarterly producton plan; dentfed customer servce ssues and evaluated the mpact of contnuous mprovement efforts. Sgnfcance: Keywords: Producton plans are often evaluated n a statc envronment. Ths paper descrbes a ter two automotve suppler s process of ntegratng the smulaton model nto the producton plannng and schedulng process. Smulaton, capacty analyss, capacty allocaton. (Receved 13 May 2006; Accepted n revsed form 20 May 2007) 1. INTRODUCTION Although many companes have adopted lean methods as ther preferred approach to addressng desgn and operatonal ssues of ther producton systems, many dscrete part manufacturers stll depend on manufacturng resource plannng or MRPII systems. These systems approach producton and capacty plannng as ntegrated processes. Aggregate producton and resource requrement plans produce the master producton schedules (MPS) and rough-cut capacty plans (RCCP) respectvely. The RCCP dentfes capacty ssues for crtcal work centers. Addtonal capacty ssues become obvous after bll of materal (BOM) explosons create the materals requrement plan (MRP). Usng nformaton from the MRP plan, the capacty requrements plan (CRP) valdates the capacty of ndvdual work centers before assgnng shop floor schedules (Spper and Bulfn, 1997). The MRP approach to settng shop floor schedules has well documented lmts (Watson et al. 1997), and affects of demand varablty and equpment avalablty compel the use of alternate technques to ensure that suffcent capacty exsts for producton schedules. The objectve of ths paper s to descrbe the approach of a make to order (MTO) company n the fabrcated metal product ndustry ncorporatng dscrete event smulaton to assess ther methods of allocatng producton capacty. Ths manufacturer s producton system contans many of the same manufacturng processes that are common n stamped and fabrcated metal wre products ndustres. The manufacturer receves orders for customzed products and flls them based on customer due dates. Long-term customer contracts produce most of the customer demand but shorter term contracts create producton demand for perodcally produced products. A product s processng characterstcs determne the specfc flow lne assgnment for manufacture. All of the product s processng operatons occur wthn ther assgned ISSN INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING
2 Assessng and Allocatng Producton Capacty Usng Smulaton Models flow lne snce there s no ntra-flow lne travel. Steel cols, varyng n materal type, gage, and slt wdth, are the raw materal needed for ths manufacturng process. These cols run through a contnual formng operaton that transforms the steel nto flexble, usable subcomponents n the automotve ndustry. Wndng the fnal product onto spools s the last step n the manufacturng process. These spools are stored n fnshed goods nventory untl shpments of product are sent to the customers. The customer, a ter two automotve suppler, assembles the products wth other tems to form a subassembly for the orgnal equpment manufacturer (OEM). The producton plannng process dentfes whch products, out of over seventy current products, are scheduled for producton on a repettve cycle and assgns those products to ther assocated flow lne. The producton of the products, not scheduled for repettve producton, occurs when the flow lnes have avalable or open capacty gaps. The producton plannng process does not have the ablty to account for varablty n spool avalablty caused by logstcs of spool transport between the manufacturer and the customer. At the begnnng of a product launch the customer supples the manufacturer wth a negotated quantty of spools. As the manufacturer fulflls customer orders and wnds the fnshed product onto these spools, they are then shpped to over twenty customers. The customers use these products n ther manufacturng processes, emptyng the spools of product, then returnng the empty spools to the manufacturer. A sgnfcant constrant n the system s that only customer specfc product can be wound onto each ndvdual customer s spools and spool sharng between customers does not occur. Managng the spools n the system s a key operatonal ssue n the producton system. Snce the number of spools n the system s fnte, the producton system acts as a closed queung system. The objectve of ntegratng the smulaton analyss nto the producton plannng process s not only to assess the avalable capacty n the system but also to evaluate the requred number of customer spools to allow for the most effcent operaton of the producton system. 2. LITERATURE SURVEY A sgnfcant amount of lterature on capacty allocaton problems are ether approached by developng mathematcal models to analyze capacty costs or by mplementng optmzaton approaches, such as dscrete event smulaton, to address capacty concerns. Some recent lterature has been addressng strategc capacty ssues. Dekkers (2003) examned capacty management of companes that developed and manufactured products based on customer demands on a strategc level. Results suggested that companes that dd not effectvely manage capacty decsons ncurred ncreased manufacturng costs and lead tme delays. Huh et al. (2006) also examne capacty decsons based on strategc ssues. Two dfferent capacty allocaton procedures, lost sales cost mnmzaton and unform fll rate producton, were consdered when demand exceeded supply. Chou et al. (2001) descrbed a process for ntegratng the three most common methods of capacty analyss; smulaton, statc capacty and queung capacty modelng. Varous mathematcal modelng approaches are also evdent. Ozdamar and Yazgac (1997) dscussed developng a lnear capacty plannng model whch focused on levelng producton loads n bottleneck departments. The model that they developed created a MPS that was feasble n terms of capacty utlzatons. Abdul-Kader and Gharb (2002) consdered the case of seral multstage producton lnes wth defned sequences. Ther approach used expermental desgn to determne the sze and placement of buffers as well the affect of these buffers on the capacty of the producton lne. The prevous work of Furmero and Vercells (1994) reasoned the volatlty and uncertanty n mxed make-to-order/assemble-to-order envronments have great dffculty n establshng requred capacty levels. They developed a mxed nteger optmzaton model for determnng capacty levels. Specfc capacty allocaton polces and ther cost mplcatons for frms tryng to address short-term varaton n product demand were analyzed by Bsh et al. (2005). They consdered nonflexble, fxed proporton and fully flexble allocaton polces. Comparson of these polces ncluded the mpact of product contrbuton margns and customer locatons. Aknc and Meredth (2006) analyzed capacty decsons n a make-to-forecast (MTF) envronment. They consdered a MTF envronment a specal case of a make-to-stock (MTS) envronment n whch producton occurs n antcpaton of customer demand and s not drven by forecasts or frm orders. A Markov analyss was used to compared order orphans (products wthout customers) to order rejectons (nsuffcent capacty). There was a wde varety of lterature that ncorporated smulaton technques nto ther approach for solvng capacty allocaton ssues. Guterrez and Crspn (2005) developed a decson support system that provded producton personnel wth recommendaton for schedulng products on ndvdual machnes n hybrd producton envronments. Although smulaton was not drectly ncorporated nto ther approach, they suggested that smulaton modelng would mprove the provded solutons by ncorporatng the effect of varablty nto the soluton. Volker and Gmlkowsky (2003) dscussed that effectve smulaton modelng of producton systems requre a sgnfcant amount of smulaton runs to analyze alternatve producton plans. They proposed a model reducton method and use of smulaton metamodels that would reduce the cost of smulaton runs. Although dscrete event smulaton has been used wdely for analyss of dscrete producton systems Mehra et al. (2006) dscussed the effects of reducng batch tmes n contnuous process ndustres. The reducton of batch szes n contnuous process ndustres showed mprovement to the producton systems usng Theory of Constrant 167
3 Marvel et al. performance measures. Byrne and Heavey (2006) expanded the use of smulaton to develop a decson support system for an ntegrated supply chan to address strategc decsons n addton to producton stockng strateges. Improvements to the drect applcaton of smulaton models were dscussed by Kumar and Nottestad (2006) and Truong and Azadvar (2003). Kumar and Nottestad (2006) ncorporated desgn of experments (DOE) nto ther smulaton analyss. Ther research suggested that hgh order nteractons are often gnored n analyzng producton systems wth smulaton models. Integratng DOE nto the smulaton analyss reduced tme requred to develop an understandng of the producton system characterstcs. Truong and Azadvar (2003) used smulaton analyss to analyze structural decsons, ncludng capacty consderatons, and coordnaton decsons to analyze the performance of a supply chan network. The smulaton analyss ncorporated genetc algorthms and mxed nteger programmng as well as smulaton to analyze the complextes of a supply chan confguraton. 3. METHODOLOGY The producton plannng process begns by classfyng the customer s products as ether hgh volume or low volume. Manufacture of the hgh volume products, referred to as the repettve products, occurs each producton cycle, whle manufacture of the low volume products, referred to as the perodc products occurs on an ntermttent bass. A Pareto analyss of the producton volumes durng the prevous plannng perods dentfed each product as ether low volume or hgh volume. The results of ths analyss dentfed that 30% of the products manufactured produced 80% of the demand for the producton resources. These hgh volume products were assgned to part famles, and subsequently assgned to process specfc flowlnes, usng clusterng technques that dentfed smlartes n the processng features. The producton system desgn focused on producng the hgh volume products effcently by schedulng ther producton n repeatng producton cycles. The low volume products were produced on an as-needed bass, based on customer orders, durng the perods n the producton cycle when open gaps of capacty were avalable. 3.1 Daly Demand Kanban A fundamental task of the producton plannng process s computng the daly demand kanbans for both the repettve and perodcally manufactured products. The daly demand kanbans represented the effectve daly demand for the products and not the daly producton quanttes. These products would be manufactured n specfc sequences durng the producton cycle on each flowlne. Dfferent flowlnes cycles would not necessarly be desgned wth the same duraton. Multplyng the producton cycle length and the daly demand kanban calculated the requred producton quanttes for each product. The prevous quarter s producton shpment requrements helped set the daly producton kanban for each product. An example of a product s daly shpment pattern s shown n Fgure Daly Demand (unts) Daly Demand Kanban (upper bound) Daly Demand Kanban (lower bound) Producton Calendar Day Fgure 1: Daly Producton Demands wth Upper and Lower Bound Demand Kanbans The hstorcal shpment patterns facltated calculatng the upper bound and lower bound daly demand kanbans. A lnear programmng model, shown n Equaton 1, calculated the upper bound kanban ensurng all producton demands would be met wth no backorders or delays. An ssue wth ths approach was there would be overproducton of products toward the end of the quarter. Three large shpments on producton calendar days 19, 44, and 48 caused the overproducton n ths example. Snce producton control would be aware of these large orders well n advance and could adjust producton capactes wth overtme or addtonal shfts, the lower bound producton quota was calculated based on preprocessng of the daly shpment data. The demand data was preprocessed so that the maxmum demand used n the lnear programmng model for any sngle day would not exceed twce the average demand for the prevous perod. 168
4 Assessng and Allocatng Producton Capacty Usng Smulaton Models The results for the calculatons for the upper and lower bound daly kanbans, based on an ntal nventory of 20 unts for ths product, are shown n Table 1. As expected the upper bound quota elmnated any backorders but resulted n an average nventory more that twce that of the lower bound and an endng nventory level over 300% larger than that of the lower bound. The lower bound resulted n 21 days where nventory levels were nadequate to meet demand. The upper and lower bounds were fences for the actual daly demand kanbans. The actual demand kanbans were set usng ths nformaton n consultaton wth marketng and sales to dentfy the proper daly demand kanban. In ths example, f the daly demand kanban were 10 unts, halfway between the upper and lower bound, there would have been only two days where demand could not be met wth nventory. Ths scenaro would result n an average nventory of 58.5 unts. mnmze Z = s.t. I I K + d +1 for = 1 to n K K = 0 for = 1 to n-1 K nteger n = K n for = 1 to n where: I I 0 = nventory poston at the end of producton calendar day = nventory poston at the end of the prevous analyss perod d = producton demand for producton calendar day K = daly demand kanban for producton calendar day n = number of producton calendar days (1) Table 1: Inventory Level Comparsons for Upper and Lower Bound Daly Demand Kanbans 3.2 Producton Cycle Selecton and Implcatons After assgnng products to ther flowlnes, the next step was settng the producton cycle length for each flowlne. The producton cycle length, representng the number of productve days, acts as a multpler for the daly demand kanban. Selectng cycle days s not arbtrary and has a sgnfcant mpact on the nventory coverage. A representaton of a flowlne cycle (see Fgure 2) shows that wthn the set number of cycle days four of the repettvely manufactured products wll be produced as well as an empty slot of open capacty. Product A Product B Product C Product D Open Cycle Days 169
5 Marvel et al. Fgure 2: Example of Flowlne Cycle The empty slot of open capacty s reserved for producng the perodc products. These products represent 70% of the products that produce the lower 20% of the product demand. For example, f the number of cycle days for Flowlne #1 s 12 days, then on Day 1 of that producton cycle, twelve tmes the daly demand quota of Product 1 s scheduled for producton. The producton plan wll not schedule Product 1 to be run on that flowlne agan untl Day 13. The focus of the product cycle selecton process s to ensure that there s enough gross capacty to satsfy the demand requrements for the repettvely produced products and to allow open slots of capacty to exst n the producton cycle n order to manufacture the perodc products. 3.3 Incorporatng Smulaton nto the Capacty Allocaton Analyss A statc spreadsheet analyss could evaluate the gross capacty of the producton system after: 1) assgnng the repettve products to the flowlnes; 2) calculatng the daly demand kanbans for the products and 3) settng producton cycle length for each flowlne. There are several crtcal ssues that cannot be addressed by spreadsheet analyss whch requre a producton system smulaton. Some of the ssues that a gross capacty analyss could not address nclude: Are there enough spools n the system to meet the market demand requrements consderng logstcal constrants? Are there enough open capacty slots to meet the demand for the perodc products? Does the sequencng of products mpact the system performance? How effcent are the schedules for product transportaton to the customer? The smulaton model, as descrbed n Secton 4, usng the producton system desgn outputs as model nputs, was desgned to provde answers to these questons. 4. SIMULATION MODEL The smulaton model analyzed producton system performance, specfcally for capacty allocaton and avalablty, based on the producton plan outputs. Snce the producton system acted as a closed queung network, because of the fnte number of spools n the system, the smulaton model tracked spools as the prmary entty flowng through the manufacturng and dstrbuton system. Each flowlne processed a specfc sequence of repettve products as determned by the producton plan. At the begnnng of the product cycle, the requred number of empty spools for processng the current product, was sent to the flowlne. As the product was manufactured t was wound onto the empty spool. After wndng a spool wth product, the spool was move to fnshed goods nventory (FGI). Subsequent to recevng customer orders for the products, the spools were transported to the customer. As these products were used n ther processes, producng the subassembles for the OEM, the spools would be depleted of product and the customer would then accumulate a batch of empty spools. Based on transportaton polces, the empty spools would be returned to the manufacturer. Fgure 3 shows how the spools flow through the manufacturng and dstrbuton system. Fgure 3: Customer Spool Flow through Manufacturng and Dstrbuton System 4.1 Model Inputs The smulaton model was desgned to be as flexble as possble to changes n products, customers, and processes. The outputs from the producton plan created the nputs to the smulaton model. A spreadsheet model ntegrated these outputs nto user nterface that allowed producton personnel to nteract wth the smulaton model. Ths was an mportant part of 170
6 Assessng and Allocatng Producton Capacty Usng Smulaton Models the smulaton project snce the producton personnel usng ths smulaton model to evaluate changes n the producton system dd not have any tranng usng smulaton software. The user nterface ntegrated all of the necessary system data nto sx major modules. These modules were: producton plan parameters, product attrbutes, spool nventores, customer orders, product transportaton polces, and system parameter modfcatons (see Fgure 4) Producton Plan Parameters The producton plannng process prmary outputs were assgnng and schedulng of repettve products to flowlnes and settng the producton cycle length for each flowlne as well as adjustng the daly demand kanban to the correct level. Other flowlne specfc nformaton transferred from the producton plan to the smulaton model ncludes the shft schedules for the flowlnes and any planned overtme or weekend shfts. Although a gross capacty analyss s performed n the producton plannng process, the smulaton model uses ths nformaton n calculatng the open capacty that exts on each flowlne at the end of the product cycle. The smulaton model decdes whch perodc products should be scheduled nto the open capacty gaps based of due dates, total processng tme, etc Product Attrbutes Product attrbutes transferred to the smulaton model nclude the processng parameters such as producton rates, product changeover tmes, spool capacty, customer, and product code. Product codes are customer specfc so t s possble for dfferent customers to purchase smlar product wth unque product codes. Parameters for perodc products also nclude dentfyng whch flowlne can process these products Spool Inventores Snce the number of spools n the system s fnte, the smulaton model s ntalzaton procedures must dentfy the spools locaton, whether they are located at the manufacturer s or the customer s faclty, and quantty of spools at these locatons. Further, the system must be able to dentfy whether the spools are empty or contan product Customer Orders The customer orders are entered nto the system to valdate the producton plan. These orders dentfy the customers and ther product requrements Product Transportaton Polces Although customer orders dentfy specfc order dates, most of the customers have negotated product transportaton polces wth the manufacturer. Product transportaton polces nclude frequency of shpments as well as days of the week n whch delveres can be made. As part of evaluatng the producton plan performance, late delveres occur when the products are not avalable for transport on ther apponted schedule. Products are scheduled by ether ndvdual product or batches of products for customers. Transportaton polces are evaluated by examnng vehcle utlzatons of the transports durng the plannng horzon System Parameter Adjustments The smulaton model evaluates the producton plan n an teratve fashon. The user nterface allows changng plan parameters wthout reenterng the producton plan. At the end of the smulaton run the modfed parameters are output to the producton plan. The user nterface allows overrdng the producton plan parameters of daly kanban requrements, producton rates, and producton cycle length. The user has the ablty to adjust these parameters n order to mprove system performance. Another parameter controlled by the user can control s the percentage of spools requred to start the producton run. Users have the ablty to allow the producton system to start producng an order for a product wthout a full complement of spools. The theory s that some of these producton runs wll span multple days and the balance of spools needed to complete the order could be n transport. Also, the user has the ablty to select whch customers can use common spools whch are held n reserve by the manufacturer. The use of these common spools s dscussed further n Secton
7 Marvel et al. Fgure 4: Smulaton Model Inputs and Outputs 4.2 Spool Processng Logc The model tracks and reacts to the flow of a fnte number of spools through the producton and dstrbuton system. The spools are assgned to specfc customers and only customer specfc product could be placed on these spools. Typcally any ndvdual customer would not contract the manufacture of more than 5 unque products. When the contracts were establshed between the customer and the manufacturer, the customer agreed to supply a fxed number of spools to the manufacturer before the ntal producton run. A major dsrupton to the producton system occurs when the manufacturer does not have avalable the correct amount of spools for a producton run. In ths case, the producton run would need to be rescheduled or canceled resultng n delays of customer shpments. The manufacturer owns a supply of common spools to prevent these types of dsruptons to the producton system. The producton schedule s ntegrty can be mantaned by usng these common spools to supplement any customer spool shortage. Producton control decdes when to use these common spools n the process but also has knowledge of the shpments of customer spools to the manufacturer. Operatons allows producton runs to start wthout the necessary amount of customer spools needed to complete the producton run f there s evdence that the balance of customer spools wll arrve durng the producton process. The model ncorporates ths logc by allowng the user to set a start percentage. The smulaton logc uses the start percentage to evaluate f the quantty of spools avalable s suffcent to start the producton run (see Fgure 5). The smulaton model processng wll follow the logc shown n Fgure 5 to process all repettve products for each flowlne. If the lack of spools, customer or common, causes a repettve product to be skpped n the sequence, the smulaton logc wll attempt to reschedule at the end of the producton cycle. 4.3 Schedulng Perodc Products The smulaton model logc attempts to process all repettve products durng the product cycle. After processng all requred repettve products, the model evaluates the remanng open capacty n the current product cycle. It s durng ths tme when the flowlnes have open capacty the perodc products wll be scheduled for producton. The model logc examnes unfulflled orders for these products, up to 28 days n the future, that can be processed on that flowlne n the allowable amount of tme untl the begnnng of the next product cycle. The earlest due date (EDD) s the selecton scheme for assgnng these products nto the avalable capacty. Products wth the same due date are then prortzed by the percentage of busness that these products represent. Daly kanban requrements do not control the perodc products producton quanttes, but nstead they follow a strct make-to-order approach, producng only the product quantty ordered by the customer. After producng all of the perodc products usng the avalable capacty n the flowlnes, the smulaton wll wat untl the begnnng of the next product cycle to repeat ths process of producton. 4.4 Addtonal Modelng Issues The projected demand for the products controls the daly kanban requrements. There wll be producton cycles n whch the producton of one or more of the repettve products s not necessary. The smulaton logc wll examne scheduled shpment requrements for the products and evaluate whether the current cycle s producton for that product can be skpped. Skppng a producton run for a product occurs when there s suffcent nventory to cover the demand for the product and there wll be no mpact of shpments of that product. As part of the prortzaton logc for selectng product processng on the flowlnes, the smulaton logc dentfes products that have backorders. In backorder stuatons, the spool requrement logc for startng a producton run s gnored, and the producton wll attempt to produce as many spools as possble wth the current nventory of empty spools. Processng perodc products s authorzed when there exsts suffcent capacty to produce that customer order. There are stuatons when logc would not approve producton when the capacty of the flowlne was slghtly nadequate. For example, a customer order that needed 10 hours of producton tme would not be authorzed f the open capacty on the 172
8 Assessng and Allocatng Producton Capacty Usng Smulaton Models flowlne was only 9 hours. In practce, operatons would authorze the extra hour of overtme to complete ths product. The smulaton also ncorporates ths logc. If the requred amount of overtme to complete an order s fewer than two hours, the smulaton logc wll adjust the shft tme to complete the order. Table 4. The fuzzy relatonshp between CR and TM TM1 TM2 TM3 TM4 TM5 CR1 (4.57,6,7.43) (5.43,7.43,8.86) (3.14,4.29,6) (5.14,6.57,8) (4,5.43,6.86) CR2 (5.71,7.43,8.57) (5.43,7.14,8.57) (3.71,4.86,6.57) (2.86,4.57,6.29) (3.43,5.14,6.86) CR3 (4.29,6,7.71) (4.29,6.29,7.71) (5.43,7.43,8.86) (4.86,6.29,7.71) (5.43,7.43,8.86) 5. SIMULATION RESULTS Fgure 5: Smulaton Model Inputs and Outputs The smulaton model was bult usng commercal off-the-shelf dscrete event smulaton software that was capable of mportng and exportng data between the model and spreadsheet software. The nterface was desgned n the spreadsheet program to allow producton plannng personnel to use the smulaton tool wthout havng expertse n programmng the smulaton model. The smulaton results answered the four questons posed n the Methodology secton. Fgure 6 shows, by product, the summarzed results of the smulaton run. The frst queston addressed was to decde f there were suffcent spools n the system to meet the current producton plan. The output dentfed the number of nstances when spool shortages caused delays to the scheduled runs of the products or when the scheduled producton runs were delayed untl later n the producton cycle. Ths output provded producton planners wth the dentty of products whose spool supples caused nterruptons n the producton system. The producton planners could then perform what-f analyses to determne how to 173
9 Marvel et al. resolve the problem. The what-f analyses were also able to evaluate the affect of the sequencng of the repettve products wthn each flowlne. The product sequence n the flowlnes were changed to dscover f there was a postve mpact on system performance parameters such as backorders and skpped or delayed producton runs. The producton planners can adjust certan parameters, such a daly kanban, processng speeds, and the sequence of scheduled products durng a smulaton run. The adjusted quanttes n the column ttles n Fgure 6 dentfy when the smulaton model user has adjusted an nput parameter. Fgure 6: Summarzed Smulaton Output The second queston to be answered related to the adequacy of the amount of open capacty for producng perodc products. The smulaton output dentfed the number of tmes that customer orders could not be flled. For the perodc products ths correlated to the avalablty of open capacty slots nto whch the perodc products could be scheduled. Addtonal output from the smulaton was also able to show the length of tme that these products remaned n backorder. The last major queston addressed by the smulaton results related to the effcency of transportng the fnshed product to the customer. Product shpments were arranged based on customer and product on weekly, bweekly, and monthly schedules. Based on the producton schedules there were nstances when less than 50% of the capacty of the truck storage capacty was utlzed. The smulaton model ntegrated the customer shpment schedules and was able to provde the producton planners wth an analyss of the effcency, based on percentage of truck capacty utlzed, of the customer shpments (see Fgure 7). The weghted effcency calculaton accounted for the number of spools shpped to the customers so neffcent shpments of only a few spools dd not skew the calculaton. The producton planners were then able to modfy shpment schedules to mprove shpment effcences. 5.1 Smulaton Valdaton and Verfcaton The smulaton model was desgned as a closed-queung network based on a fnte number of spools n the system. The valdaton and verfcaton technques descrbed by Sargent (1999) were used to evaluate the model. The model was run for a smulated 90 days of producton. Durng ths run there were 3376 enttes n the system and the smulaton output verfed the total enttes n the system remaned constant durng the processng of all the enttes. Statc spreadsheet modelng of one of the flowlnes, usng determnstc processng values, verfed the logc for shft tme consumpton durng the producton runs of the repettve products as well as the schedulng and processng of the perodc products nto open capacty gaps was correct. Fgure 7: Customer Shpment Effcences 174
10 Assessng and Allocatng Producton Capacty Usng Smulaton Models 6. CONCLUSION A ter two automoble suppler s producton faclty requred manufacturng two classes of products; those produced on a repettve bass and those produced on a perodc bass. Lnear programmng technques were used to set the daly demand kanbans. A smulaton model was developed, usng the daly demand kanbans as well as other system parameters, to ad the producton planners n evaluatng the quarterly producton plan. The model s outputs were able to assess the system performance regardng spool avalablty, transportaton effcences, backorders and delayed or skpped producton runs. The producton planners used the model s output to trace nterruptons n producton back to the root cause. The what-f analyses provded quanttatve mpacts on proposed mprovements to producton system parameters. 7. REFERENCES 1. Abdul-Kader, W. and Gahrb, A. (2002). Capacty estmaton of a mult-product unrelable producton lne. Internatonal Journal of Producton Research, 40(18): Aknc, U. and Meredth, J. (2006). Choosng the approprate capacty for a make-to-forecast producton envronment usng a Markov analyss approach. IIE Transactons, 38: Bsh, E.K., Murel, A., and Bller, S. (2005). Managng Flexble Capacty n a Make-to-Order Envronment. Management Scence, 51(2): Byrne, P.J. and Heavey, C. (2006). Smulaton Model of a Vertcally Integrated Supply Chan: A Case Study. Internatonal Journal of Industral Engneerng, 13(2): Chou, Y-C., Wu, C-S., Kao, C-E., and Hseh, S-H. (2001). Integraton of Capacty Plannng Technques for Tool Portfolo Plannng n Semconductor Manufacturng. Internatonal Journal of Industral Engneerng, 8(4): Dekkers, R. (2003). Strategc capacty management: meetng technologcal demands and performance crtera. Journal of Materals Processng Technology, 139: Fumero, F. and Vercells, C. (1994). Capacty analyss n repettve assemble-to-order manufacturng systems. European Journal of Operatons Research, 78: Guterrez, R.S. and Crspn, M.A. (2005). An Order Schedulng System n MTO/MTS Envronments: A Case Study. Internatonal Journal of Industral Engneerng, 12(1): Huh, W.T., Roundy, R.O., and Cakanyldrm, M. (2006). A General Strategc Capacty Plannng Model under Demand Uncertanty. Naval Research Logstcs, 53: Kumar, S. and Nottestad, D.A. (2006). Capacty desgn: an applcaton usng dscrete-event smulaton and desgned experments. IIE Transactons, 38: Marvel, J.H., Schaub, M. and Weckman, G. (2005). Valdatng the Capacty Plannng Process and Flowlne Product Sequencng through Smulaton Analyss. Proceedngs of the 2005 Wnter Smulaton Conference, Insttute of Electrcal and Electroncs Engneers, pp Mehra, S., Inman, R.A., and Tute, G. (2006). A smulaton-based comparson of batch szes n a contnuous processng ndustry. Producton Plannng & Control, 17(1): Ozdamar, L. and Yazgac, T. (1997). Capacty drven due date settngs n make-to-order producton systems. Internatonal Journal of Producton Economcs, 49: Sargent, R. G. (1999). Valdaton and verfcaton of smulaton models Proceedngs of the 2005 Wnter Smulaton Conference, Insttute of Electrcal and Electroncs Engneers, pp Spper, D. and R. L. Bulfn Jr. (1997). Producton: Plannng, Control, and Integraton. New York: The McGraw-Hll Companes, Inc. 16. Truong, T.H. and Azadvar, F. (2003). Smulaton Based Optmzaton for Supply Chan Confguraton Desgn. Proceedngs of the 2003 Wnter Smulaton Conference, Insttute of Electrcal and Electroncs Engneers, pp Volker, S. and Gmlkowsky, P. (2003). Reduced Dscrete-Event Smulaton Models for Medum-Term Producton Schedulng. Systems Analyss Modelng Smulaton, 43(7): Watson, E. F., D. J. Mederos, and R. P. Sadowsk. (1997). A smulaton-based backward plannng approach for orderrelease. Proceedngs of the 1997 Wnter Smulaton Conference, Insttute of Electrcal and Electroncs Engneers, pp
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