LRm Laboratory for Responsible Manufacturing

Transcription

1 LRm Laboratory for Responsble Manufacturng Bblographc Informaton Udomsawat, G., Gupta, S. M. and Al-Turk, Y. A. Y., "Push and Pull Control Systems n Dsassembly Lnes", Proceedngs of the 2004 Decson Scences Insttute Conference, Boston, Massachusetts, pp , November 20-23, Copyrght Informaton Copyrght 2004, Surendra M. Gupta. Contact Informaton Dr. Surendra M. Gupta, P.E. Professor of Mechancal and Industral Engneerng and Drector of Laboratory for Responsble Manufacturng 334 SN, Department of MIE Northeastern Unversty 360 Huntngton Avenue Boston, MA 02115, U.S.A. (617) Phone (617) Fax gupta@neu.edu e-mal address Home Page

2 PUSH AND PULL CONTROL SYSTEMS IN DISASSEMBLY LINES Gun Udomsawat, Northeastern Unversty, Boston, MA 02115, (617) Surendra M. Gupta, Northeastern Unversty, Boston, MA 02115, (617) Yousef A. Y. Al-Turk, Kng Abdulazz Cty for Scence and Technology, Ryadh, (Saud Araba) ABSTRACT Increasng envronmental concerns durng the last decade have caused many governments to persuade manufacturers to take back used products so that the components and materals recovered from the products could be reused and/or recycled. The dsassembly process s the frst step for recoverng components and materals. We dscuss some of the complcatons n plannng and schedulng of dsassembly on a dsassembly lne. We show how to overcome them by mplementng a mult-kanban mechansm n the dsassembly lne settng. We then nvestgate the mult-kanban mechansm usng smulaton and demonstrate that n the mult-product, mult-demand envronment, such mechansm performs superor to the tradtonal push system. Keywords: Dsassembly Lne, Just-In-Tme Systems, Producton and Inventory Control Systems. INTRODUCTION Dsassembly s the process of separatng the desred components, subassembles, and materals from endof-lfe or returned products. Its key objectves nclude recoverng components and/or materals n order to preserve ther functonalty and/or values for future use [4]. For further nformaton on dsassembly and product recovery, see Brennan et al. [1] and Lambert [11]. Dsassembly lne has become the subject of recent nterest [2], [3]. A dsassembly lne s qute dfferent from an assembly lne n terms of materal movement, demand arrval and nventory level fluctuaton. For example, as opposed to the normal "convergent" flow on an assembly lne, n a dsassembly lne, the flow process s "dvergent" (a sngle product s broken down nto many subassembles and parts). A dsassembly lne has sgnfcant nventory problems because of the dsparty between the demands for certan parts or subassembles and ther yeld from dsassembly. In addton, on a dsassembly lne, not only can the demand arrve at the last workstaton, t can also arrve at any of the other workstatons n the system. Moreover, end-of-lfe products can arrve at any workstaton and can be n varous forms or combnatons requrng dfferent dsassembly sequences. It s crucal to consder many factors when choosng a proper producton control mechansm for a dsassembly lne. Push system and pull system are two broad types of systems used to control the operatons of an assembly lne. However, dsassembly lnes tend to be hghly nventory ntensve and thus t makes the pull system a vable alternatve here as well. Kanbans are often used n a pull system to control nventory levels and can be easly mplemented on an assembly lne. However, desgnng a kanban mechansm for a dsassembly lne has not been thoroughly studed n the past. In ths paper, we dscuss the complcatons encountered n a dsassembly lne and suggest a proper way to control t usng the pull system concept. We ntroduce a mult-kanban system and ts routng mechansm n order to drect kanbans n an effcent way. We demonstrate the effectveness of the proposed methodology by comparng t to the push system usng a smulaton model and a numercal example. DISASSEMBLY LINE Dsassembly lne can be descrbed as a seres of workstatons operatng n a sequence to dsassemble the end-of-lfe (EOL) products to meet certan demand for subassembles and/or components. In a dsassembly lne, the arrvng products may consst of dfferent combnatons of components from a gven Correspondng author 7811

3 set of components. From a set of N components, the total number of possble combnatons of components, Q (N) s gven by: N Q( ) = 2 N 1 N (1) For example, a set of 4 components (A, B, C, D) can produce up to 11 possble product combnatons (vz., AB, ABC, ABCD, ABD, AC, ACD, AD, BC, BCD, BD, CD). By addng one more component to the set, the number of possble combnatons ncreases to 26, that s, the number of combnatons ncreases exponentally wth the ncrease n the number of components. The varety of EOL products enterng the system destablzes the dsassembly lne by causng an overflow of materals at one workstaton whle starvng some other workstatons leadng to undesrable fluctuatons of nventory n the system. It s therefore crucal to balance the lne and manage the materals flow of the lne. The arrval pattern of demand n a dsassembly lne s much more complcated than n a typcal assembly lne. The key reason s the multlevel arrval pattern of demand. Demand can occur at any level of the dsassembly process. In most assembly lnes, demand arrves only at the last workstaton. In that case, even f multlevel arrval of demand were consdered, ts effect would be relatvely bengn because the product does not go forward from there on as t s taken off the lne to fulfll the demand. In a dsassembly lne settng, however, the arrvals of external demand at workstatons other than the one n the end creates a dsparty between the number of demanded components and the number of partally dsassembled products. Thus, f the system responds to every request for components, t would end up wth a sgnfcant amount of extra nventory of components that are n low demand. All ths creates chaos n the system. The problem becomes even more crtcal when products cannot be stored for long perods of tme due to the market condtons, space lmtatons, or need for controlled envronments. Even though mechansms such as kanban, base stock and CONWIP have been used to control nventory n an assembly lne settng, they have not been deployed n a dsassembly lne settng. Ths s true because these mechansms are not suted for the dsassembly envronment n ther current forms. Ths, therefore, leads to the need for developng better producton control systems n order to effcently work n a dsassembly lne settng. In general, there are two types of control mechansms: push mechansm and pull mechansm. The push mechansm reles on a predetermned producton schedule based on the expected demand of fnshed products. On the other hand, the producton n pull mechansm s trggered by the actual demand and causes a flow of materals throughout the system. The push system has advantages n terms of experence n mplementng t and provdng hgher levels of customer servce n certan producton scenaros because the system tends to buld up nventory. On the other hand, the pull mechansm has an advantage that t does not generate large amounts of nventory. However, t reles heavly on consstency of raw materals supples and aglty of the server. Ths s the man reason why pull mechansm s more lkely to perform better than push mechansm n a dsassembly lne. Kanban [7] s one of the most commonly used pull mechansm tools avalable. Advantages of the kanban system nclude ts ablty to control producton, restrcton of system s nventory, ts smplcty n producton schedulng, reduced burden on operators, ease of dentfcaton of parts by the kanbans attached to them, and substantal reducton n paper work. Tradtonally, kanbans are desgned for a determnstc envronment. Its performance s, therefore, optmum n that envronment. However, once mplemented n a dsassembly lne settng, the kanban system s fraught wth numerous types of uncertantes [5], [6]. A modfcaton of the mechansm s therefore needed to mprove ts performance by reducng the mpact of these uncertantes [9]. 7812

4 MULTI-KANBAN MODEL Product 1 Subassembly 2 Subassembly 3 Subassembly N-1 External Demand Kanban Flow or Internal Subassembly Flow Extenal Product / Subassembly Arrval Kanban Routng Mechansm FIGURE 1. Kanbans and Materals Flows n a Dsassembly Lne Two basc types of materals n the system are components and subassembles. A component s a sngle tem that cannot be further dsassembled and s watng to be retreved va a customer demand. On the other hand, a subassembly s somethng that can stll be dsassembled. Consder workstaton j, where 1 j N-1. When a demand for component j arrves at the component buffer of workstaton j, one unt of component j s retreved and the component kanban j attached to t s routed to the most desrable workstaton. The procedure for determnng the most desrable workstaton to route component kanban j s gven below. (Note that ths procedure s not applcable to component kanbans N-1 and N. In both cases the kanbans are routed to the nput buffer of the last workstaton). A component kanban orgnatng from workstaton j wll be routed to a workstaton, where 1 < j, or workstaton j dependng on the avalablty and the desrablty of the subassembly that contans component j. Routng component kanban j to workstaton, where 1 (j-1), wll result n an mmedate separaton of component j from component. Thus, the only subassembly located at the nput buffer of workstaton that would be useful s a subassembly that contans only components and j. If ths type of subassembly exsts n the nput buffer of workstaton, then workstaton s qualfed. Smlarly, f there s at least one subassembly n the nput buffer of workstaton j, then workstaton j s qualfed. Next, we need to select the most desrable workstaton to route component kanban j to, among the qualfed ones, such that, f chosen, wll cause the least amount of extra nventory n the system. Choosng workstaton wll ncrease the nventory level of component by an addtonal unt. Thus, the best workstaton s the one that s most starvng for ts component. By checkng the backorder level for demand, we could determne the most starvng workstaton. If there s a te, select the most downstream workstaton. Choosng workstaton j wll create a resdual subassembly that wll be further dsassembled at downstream workstatons. If workstaton j s chosen, then a proper subassembly must be chosen to dsassemble. For example, f a backorder exsts at the component buffer of workstaton k, where j< k (N-1), then, f avalable, we mght try to dsassemble a subassembly that contans only components and k. If more than one workstaton qualfy as starvng workstatons, then the one that s most starvng among them s chosen. If there s a te, then the most downstream workstaton s selected. We can now compare the starvng levels of workstatons and j. If the hghest starvng level of workstaton s greater than or equal to the starvng level of workstaton j then we wll route the component kanban j to workstaton, otherwse, we wll route t to workstaton j. Note that whenever an external subassembly s avalable, t wll always be chosen frst. Internal subassembles wll only be used when no external subassembly of the desred knd s avalable. Subassembly kanbans are routed n a fashon smlar to component kanbans. 7813

5 Selecton of Products Because we allow multple combnatons of products, the worker may have several optons when selectng the product for dsassembly. If the authorzaton of dsassembly s ntated by the subassembly kanban (j x ), whch can occur only at workstaton, where 1 < j, the workers wll have no opton but to select the subassembly that results n mmedate separaton of subassembly (j x ), vz., subassembly (j x ). If the authorzaton of dsassembly s ntated by component kanban j at workstaton, where 1 < j, the worker wll have to remove subassembly (j) from the product buffer wth no other optons because the only subassembly that results n mmedate separaton of component j s the subassembly (j). However, f the component kanban j arrves at workstaton j, there are multple optons because every subassembly located n the product buffer contans component j and always results n mmedate separaton of component j. In ths case, we determne whether or not the resdual that s created by the dsassembly wll result n overflow of nventory. We choose the subassembly (j x ) where x s the most desrable resdual rankng based on the request of subassembly kanban x at workstaton j (exstng kanban x at the workstaton j) or current nventory level of subassembly (component) x, respectvely. Determnng the Kanban Level The kanban level plays an mportant role n the mult-kanban mechansm as t mantans a proper flow of components and subassembles at a desred level throughout the system. It can be determned by consderng product arrval rate, demand arrval rate and dsassembly tme. The number of kanbans for both the component kanban, k and the subassembly kanban, k j can be computed, at any pont n the dsassembly lne, usng the followng general expressons: k = max( 1, R F ) (2) k j = max( 1, Rj Fj ) (3) where R s the request rate of component, F s the furnsh rate of component, R j s the request rate of subassembly j, and of F j s the furnsh rate subassembly j. These request rates and furnsh rates can be calculated as follows: R =, for 1 N (4) d F = s(, w), for 1 N (5) w= 1 j s R =, s the next component to be dsassembled n the sequence (6) m 1 + F j = a j s(, w), s the latest component dsassembled n the sequence (7) w= 1 Where d s the demand arrval rate of component, s (,w) s the dsassembly rate of component at workstaton w, s j s the dsassembly rate of subassembly j, a j s the arrval rate of subassembly j (from external source), m s the current workstaton ndex, N s the maxmum number of component, and N-1 s the maxmum number of workstaton. For the case of component kanban, whch s requested only from a sngle source, request rate s equal to the customer demand arrval rate. However, because the component kanban arrves from several sources n the system, the furnsh rate s the summaton of arrval rates from all possble sources. For the case of subassembly kanban, the furnsh rate s nfluenced by both the dsassembly rate and the external subassembly arrval rate. Thus, we take all external and nternal arrval rates of subassembles at the buffer nto account. Smlarly, the two requestng sources, vz., the demand for target component and the demand for resdual subassembly affect the request rate. The number of kanbans s determned at the begnnng of the dsassembly process. It s clear that demand, supples, dsassembly tme, and product structure, all affect the computaton of the number of kanbans. 7814

6 EXPERIMENT, RESULTS AND DISCUSSION We created a smulaton model usng the PC verson of SIMAN and ARENA [8]. Run tme was set to 48 hours after 5 hours of warm-up perod for each replcaton. The steady state was reached and confrmed usng Welch Procedure [10]. We collected data for three performance measures, vz., the number of satsfed demand (SD), the average work n process (WIP), and the average order completon tme (OCT), for the tradtonal push system (TPS) and the mult-kanban system (MKS). In order to determne the central tendency and varablty n the performance measures, we performed 20 ndependent replcatons. The example consdered s a dsassembly lne consstng of 4 workstatons. Eght dfferent products (ABCDE, ACDE, ABC, AC, BCDE, BC, CDE and DE) are to be dsassembled n order to fulfll the demands for fve components. Note that we allow any of the subassembles to arrve from external sources at the approprate pont on the dsassembly lne. The kanban level s calculated usng equatons (2) and (3). We ran two sets of experments representng the push system and the mult-kanban system. In the push system, all arrvng products were processed contnuously under the FCFS rule. Table 1 presents a summary of average values of the three performance measures wth ther standard devatons shown n parentheses. The dfference n WIP s statstcally sgnfcant at 0.05 level The varablty n OCT s sgnfcantly reduced because the MKS allows the dsassembly process to start only when there s a demand for a component. Ths should be helpful when predctng the projected order completon tme. The dfference n average nventory for each component (WIP) for the TPS and MKS s statstcally sgnfcant from each other because the MKS routes kanban to the staton that always results n the most needed component. Ths creates the pullng of materals exactly where and when they are needed. Results confrm that the MKS s capable of meetng customer demand that s comparable to the TPS. Fgure 2 shows that the MKS has lower average nventory of all fve components than the TPS. The TPS bulds up nventory n order to fulfll customers' demands. On the other hand, the MKS system deals wth fluctuaton among demands by routng the kanbans to the most sutable workstaton after determnng that there s no overflow part avalable. By examnng the number of parts beng requested n real tme and the number of avalable source products, the MKS selects the best destnaton for the kanban. In ths case, the system was able to reduce the nventory level by an average of 81% whle fulfllng customers' demands usng the suggested kanban levels n the system. TABLE 1. The Performance of TPS and MKS DS WIP OCT A B C D E TPS MKS TPS MKS TPS MKS TPS MKS TPS MKS (7.3) (8.7) (6.7) (6.3) (5.8) (7.7) (9.7) (8.9) (6.8) (7.8) (5.6) (2.3) (4.9) (1.4) (6.9) (3.9) (7.1) (3.2) (7.3) (3.3) (2.6) (2.1) (5.3) (3.7) (5.7) (3.3) (8.3) (4.2) (8.4) (4.7) Push System Mult-Kanban System Average Inventory (Unts) A B C D E FIGURE 2. WIP level for TPS and MKS 7815

7 REFERENCES [1] Brennan L., Gupta S. M. and Taleb K. N., Operatons Plannng Issues n an Assembly/Dsassembly Envronment, Internatonal Journal of Operatons and Producton Management, Vol. 14, No. 9, 57-67, [2] Gungor A. and Gupta S. M., A Soluton Approach to the Dsassembly Lne Balancng Problem n the Presence of Task Falures, Internatonal Journal of Producton Research, Vol. 39, No. 7, , [3] Gungor A. and Gupta S. M., Dsassembly Lne n Product Recovery, Internatonal Journal of Producton Research, Vol. 40, No. 11, , [4] Gungor A. and Gupta S. M., Issues n Envronmentally Conscous Manufacturng and Product Recovery: A Survey, Computer and Industral Engneerng, Vol. 36, No. 4, , [5] Gupta S. M. and Al-Turk Y. A. Y., The Effect of Sudden Materal Handlng System Breakdown on the Performance of a JIT System, Internatonal Journal of Producton Research, Vol. 36, No. 7, , [6] Gupta S. M., Al-Turk Y. A. Y. and Perry R. F., Flexble Kanban System, Internatonal Journal of Operatons and Producton Management, Vol. 19, No. 10, , [7] Hopp W. J. and Spearman M. L., Factory Physcs, Second Edton, McGraw-Hll, New York, [8] Kelton D. W., Sadowsk R. P. and Sadowsk, D. A., Smulaton wth Arena, WCB, McGraw- Hll, New York, [9] Korugan, A. and Gupta, S. M., Adaptve Kanban Control Mechansm for a Sngle Stage Hybrd System, Proceedngs of the SPIE Internatonal Conference on Envronmentally Conscous Manufacturng II, Newton, Massachusetts, October 28-29, pp , [10] Law, A. M. and Kelton, D.W., Smulaton Modelng and Analyss, McGraw-Hll, New York, [11] Lambert A. J. D., Dsassembly Sequencng: A Survey, Internatonal Journal of Producton Research, Vol. 41, No. 16, ,