Literature review of JIT-KANBAN system

Size: px
Start display at page:

Download "Literature review of JIT-KANBAN system"

Transcription

1 Int J Adv Manuf Technol (2007) 32: DOI /s ORIGINAL ARTICLE C. Sendil Kumar. R. Panneerselvam Literature review of JIT-KANBAN system Received: 9 February 2005 / Accepted: 9 September 2005 / Published online: 22 March 2006 # Springer-Verlag London Limited 2006 Abstract In this paper, JIT (Just-In-Time)-KANBAN literature survey was carried out and presented. The introductory section deals with the philosophy of JIT, and the concept involved in the push and pull system. The blocking mechanisms in the kanban system are also discussed elaborately. Besides these sections, the importance of measure of performance (MOP) and the application of the same with respect to JIT-KANBAN are presented. The recent trends in the JIT-KANBAN are discussed under the heading Special cases. In this review, 100 state-of-art research papers have been surveyed. The directions for the future works are also presented. Keywords JIT. KANBAN. Blocking Mechanisms. CONWIP. Measure of performances (MOP). Simulation 1 Introduction Just -In-Time (JIT) manufacturing system was developed by Taiichi Ohno which is called Japanese Toyota production system. JIT manufacturing system has the primary goal of continuously reducing and ultimately eliminating all forms of wastes (Brown et al. [5], Ohno [54], Sugimori et al. [82]). Based on this principle, Japanese companies are operating with very low level of inventory and realizing exceptionally high level of quality and productivity (Richard J. Tersine [62], James H. Greene [30]). JIT emphasizes zero concept which means achievement of the goals of zero defects, zero queues, C. Sendil Kumar Neyveli Lignite Corporation, Neyveli, India R. Panneerselvam (*) Department of Management Studies, School of Management, Pondicherry University, Pondicherry , India panneer_dms@yahoo.co.in zero inventories, zero breakdown and so on. It ensures the supply of right parts in right quantity in the right place and at the right time. Hence, the old system of material acquisition and, buyer and seller relationships are changed to new revolutionary concepts (Womack et al. [91], Womack and Jones [92], Markey et al. [45]). Similarly, JIT becomes an inevitable system at plant level, which integrates the cellular manufacturing, flexible manufacturing, computer integrated manufacturing and Robotics (Schonberger [63], Golhar [12]). Due to the technological advancement, the conventional method of push production system linked with Material Requirement Planning (MRP) was changed to pull type JIT production system to meet out the global competition, where the work-in-process (WIP) can be managed and controlled more accurately than the push- production system (Mason Paul [46]). KANBAN system is a new philosophy, which plays a significant role in the JIT production system. Kanban is basically a plastic card containing all the information required for production/assembly of a product at each stage and details of its path of completion. The kanban system is a multistage production scheduling and inventory control system. These cards are used to control production flow and inventory. This system facilitates high production volume and high capacity utilization with reduced production time and work-in-process. The objectives of this paper are as listed below 1) Critical review of JIT literature. 2) Segregating the different research articles of JIT. 3) Exploring the recent trends in JIT-Kanban system and deriving directions for future research. In this paper, the articles are reviewed and an appropriate classification is presented.the kanban study was made elaborately, since it acts as a basic communicator and feedback agent to the JIT system. Push and pull system, principle of operation of kanban cards, Blocking mechanism, Toyota s formula, and the measures of performances (MOP) are also discussed in this paper. The latest trends in JIT-Kanban system are also addressed separately under the

2 394 heading Special cases. Finally, the directions for future researches are presented. Request for items Request for items 2 Push and pull systems Push and Pull system are two types of production systems, which operate equally in opposite sense and have their own merits and demerits (Monden [50], Villeda Ramiro et al. [89]). Push system It is a conventional system of production. When a job completes its process in a workstation, then it is pushed to the next workstation where it requires further processing or storing. In this system, the job has a job card and the job card is transferred stage by stage according to its sequence. In this method, due to unpredictable changes in demand or production hinder-ness, the job happens to deviate from its schedule and it causes accumulation of work-in-process inventory. Hence, inventory planners pessimistically fix the safety stock level on the higher side. A schematic representation of the push system is shown in Fig. 1. In Fig. 1, WS j is the j th workstation and the product line consists of n workstations. Pull system A pull type production system consists of a sequence of workstations involving value addition in each workstation (WS). In the pull system, from the current workstation (j), each job is withdrawn by its succeeding workstation (j+1). In other words, the job is pulled by the successive workstation instead of being pushed by its preceding workstation. The flow of parts throughout the product line is controlled by Kanban Cards (Turbo [87]). In practice, these kanban cards can be either single-card system or two-card system. Each workstation has an inbound stocking point and an outbound stocking point. The primary advantage of the pull system is the reduced inventory and hence the associated cost of inventory reduction. A schematic view of the pull system with two workstations and store is shown in Fig. 2. A kanban system operates only with single card is called production order kanban (POK) (J. Berkley [4], Sarathapreeyadarishini et al. [78]). If the distance between the consecutive workstations is very short, a single buffer mode is made available between the workstations. This buffer mode acts as both outbound buffer for the current workstation j and inbound buffer for the succeeding workstation j+1, respectively. A schematic diagram of a single-card system is shown in Fig. 3. In the two-card system, where the distance between the two consecutive work stations are more, each work station will have separate inbound buffer and outbound buffer (Kimura O. et al. [36], Hemamalini et al. [21]) and the cards are called as Production Order Kanban (POK) and Withdrawal Kanban WS 1 Fig. 2 Pull system Items movement WS 2 Items movement (WK), respectively. A schematic diagram of a two-card system is shown in Fig Operation of two-card kanban system STORE The two-card kanban pull system which works in the Assembly/Manufacturing line is elaborated by Panneerselvam [56], Edward J. Hay [17], Kimura and Terada [36], Hunglin Wang et al. [25] and Hemamalini et al. [21] and Shahabudeen et al. [76]. Basically it has plastic cards, which give information about the parts and also things to be done. The production order kanban (POK) is a production order, which instructs the preceding workstation to produce the required number of units. The withdrawal kanban (WK) gives the message to the succeeding process about the number of units it should withdraw. The simple steps adopted in kanban system are as follows 1) The container of the succeeding workstation j+1 is moved to the preceding workstation j with the withdrawal kanban (WK) and placed it in its output buffer. 2) a) Consequently it pulls the parts from output buffer of the workstation j and detach the production order kanban (POK) attached to those parts and then places the POK in the POK-post of the workstation j. b) Work station j starts its production as per the production order in its POK post. 3) The container along with the parts and WK moves again to its succeeding workstation j+1. Then it delivers the parts to the input buffer of the workstation j+1 and places the WK to the WK-post of the workstation j+1. POK POST Card + Parts movement Only card movement WS j WS j+1 WS 1 WS 2 o o o WS j ooo WS n STORE Fig. 1 Push system BUFEER Fig. 3 Schematic diagram of a single card system

3 395 WS j POK POST ( j ) POK Output Buffer of Workstation j 4) When the parts in the containers of the workstation j+1 are fully used, then the steps from 1 to 3 are repeated. 3 Blocking mechanisms WK+ Parts WK Each workstation of a production/assembly line requires sufficient space for storing parts in its output buffers. When the buffer capacity of a workstation is fully occupied, no further storage is possible. Because of this fact, the workstation can not release the parts and hence, it can not process components. This condition is called Blocking. The blockings are categorized according to the types as presented in Table Single card -instantaneous WK POST (j+1) As discussed earlier, if the workstations are situated closer to each other, the output buffer of the workstation j and the input buffer of the workstation j+1 are one and the same. Under such situation, a single card instantaneous kanban is used. Berkley [4] and Sharadhapreeyadarishini et al. [77] have discussed the blocking mechanism of single card type in detail. Table 1 Categories of blocking mechanisms Single Card -Instantaneous Two Card - Non Instantaneous 1) Blocking due to part-type. 1) Blocking due to part-type. 2) Blocking due to queue size. 2) Blocking due to queue size. 3) Dual blocking mechanism. 3) Dual blocking mechanism. Blocking mechanism Operative on Material Handling. 4) Blocking due to part-type. 5) Blocking due to queue size. 6) Dual blocking mechanism. WK Input Buffer of Workstation j+1 Fig. 4 Schematic diagram of two card system WS j Blocking due to part type This type of blocking occurs due to restriction in the number of parts (containers) that can be stored in the buffer between workstation j and the workstation j+1. The workstation j will not process the particular part p, since there is no reserved space in the buffer storage for the particular part type. Let Q(p, j, j+1) be the maximum number of units (container) of part type p that can be stored in the buffer storage between the workstation j and the workstation j+1. Then the workstation j can process the p type parts, only if the actual number of units (container) of the part type p in the buffer storage less than Q(p, j, j+1); otherwise, the workstation j is blocked due to part type p alone. The workstation can process any other part type provided that workstation is not blocked with respect to that part type Blocking due to queue size This type of blocking occurs due to restriction in the total number of containers of all part types in the buffer between workstation j and the workstation j+1. The workstation j will not process any part type if there is no space in the buffer storage between the workstation j and the workstation j+1, irrespective of part type and container. Let Q (j, j+1) be the maximum number of containers irrespective of the part types that can be stored in the buffer storage between the workstation j and the workstation j+1. Then the workstation j can process part types, only if the actual total number of containers in the storage between the workstation j and the workstation j+1 is less than Q(j, j+1); Otherwise, the work station j is said to be blocked due to the queue size constraint Dual blocking mechanism If both the above blocking mechanisms operate simultaneously, then it is called Dual blocking mechanism. The work station j is said to be blocked if the actual number of units (containers) of the part p in the buffer storage between the workstation j and the workstation j+1 is equal to Q(p, j, j+1) and the actual total number of containers in the buffer storage between the workstation j and the workstation j+1 is equal to Q(j, j+1). Subsequently, when a container of the part type p is taken by workstation j+1, then the blocking is released and the workstation j can start processing the part p. If the work station j+1 takes the container of any part other than that of p, then the work station j is still blocked with respect to part p and it is not blocked with respect to other part types. 3.2 Two card- non-instantaneous If the distance between consecutive workstations is more, there will be independent input and output buffer points for

4 396 each workstation. In this system, the blocking can occur due to stagnation of parts in the output buffer of that workstation. Berkley J. [4] et al. [21] have studied this type of blocking Blocking due to part type This type of blocking occurs due to restriction in the number of parts (containers) that can be stored in the output buffer of the workstation j. The workstation j can not process the particular part p, since there is no reserved space for the part type p in the output buffer of the workstation j. Let Q(p, j) be the maximum number of units (containers) of the part type p that can be stored in the output buffer storage of workstation j. Then the workstation j can process the parts of the part type p, only if the actual number of units (containers) of p in the output buffer storage of the workstation j is less than Q(p, j); otherwise, the workstation j is blocked due to the part type p alone. The workstation j can process parts of any other part type provided that the workstation is not blocked with respect to that part type Blocking due to queue size This type of blocking occurs due to restriction in the total number of containers of all part types in the output buffer of the workstation j. The workstation j will not process any of the part types since there is no space in the output buffer storage of the workstation j, irrespective of part type and container. Let Q(j) denotes the maximum number of containers irrespective of part type that can be stored in the output buffer storage of the workstation j. Then the workstation j can process parts only if the actual total number of containers in the output buffer of the workstation j is less than Q(j); otherwise, the workstation j is said to be blocked due to the queue size constraint Dual blocking mechanism If both of the above blocking mechanisms operate simultaneously, then it is called dual blocking mechanism. The workstation j is said to be blocked if the actual number of units (containers) of part type p in the output buffer of workstation j is equal to Q(p, j) and the actual total number of containers in the output buffer of workstation j is equal to Q(j). Subsequently, when a container of part type p is taken to the input buffer of the workstation j+1, the blocking will be released and the workstation j can start processing the part type p. If the input buffer of workstation j+1 takes a container of parts other than the part p, then the workstation j is still blocked with respect to the part type p. 3.3 Blocking mechanisms operative on material handling Material handling operation between the workstation j and the workstation j+1 can be blocked due to part type, queue size or both. This is similar to the above types but the blocking is due to Material Handling (MH) between output buffer of the workstation j and the input buffer of the workstation j+1. This was studied by Berkley J. [4] and Hemamalini et al. [21] Blocking mechanism due to part type This type of blocking occurs due to restriction in the number of parts (containers) that can be stored in the input buffer of the workstation j+1. Let M(p, j+1) denotes the maximum number of units (containers) of part type p that can be stored in the input buffer storage of the workstation j+1. Then, materials handling is permitted from the output buffer of work station j to the input buffer of workstation j+1, if the actual number of units (containers) of part p in the input buffer of work station j+1 is less than M(p, j+1) Blocking mechanism due to queue size This type of blocking occurs due to restriction in the total number of containers of all part types that can be stored in the input buffer of the workstation j+1. Let M(j+1) be the maximum number of containers irrespective of part types that can be stored in the input buffer storage of workstation j+1. Then, the material handling is permitted from output buffer of the workstation j to the input buffer of workstation j+1 only, if the actual total number of containers in the input buffer of work station j+1 is less than M(j+1) Dual blocking mechanism If both blocking mechanisms discussed in sections and operate simultaneously then it is called dual blocking mechanism. The material handling operation is said to be blocked, if the actual number of units (containers) of part type p in the input buffer of the workstation j+1 is equal to M(p, j+1) and the actual total number of containers in the input buffer of the workstation j+1 is equal to M(j+1). Subsequently, when a container of the part type p is taken from the input buffer of workstation j+1, the blocking will be released and the material handling starts to clear the parts from the output buffer of the work station j. Now, the workstation j can start processing the part p. If the workstation j+1 takes a container of parts other than that of part p from the input buffer of the workstation j+1, then the material handling is not possible for the part p. So the workstation j is continued to be in blocked state with

5 397 respect to the part p. However, the workstation j is not blocked with respect to other part types. 4 Toyota s kanban formula The formula used by Toyota Motor Company to determine the number of kanbans is called Toyota formula. (Berkley [4], Chan [6], Henry et al. [10], Hunglin Wang et al. [25], Ohno et al. [53], Monden Y. [50], Philipoom et al. [60] and Yavuz et al. [98]). The Toyota s kanban formula is presented below. K DLð1 þ αþ C where, K is the number of kanbans, D is the demand per unit time, L is the lead-time, α is the safety factor and C is the container capacity From these literatures, it was noted that the lead-time includes waiting time, processing time, conveyance time and kanban collecting time. The safety stock serves as a buffer against variations in both supply and demand. Henry et al. [10] has suggested some practical values for the variables C and α. The value of C is limited to a maximum of 10% of demand and α is a policy variable, which is decided by the management up to 10% of the demand. The variable K is the number of kanbans, which is related to the stock. If the value of K increases, the stock of the parts also increases. As a result, idle stock occurs. Similarly, if the value of K decreases, the stock of parts also decreases and shortage occurs. Hence, the JIT production system applies trade-off between the above parameters to find the optimum number of kanbans. Many researches have been carried out to find the optimum number of kanbans using different methodologies and tools such as simulation, queuing models, mathematical models, Artificial Intelligent approach and so on. From the Toyota s empirical equation, one can find the number of kanbans required for the system. 5 Measure of performance (MOP) For any system, the efficiency is measured through a function of related parameters/ factors. Hence these factors must obviously establish close relationship with the focused problem. These factors individually or jointly represent a performance. Blair Berkly J. [4] has given a note on workstation performance in kanban controlled shops in terms of average inventories, quality and the ability to meet the demands. Our study reveals that various researchers have used thirteen factors and they are shown in Table 2. From Table 2, it is inferred that the average work-inprocess (WIP), average flow time, mean cumulative throughput rate and weighted earliness of the job are Table 2 Factors used by various researchers Sl. No Factors used for MOP The reference numbers of research articles which use the MOP 1 Average WIP [2, 6, 15, 48, 76, 79, 89, 90, 98] 2 Demand [6, 15, 76] 3 Fill Rate [6] 4 Average kanban [74, 77, 98] waiting/queue time 5 Average Flow [2, 6, 22, 31, 51, 61, 76, 77, 98] (production lead) time 6 Average setup/process [98] time ratio 7 Average input/output [79, 89] inventory 8 Mean cumulative [48, 74, 88, 90, 98] throughput rate 9 Mean line utilization [48, 89, 98] 10 Mean demand satisfaction lead time [98] 11 Mean staging delay of job [49, 51] 12 Mean Tardiness [2, 22, 51, 61, 79] 13 Weighted earliness of the job [22, 61] frequently used as performance measures. Some important definitions for the factors, which are used in different MOPs by various researchers, are discussed below. Yavuz and Satir [98] have used seven factors in their study, which are as presented below. 1) Mean Cumulative Throughput Rate: It is the ratio of total satisfied demand to the total generated demand. 2) Mean Total Production Lead Time: It is the amount of time spent by a job from entering the system to until completion of all operations, averaged over all completed job. 3) Mean Total Demand Satisfaction Lead Time: It is the time interval between arrival of the demand and satisfaction of the demand. 4) Mean Utilization of Line: It is the mean utilization of the last station in the line. 5) Mean Setup/Run Time Ratio of Line: It is the ratio between the setup time and the run time of last station. 6) Mean Total WIP Length: It is the mean of all inprocess-inventory levels for the products excluding finished goods (FG). 7) Mean Total Waiting Time: It is the waiting time of all products in all processes and finished goods inventory (FGI). A general purpose analytical model to evaluate the performance of multistage kanban controlled production system was developed by Di Mascolo et al. [15]. The performance measures used by them are percentage of demand for back-order, average waiting time of backorder and average work-in-process.

6 398 A simulation experiment to evaluate the relative effectiveness of various rescheduling policies in capacity-constrained, JIT make-to-stock production environment is examined by Kern et al. [34]. Three performance measures analyzed by them are average finished goods inventory, total units of sales lost, and measure of schedule instability. Jing-Wen Li [31] has measured three factors for shop performance which are average work-in-process (WIP) inventory, average flow time and average set up time to processing time ratio (ASOTR), which is the ratio of total amount of time spent for setting up machines to the total amount of time spent for processing parts averaged over all machines. Uday S. Karmarker [88] used throughput rate for total work performance. In another study, the priority rule assignment was checked by the following factors by Nabil R. Adam et al. [51]. 1) The lead time of a job 2) The flow time of the job 3) The staging delay of a job 4) Mean Tardiness Hemamalini et al. [22] considered the objective function to minimize the sum of weighted flow time, weighted earliness of jobs and weighted tardiness of containers. Shahabudeen et al. [76] used an universal test which may be suited for the MOP in any JIT system, which are percentage zero demand (PZD), mean lead time (MLT) and mean total WIP (MTW) as explained below. 1) Percentage zero demand: It is the percentage of total demand immediately satisfied to the total generated demand. 2) Mean lead time: It is the sum of the waiting time, processing time and moving time averaged per station. It is also called as mean flow time. 3) Mean total WIP: It is the average number of kanbans waiting for each part type at each workstation. Here, PZD is a maximization measure and, MLT and MTW are the minimization measures and hence the sum of the objective MOPs is changed as Z max (a 1 PZD +a 2 RMLT +a 3 RMTW), where a 1, a 2 and a 3 are weights of the respective measures and, RMLT and RMTW are modified values of MLT and MTW, respectively. Chan F.T.S. [6] has done a work on how the MOP changes in different production systems, while increasing the kanban size. The measures of performance taken by him are as listed below 1) Unsatisfied order, which is the difference between the actual number of unit produced and the level of demand. 2) Manufacturing lead-time, which is the time between the customer order and the completion of order. 3) In-process-inventory is the total number of work-inprocess (WIP) inventory in units excluding finished goods inventory. 4) Fill rate is the percentage of demand satisfied. The results of his study as a function of the kanban size are shown in Table 3. Table 3 Results of the study by Chan [6] MOP Pull (Single product) Hybrid (Single product) 6 Literature review Hybrid (Multi-product) 1) Fill rate Decrease Decrease Increase 2) In-processinventory Increase Increase Increase 3) Manufacturing lead time Increase Increase Decrease Golhar et al. [12] have classified the JIT literature as elimination of waste, employee participation, supplier participation and total quality control. A similar work was done by Berkly [4] for kanban production process. He has selected 24 elements in the kanban production system as operational design factors. In this section, the different topics associated with JIT- KANBAN studied by various researchers have been grouped and presented as shown in Fig. 5. The Table 4 shows the reference numbers of the articles with respect to the classifications shown in Fig. 5. Obviously, most of the researchers were focusing on the determination of number of kanbans and determining corresponding solutions by using suitable models and tools. Some authors have developed simulations model and meta-heuristics like, genetic algorithm (GA), tabu search (TS), and simulated annealing (SA) for JIT-Kanban for better solutions. The Table 5 shows the number of articles dealt in different periods. From, Table 5, it is clear that, during last two 5 years period ( & ), the number of researches are more. Further, more researches have been done in empirical theory, flow shop, simulation, variability and its effects, CONWIP and special cases. Many researchers have worked in JIT system with different objectives. Here, the authors have grouped some important KANBAN FLOW SHOP ASSEMBLY LINE BATCH JIT CONWIP POLCA SPECIAL CASES SCM VARIABILITY & ITS EFFECTS DIFFERENT MODELS MATHEMATICAL QUEUING MARKOVIANS SIMULATIONS COST MINIMIZATION Fig. 5 Flowchart showing the classification of literature review

7 399 Table 4 Details of classification of review articles Area of Research Reference numbers of related Articles JIT [4, 12] Kanban-Empirical theory [10, 33, 50, 59, 71, 93, 95] Flow shop [7, 22, 58, 61, 77, 78] Assembly line [16, 89, 94] Batch Production [35, 86] System Modeling Approach: [3, 36] Mathematical Queuing [73, 99] Marko-chain [14, 28, 52, 90] Simulation [1, 9, 13, 19, 26, 66, 67, 74, 75] Cost minimization [53, 68, 72] Variability and its effects [7, 24, 48, 89, 98] CONWIP [8, 44, 55, 69, 70, 79, 96] POLCA [69] SCM [18, 29, 47, 80] Special Cases [6, 38, 40, 41, 43, 60, 69, 84, 85, 97, 100] objectives of the researches into six headings as shown in Table 6. From Table 6, it is clear that the following objectives attracted more researchers. Design of kanban system Performance behaviour Sequencing and scheduling 6.1 Empirical theory In the paper by Monden Y. [50], a comprehensive presentation of Toyota production system is given. A successful kanban system will drastically reduce the throughput time and lead time (Philipoom et al. [59]). Table 6 Objective based classification and their references Classification Reference numbers of articles A. Principles of [21, 50, 55, 59, 96] JIT-Kanban system B. Operating Factors [4, 12, 80] C. Design of Kanban [1, 10, 13, 19, 26, 52, 53, 66, 71, 74, 75, System 93] D. Performance [7, 24, 33, 36, 48, 58, 73, 81, 89, 90, 98, behaviour 99] E. Sequencing [18, 22, 57, 58, 61, 77, 78, 94] & Scheduling F. Inventory/Buffer [3, 68, 86] Control Karmarker and Kekre [33] have concluded from their studies that the reduction in container size and increase in number of kanbans lead to better results. Many researchers were interested in finding the optimal number of kanbans. The Toyota formula is very much useful in determining the optimal number of kanbans. Co Henry et al. [10] used the Toyota formula and also investigated the safety stock allocations in an uncertain dynamic environment. A similar work was considered by Sarkar et al. [71] to find number of kanbans between two adjacent workstations. Yale T. Herer et al. [95] presented a study for kanban system, CONWIP and buffered production lines. In this study, they incorporated a non-integral approach using simulation. The use of non-integral approach helps production planners to obtain discrete number of kanbans. Woolsey et al. [93] have developed a simple spreadsheet optimization program to determine the corresponding number of kanbans with respect to user-defined safety stock levels and other values. It gives a close-form of Table 5 Details of researches in different periods Area of Research Total JIT 2 2 Kanban- Empirical theory Flow shop Assembly line Batch Modelling Approach: Mathematical Queueing 2 2 Markovians Simulation Cost minimization Variability and its effects CONWIP POLCA 1 1 SCM 4 4 Special cases Total

8 400 solution to the problem. This means that an answer for any problem size may be instantaneously obtained Flow shop Kanban system is widely implemented in repetitive manufacturing environment. For a single card operational system, Sharadhapriyadarishini et al. [77] have developed two heuristics and proved that these are more efficient. Saradhapriyadarishini et al. [78] have proposed a recursive equation for scheduling the single card kanban system with dual blocking. They proposed a heuristic with twin objectives of minimizing the sum of total weighted time of containers and weighted flow time of part-types. Rajendran [61] has done a work on two card flow shop scheduling with n part-types. In this paper, mathematical models for time tabling of containers for different problems have been formulated. Then, a heuristic was developed to minimize the sum of weighted flow time, weighted earliness, and weighted tardiness of containers. Hemamalini et al. [22] have done similar work. In this work, the heuristic developed is simulated annealing algorithm. This is compared with random search method. In these papers, the comparisons are done only based on mean relative percentage increase. Instead of this approach, comparisons based on complete ANOVA experiments would provide reliable inference. Peter Brucker et al. [58] have carried out research on flow shop problem with a buffer of limited capacity between two adjacent machines. After finishing the processing of a job on a machine, either the job is to be processed on the following machine or it is to be stored in the buffer between these machines. If the buffer is completely occupied, the job has to wait on its current machine but blocks this machine for other jobs. In this paper, they determined a feasible schedule to minimize the makespan using tabu search. The results of the problem using tabu search were compared with that of benchmark instances. The comparisons are done only based on relative improvements. Instead of this approach, comparisons based on complete ANOVA experiments would provide reliable inference Assembly line Assembly lines are similar to the flow shops in which assembly of parts are carried out in a line sequence. In a multi product assembly line, the sequencing of the jobs is a challenging task. Drexl et al. [16] considered an assembly line sequencing mixed model problem. It is a combinatorial problem. They formulated this combinational problem as integer programming model. This model can be used only for small size problems due to the limitations of operations research software with respect to handling the number of variables and constraints, which are present in the integerprogramming model. Xiaobo et al. [94] have considered similar work on mixed model assembly line sequencing problem with conveyor stoppages. They proposed branch and bound algorithm, and simulated annealing algorithm for finding the optimal solution and sub-optimal solution of the mixed-model sequencing problem, respectively to minimize the total conveyor stoppage time. The branchand-bound method was devoted to find the optimal solution of small-sized problems, whereas the simulated annealing method was used to cope with large-scale problems to obtain a good sub-optimal solution. Future, research on simulated annealing applied to this problem can be directed to establish a better seed generation algorithm. However, the practitioner should spend considerable time in fixing the parameter called temperature (T) in the simulated annealing algorithm by trail and error method before actually solving the problem Batch production system In a batch production system, the switching over from one product to other product depends on many factors such as stock reaching to the threshold level, different priority schemes, economical setups, etc. Tafur Altiok et al. [86] have dealt this issue differently for the pull type manufacturing system with multi product types. In this paper, they developed an iterative procedure to approximately compute the average inventory level of each product as finished goods using different priority schemes. In this paper, the demand arrival process is assumed to be a poisson distribution and processing times and the set-up times are arbitrarily distributed. But, in practice, the processing times may follow other distributions, viz., normal, uniform, exponential, etc. which are not experimented in this paper. Khan et al. [35] addressed the problem of manufacturing system that procures raw materials from vendors in lot and convert them into finished products. They estimated production batch sizes for JIT delivery system and designed a JIT raw material supply system. A simple algorithm was developed to compute the batch sizes for both manufacturing and raw material purchasing policies. 6.2 Modeling approach Modelling approach aims to obtain the optimal solution. This subsection reviews different modeling approaches Mathematical model Kimera and Terada [36] have developed a mathematical model in the area of kanban system. They have given a basic balance equation for multi stage systems, which shows how the fluctuation of final demand influences the fluctuation of production and inventory volumes. Bitran and Chang [3] have designed an optimization model for the kanban system. The model is intended for a deterministic multi-stage capacitated assembly-type production setting. In this paper, a non-linear model developed by them is

9 401 converted into a linear model with deterministic demand. This deterministic model is designed to find the choice of the number of kanbans to be used at each stage of a given problem and to control the level of inventory. But this analysis does not include uncertainties directly. Hence, the utility of this model is very much limited Queuing model Seki et al. [73] have designed a single-stage kanban system with poisson demand arrivals. The system is formulated as a queuing system under piecewise constant load, and a numerical method by transient solutions of the queue is applied. This method, which shows the transient behavior of the kanban system, gives a better result. Yoichi Seki et al. [99] did similar work on the single stage kanban system with poisson demand and erlang production times. The objective of this work is to determine the number of kanbans, when a change of load to the system is planned. They mainly proposed a numerical method by transient solutions of the queueing system which was developed under piecewise constant load. This method also shows that the transient behavior of the kanban system operates better with other parameters. In this paper, the load distribution is assumed to be piecewise linear. Instead, it can be assumed as a continuous distribution and the corresponding results using simulation can be compared with `the results of this paper Markovians model Vito Albino et al. [90] studied a model of kanban controlled manufacturing system based on Markovian assumption. An approximate approach was developed to solve the model, which permits reliable evaluation of performance in terms of throughput time and work-in-process (WIP). Further, they validated the results using discrete-event simulation applied to their problem. It was observed that the results of the approximation approach did not deviate much from that of the simulation approach. The errors were always within 5% even for moderate size problems with 20 stages. The comparisons made in this paper were based on absolute value of percentage relative errors. Instead of this approach, they should have done comparisons through a carefully designed ANOVA experiments. Nori and Sarkar [52] have modeled the kanban system using Markov-chain to determine the optimum number of kanbans between adjacent workstations. Deleersnyder et al. [14] have modeled a blocking situation in the queues of the kanban system using discrete time Markovian chain to study the effect of number of kanbans, machine reliability, processing time and demand variability. Markham et al. [28] formed a procedure based rule induction approach for determining the number of kanbans and other factors in JIT. They applied classification and regression tree (CART) technique to generate the production rule, based on decision trees. This system approach involves 3 steps methodology, viz., 1) data collection, 2) formation of decision tree, and 3) interpretation of decision tree. This method helps to set kanban levels under high demand variability. The results show that rule induction using CART is a viable solution to the knowledge acquisition bottleneck. Hence, an extended work on knowledge acquisition for this domain will be a significant contribution to literature Simulation based studies There are many simulation softwares available in the market, such as GPSS, Q-GERT, SLAM-II, SIMAN, SIMSCRIPT, EXTEND, ARENA, and SIMULINK. Simulation uses the attributes/parameters of a problem to arrive the results. As for as designing of kanban system, a basic simulation study was done by Davis et al. [13] and Gabriel et al. [19] to determine the number of kanbans. In another work by Rudi De Smet et al. [67], a simulation model was developed to study the feasibility of plans to produce some subparts of the product in a kanban-controlled manner to determine the operational parameters such as number of kanbans and container size. This feasibility study was carried out for two situations, namely (1) all subpart types are produced in a kanban controlled manner and (2) only the production of fast-movers on two (out of three) machines is kanban controlled. The result assures that the kanban control is the best method for fast moving parts. In a kanban control system, the main decision parameters are the number of kanbans and lot size. Alabas et al. [1] developed three-meta heuristics viz., genetic algorithm (GA), simulated annealing (SA) and tabu search (TS) coupled with a simulation model to find the optimum number of kanbans with the minimum cost. In addition, a neural network metamodel was developed and compared with the heuristic procedures in terms of solution accuracy. They found that the tabu search requires less computational efforts when compared to the other two meta-heuristics and the neural network meta-model. In a similar work by Hurrion R.D. [26], simulation and neural network metamodel have been used for designing the kanban system. In this paper, an approximate solution is found using neural network meta-mdoel and then it is used as the starting point in simulation to find the optimum number of kanbans of a manufacturing system. Actually, the word optimum should have been avoided in his paper, because neither the proposed meta-model nor the simulation approach will give optimal number of kanbans. The optimum number of kanbans may be called as the minimum number of kanbans. In this context, an attempt has been made by Shahabudeen et al. [75] to set the number of kanbans as well as lot size at each station using simulated annealing algorithm. A simulation model with a single-card system has been designed and used in the analysis. A bi-criterion objective function comprising of mean throughput rate and aggregate average kanban queue, has been used for evaluation. In another work of them (Shahabudeen and Krishnaiah [74]), they have set the number of production

10 402 kanbans and withdrawl kanbans at each workstation, and lot size using genetic algorithm (GA). The solution of the genetic algorithm is found to be better than the random search procedure. They concluded that the genetic algorithm gives better solution for the assumed kanban system. A paper by Royce O. Bowden et al. [66] describes the use of evolutionary programming (EP) integrated with a simulation model of manufacturing system to determine the minimum number of kanbans and corresponding production trigger values required to meet the demand. In this paper, the inference is drawn for each measure, based on single replication under each solution-technique. The authors could have designed a single factor ANOVA experiment for each measure in which Solution Technique as the factor, with desirable number of replications to obtain reliable inference of their simulation study. Christos G. Panayioton et al.[9] have developed a simulation based algorithm for determining the minimum number of kanbans in a serial production system in order to maximize the throughput rate and minimize work-inprocess inventory. The finite perturbation analysis (FPA) technique was used in the simulation and to get sensitivity results. They have considered single product in the production line. But, in most of the cases, production lines will be manufacturing multi-products. The assumptions of arbitrary arrival and service process distributions limit the scope of application of this paper in practice Cost minimization model Ohno et al. [53] proposed an algorithm to determine the optimal number of kanbans for each of the two kinds of kanban (production ordering and supplier kanbans) under stochastic demand. An algorithm was devised for determining the optimal number of kanbans that minimizes the expected average cost per period. Since, no safety stock is assumed in this paper, this can be regarded as a procedure for determining the safety stock also. Sarkar et al.[72] studied a multi stage kanban system for short life-cycle product in the market. In this research, the problem is to find optimally the number of orders for raw-materials, kanbans circulated between workstations, finished goods shipments to the buyers, and the batch size for each shipment (lot) with minimum total cost of the inventory. A cost function was developed based on the costs incurred for the raw materials, the work-in-process and the finished goods. The optimal number of raw material orders that minimizes the total cost is obtained first, which is then used to find the minimum number of kanbans, finished goods shipments, and the batch sizes of shipments. This paper discusses a stage-wise optimization. Instead, a fully integrated approach may be followed. Further, this paper considers single product, with constant production rate at each workstation in a serial production line. So, the work may be extended for multi-product with varying production rate at each workstation in an assembly-type production. During preventive maintenance, a JIT buffer is needed so that the normal operation will not be interrupted. The optimal JIT buffer level is determined from a cost analysis using trade-off between the holding cost per unit of time and the shortage cost per unit time such that their sum is minimized (Salmark et al. [68]). 6.3 Variability and its effects Mehmet Savsar et al. [48] studied a simulation model to investigate the effect of different operational conditions, including kanban withdrawal policies on three performance measures of JIT, viz., average throughput rate, average station utilization and average work-in-process. Unlike other simulation studies that use exponential or truncated normal distribution, this model uses Erlang and Gama distribution. It is observed that the throughput rate as well as the average station utilization is significantly affected by the variability in processing time and demand intervals. They proposed two types of kanban withdrawal cycles, namely fixed withdrawal policy and variable withdrawal policy. Under the fixed withdrawal policy, the time interval between consecutive visits of a part-carrier to a workstation for kanban removal is fixed, but the order quantity (number of kanbans carried) is variable whereas under the variable withdrawal policy, the time interval between consecutive visits of a part-carrier to a workstation for kanban removal is variable, but the order quantity is fixed. As an extension of this work, the effects of different combinations of the two kanban withdrawal policies and number of kanbans between workstations, on the performance measures can be compared. Huang et al. [24] have found that overtime required will be increased when the variation in processing time is increased. Also, they emphasized that a kanban system would not be effective with high variable processing or set up time. Villeda et al. [89] performed a simulation study for a final assembly consisting of 3 sub-assembly lines and 4 stages repetitive production systems with kanbans. They concluded that improved productivity obtained through unbalancing the processing time at all workstations increases directly with the variability in the final assembly. Chaturvedi and Golhar [7] simulated a kanban based flow production line for a product in nine sequentially arranged workstations. They observed that the system performance was worst for exponential processing time distribution and variability affected station utilization, throughput time and WIP inventory. Yavuz and Satir [98] have studied the simulation of multi-item, multi-stage flow line operating under the JIT philosophy with a two-card kanban technique. The flow line produces four products through five stations. This study uses partial factorial design for experimentation. Seven experimental clusters are designed, each composed of at most three factors. The F ratios and the degrees of freedom of the model are obtained from multi-variate analysis of variance (MANOVA). They found that decrease in lot size reduces mean length and waiting times in work-in-process points at all kanbans levels. An increase in the uncertainty of demand arrival rates and demand sizes increases the probability of sudden over-

11 403 loading. An increase in the coefficient of variation in processing times brings about higher line utilization and a decrease in throughput rate. The scheduling rules tested in this paper are found to yield no significant differences in the utilization of line and on the behaviours of work-inprocess. Feeder lines may be introduced into the pull system configuration, where lines feed the final assembly line. Further, alternate operating routes for the products along the line may be introduced. 6.4 CONWIP CONWIP is a kanban system working with constant workin-process. CONWIP is a generalized form of kanban. Like, kanban system, it relies on signals, which could be electronic and it is equivalent to kanban cards. In a CONWIP system, the cards traverse a circuit that includes the entire production line. A card is attached to a standard container of parts at the beginning of the line. When the container is used at the end of the line, the card is removed and sent back to the beginning of the line where it waits in a card queue to eventually be attached to another container of parts. Oscar Rubiane et al. [55] have reviewed the literatures and presented the benefits and comparison of the CONWIP systems. Most of the articles reveal that the CONWIP system works more efficiently than the conventional kanban systems. Yang and Kum Khiong [96] compared 3 different systems viz., Single Kanban, Dual Kanban and Conwip. The results show that CONWIP consistently produces the shortest mean customer waiting time and lowest total work-in-process. Spearman et al. [79] have stressed that the flexibility of CONWIP system allows it to be used by any product-line where the utility of kanban system is limited. Hence, the superiority of CONWIP pull system is an alternative to kanban system. They present theoretical arguments and simulation study of CONWIP. Christelle Duri et al. [8] have analyzed CONWIP system, which consists of three stations in series. When a finished part is consumed by a demand, a raw part is released immediately and gets processed at each station sequentially. The processing at each station does not always meet the requirement of quality. Hence, at the end of processing in a station, the part is checked for quality and if it is not as per the standard, then it is sent back to the same station for reprocessing. They proposed an analytical method to evaluate the performance of this kind of system. In this paper, only three stations in series are considered. As an extension, a CONWIP system with generalized, n stations in series may be analyzed. 6.5 SCM There are number of articles in SCM (Supply Chain Management). In this present survey, a few JIT-SCM related articles are reviewed. In pull production management systems such as JIT, deliveries must be made on an as-needed basis only, and production begins only when requested. It is supposed to match customer demand, that is, producing only enough to replenish what the customer has used or sold. F. Elizabeth Vergara et al.[18] have dealt the coordination between different parts of simple supply chain. Materials should be moved from one supplier to other supplier as per the JIT. For this, an evolutionary algorithm was used which identifies the optimal or near optimal, synchronized delivery cycle time and suppliers component sequences for a multi-supplier, multi-component simple supply chain. The evolutionary algorithm also calculates a synchronized delivery cycle time for the entire supply chain, the cumulative cost throughout the supply chain, and the cost to each supplier. The results of this algorithm were compared with enumeration method and found that the evolutionary algorithm gives better solution in quick manner. This algorithm uses only two-point crossover genetic operators. A third genetic operator may be introduced to further improve the performance of the evolutionary algorithm. The evolutionary algorithm may be modified to handle complex supply chain problem. Stefan Minner [80] did a comprehensive review of multiple-supplier inventory models in supply chain management. SCM discusses strategic aspects of supplier competition, operation flexibility, global sourcing and inventory models. Further it was extended to logistics and multi echelon system. The emerging importance of E- business, especially E-procurement possibilities with the use of Internet technologies reduces transaction costs for supplier search and order placement with several suppliers and therefore multiple-supplier models are more attractive when compared to single sourcing alternative. This type of market with spot offers, continuously changing suppliers and high uncertainty with respect to lead-time and reliability of supplies, makes multiple-supplier replenishment and inventory strategies outperform single sourcing policies. Matheo et al. [47] have carried out a case study on inventory management in a multi-echelon spare parts supply chain. This paper clearly shows the close relationship between supply chain structure and demand patterns. The problems of managing supply chain with various numbers of echelons, multi model, extremely variable demand and lack of visibility over the distribution channel are discussed. They provided an algorithmic solution through the comprehension of the sources of demand variability and through a probabilistic forecast and inventory management. Isreal David et al. [29] have enumerated the vendor-buyer inventory production models. They argue that there should be a certain degree of independence between successive links of the supply chain, to allow flexibility in production management in individual links. They identified the degree of independence and level of flexibility in terms of lot sizing and delivery scheduling in a single-vendor-single-buyer system. In these lines, appropriate two-sided vendor-buyer inventory production models are formulated and analyzed. In all the papers, simulation as well as meta-heuristics can be used as powerful tools to derive results under

Uncertain Supply Chain Management

Uncertain Supply Chain Management Uncertain Supply Chain Management 2 (2014) 15 26 Contents lists available at GrowingScience Uncertain Supply Chain Management homepage: www.growingscience.com/uscm A state of art review on optimization

More information

CHAPTER 2 LITERATURE REVIEW

CHAPTER 2 LITERATURE REVIEW CHAPTER 2 LITERATURE REVIEW A manufacturing system is a combination of human, machinery and equipment bounded by material and instruction. A detailed literature review is carried out in the areas of manufacturing

More information

Performance Analysis of Single Flow line Manufacturing A Modeling Simulation approach

Performance Analysis of Single Flow line Manufacturing A Modeling Simulation approach Performance Analysis of Single Flow line Manufacturing A Modeling Simulation approach G. G. Sastry # Dr Mukesh Saxena Dr Rajnish Garg ABSTRACT The flow line manufacturing with pull production control mechanisms

More information

Analysis and Modelling of Flexible Manufacturing System

Analysis and Modelling of Flexible Manufacturing System Analysis and Modelling of Flexible Manufacturing System Swetapadma Mishra 1, Biswabihari Rath 2, Aravind Tripathy 3 1,2,3Gandhi Institute For Technology,Bhubaneswar, Odisha, India --------------------------------------------------------------------***----------------------------------------------------------------------

More information

An Adaptive Kanban and Production Capacity Control Mechanism

An Adaptive Kanban and Production Capacity Control Mechanism An Adaptive Kanban and Production Capacity Control Mechanism Léo Le Pallec Marand, Yo Sakata, Daisuke Hirotani, Katsumi Morikawa and Katsuhiko Takahashi * Department of System Cybernetics, Graduate School

More information

JOB SEQUENCING & WIP LEVEL DETERMINATION IN A CYCLIC CONWIP FLOWSHOP WITH BLOCKING

JOB SEQUENCING & WIP LEVEL DETERMINATION IN A CYCLIC CONWIP FLOWSHOP WITH BLOCKING International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 9, September 2017, pp. 274 280, Article ID: IJMET_08_09_029 Available online at http://www.iaeme.com/ijmet/issues.asp?jtype=ijmet&vtype=8&itype=9

More information

JOB SEQUENCING & WIP LEVEL DETERMINATION IN A CYCLIC CONWIP FLOWSHOP WITH BLOCKING

JOB SEQUENCING & WIP LEVEL DETERMINATION IN A CYCLIC CONWIP FLOWSHOP WITH BLOCKING International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 9, September 2017, pp. 274 280, Article ID: IJMET_08_09_029 Available online at http://www.iaeme.com/ijmet/issues.asp?jtype=ijmet&vtype=8&itype=9

More information

Kanban Applied to Reduce WIP in Chipper Assembly for Lawn Mower Industries

Kanban Applied to Reduce WIP in Chipper Assembly for Lawn Mower Industries Kanban Applied to Reduce WIP in Chipper Assembly for Lawn Mower Industries Author Rahman, A., Chattopadhyay, G., Wah, Simon Published 2006 Conference Title Condition Monitoring and Diagnostic Engineering

More information

CHAPTER 3.0 JUST-IN-TIME (JIT) MANUFACTURING SYSTEMS

CHAPTER 3.0 JUST-IN-TIME (JIT) MANUFACTURING SYSTEMS CHAPTER 3.0 JUST-IN-TIME (JIT) MANUFACTURING SYSTEMS 3.1 Abstract The Just-In-Time technique based manufacturing system, developed and implemented in the Toyota Motor Company may be defined as manufacturing

More information

PERFORMANCE ANALYSES OF CONWIP CONTROLLED PRODUCTION SYSTEM USING SIMULATION

PERFORMANCE ANALYSES OF CONWIP CONTROLLED PRODUCTION SYSTEM USING SIMULATION PERFORMANCE ANALYSES OF CONWIP CONTROLLED PRODUCTION SYSTEM USING SIMULATION ROTARU Ana University of Pitesti, Faculty of Mechanics and Technology, Department of Management and Technology e-mail: ana_c_rotaru@yahoo.com

More information

THE APPLICATIONS OF TIME INTERVAL ALIGNMENT POLICIES IN SUPPLY CHAIN SYSTEM

THE APPLICATIONS OF TIME INTERVAL ALIGNMENT POLICIES IN SUPPLY CHAIN SYSTEM THE APPLICATIONS OF TIME INTERVAL ALIGNMENT POLICIES IN SUPPLY CHAIN SYSTEM Meimei Wang Decision Support, Beth Israel Deaconess Medical Center, 1135 Tremont Street, Boston, MA 02120, mmwang@bidmc.harvard.edu

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION 1.1 MANUFACTURING SYSTEM Manufacturing, a branch of industry, is the application of tools and processes for the transformation of raw materials into finished products. The manufacturing

More information

OPTIMAL BATCHING AND SHIPMENT CONTROL IN A SINGLE-STAGE SUPPLY CHAIN SYSTEM

OPTIMAL BATCHING AND SHIPMENT CONTROL IN A SINGLE-STAGE SUPPLY CHAIN SYSTEM Abstract OPIMAL BACHING AN SHIPMEN CONROL IN A SINGLE-SAGE SUPPLY CHAIN SYSEM Shaojun Wang epartment of Industrial & Engineering echnology Southeast Missouri State University Cape Girardeau, MO 6370, USA

More information

EXAMINATION OF THE EFFECTS OF BOTTLENECKS AND PRODUCTION CONTROL RULES AT ASSEMBLY STATIONS

EXAMINATION OF THE EFFECTS OF BOTTLENECKS AND PRODUCTION CONTROL RULES AT ASSEMBLY STATIONS EXAMINATION OF THE EFFECTS OF BOTTLENECKS AND PRODUCTION CONTROL RULES AT ASSEMBLY STATIONS By TIMOTHY M. ELFTMAN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

More information

EasyChair Preprint. Economic Investigation in Variable Transfer Batch Size, in CONWIP Controlled Transfer Line

EasyChair Preprint. Economic Investigation in Variable Transfer Batch Size, in CONWIP Controlled Transfer Line EasyChair Preprint 57 Economic Investigation in Variable Transfer Batch Size, in CONWIP Controlled Transfer Line Guy Kashi, Gad Rabinowitz and Gavriel David Pinto EasyChair preprints are intended for rapid

More information

OPERATIONS RESEARCH Code: MB0048. Section-A

OPERATIONS RESEARCH Code: MB0048. Section-A Time: 2 hours OPERATIONS RESEARCH Code: MB0048 Max.Marks:140 Section-A Answer the following 1. Which of the following is an example of a mathematical model? a. Iconic model b. Replacement model c. Analogue

More information

RE-EXAMINING THE PERFORMANCE OF MRP AND KANBAN MATERIAL CONTROL STRATEGIES FOR MULTI-PRODUCT FLEXIBLE MANUFACTURING SYSTEMS

RE-EXAMINING THE PERFORMANCE OF MRP AND KANBAN MATERIAL CONTROL STRATEGIES FOR MULTI-PRODUCT FLEXIBLE MANUFACTURING SYSTEMS RE-EXAMINING THE PERFORMANCE OF MRP AND KANBAN MATERIAL CONTROL STRATEGIES FOR MULTI-PRODUCT FLEXIBLE MANUFACTURING SYSTEMS Ananth Krishnamurthy Department of Decision Sciences and Engineering Systems,

More information

A SIMULATION APPROACH FOR PERFORMANCE EVALUATION OF PULL, PUSH SYSTEMS

A SIMULATION APPROACH FOR PERFORMANCE EVALUATION OF PULL, PUSH SYSTEMS Proceedings of the 2005 International Conference on Simulation and Modeling V. Kachitvichyanukul, U. Purintrapiban, P. Utayopas, eds. A SIMULATION APPROACH FOR PERFORMANCE EVALUATION OF PULL, PUSH SYSTEMS

More information

LOADING AND SEQUENCING JOBS WITH A FASTEST MACHINE AMONG OTHERS

LOADING AND SEQUENCING JOBS WITH A FASTEST MACHINE AMONG OTHERS Advances in Production Engineering & Management 4 (2009) 3, 127-138 ISSN 1854-6250 Scientific paper LOADING AND SEQUENCING JOBS WITH A FASTEST MACHINE AMONG OTHERS Ahmad, I. * & Al-aney, K.I.M. ** *Department

More information

There are three options available for coping with variations in demand:

There are three options available for coping with variations in demand: Module 3E10 Operations management for Engineers - Crib 1 (a) Define the theoretical capacity of a manufacturing line. Explain why the actual capacity of a manufacturing line is often different from its

More information

PLUS VALUE STREAM MAPPING

PLUS VALUE STREAM MAPPING LEAN PRINCIPLES PLUS VALUE STREAM MAPPING Lean Principles for the Job Shop (v. Aug 06) 1 Lean Principles for the Job Shop (v. Aug 06) 2 Lean Principles for the Job Shop (v. Aug 06) 3 Lean Principles for

More information

Outline. Push-Pull Systems Global Company Profile: Toyota Motor Corporation Just-in-Time, the Toyota Production System, and Lean Operations

Outline. Push-Pull Systems Global Company Profile: Toyota Motor Corporation Just-in-Time, the Toyota Production System, and Lean Operations JIT and Lean Operations Outline Push-Pull Systems Global Company Profile: Toyota Motor Corporation Just-in-Time, the Toyota Production System, and Lean Operations Eliminate Waste Remove Variability Improve

More information

INDIAN INSTITUTE OF MATERIALS MANAGEMENT Post Graduate Diploma in Materials Management PAPER 18 C OPERATIONS RESEARCH.

INDIAN INSTITUTE OF MATERIALS MANAGEMENT Post Graduate Diploma in Materials Management PAPER 18 C OPERATIONS RESEARCH. INDIAN INSTITUTE OF MATERIALS MANAGEMENT Post Graduate Diploma in Materials Management PAPER 18 C OPERATIONS RESEARCH. Dec 2014 DATE: 20.12.2014 Max. Marks: 100 TIME: 2.00 p.m to 5.00 p.m. Duration: 03

More information

Optimizing Inplant Supply Chain in Steel Plants by Integrating Lean Manufacturing and Theory of Constrains through Dynamic Simulation

Optimizing Inplant Supply Chain in Steel Plants by Integrating Lean Manufacturing and Theory of Constrains through Dynamic Simulation Optimizing Inplant Supply Chain in Steel Plants by Integrating Lean Manufacturing and Theory of Constrains through Dynamic Simulation Atanu Mukherjee, President, Dastur Business and Technology Consulting,

More information

This is a refereed journal and all articles are professionally screened and reviewed

This is a refereed journal and all articles are professionally screened and reviewed Advances in Environmental Biology, 6(4): 1400-1411, 2012 ISSN 1995-0756 1400 This is a refereed journal and all articles are professionally screened and reviewed ORIGINAL ARTICLE Joint Production and Economic

More information

Mass Customized Large Scale Production System with Learning Curve Consideration

Mass Customized Large Scale Production System with Learning Curve Consideration Mass Customized Large Scale Production System with Learning Curve Consideration KuoWei Chen and Richard Lee Storch Industrial & Systems Engineering, University of Washington, Seattle, U.S.A {kwc206,rlstorch}@uw.edu

More information

University Question Paper Two Marks

University Question Paper Two Marks University Question Paper Two Marks 1. List the application of Operations Research in functional areas of management. Answer: Finance, Budgeting and Investment Marketing Physical distribution Purchasing,

More information

Numerical investigation of tradeoffs in production-inventory control policies with advance demand information

Numerical investigation of tradeoffs in production-inventory control policies with advance demand information Numerical investigation of tradeoffs in production-inventory control policies with advance demand information George Liberopoulos and telios oukoumialos University of Thessaly, Department of Mechanical

More information

A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEUDULING

A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEUDULING A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEUDULING PROF. SARVADE KISHORI D. Computer Science and Engineering,SVERI S College Of Engineering Pandharpur,Pandharpur,India KALSHETTY Y.R. Assistant Professor

More information

International Journal for Management Science And Technology (IJMST)

International Journal for Management Science And Technology (IJMST) Volume 3; Issue 2 Manuscript- 3 ISSN: 2320-8848 (Online) ISSN: 2321-0362 (Print) International Journal for Management Science And Technology (IJMST) VALIDATION OF A MATHEMATICAL MODEL IN A TWO ECHELON

More information

Scheduling Problems in the Lot Production Lines of the Toyota Production System

Scheduling Problems in the Lot Production Lines of the Toyota Production System J Jpn Ind Manage Assoc 65, 321-327, 2015 Original Paper Scheduling Problems in the Lot Production Lines of the Toyota Production System Shigenori KOTANI 1 Abstract: The focus of this paper is the scheduling

More information

Finished goods available to meet Takt time when variations in customer demand exist.

Finished goods available to meet Takt time when variations in customer demand exist. Delaware Valley Industrial Resource Center 2905 Southampton Road Philadelphia, PA 19154 Tel: (215) 464-8550 Fax: (215) 464-8570 www.dvirc.org Term Batch-and-Queue Processing Buffer Stock Catchball Cell

More information

Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa. JIT --Intro 02/11/03 page 1 of 28

Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa. JIT --Intro 02/11/03 page 1 of 28 Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa JIT --Intro 02/11/03 page 1 of 28 Pull/Push Systems Pull system: System for moving work where a workstation pulls output

More information

ISE480 Sequencing and Scheduling

ISE480 Sequencing and Scheduling ISE480 Sequencing and Scheduling INTRODUCTION ISE480 Sequencing and Scheduling 2012 2013 Spring term What is Scheduling About? Planning (deciding what to do) and scheduling (setting an order and time for

More information

Implementation of Just-In-Time Policies in Supply Chain Management

Implementation of Just-In-Time Policies in Supply Chain Management Implementation of Just-In-Time Policies in Supply Chain Management AYDIN M. TORKABADI RENE V. MAYORGA Industrial Systems Engineering University of Regina 3737 Wascana Parkway, Regina, SK, S4S 0A2 CANADA

More information

Production Planning under Uncertainty with Multiple Customer Classes

Production Planning under Uncertainty with Multiple Customer Classes Proceedings of the 211 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, January 22 24, 211 Production Planning under Uncertainty with Multiple Customer

More information

PRODUCTION ACTIVITY CONTROL (PAC)

PRODUCTION ACTIVITY CONTROL (PAC) PRODUCTION ACTIVITY CONTROL (PAC) Concerns execution of material plans Contains shop floor control (SFC), and vendor scheduling and follow-up SFC encompasses detailed scheduling and control of individual

More information

JOB SHOP SCHEDULING TO MINIMIZE WORK-IN-PROCESS, EARLINESS AND TARDINESS COSTS ZHU ZHECHENG A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

JOB SHOP SCHEDULING TO MINIMIZE WORK-IN-PROCESS, EARLINESS AND TARDINESS COSTS ZHU ZHECHENG A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY JOB SHOP SCHEDULING TO MINIMIZE WORK-IN-PROCESS, EARLINESS AND TARDINESS COSTS ZHU ZHECHENG A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF INDUSTRIAL AND SYSTEMS ENGINEERING NATIONAL

More information

IMPROVING THE SCHEDULING ALGORITHM OF LIMIS PLANNER

IMPROVING THE SCHEDULING ALGORITHM OF LIMIS PLANNER IMPROVING THE SCHEDULING ALGORITHM OF LIMIS PLANNER MASTER THESIS INDUSTRIAL ENGINEERING AND MANAGEMENT Student: First Supervisor : Second Supervisor: Roel Kikkert s0141178 Industrial Engineering and Management

More information

Simulation of Lean Principles Impact in a Multi-Product Supply Chain

Simulation of Lean Principles Impact in a Multi-Product Supply Chain Simulation of Lean Principles Impact in a Multi-Product Supply Chain M. Rossini, A. Portioli Studacher Abstract The market competition is moving from the single firm to the whole supply chain because of

More information

Tilburg University. Customized Pull Systems for Single-Product Flow Lines Gaury, E.G.A.; Kleijnen, Jack; Pierreval, H. Publication date: 1998

Tilburg University. Customized Pull Systems for Single-Product Flow Lines Gaury, E.G.A.; Kleijnen, Jack; Pierreval, H. Publication date: 1998 Tilburg University Customized Pull Systems for Single-Product Flow Lines Gaury, E.G.A.; Kleijnen, Jack; Pierreval, H. Publication date: 1998 Link to publication Citation for published version (APA): Gaury,

More information

Metaheuristics. Approximate. Metaheuristics used for. Math programming LP, IP, NLP, DP. Heuristics

Metaheuristics. Approximate. Metaheuristics used for. Math programming LP, IP, NLP, DP. Heuristics Metaheuristics Meta Greek word for upper level methods Heuristics Greek word heuriskein art of discovering new strategies to solve problems. Exact and Approximate methods Exact Math programming LP, IP,

More information

SCHEDULING AND CONTROLLING PRODUCTION ACTIVITIES

SCHEDULING AND CONTROLLING PRODUCTION ACTIVITIES SCHEDULING AND CONTROLLING PRODUCTION ACTIVITIES Al-Naimi Assistant Professor Industrial Engineering Branch Department of Production Engineering and Metallurgy University of Technology Baghdad - Iraq dr.mahmoudalnaimi@uotechnology.edu.iq

More information

LECTURE 8: MANAGING DEMAND

LECTURE 8: MANAGING DEMAND LECTURE 8: MANAGING DEMAND AND SUPPLY IN A SUPPLY CHAIN INSE 6300: Quality Assurance in Supply Chain Management 1 RESPONDING TO PREDICTABLE VARIABILITY 1. Managing Supply Process of managing production

More information

PULL REPLENISHMENT PERFORMANCE AS A FUNCTION OF DEMAND RATES AND SETUP TIMES UNDER OPTIMAL SETTINGS. Silvanus T. Enns

PULL REPLENISHMENT PERFORMANCE AS A FUNCTION OF DEMAND RATES AND SETUP TIMES UNDER OPTIMAL SETTINGS. Silvanus T. Enns Proceedings of the 2007 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds. PULL REPLENISHMENT PERFORMANCE AS A FUNCTION OF DEMAND RATES

More information

Just In Time (JIT) Quality and Reliability Engg. (171906) H I T. Hit suyo na mono O Iru toki iru dake Tasukuran

Just In Time (JIT) Quality and Reliability Engg. (171906) H I T. Hit suyo na mono O Iru toki iru dake Tasukuran Just In Time (JIT) H I T Hit suyo na mono O Iru toki iru dake Tasukuran (What is needed) (When it is needed and in what quantity) (Make) The crux is, if you cannot use it now do not make it now. Quality

More information

Re-Examining the Performance of MRP and Kanban Material Control Strategies for Multi-Product Flexible Manufacturing Systems

Re-Examining the Performance of MRP and Kanban Material Control Strategies for Multi-Product Flexible Manufacturing Systems The International Journal of Flexible Manufacturing Systems, 16, 123 150, 2004 c 2004 Kluwer Academic Publishers. Manufactured in The Netherlands. Re-Examining the Performance of MRP and Kanban Material

More information

PROFIT MAXIMIZATION OF A KANBAN-BASED SUPPLY CHAIN. A Thesis. presented to. the Faculty of the Graduate School. at the University of Missouri-Columbia

PROFIT MAXIMIZATION OF A KANBAN-BASED SUPPLY CHAIN. A Thesis. presented to. the Faculty of the Graduate School. at the University of Missouri-Columbia PROFIT MAXIMIZATION OF A KANBAN-BASED SUPPLY CHAIN A Thesis presented to the Faculty of the Graduate School at the University of Missouri-Columbia In Partial Fulfillment of the Requirements for the Degree

More information

Stochastic Lot-Sizing: Maximising Probability of Meeting Target Profit

Stochastic Lot-Sizing: Maximising Probability of Meeting Target Profit F1 Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 6, 2012 Stochastic Lot-Sizing: Maximising Probability of Meeting Target

More information

Infor CloudSuite Industrial Whatever It Takes - Advanced Planning & Scheduling for Today s Manufacturer

Infor CloudSuite Industrial Whatever It Takes - Advanced Planning & Scheduling for Today s Manufacturer Infor CloudSuite Industrial Whatever It Takes - Advanced Planning & Scheduling for Today s Manufacturer May 2017 CloudSuite Industrial Where Did APS Come From? APS grew out of the convergence of two movements.

More information

UNIFIED MODELLING FRAMEWORK OF MULTI-STAGE PRODUCTION-INVENTORY CONTROL POLICIES WITH LOT SIZING AND ADVANCE DEMAND INFORMATION

UNIFIED MODELLING FRAMEWORK OF MULTI-STAGE PRODUCTION-INVENTORY CONTROL POLICIES WITH LOT SIZING AND ADVANCE DEMAND INFORMATION Chapter 1 UNIFIED MODELLING FRAMEWORK OF MULTI-STAGE PRODUCTION-INVENTORY CONTROL POLICIES WITH LOT SIZING AND ADVANCE DEMAND INFORMATION George Liberopoulos Department of Mechanical and Industrial Engineering

More information

LEAN IN THE SOFTWARE TEST LAB

LEAN IN THE SOFTWARE TEST LAB LEAN IN THE SOFTWARE TEST LAB Kathy Iberle Iberle Consulting Group, Inc. Pacific Northwest Software Quality Conference October 15, 2013 HELLO, MY NAME IS KATHY IBERLE Kathy Iberle has been working with

More information

SINGLE MACHINE SEQUENCING. ISE480 Sequencing and Scheduling Fall semestre

SINGLE MACHINE SEQUENCING. ISE480 Sequencing and Scheduling Fall semestre SINGLE MACHINE SEQUENCING 2011 2012 Fall semestre INTRODUCTION The pure sequencing problem is a specialized scheduling problem in which an ordering of the jobs completely determines a schedule. Moreover,

More information

A Simple EOQ-like Solution to an Inventory System with Compound Poisson and Deterministic Demand

A Simple EOQ-like Solution to an Inventory System with Compound Poisson and Deterministic Demand A Simple EOQ-like Solution to an Inventory System with Compound Poisson and Deterministic Demand Katy S. Azoury San Francisco State University, San Francisco, California, USA Julia Miyaoka* San Francisco

More information

Contents PREFACE 1 INTRODUCTION The Role of Scheduling The Scheduling Function in an Enterprise Outline of the Book 6

Contents PREFACE 1 INTRODUCTION The Role of Scheduling The Scheduling Function in an Enterprise Outline of the Book 6 Integre Technical Publishing Co., Inc. Pinedo July 9, 2001 4:31 p.m. front page v PREFACE xi 1 INTRODUCTION 1 1.1 The Role of Scheduling 1 1.2 The Scheduling Function in an Enterprise 4 1.3 Outline of

More information

and type II customers arrive in batches of size k with probability d k

and type II customers arrive in batches of size k with probability d k xv Preface Decision making is an important task of any industry. Operations research is a discipline that helps to solve decision making problems to make viable decision one needs exact and reliable information

More information

Allocating work in process in a multiple-product CONWIP system with lost sales

Allocating work in process in a multiple-product CONWIP system with lost sales Allocating work in process in a multiple-product CONWIP system with lost sales S. M. Ryan* and J. Vorasayan Department of Industrial & Manufacturing Systems Engineering Iowa State University *Corresponding

More information

IMPROVING MANUFACTURING PROCESSES USING SIMULATION METHODS

IMPROVING MANUFACTURING PROCESSES USING SIMULATION METHODS Applied Computer Science, vol. 12, no. 4, pp. 7 17 Submitted: 2016-07-27 Revised: 2016-09-05 Accepted: 2016-09-09 computer simulation, production line, buffer allocation, throughput Sławomir KŁOS *, Justyna

More information

5.3 Supply Management within the MES

5.3 Supply Management within the MES Technical 6x9 / Manufacturing Execution Sytems (MES): Design, Planning, and Deployment / Meyer / 0-07-162383-3 / Chapter 5 Core Function Production Flow-Oriented Planning 85 Customer data (e.g., customer

More information

Hybrid Model applied in the Semiconductor Production Planning

Hybrid Model applied in the Semiconductor Production Planning , March 13-15, 2013, Hong Kong Hybrid Model applied in the Semiconductor Production Planning Pengfei Wang, Tomohiro Murata Abstract- One of the most studied issues in production planning or inventory management

More information

INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 2, No 3, 2011

INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 2, No 3, 2011 Minimization of Total Weighted Tardiness and Makespan for SDST Flow Shop Scheduling using Genetic Algorithm Kumar A. 1 *, Dhingra A. K. 1 1Department of Mechanical Engineering, University Institute of

More information

JIT AND Lean Operations 14-1

JIT AND Lean Operations 14-1 Chapter 15 JIT AND Lean Operations 14-1 Product Structure Tree = Shop floor layouts A B(4) C(2) D(2) E(1) D(3) F(2) MRP vs. JIT 14-2 JIT/Lean Production Just-in-time: Repetitive production system in which

More information

Operation and supply chain management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology Madras

Operation and supply chain management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology Madras Operation and supply chain management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology Madras Lecture - 37 Transportation and Distribution Models In this lecture, we

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION 1.1 FACILITY LAYOUT DESIGN Layout design is nothing but the systematic arrangement of physical facilities such as production machines, equipments, tools, furniture etc. A plant

More information

Inventory Management 101 Basic Principles SmartOps Corporation. All rights reserved Copyright 2005 TeknOkret Services. All Rights Reserved.

Inventory Management 101 Basic Principles SmartOps Corporation. All rights reserved Copyright 2005 TeknOkret Services. All Rights Reserved. Inventory Management 101 Basic Principles 1 Agenda Basic Concepts Simple Inventory Models Batch Size 2 Supply Chain Building Blocks SKU: Stocking keeping unit Stocking Point: Inventory storage Item A Loc

More information

An Integrated Three-Tier Logistics Model

An Integrated Three-Tier Logistics Model An Integrated Three-Tier Logistics Model Sarawoot Chittratanawat and James S. Noble Department of Industrial and Manufacturing Systems University of Missouri Columbia, MO 65211 Abstract A three-tier logistics/production

More information

Journal of Business & Economics Research May 2008 Volume 6, Number 5 ABSTRACT

Journal of Business & Economics Research May 2008 Volume 6, Number 5 ABSTRACT Comparative Analysis Of Production Control Systems Through Simulation S. Hossein Cheraghi, Wichita State University Mohammad Dadashzadeh, Oakland University Mahesh Soppin, Wichita State University ABSTRACT

More information

Inventory Control. Inventory. Inventories in a manufacturing plant. Why inventory? Reasons for holding Raw Materials. Reasons for holding WIP

Inventory Control. Inventory. Inventories in a manufacturing plant. Why inventory? Reasons for holding Raw Materials. Reasons for holding WIP Inventory Control Inventory the oldest result of the scientific management efforts inventory plays a key role in the logistical behavior of virtually all manufacturing systems the classical inventory results

More information

A System Dynamics Model for a Single-Stage Multi-Product Kanban Production System

A System Dynamics Model for a Single-Stage Multi-Product Kanban Production System A System Dynamics Model for a Single-Stage Multi-Product Kanban Production System L. GUERRA, T. MURINO, E. ROMANO Department of Materials Engineering and Operations Management University of Naples Federico

More information

OPTIMAL ALLOCATION OF WORK IN A TWO-STEP PRODUCTION PROCESS USING CIRCULATING PALLETS. Arne Thesen

OPTIMAL ALLOCATION OF WORK IN A TWO-STEP PRODUCTION PROCESS USING CIRCULATING PALLETS. Arne Thesen Arne Thesen: Optimal allocation of work... /3/98 :5 PM Page OPTIMAL ALLOCATION OF WORK IN A TWO-STEP PRODUCTION PROCESS USING CIRCULATING PALLETS. Arne Thesen Department of Industrial Engineering, University

More information

MODELING OF A SHAFT PRODUCTION SYSTEM

MODELING OF A SHAFT PRODUCTION SYSTEM , TECHNOLOGIES IN MACHINE BUILDING, ISSN 11-5, 15 MODELING OF A SHAFT PRODUCTION SYSTEM Florin BURUIANA 1, Mihaela BANU, Alice BURUIANA 1 1 Department of Manufacturing Engineering, Faculty of Engineering,

More information

SCHEDULING RULES FOR A SMALL DYNAMIC JOB-SHOP: A SIMULATION APPROACH

SCHEDULING RULES FOR A SMALL DYNAMIC JOB-SHOP: A SIMULATION APPROACH ISSN 1726-4529 Int j simul model 9 (2010) 4, 173-183 Original scientific paper SCHEDULING RULES FOR A SMALL DYNAMIC JOB-SHOP: A SIMULATION APPROACH Dileepan, P. & Ahmadi, M. University of Tennessee at

More information

Lean and Agile Systems. Rajiv Gupta FORE School of Management October 2013 Session 6

Lean and Agile Systems. Rajiv Gupta FORE School of Management October 2013 Session 6 Lean and Agile Systems Rajiv Gupta FORE School of Management October 2013 Session 6 Module 1 Recap of Session 5 Module 2 Pull Production Rules of Kanban Module 3 Small Batch Production Level Production

More information

Scheduling and Coordination of Distributed Design Projects

Scheduling and Coordination of Distributed Design Projects Scheduling and Coordination of Distributed Design Projects F. Liu, P.B. Luh University of Connecticut, Storrs, CT 06269-2157, USA B. Moser United Technologies Research Center, E. Hartford, CT 06108, USA

More information

Just-In-Time (JIT) Manufacturing. Overview

Just-In-Time (JIT) Manufacturing. Overview Just-In-Time (JIT) Manufacturing Overview The Just-in-Time (JIT) Manufacturing Philosophy Prerequisites for JIT Manufacturing Elements of JIT Manufacturing Benefits of JIT Manufacturing Success and JIT

More information

WORKLOAD CONTROL AND ORDER RELEASE IN COMBINED MTO-MTS PRODUCTION

WORKLOAD CONTROL AND ORDER RELEASE IN COMBINED MTO-MTS PRODUCTION WORKLOAD CONTROL AND ORDER RELEASE IN COMBINED MTO-MTS PRODUCTION N.O. Fernandes 1, M. Gomes 1, S. Carmo-Silva 2 1 Polytechnic Institute of Castelo Branco, School of Technology Castelo Branco, Av. do Empresário

More information

Optimizing the supply chain configuration with supply disruptions

Optimizing the supply chain configuration with supply disruptions Lecture Notes in Management Science (2014) Vol. 6: 176 184 6 th International Conference on Applied Operational Research, Proceedings Tadbir Operational Research Group Ltd. All rights reserved. www.tadbir.ca

More information

Determining the Effectiveness of Specialized Bank Tellers

Determining the Effectiveness of Specialized Bank Tellers Proceedings of the 2009 Industrial Engineering Research Conference I. Dhillon, D. Hamilton, and B. Rumao, eds. Determining the Effectiveness of Specialized Bank Tellers Inder S. Dhillon, David C. Hamilton,

More information

AN ABSTRACT OF THE DISSERTATION OF

AN ABSTRACT OF THE DISSERTATION OF AN ABSTRACT OF THE DISSERTATION OF SeJoon Park for the degree of Doctor of Philosophy in Industrial Engineering presented on December 6, 2011. Title: Container Fleet-Sizing for Part Transportation and

More information

Determination of the Number of Kanbans and Batch Sizes in a JIT Supply Chain System

Determination of the Number of Kanbans and Batch Sizes in a JIT Supply Chain System Transaction E: Industrial Engineering Vol. 17, No. 2, pp. 143{149 c Sharif University of Technology, December 2010 Research Note Determination of the Number of Kanbans and Batch Sizes in a JIT Supply Chain

More information

Industrial & Sys Eng - INSY

Industrial & Sys Eng - INSY Industrial & Sys Eng - INSY 1 Industrial & Sys Eng - INSY Courses INSY 3010 PROGRAMMING AND DATABASE APPLICATIONS FOR ISE (3) LEC. 3. Pr. COMP 1200. Programming and database applications for ISE students.

More information

A New Fuzzy Modeling Approach for Joint Manufacturing Scheduling and Shipping Decisions

A New Fuzzy Modeling Approach for Joint Manufacturing Scheduling and Shipping Decisions A New Fuzzy Modeling Approach for Joint Manufacturing Scheduling and Shipping Decisions Can Celikbilek* (cc340609@ohio.edu), Sadegh Mirshekarian and Gursel A. Suer, PhD Department of Industrial & Systems

More information

We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors

We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists 4,000 116,000 120M Open access books available International authors and editors Downloads Our

More information

PULL PRODUCTION POLICIES: COMPARATIVE STUDY THROUGH SIMULATIVE APPROACH

PULL PRODUCTION POLICIES: COMPARATIVE STUDY THROUGH SIMULATIVE APPROACH PULL PRODUCTION POLICIES: COMPARATIVE STUDY THROUGH SIMULATIVE APPROACH Mosè Gallo (a), Guido Guizzi (b), Giuseppe Naviglio (c) (a) (b) (c) Department of Materials Engineering and Operations Management

More information

JUST IN TIME. Manuel Rincón, M.Sc. October 22nd, 2004

JUST IN TIME. Manuel Rincón, M.Sc. October 22nd, 2004 JUST IN TIME Manuel Rincón, M.Sc. October 22nd, 2004 Lecture Outline 1. Just-in-Time Philosophy 2. Suppliers Goals of JIT Partnerships Concerns of Suppliers 3. JIT Layout Distance Reduction Increased Flexibility

More information

OM (Fall 2016) Outline

OM (Fall 2016) Outline Lean Operations Outline Global Company Profile: Toyota Motor Corporation Lean Operations Lean and Just-in-Time Lean and the Toyota Production System Lean Organizations Lean in Services 2 Toyota Motor Corporation

More information

A comparison between Lean and Visibility approach in supply chain planning

A comparison between Lean and Visibility approach in supply chain planning A comparison between Lean and Visibility approach in supply chain planning Matteo Rossini, Alberto Portioli Staudacher Department of Management Engineering, Politecnico di Milano, Milano, Italy (matteo.rossini@polimi.it)

More information

Simultaneous Perspective-Based Mixed-Model Assembly Line Balancing Problem

Simultaneous Perspective-Based Mixed-Model Assembly Line Balancing Problem Tamkang Journal of Science and Engineering, Vol. 13, No. 3, pp. 327 336 (2010) 327 Simultaneous Perspective-Based Mixed-Model Assembly Line Balancing Problem Horng-Jinh Chang 1 and Tung-Meng Chang 1,2

More information

A Case Study of Capacitated Scheduling

A Case Study of Capacitated Scheduling A Case Study of Capacitated Scheduling Rosana Beatriz Baptista Haddad rosana.haddad@cenpra.gov.br; Marcius Fabius Henriques de Carvalho marcius.carvalho@cenpra.gov.br Department of Production Management

More information

PRODUCT-MIX ANALYSIS WITH DISCRETE EVENT SIMULATION. Raid Al-Aomar. Classic Advanced Development Systems, Inc. Troy, MI 48083, U.S.A.

PRODUCT-MIX ANALYSIS WITH DISCRETE EVENT SIMULATION. Raid Al-Aomar. Classic Advanced Development Systems, Inc. Troy, MI 48083, U.S.A. Proceedings of the 2000 Winter Simulation Conference J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, eds. PRODUCT-MIX ANALYSIS WITH DISCRETE EVENT SIMULATION Raid Al-Aomar Classic Advanced Development

More information

Flow and Pull Systems

Flow and Pull Systems Online Student Guide Flow and Pull Systems OpusWorks 2016, All Rights Reserved 1 Table of Contents LEARNING OBJECTIVES... 4 INTRODUCTION... 4 BENEFITS OF FLOW AND PULL... 5 CLEARING ROADBLOCKS... 5 APPROACH

More information

Inventory Optimization using ERP to reduce final product lead time, Inventory value an inbound logistics cost for MTO, FMCG Company

Inventory Optimization using ERP to reduce final product lead time, Inventory value an inbound logistics cost for MTO, FMCG Company IJMS 2016 vol. 3 (2): 53-60 International Journal of Multidisciplinary Studies (IJMS) Volume 3, Issue 2, 2016 Inventory Optimization using ERP to reduce final product lead time, Inventory value an inbound

More information

International Journal of Industrial Engineering Computations

International Journal of Industrial Engineering Computations International Journal of Industrial Engineering omputations (200) 39 46 ontents lists available at GrowingScience International Journal of Industrial Engineering omputations homepage: www.growingscience.com/iiec

More information

Factory Operations Research Center (FORCe II)

Factory Operations Research Center (FORCe II) Factory Operations Research Center (FORCe II) SRC/ISMT 2004-OJ-1214 Configuration, monitoring and control of semiconductor supply chains Jan. 7, 2005 Shi-Chung Chang (task 1) Argon Chen (task 2) Yon Chou

More information

Scheduling a dynamic job shop production system with sequence-dependent setups: An experimental study

Scheduling a dynamic job shop production system with sequence-dependent setups: An experimental study Robotics and Computer-Integrated Manufacturing ] (]]]]) ]]] ]]] www.elsevier.com/locate/rcim Scheduling a dynamic job shop production system with sequence-dependent setups: An experimental study V. Vinod

More information

1 Introduction Importance and Objectives of Inventory Control Overview and Purpose of the Book Framework...

1 Introduction Importance and Objectives of Inventory Control Overview and Purpose of the Book Framework... Contents 1 Introduction... 1 1.1 Importance and Objectives of Inventory Control... 1 1.2 Overview and Purpose of the Book... 2 1.3 Framework... 4 2 Forecasting... 7 2.1 Objectives and Approaches... 7 2.2

More information

A COMPARATIVE STUDY OF POLCA AND GENERIC CONWIP PRODUCTION CONTROL SYSTEMS IN ERRATIC DEMAND CONDITIONS. Ozgur Kabadurmus

A COMPARATIVE STUDY OF POLCA AND GENERIC CONWIP PRODUCTION CONTROL SYSTEMS IN ERRATIC DEMAND CONDITIONS. Ozgur Kabadurmus A COMPARATIVE STUDY OF POLCA AND GENERIC CONWIP PRODUCTION CONTROL SYSTEMS IN ERRATIC DEMAND CONDITIONS Ozgur Kabadurmus Department of Industrial and Systems Engineering, Auburn University Abstract The

More information

Subbu Ramakrishnan. Manufacturing Finance with SAP. ERP Financials. Bonn Boston

Subbu Ramakrishnan. Manufacturing Finance with SAP. ERP Financials. Bonn Boston Subbu Ramakrishnan Manufacturing Finance with SAP ERP Financials Bonn Boston Contents at a Glance 1 Overview of Manufacturing Scenarios Supported by SAP... 25 2 Overview of Finance Activities in a Make-to-Stock

More information

SimBa: A Simulation and Balancing System for Manual Production Lines

SimBa: A Simulation and Balancing System for Manual Production Lines 19 SimBa: A Simulation and Balancing System for Manual Production Lines Isabel C. Pra9a, Adriano S. Carvalho Faculdade de Engenharia da Universidade do Porto Instituto de Sistemas e Rob6tica - Grupo de

More information

Simulation Model and Analysis for Uncertain Demand and Replenishment Policies in Supply Chain

Simulation Model and Analysis for Uncertain Demand and Replenishment Policies in Supply Chain Simulation Model and Analysis for Uncertain Demand and Replenishment Policies in Supply Chain Jihyun Kim and Jaehyun Han College of Business Administration, Kwangwoon University 447-1 Wolgye-dong, Nowon-gu,

More information